diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 280801786..c8c90eead 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -31,6 +31,35 @@ jobs:
- name: Public tree audit
run: bash scripts/audit_public_tree.sh
+ documentation-history:
+ if: github.event_name == 'pull_request'
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ with:
+ fetch-depth: 0
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.12"
+ - name: Audit documents in every new commit
+ env:
+ DOCUMENTATION_BASE: ${{ github.event.pull_request.base.sha }}
+ run: bash scripts/audit_public_tree.sh --history-base "$DOCUMENTATION_BASE"
+
+ sandbox-contracts:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.12"
+ - name: Install optional runtime and practice
+ run: python -m pip install -e '.[dev,sandbox]' -e practices/h_cluster
+ - name: SDK contracts without allocation
+ run: python scripts/sandbox_sdk_contract.py
+ - name: Optional lifecycle and practice tests
+ run: python -m pytest tests/harborrl/test_sandbox_cleanup.py practices/h_cluster/tests
+
package:
runs-on: ubuntu-latest
needs: cpu
diff --git a/.gitignore b/.gitignore
index 2f4e7f480..7755b672b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,7 +32,20 @@ harbor_datasets/
.tools/
.worktrees/
benchmarks/harbor/0.5-mvp/
-docs/
+docs/*
+!docs/agents/
+docs/agents/*
+!docs/agents/environment-check.md
+!docs/configuration-wizard.md
+!docs/deployment.md
+!docs/sandbox-rollout.md
+!docs/train-inference-consistency.md
+!docs/web-console.md
+practices/**/VALIDATION.md
+practices/**/HISTORICAL_VALIDATION.md
+integration-report-*.md
+integration-roadmap-*.md
+*.bundle
local/
configs/site/
examples/**/private*
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 98b03e866..46f7e2791 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -13,6 +13,25 @@ HarborRL accepts changes through pull requests from a public branch.
6. Submit a reviewable commit series and describe external versions, task
provenance, and any acceptance evidence.
+## Public documentation
+
+Commit documentation that helps users install, configure, run or troubleshoot
+HarborRL. Keep development plans, integration reports, experiment logs and
+machine-specific validation records outside the repository. Summarize current
+support and limitations in the relevant user guide without development history.
+
+The public-document allowlist is maintained in `scripts/audit_public_tree.sh`.
+Update it deliberately when adding a user guide. Check both the final tree and
+every new commit before opening a pull request:
+
+```bash
+bash scripts/audit_public_tree.sh --history-base origin/main
+```
+
+Deleting a private document in a later commit does not remove it from history.
+Clean unpublished commits before pushing; do not merge a development branch
+whose history contains private documents into a public branch.
+
Configuration and task fixtures must use generic placeholders. Absolute personal
paths, internal DNS names, private registry references, and unexplained large
files will fail review.
diff --git a/NOTICE.md b/NOTICE.md
index 4dc4f09ac..3ab8ecf6f 100644
--- a/NOTICE.md
+++ b/NOTICE.md
@@ -2,6 +2,10 @@
HarborRL is MIT-licensed by Princeton-AI.
+The local web console bundles Lucide 1.61.0 (ISC license). Its unmodified browser
+distribution and license are in `harborrl/webui/static/lucide.min.js` and
+`harborrl/webui/static/lucide-LICENSE.txt`. No CDN connection is required.
+
The public package does not vendor Slime, Megatron-LM, SGLang, Harbor, Claude
Code, or model weights. Operators are responsible for reviewing the licenses of
the exact external versions they pin and for complying with model and dataset
diff --git a/README.md b/README.md
index cc82cddd7..1b1b8ac7d 100644
--- a/README.md
+++ b/README.md
@@ -2,9 +2,9 @@
A lightweight agentic reinforcement learning framework for everyone
@@ -25,6 +25,8 @@
## News
+- **2026-10-26** Added an [offline configuration wizard](docs/configuration-wizard.md) and a [local Web console](docs/web-console.md) for configuration, validation, dry-runs, training launch, and logs.
+- **2026-10-26** Added [controller/GPU deployment configuration](docs/deployment.md), optional [OpenSandbox execution](docs/sandbox-rollout.md), and opt-in [train/inference consistency checks](docs/train-inference-consistency.md).
- **10/2026** 📣📣📣 HarborRL released
## About
@@ -40,6 +42,14 @@ HarborRL supports flexible combinations of **Models × Algorithms × Tasks × Ha
## AI-assisted Configure
+For the offline options catalog, interactive configuration, and answer-file setup,
+see the [configuration CLI guide](docs/configuration-wizard.md).
+Start with `harborrl options` or `harborrl init --help`.
+
+Run `harborrl web` to open the local browser console for configuration, validation,
+dry-runs, training launch, and logs. See the [web console guide](docs/web-console.md)
+for remote-controller access and lifecycle details.
+
If you want an AI agent to help deploy this repository and verify training,
ask it to first read and strictly follow
[docs/agents/environment-check.md](docs/agents/environment-check.md).
@@ -78,6 +88,12 @@ Confirm the result yourself against this checklist:
## Quick Start
+The default CPU install does not need a Sandbox SDK or cluster account.
+For controller placement, see [deployment](docs/deployment.md). For optional
+standard OpenSandbox execution, see [Sandbox rollout](docs/sandbox-rollout.md).
+The independently installed [H-cluster practice](practices/h_cluster/README.md)
+contains Brainbox-specific deployment and restricted-network relay examples.
+
### Install
Use Python 3.10 or newer. Runtime dependencies, development dependencies, and the `harborrl` command are locked by [pyproject.toml](pyproject.toml) and [uv.lock](uv.lock). The CPU development install does not require CUDA, Docker, model weights, Slime, Megatron-LM, or SGLang.
@@ -128,7 +144,7 @@ The command style is a shorthand over `config.yaml` in the working directory (or
## Task Configuration
-Training is configured with one YAML file that has a fixed section and field set. Relative paths resolve against the YAML file, and repeated `--set section.field=value` overrides are parsed as YAML values and validated again. Start site-specific configurations from [examples/native/train_qwen_native.yaml](examples/native/train_qwen_native.yaml).
+Training is configured with one YAML file that has a fixed section and field set. Relative paths resolve against the YAML file, and repeated `--set section.field=value` overrides are parsed as YAML values and validated again. Start site-specific configurations from [examples/native/train_qwen_native.yaml](examples/native/train_qwen_native.yaml). Optional [train/inference consistency checks](docs/train-inference-consistency.md) compare frozen rollout scores with the pre-update trainer and probe synchronized inference engines.
The configuration has three broad parts: `execution` is fixed to `harbor_job`; `harbor`, `gateway`, and `deployment` describe rollout workers, service endpoints, and GPU topology; `tasks`, `harness`, `model`, `training`, `sampling`, and `output` describe the experiment and its outputs.
diff --git a/README_zh.md b/README_zh.md
index 18a33b25c..f75e744a9 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -25,6 +25,8 @@
## News
+- **2026-10-26** 新增[离线配置向导](docs/configuration-wizard.md)和[本地 Web 控制台](docs/web-console.md)，支持配置、校验、dry-run、训练启动和日志查看。
+- **2026-10-26** 新增 [controller/GPU 部署配置](docs/deployment.md)、可选的 [OpenSandbox 执行能力](docs/sandbox-rollout.md)，以及按需启用的[训练与推理一致性检查](docs/train-inference-consistency.md)。
- **10/2026** 📣📣📣 HarborRL released
## 项目介绍
@@ -43,6 +45,11 @@ HarborRL 支持 **Models × Algorithms × Tasks × Harnesses (MATH)** 的灵活
如果你希望让 AI agent 辅助完成本 repo 的部署和训练验证，请让它先阅读并严格遵循
[docs/agents/environment-check.md](docs/agents/environment-check.md)。
+离线选项目录、交互式配置和 answer-file 配置方式见[配置 CLI 指南](docs/configuration-wizard.md)。
+可以从 `harborrl options` 或 `harborrl init --help` 开始。
+运行 `harborrl web` 可打开本地浏览器控制台，完成配置、校验、dry-run、训练启动和日志查看；
+远程访问与生命周期说明见 [Web 控制台指南](docs/web-console.md)。
+
推荐的实际操作流程是把下面这段提示词交给 agent，并把尖括号内容替换成你的机器信息：
```text
@@ -73,6 +80,10 @@ doctor 和 launch_native --dry-run。
## 快速开始
+默认 CPU 安装不需要 Sandbox SDK 或集群账号。
+controller 与 GPU 的部署方式见[部署指南](docs/deployment.md)，可选的标准 OpenSandbox 执行方式见[Sandbox rollout 指南](docs/sandbox-rollout.md)。
+独立安装的 [H-cluster 实践包](practices/h_cluster/README.md)提供 Brainbox 部署与受限网络中继示例。
+
### 安装
使用 Python 3.10 或更新版本。Python 依赖、开发依赖和 `harborrl` 命令由 [pyproject.toml](pyproject.toml) 与 [uv.lock](uv.lock) 锁定。CPU 开发环境不需要 CUDA、Docker、模型权重、Slime、Megatron-LM 或 SGLang。
diff --git a/docs/configuration-wizard.md b/docs/configuration-wizard.md
new file mode 100644
index 000000000..441a6138b
--- /dev/null
+++ b/docs/configuration-wizard.md
@@ -0,0 +1,140 @@
+# Configuration CLI
+
+The configuration tools run with the core package (Python 3.10+ and PyYAML).
+They enumerate implemented capabilities, check configuration offline, and create
+consistent training schema 2 / deployment schema 1 files. They do not install
+software, download tasks or models, connect over SSH, or allocate Sandbox/GPU jobs.
+
+## Browse and validate
+
+```bash
+harborrl options
+harborrl options deployments --json
+harborrl options fields --json
+harborrl validate site/config.yaml --deployment site/deployment.yaml --json
+harborrl validate site/config.yaml --deployment site/deployment.yaml --check-files
+```
+
+Catalog dimensions are `models`, `algorithms`, `tasks` (task providers),
+`harnesses`, `datasets`, `deployments`, `fields`, and `compat`. JSON keeps the
+`catalog_version` and `schema_versions` envelope when selecting a dimension.
+The catalog reflects registered implementations, not roadmap claims. Dataset
+`status` describes support; `available` describes local task files. A wheel does
+not include repository examples, and planned datasets are not ready to train.
+
+Validation defaults to schema, semantics, and cross-file consistency. It does
+not open referenced profiles or tasks until `--check-files` is supplied. Run file
+checks on the controller: its source/interpreter, profile, task catalog and
+Sandbox configuration belong there. Local GPU and runner paths are checked
+locally; SSH target paths are deferred. The shared root may be created by a later
+run and is not required to exist during configuration. Credentials are never read.
+
+Checks are `passed`, `failed`, or `deferred`. The report is `invalid` if a check
+fails, otherwise `valid_with_deferred_checks` while live checks remain. Exit codes
+are 0 for no failed checks, 1 for validation/creation failure, and 2 for CLI usage
+errors. `--json` writes one JSON object to stdout. Schema validators may report
+only their first error. Without `--deployment`, the existing CLI discovery of
+`./deployment.yaml` and embedded fallback is preserved and reported.
+
+GPU memory fit, model loading, live tokenizer fingerprints, package versions,
+connectivity and platform capacity are not established by offline validation.
+Sandbox image bindings are checked locally; trusted site adapters and credentials
+are checked later by runner preflight. Sandbox installation/profile timing
+compatibility is additionally checked by the existing training dry-run.
+
+## Prepare inputs
+
+1. Obtain Harbor-format tasks and run `harborrl hub inspect --tasks-dir /path/tasks
+ --dataset my-tasks --revision pinned-revision --run-dir /path/inspection`.
+ The resulting JSONL is an inspection report, not a training catalog. Build a
+ JSON list (or JSONL records with a `.jsonl` suffix) containing exactly `id`,
+ `revision`, `path`, `task_digest`, and `reward_profile` for each selected task.
+ Copy `path` and `task_digest` from a `NEEDS_PROBE` inspection record, assign a
+ unique stable `id`, retain the pinned revision, and explicitly set the verifier's
+ reward contract (for example `reward_profile: {key: reward}`). See
+ [the catalog example](../examples/native/catalog.jsonl). `INVALID` and
+ `UNSUPPORTED` inspection results cannot be made trainable by changing labels.
+ Do not replace computed digests with placeholder values.
+2. Prepare a pinned Claude Code profile. See
+ [the native examples](../examples/native) for profile structure. It must match
+ the agent and task requirements of your deployment.
+3. Stage checkpoint and reference directories on the GPU target. In its existing
+ training environment, load the actual local tokenizer with the same options as
+ the runtime and call `harborrl.rollout.runtime.tokenizer_fingerprint(tokenizer)`.
+ Its return values are `tokenizer_digest, template_digest`. This is an explicit
+ preparation step, not an operation performed by the wizard. Hashing a filename
+ or guessing an arbitrary 64-character value does not establish compatibility.
+4. Identify controller/GPU/runner Python executables, checkout paths, SSH aliases
+ and shared storage. Service origins must include their declared ports and be
+ reachable by the participating machines.
+5. For Sandbox, prepare the existing platform configuration and image bindings.
+ H-cluster users follow [the optional H practice](../practices/h_cluster/README.md)
+ and reference its site file. The core CLI never asks for H project/environment
+ identifiers or embeds private endpoints or credentials.
+
+## Generate
+
+```bash
+harborrl init --out-dir ./site-new
+harborrl init --answers my-answers.yaml --out-dir ./site-new
+```
+
+Interactive mode asks for deployment roles, existing inputs, model fingerprints,
+GPU layout and a few sampling settings, then previews both configurations and
+requires `yes` to publish. Entering no or EOF cancels. Non-terminal use requires
+`--answers`; missing required answers never fall back to prompting.
+
+Start from one of these complete answer templates and replace every `REPLACE_*`
+value and example path before running:
+
+- [embedded-local](../examples/options/embedded-local.answers.yaml): one machine,
+ local Harbor runner, colocated training/serving by default.
+- [service-ssh](../examples/options/service-ssh.answers.yaml): controller, SSH GPU
+ target and explicit SSH Docker workers.
+- [service-sandbox-local-runner](../examples/options/service-sandbox.answers.yaml):
+ controller and runner here, SSH GPU target, Sandbox task execution.
+
+Templates deliberately contain invalid digest placeholders. They cannot claim
+training readiness without your real inputs. Default one-GPU colocate is a
+configuration starting point, not a Qwen model memory-fit recommendation.
+
+Answers allow only `answers_version: 1`, `preset`, `training`, `deployment`, and
+optional `sandbox_config`. Overrides use the existing schema section/field names;
+unknown keys fail. No expressions or shell commands are evaluated. Gateway fields,
+shared run root, GPU budget and worker destinations are derived when only one
+side is provided. Contradictory explicit values fail. Supply matching origin URLs
+when changing ports. Paths in training overrides are relative to the **final
+output directory**, not the answers file. Deployment machine paths are absolute.
+
+The Sandbox preset can also describe remote runners through an answers file:
+set `training.harbor.runner_transport: ssh` and provide deployment rollout workers
+with `docker: false`. The builder derives training worker destinations and the
+first worker's runner Python. All configured interpreters still require preflight.
+
+`--probe-local` explicitly enables bounded local `nvidia-smi` UUID and current
+Python version queries. It also reads explicit SSH Host aliases (no wildcard,
+Match execution or Include expansion) and gives a parent-directory access hint.
+Missing tools and failed commands return `unknown`, not zero GPUs. Suggestions
+never silently change answers; no remote probe or write permission test occurs.
+
+The output directory must not exist, and its parent must exist. Publication uses
+an exclusive per-target lock, same-parent staging and a directory rename. Existing
+files are never overwritten. Failures clean up only this invocation's staging and
+lock. After an abrupt process termination, inspect any remaining `.NAME.init.lock`
+and staging directory before removing them; automatic stale-lock recovery is not
+attempted. The lock coordinates wizard processes, not arbitrary external writers.
+
+The bundle contains `config.yaml`, `deployment.yaml`, and `setup-manifest.json`.
+The manifest records schema versions, configuration digests, references and the
+offline validation scope, including deferred checks. It does not copy credentials.
+
+The CLI prints these next commands without executing them:
+
+```bash
+harborrl validate site-new/config.yaml --deployment site-new/deployment.yaml --check-files
+harborrl train site-new/config.yaml --deployment site-new/deployment.yaml --dry-run
+harborrl doctor site-new/config.yaml --deployment site-new/deployment.yaml
+```
+
+`doctor` performs live environment/connectivity checks. Actual training remains a
+separate action. Command examples use POSIX shell quoting.
diff --git a/docs/deployment.md b/docs/deployment.md
new file mode 100644
index 000000000..2f35a8501
--- /dev/null
+++ b/docs/deployment.md
@@ -0,0 +1,110 @@
+# Deployment configuration and the controller placement
+
+A `deployment.yaml`
+describes where the manager, GPU training target and rollout workers live and
+how they reach each other; the training YAML keeps every training-side field
+(learning rate, sampling, task catalog) and is unchanged.
+
+```bash
+harborrl train train.yaml --deployment deployment.yaml
+```
+
+Without `--deployment`, a `deployment.yaml` in the working directory is picked
+up; when no file exists the compatible embedded-local topology is used and the
+legacy same-machine behavior is preserved bit-for-bit (recorded in the plan as
+`deployment.source: embedded-local`).
+
+## What the schema covers (v1)
+
+- `manager` — the HarborRL controller node: `embedded` or `service` mode, bind
+ address, control/gateway ports and the URLs other machines use to reach them.
+ In `service` mode both URLs must be plain origins that match their ports and
+ must not be loopback.
+- `gpu_target` — the Slime training machine: `local` or `ssh` transport, the
+ checkout (`source_dir`), training interpreter (`train_python`), an optional
+ controlled `environment_file` (a JSON variable map, never a shell), and
+ `expected_gpus`.
+- `storage` — `shared_fs` mode with the shared `run_root` and writability
+ expectations per role.
+- `rollout` — `ssh_docker` workers: SSH destination, runner interpreter,
+ Docker requirement and task roots. The `embedded` provider keeps worker
+ discovery in the training config.
+- `connectivity` — timeouts and which control-loop hops are required.
+
+Secrets are rejected by the schema: no passwords, keys or API tokens. SSH
+destinations resolve through the invoking user's SSH config, agent or a
+restricted identity file. Paths are interpreted in target-machine semantics —
+the manager never assumes its own `sys.executable` works on the GPU or workers.
+See `examples/native/deployment-embedded.yaml` and
+`examples/native/deployment-service.yaml`.
+
+## Preflight graph
+
+`train` and `doctor` share one check graph, executed at the earliest launch
+stage. Each check records the role and machine it ran against, the outcome,
+timing, and a user-executable fix. Any failed check exits nonzero before a run
+directory or training process is created.
+
+Layer order: manager local state (run root, token file, ports) → GPU target
+(SSH reachability, paths, model paths, visible GPU budget) → rollout workers
+(SSH, runner Python >= 3.12, pinned Harbor version, Docker, task roots) →
+control-loop addresses (DNS resolution now, live health dials after components
+start). `--dry-run` parses both configs and prints the resolved plan without
+any remote probing or side effects.
+
+When the GPU target is remote, machine-local dependency probes (Ray, Torch,
+Slime imports, SGLang image, `nvidia-smi`) do not run on the manager: they are
+enforced fail-closed by `slime_train.sh` at GPU job startup. The manager CPU
+needs no GPU dependencies.
+
+## TrainingTarget
+
+`harborrl/deployment/training_target.py` launches one Slime job either as a
+local child (legacy path) or over SSH (service mode). The SSH target writes a
+generated `gpu-launcher.sh` into the shared run directory, starts it detached
+(`setsid`) on the GPU machine, records the launcher pid, polls the shared
+`exit-code` record, and cancels by signalling the remote process group
+(INT → TERM → KILL). It never resubmits or restarts a job.
+
+## RolloutService (manager mode)
+
+`harborrl/rollout/service.py` is the manager-side control plane: HTTP JSON
+with run-scoped tokens.
+
+- `GET /healthz`, `GET /readyz`
+- `POST /runs/{run_id}/register` — idempotent by manifest digest; the same id
+ with a different manifest is a 409.
+- `POST /runs/{run_id}/groups` — idempotent group submission. Same group id
+ and content returns the existing state; same id with different content is a
+ 409; submitting against a non-admitted policy version is a 409. On timeout
+ the response says `running` and points at the status endpoint — the client
+ must query, never resubmit.
+- `GET /runs/{run_id}/groups/{group_id}`, `POST .../cancel`
+- `POST /runs/{run_id}/policy/activate` — validates the whole serving pool is
+ healthy on the expected version, drains old admission, then opens the new
+ version and publishes `policy-pool.json` into the shared run directory.
+- `POST /runs/{run_id}/serving/announce` — the GPU job publishes its inference
+ endpoint; the manager never hand-configures it.
+- `POST /runs/{run_id}/drain`, `POST /runs/{run_id}/cancel`
+
+Bulky data (trajectory manifests, token ids, logprobs, loss masks, group
+export) is written to the shared run directory. HTTP responses carry paths
+plus SHA-256 digests; `GroupResult` also carries per-slot identity, finish
+reason, reward, trace seal status and verifier digest.
+
+The GPU-side adapter (`harborrl/backends/slime_v032/rollout_service_client.py`)
+recomputes every manifest digest, checks identities against the request,
+requires a complete group of at least two slots on the locked policy version,
+and only then converts trajectories into Slime samples. It is selected by
+`HARBORRL_ROLLOUT_SERVICE_URL` in the GPU job environment.
+
+## Operational limits
+
+Service mode requires shared storage visible at the configured paths on the
+controller, GPU target and runners. A successful dry run checks configuration;
+it does not establish network reachability, available capacity or training
+quality. Check connectivity with doctor and inspect run artifacts after launch.
+
+Choose worker and group concurrency for the target deployment. Instance
+readiness does not imply that an agent is running, and a completed checkpoint
+does not establish learning improvement.
diff --git a/docs/sandbox-rollout.md b/docs/sandbox-rollout.md
new file mode 100644
index 000000000..6fdc9d19a
--- /dev/null
+++ b/docs/sandbox-rollout.md
@@ -0,0 +1,72 @@
+# Sandbox rollout
+
+Sandbox is an optional execution environment under the common Controller,
+RolloutService, Runtime and Harbor Runner. Docker execution remains the default.
+The core does not import the SDK until an explicitly selected runner needs it.
+
+## Standard OpenSandbox
+
+Use Python 3.12 or newer for the runner and install `pip install -e '.[sandbox]'`.
+Start with `examples/native/sandbox.yaml`. Set the service origin (without a
+trailing /v1), a private credential-file reference and exact task image bindings.
+Standard credentials are opaque tokens, not a platform-specific AK:SK format.
+
+Select the environment in your training config:
+
+```yaml
+harbor:
+ python: /opt/harborrl/runner/bin/python
+ version: 0.23.0
+ runner_transport: local
+ workers: []
+ environment:
+ runtime: sandbox
+ config: /srv/harborrl/site/sandbox.yaml
+```
+
+For service placement, use `rollout.provider: sandbox` in the deployment file.
+A local runner requires an empty deployment worker list. SSH runners require
+explicit workers, the same shared task/evidence paths and the adapter installed
+on each runner. Runner placement is independent of the environment provider.
+The target and route must be validated before claiming that an SSH/Sandbox
+deployment works on a real platform.
+
+The standard adapter uses Harbor's OpenSandbox implementation and adds durable
+allocation tracking, bounded lifecycle operations and cleanup-debt records.
+Custom trusted adapters subclass ManagedOpenSandboxEnvironment and are selected
+through `adapter: module:Class`; arbitrary task input cannot select code.
+Provider settings belong in `options` and per-image binding `options`.
+They must not contain inline secrets. Core plan construction does not import
+the adapter, read credentials, connect to a platform or allocate instances.
+
+The optional `installation` block supports a pinned offline Claude binary:
+`mode: offline_binary`, `version`, `binary`, and its 64-character `sha256`.
+Without it, Harbor performs normal agent setup, which can need network access.
+
+## Concurrency and evidence
+
+`max_live_instances` bounds all active instances; each image binding has its
+own limit. Binding capacity is acquired before global capacity to avoid
+starvation. `startup_concurrency` separately bounds simultaneous SDK startup
+using process-shared locks. Shared-filesystem flock support is required when
+runners use the same lock directory across hosts.
+
+Groups use the same identity, policy barrier, verifier and consumption audit
+as Docker. Failed slots cancel their group siblings; submitted independent
+groups finish before a partial batch is rejected. Unconfirmed cleanup blocks
+new admission. Production export never accepts synthetic evidence.
+
+## Checks
+
+`python scripts/sandbox_sdk_contract.py` checks installed APIs and fake lifecycle
+cleanup without contacting a platform. `sandbox_probe` checks the selected
+adapter, SDK, credential-file permissions and optional binary locally.
+Its explicit `--network` mode is adapter-specific and reports skipped checks.
+
+`python scripts/sandbox_smoke.py CONFIG --output NEW_DIRECTORY --gateway URL`
+is a live allocation test and consumes platform resources. A successful CPU
+test or dependency probe does not establish live training acceptance.
+
+For Brainbox and developer-mediated networking, see the independently
+installable [H-cluster practice](../practices/h_cluster/README.md). H-specific
+configuration from the experimental branch must be migrated as described there.
diff --git a/docs/train-inference-consistency.md b/docs/train-inference-consistency.md
new file mode 100644
index 000000000..f4bd4d2d2
--- /dev/null
+++ b/docs/train-inference-consistency.md
@@ -0,0 +1,79 @@
+# Train / inference consistency
+
+Consistency checks compare log probabilities of the **same response tokens at
+the same prefix**, before an optimizer update. This measures backend drift;
+it does not measure model quality or full-distribution KL divergence.
+
+The three policies are the observed behavior `q`, frozen pre-update trainer
+`p0`, and current trainer `pθ`. Backend drift is `log p0 - log q`; optimization
+drift is `log pθ - log p0`. The Native loss remains unchanged. No TIS, clipping
+correction, or new training objective is introduced.
+
+## Configuration
+
+Native schema 2 accepts an optional `consistency` mapping. Its default mode is
+`off`. Existing configurations remain valid. `audit` records numerical violations;
+`enforce` rejects violations before
+training or serving admission. Invalid evidence always rejects, including in
+audit mode. Enforce requires an explicit `max_abs_delta`. Calibrate tolerances
+for the actual model, precision, parallel layout and kernels.
+
+```yaml
+consistency:
+ mode: audit
+ max_abs_delta: null
+ max_mean_abs_delta: null
+ bad_token_delta: null
+ max_bad_fraction: 0.0
+ worst_tokens: 8
+```
+
+Native overrides use the existing `--set consistency.mode=audit` interface.
+For enforce, configure a deployment-specific `max_abs_delta` in YAML before
+loading the configuration. The configuration is preserved in launch evidence.
+
+## Native Megatron / SGLang
+
+The enabled launcher runs `harborrl.consistency.train`, which uses the pinned
+Slime training loop and its supported actor factory. It never edits upstream
+Slime. The existing pre-update forward supplies actual packed-batch `p0` values.
+Immutable serving sidecars bind token IDs, float32-normalized behavior scores,
+response masks, policy version, tokenizer and template identity. Missing or
+mismatched sidecars reject the batch.
+
+Before each weight synchronization, the actor evaluates two fixed token
+fixtures using Megatron. Every synchronized SGLang engine then teacher-forces
+those exact sequences. Generation-time and post-probe versions must both match.
+Each engine must satisfy the numerical limits independently. Checks also cover
+the final sync, even when no subsequent rollout occurs. Probe records are reused
+only for the same pool, configuration and immutable reference. A fixed canary
+is a smoke check; actual rollout-batch checks provide broader coverage.
+
+Supported initial scope: dense models, synchronous full NCCL synchronization,
+PP=CP=1, with TP and DP. Temperature and top-p must be 1; top-k must be disabled.
+Critic, old-actor queues, MoE, release-train and routing replay are rejected.
+Transformed sampling would require a separate definition of behavior scores.
+
+`RUN_DIR/consistency` contains sync references, pool admissions, serving-turn
+sidecars and `batch-*.json` reports. References include a bounded sampled
+parameter fingerprint per rank; this is **not** a full weight checksum. Reports
+contain valid-token-weighted absolute/signed errors, log-ratio range, bad-token
+fraction, bounded worst positions and histogram percentile **upper bounds**.
+They do not copy raw prompt or response text. TP duplicate counts are excluded.
+Distributed validation collects rank errors before all ranks reject.
+
+## Validation and limitations
+
+Run `pytest -q tests/test_consistency.py` for the dependency-free checks. With
+Torch installed, run `pytest -q tests/test_consistency_torch.py` for two-rank
+failure propagation. These tests do not run a separate training loop.
+
+GPU acceptance must additionally exercise the locked Megatron / SGLang versions
+on the target hardware: initial sync, actual rollout tokens, optimizer update,
+final sync and checkpoint resume. Record dtype, attention backend, TP/DP, model
+revision and measured error distribution. CPU tests and mocked HTTP responses
+cannot certify SGLang input-logprob behavior or CUDA numerical tolerances.
+This implementation has not yet passed that live GPU acceptance gate.
+
+H-cluster relay topology remains a deployment practice. No H-cluster hostnames,
+private paths or machine-specific numerical thresholds belong in this module.
diff --git a/docs/web-console.md b/docs/web-console.md
new file mode 100644
index 000000000..3d9603ccc
--- /dev/null
+++ b/docs/web-console.md
@@ -0,0 +1,110 @@
+# Local web console
+
+```bash
+harborrl web --workspace ./harborrl-web
+```
+
+This opens a browser on an available loopback port. Keep the terminal running.
+`--port 8765` chooses a fixed port; `--no-browser` prints the URL without opening
+it. The workspace stores saved configuration bundles and execution records.
+One console process owns a workspace at a time. The web console uses the core
+Python dependencies; Node, a frontend build and CDN access are not required.
+
+## Configure and launch
+
+1. Select Local, SSH workers, or Sandbox. Fill controller/GPU/runner paths and
+ service origins. Source and interpreter paths refer to the selected machine.
+ The controller source directory and Python executable select the checkout and
+ environment used by the launched CLI.
+2. Provide existing checkpoint/reference directories, a pinned task catalog,
+ harness profile and real tokenizer/template fingerprints. The
+ [configuration CLI guide](configuration-wizard.md) describes preparing these
+ inputs. The console never downloads or generates model inputs.
+3. Choose GPU layout, tensor parallelism, rollout batches and sampling settings.
+ Allocation defaults are configuration values, not a model memory-fit estimate.
+4. Review the generated YAML and validation. **Check local files** additionally
+ checks references on the controller. Deferred GPU, connectivity, model and
+ Sandbox checks remain explicit. Save the bundle under a new configuration name.
+5. Run **Dry-run** to build the actual CLI launch plan. **Doctor** performs live
+ checks after confirmation. **Start training** requires a separate confirmation
+ and invokes the existing `harborrl train` flow, including its preflight.
+
+Configuration names are limited to letters, digits, underscores and hyphens.
+Saved configurations are not overwritten. Reopen one to launch it again, or edit
+and save under a new name. The advanced answers editor and YAML/JSON import use
+the existing `answers_version: 1` contract. Download an individual generated file
+or export answers from the editor. Advanced remote Sandbox runners can be
+specified through imported answers; the form preset defaults to a local runner.
+
+H-cluster deployments remain the optional
+[H practice](../practices/h_cluster/README.md). Prepare its Sandbox configuration
+separately and reference the path. H project/environment identifiers and relay
+settings are not part of the core web form.
+
+Windows supports configuration and dry-run. Live training/doctor controls require
+the console to run on a POSIX controller, using the same environment and checkout
+that would normally run the training CLI. Deployment readiness still depends on
+the project's supported Linux training environment.
+
+## Runs and shutdown
+
+Only one execution is active per workspace, including doctor and dry-run. This
+prevents duplicate launches within this console; it is not a cluster-wide GPU
+scheduler. Other CLI processes or different workspaces can still use the same
+resources, and operators must coordinate those allocations.
+
+Each job keeps immutable input files and a durable `job.json`/`console.log` under
+`WORKSPACE/jobs/JOB_ID`. Execution appends `web/JOB_ID` to the configured shared
+run root and updates both config files in its snapshot. The original saved bundle
+is unchanged. The existing runtime then creates its normal `training/...` run
+directory there. This isolates artifacts while retaining the configured shared
+filesystem. Run logs show the actual snapshot and output paths.
+
+The Runs view polls status and the last 64 KiB of output. **Controller** shows the
+CLI output and errors. **GPU training** reads the separate `train-stdout.log` when
+the service deployment creates one. Embedded training generally streams into
+the controller log. Full logs remain on disk; the browser download contains the
+currently displayed tail. This initial release does not aggregate WandB curves
+or claim remote/GPU progress from a quiet controller log.
+
+**Stop** sends an interrupt through the existing CLI lifecycle. Local training
+forwards it to the training process group; service mode uses the existing SSH
+target cancellation. `stopping` means cleanup is pending, not that resources are
+already free. Closing the browser does not stop a job. Ctrl+C or SIGTERM in the
+server terminal requests cleanup; shutdown waits up to 30 seconds. A timeout
+records an unknown state without force-killing arbitrary remote processes.
+
+After a crash or interrupted shutdown, previously running jobs appear as
+`unknown`. New launches are blocked. Inspect the target processes, shared run
+records and Sandbox cleanup evidence, then use **Confirm cleanup** to acknowledge
+that external cleanup is complete. This action records operator confirmation; it
+does not kill a stored PID or automatically relaunch a job. The server never
+signals a PID recovered from an old session.
+
+## Remote controller
+
+On the controller, from the intended checkout/environment:
+
+```bash
+python -m harborrl.cli web --workspace /srv/shared/web-console --port 8765 --no-browser
+```
+
+On the workstation, create a local SSH forward:
+
+```bash
+ssh -N -L 127.0.0.1:8765:127.0.0.1:8765 your-controller-alias
+```
+
+Open the URL printed by the controller in the workstation browser. If the local
+forward uses a different port, replace only the URL port. Leave the `#token=...`
+fragment intact. If either port is occupied, choose an unused port; omitting
+`--port` selects one automatically and prints it.
+
+The listener is deliberately fixed to `127.0.0.1`. A random per-session token
+protects API reads and writes; the browser moves the URL fragment into
+sessionStorage and removes it from the address bar. State-changing requests also
+require a same-origin JSON request. Responses use CSP, frame blocking and
+no-store headers, and assets are packaged locally. Keep the token private: this
+is a local single-user console with the controller user's filesystem and training
+privileges. It is not a public multi-user service or an Internet-facing gateway.
+Do not expose it through a public reverse proxy.
diff --git a/examples/native/catalog.jsonl b/examples/native/catalog.jsonl
new file mode 100644
index 000000000..eba16a31c
--- /dev/null
+++ b/examples/native/catalog.jsonl
@@ -0,0 +1 @@
+{"id": "hello_world", "path": "tasks/hello_world", "revision": "v0.1-example", "reward_profile": {"key": "reward", "offset": 0.0, "raw_range": [0.0, 1.0], "scale": 1.0}, "task_digest": "905f496d2852cca5b1e86e9de72395b41c291d2b8f6a80807e0ed24ca19541b3"}
diff --git a/examples/native/deployment-embedded.yaml b/examples/native/deployment-embedded.yaml
new file mode 100644
index 000000000..12c5cef81
--- /dev/null
+++ b/examples/native/deployment-embedded.yaml
@@ -0,0 +1,45 @@
+# Embedded (same-machine) deployment: identical to the legacy pipeline.
+# Use this file explicitly, or simply omit --deployment to get the same
+# embedded-local topology built from the training config.
+schema_version: 1
+
+manager:
+ id: embedded-local
+ mode: embedded
+ bind: 127.0.0.1
+ control_port: 49200
+ control_url: http://127.0.0.1:49200
+ gateway_port: 49184
+ gateway_url: http://127.0.0.1:49184
+ run_token_file: /run/secrets/harborrl/run-token
+ source_dir: /opt/harborrl/source
+ controller_python: /opt/harborrl/controller/bin/python
+
+gpu_target:
+ id: local
+ transport: local
+ ssh: null
+ source_dir: /opt/harborrl/source
+ train_python: /opt/harborrl/training/bin/python
+ environment_file: null
+ expected_gpus: 8
+
+storage:
+ mode: shared_fs
+ run_root: /mnt/shared/harborrl/runs
+ manager_writable: true
+ gpu_writable: true
+ worker_writable: true
+
+rollout:
+ # The embedded provider keeps worker discovery in the training config.
+ provider: embedded
+ workers: []
+
+connectivity:
+ command_timeout_s: 30
+ ssh_timeout_s: 15
+ http_timeout_s: 10
+ gpu_to_controller: true
+ worker_to_gateway: true
+ manager_to_gpu_inference: true
diff --git a/examples/native/deployment-service.yaml b/examples/native/deployment-service.yaml
new file mode 100644
index 000000000..b3584f5f9
--- /dev/null
+++ b/examples/native/deployment-service.yaml
@@ -0,0 +1,43 @@
+# Replace example hosts and paths; no site-specific addresses are required.
+schema_version: 1
+manager:
+ id: manager
+ mode: service
+ bind: 0.0.0.0
+ control_port: 49200
+ control_url: http://manager.example:49200
+ gateway_port: 49184
+ gateway_url: http://manager.example:49184
+ run_token_file: /srv/harborrl/site/run-token
+ source_dir: /srv/harborrl/repo
+ controller_python: /opt/harborrl/controller/bin/python
+gpu_target:
+ id: trainer
+ transport: ssh
+ ssh: {host: training-host}
+ source_dir: /srv/harborrl/repo
+ train_python: /opt/harborrl/training/bin/python
+ environment_file: /srv/harborrl/site/gpu-env.json
+ expected_gpus: 8
+storage:
+ mode: shared_fs
+ run_root: /srv/harborrl/runs
+ manager_writable: true
+ gpu_writable: true
+ worker_writable: true
+rollout:
+ provider: ssh_docker
+ workers:
+ - id: worker-a
+ ssh: {host: rollout-host}
+ source_dir: /srv/harborrl/repo
+ runner_python: /opt/harborrl/runner/bin/python
+ docker: true
+ task_roots: [/srv/harborrl/tasks]
+connectivity:
+ command_timeout_s: 30
+ ssh_timeout_s: 15
+ http_timeout_s: 10
+ gpu_to_controller: true
+ worker_to_gateway: true
+ manager_to_gpu_inference: true
diff --git a/examples/native/sandbox.yaml b/examples/native/sandbox.yaml
new file mode 100644
index 000000000..4ab3788a5
--- /dev/null
+++ b/examples/native/sandbox.yaml
@@ -0,0 +1,10 @@
+# Standard OpenSandbox; install harborrl[sandbox] in a Python 3.12+ runner.
+domain: https://sandbox.example
+credential_file: /run/secrets/harborrl/sandbox-key
+max_live_instances: 2
+startup_concurrency: 1
+bindings:
+ - image: registry.example/project/task:locked-version
+ max_live_instances: 2
+# Omit installation to use normal Harbor agent setup. Offline deployments
+# may supply mode, version, binary and sha256; see docs/sandbox-rollout.md.
diff --git a/examples/options/embedded-local.answers.yaml b/examples/options/embedded-local.answers.yaml
new file mode 100644
index 000000000..7a4bc5db1
--- /dev/null
+++ b/examples/options/embedded-local.answers.yaml
@@ -0,0 +1,30 @@
+# Replace every example path and REPLACE_* value. No model fit is implied.
+answers_version: 1
+preset: embedded-local
+training:
+ tasks:
+ catalog: /srv/harborrl/inputs/tasks.jsonl
+ harness:
+ profile: /srv/harborrl/inputs/claude-code.json
+ harbor:
+ python: /opt/harbor/bin/python
+ model:
+ checkpoint: /srv/models/qwen
+ reference: /srv/models/qwen-reference
+ args_file: qwen3-8B
+ gateway:
+ tokenizer_digest: REPLACE_WITH_ACTUAL_TOKENIZER_SHA256
+ template_digest: REPLACE_WITH_ACTUAL_TEMPLATE_SHA256
+ deployment:
+ layout: colocate
+ num_gpus: 1
+ sampling:
+ group_size: 2
+ groups_per_batch: 1
+deployment:
+ manager:
+ source_dir: /srv/harborrl/source
+ controller_python: /opt/controller/bin/python
+ gpu_target:
+ source_dir: /srv/harborrl/source
+ train_python: /opt/training/bin/python
diff --git a/examples/options/service-sandbox.answers.yaml b/examples/options/service-sandbox.answers.yaml
new file mode 100644
index 000000000..a55de6d2f
--- /dev/null
+++ b/examples/options/service-sandbox.answers.yaml
@@ -0,0 +1,34 @@
+# Prepare a standard or practice-specific Sandbox config separately.
+answers_version: 1
+preset: service-sandbox-local-runner
+sandbox_config: /srv/shared/site/sandbox.yaml
+training:
+ tasks:
+ catalog: /srv/shared/inputs/tasks.jsonl
+ harness:
+ profile: /srv/shared/inputs/claude-code.json
+ harbor:
+ python: /opt/sandbox-runner/bin/python
+ model:
+ checkpoint: /srv/models/qwen
+ reference: /srv/models/qwen-reference
+ args_file: qwen3-8B
+ gateway:
+ tokenizer_digest: REPLACE_WITH_ACTUAL_TOKENIZER_SHA256
+ template_digest: REPLACE_WITH_ACTUAL_TEMPLATE_SHA256
+ deployment:
+ layout: colocate
+ num_gpus: 1
+deployment:
+ manager:
+ source_dir: /srv/shared/harborrl
+ controller_python: /opt/controller/bin/python
+ control_url: http://controller.example:49200
+ gateway_url: http://controller.example:49184
+ gpu_target:
+ ssh:
+ host: gpu-training
+ source_dir: /srv/shared/harborrl
+ train_python: /opt/training/bin/python
+ storage:
+ run_root: /srv/shared/runs
diff --git a/examples/options/service-ssh.answers.yaml b/examples/options/service-ssh.answers.yaml
new file mode 100644
index 000000000..7a5851da4
--- /dev/null
+++ b/examples/options/service-ssh.answers.yaml
@@ -0,0 +1,44 @@
+# All paths refer to the named role's machine. Replace before use.
+answers_version: 1
+preset: service-ssh
+training:
+ tasks:
+ catalog: /srv/shared/inputs/tasks.jsonl
+ harness:
+ profile: /srv/shared/inputs/claude-code.json
+ model:
+ checkpoint: /srv/models/qwen
+ reference: /srv/models/qwen-reference
+ args_file: qwen3-8B
+ gateway:
+ tokenizer_digest: REPLACE_WITH_ACTUAL_TOKENIZER_SHA256
+ template_digest: REPLACE_WITH_ACTUAL_TEMPLATE_SHA256
+ deployment:
+ layout: split
+ num_gpus: 8
+ actor_gpus: 4
+ rollout_gpus: 4
+ actor_tensor_parallel_size: 4
+ rollout_gpus_per_engine: 4
+deployment:
+ manager:
+ source_dir: /srv/shared/harborrl
+ controller_python: /opt/controller/bin/python
+ control_url: http://controller.example:49200
+ gateway_url: http://controller.example:49184
+ gpu_target:
+ ssh:
+ host: gpu-training
+ source_dir: /srv/shared/harborrl
+ train_python: /opt/training/bin/python
+ storage:
+ run_root: /srv/shared/runs
+ rollout:
+ workers:
+ - id: runner-0
+ ssh:
+ host: rollout-worker
+ source_dir: /srv/shared/harborrl
+ runner_python: /opt/harbor/bin/python
+ docker: true
+ task_roots: [/srv/shared/tasks]
diff --git a/harborrl/algorithms/__init__.py b/harborrl/algorithms/__init__.py
new file mode 100644
index 000000000..61b32100b
--- /dev/null
+++ b/harborrl/algorithms/__init__.py
@@ -0,0 +1 @@
+"""Training algorithm adapters independent of backend hook plumbing."""
diff --git a/harborrl/algorithms/grpo.py b/harborrl/algorithms/grpo.py
new file mode 100644
index 000000000..34e98d7dc
--- /dev/null
+++ b/harborrl/algorithms/grpo.py
@@ -0,0 +1,18 @@
+"""GRPO semantics backed by the existing CPU-verified implementations."""
+from __future__ import annotations
+
+from harborrl.backends.slime_v032.advantage import compute_advantages_and_returns
+from harborrl.backends.slime_v032.loss import clipped_loss, loss_function
+
+
+class GRPOAlgorithmAdapter:
+ algorithm_id = "grpo"
+
+ def compute_advantages_and_returns(self, args, rollout_data):
+ return compute_advantages_and_returns(args, rollout_data)
+
+ def clipped_loss(self, *args, **kwargs):
+ return clipped_loss(*args, **kwargs)
+
+ def backend_loss_function(self, *args, **kwargs):
+ return loss_function(*args, **kwargs)
diff --git a/harborrl/backends/slime_v032/config.py b/harborrl/backends/slime_v032/config.py
new file mode 100644
index 000000000..cc52b864d
--- /dev/null
+++ b/harborrl/backends/slime_v032/config.py
@@ -0,0 +1,125 @@
+"""Schema-2 to official Slime launch mapping; no live probes."""
+import json
+import os
+import sys
+
+CONFIG_ENV = {
+ "HF_CKPT": ("model", "checkpoint"),
+ "REF_LOAD": ("model", "reference"),
+ "MODEL_ARGS_FILE": ("model", "args_file"),
+ "MODEL_TAG": ("model", "args_file"),
+ "NUM_ROLLOUT": ("training", "num_rollout"),
+ "SAVE_INTERVAL": ("training", "save_interval"),
+ "LR": ("training", "learning_rate"),
+ "ROLLOUT_BATCH_SIZE": ("sampling", "groups_per_batch"),
+ "N_SAMPLES": ("sampling", "group_size"),
+ "ROLLOUT_MAX_RESPONSE_LEN": ("sampling", "max_tokens"),
+ "ROLLOUT_MAX_CONTEXT_LEN": ("sampling", "max_context"),
+ "NUM_GPUS": ("deployment", "num_gpus"),
+ "ACTOR_GPUS": ("deployment", "actor_gpus"),
+ "ROLLOUT_GPUS": ("deployment", "rollout_gpus"),
+ "TP_SIZE": ("deployment", "actor_tensor_parallel_size"),
+ "ROLLOUT_NUM_GPUS_PER_ENGINE": ("deployment", "rollout_gpus_per_engine"),
+ "HARBORRL_GPU_LAYOUT": ("deployment", "layout"),
+ "HARBORRL_NATIVE_RUNNER_PYTHON": ("harbor", "python"),
+ "HARBORRL_NATIVE_PROFILE": ("harness", "profile"),
+ "HARBORRL_NATIVE_CATALOG": ("tasks", "catalog"),
+ "HARBORRL_NATIVE_GATEWAY_HOST": ("gateway", "host"),
+ "HARBORRL_NATIVE_GATEWAY_PORT": ("gateway", "port"),
+ "HARBORRL_NATIVE_GATEWAY_URL": ("gateway", "advertised_url"),
+ "HARBORRL_NATIVE_TOKENIZER_DIGEST": ("gateway", "tokenizer_digest"),
+ "HARBORRL_NATIVE_TEMPLATE_DIGEST": ("gateway", "template_digest"),
+ "HARBORRL_NATIVE_MAX_ATTEMPTS": ("sampling", "max_attempts"),
+ "RUNS_ROOT": ("output", "root"),
+}
+
+
+def launch_plan(config):
+ from harborrl.backends.slime_v032.versions import runtime_contract_id
+ from harborrl.config import ROOT
+ from harborrl.config.native import validate, validate_files
+ from harborrl.math_registry import select_math
+ from harborrl.tasks.catalog import catalog
+ validate(config, ROOT / "config.yaml")
+ validate_files(config)
+ selection = select_math(config)
+ entries = catalog(config)
+ runtime = config['harbor'].get('environment', {'runtime': 'docker'})
+ sandbox = None
+ if runtime['runtime'] == 'sandbox':
+ from harborrl.rollout.harbor_job.sandbox_config import (
+ load_sandbox,
+ task_binding,
+ )
+ from harborrl.trajectories.native import read_json
+ sandbox = load_sandbox(runtime['config'])
+ profile = read_json(config['harness']['profile'])
+ if sandbox['installation'] and sandbox['installation']['version'] != profile['cli_version']:
+ raise ValueError('sandbox installation version differs from harness profile')
+ budget = sum(profile[k] for k in ('agent_timeout_sec', 'setup_timeout_sec', 'verifier_timeout_sec'))
+ if sandbox['lifecycle_minutes'] * 60 <= budget + sandbox['create_timeout_s'] + 2 * sandbox['cleanup_timeout_s']:
+ raise ValueError('sandbox lifecycle must cover setup, agent, verifier and cleanup budgets')
+ for entry in entries:
+ task_binding(sandbox, entry['path'])
+ c = config
+ worker_overrides = os.environ.get('HARBORRL_NATIVE_TASK_WORKER_OVERRIDES', '{}')
+ try:
+ overrides = json.loads(worker_overrides)
+ valid_workers = set(c['harbor']['workers'])
+ if (not isinstance(overrides, dict) or any(not isinstance(key, str) or not isinstance(value, str) or value not in valid_workers
+ for key, value in overrides.items())):
+ raise ValueError
+ except (ValueError, json.JSONDecodeError) as exc:
+ raise ValueError('invalid HARBORRL_NATIVE_TASK_WORKER_OVERRIDES') from exc
+ environment = {
+ **{name: c[section][field] for name, (section, field) in CONFIG_ENV.items()},
+ 'DATASET': selection.task.id, 'ALGO': selection.algorithm.id,
+ 'HARNESS_OPTION': f"native-{selection.harness.id.replace('_', '-')}",
+ 'TRAIN_PYTHON': sys.executable,
+ 'HARBORRL_STRUCTURED_LAUNCH': '1', 'HARBORRL_VERIFY_POLICY_POOL': '1',
+ 'HARBORRL_SKIP_GLOBAL_CLEANUP': '1', 'SLIME_RAY_PLACEMENT_GPU_PROBE': '1',
+ 'PYTORCH_CUDA_ALLOC_CONF': ('max_split_size_mb:2048' if c['deployment']['layout'] == 'colocate'
+ else 'max_split_size_mb:2048,expandable_segments:True'),
+ 'HARBORRL_NATIVE_ROLLOUT': '1',
+ 'HARBORRL_NATIVE_SLIME_CONTRACT': runtime_contract_id(c['training']['backend_contract']),
+ 'HARBORRL_NATIVE_RUNNER_WORKERS': json.dumps(c['harbor']['workers']),
+ 'HARBORRL_NATIVE_TASK_WORKER_OVERRIDES': json.dumps(overrides, sort_keys=True),
+ 'HARBORRL_NATIVE_AUDITED_LOGPROBS': str(int(c['gateway']['audited_raw_logprobs'])),
+ 'HARBORRL_NATIVE_DRAIN_TIMEOUT': '30',
+ 'WANDB_ENABLE': '0',
+ 'HARBORRL_CONSISTENCY': json.dumps(c['consistency'], sort_keys=True),
+ 'HARBORRL_CONSISTENCY_MODE': c['consistency']['mode'],
+ }
+ environment['HARBORRL_NATIVE_RUNNER_TRANSPORT'] = c['harbor'].get('runner_transport', 'ssh')
+ if sandbox is not None:
+ environment['HARBORRL_NATIVE_SANDBOX'] = json.dumps(sandbox, sort_keys=True)
+ return {'config': config, 'math': selection.asdict(),
+ 'command': [sys.executable, '-m', 'harborrl.platform.native_train'],
+ 'environment': {key: str(value) for key, value in environment.items()},
+ 'training_command': ['bash', str(ROOT / 'harborrl/platform/slime_train.sh')],
+ 'required_services': ['GPU Ray / SGLang / Messages Gateway',
+ 'Sandbox / Harbor runner' if sandbox else 'external CPU Harbor Docker workers'],
+ 'launcher': f"native {selection.harness.display_name} → Messages Gateway → "
+ f"{selection.task.display_name} verifier → "
+ f"{selection.algorithm.display_name}",
+ 'layout': {'mode': config['deployment']['layout'], 'offload': config['deployment']['layout']=='colocate'}}
+
+
+def field_usage():
+ """Account for every schema-2 field, including non-environment consumers."""
+ usage = {}
+ from harborrl.consistency.config import DEFAULTS
+ usage.update({f'consistency.{key}': ['environment:HARBORRL_CONSISTENCY'] for key in DEFAULTS})
+ for name, (section, field) in CONFIG_ENV.items():
+ usage.setdefault(f"{section}.{field}", []).append(f"environment:{name}")
+ usage.update({
+ "execution.backend": ["schema:harbor_job"],
+ "harness.name": ["schema:claude_code", "runner:Claude Code"],
+ "harbor.version": ["preflight:worker runtime version"],
+ "training.backend_contract": ["environment:HARBORRL_NATIVE_SLIME_CONTRACT", "preflight:backend hooks"],
+ "harbor.workers": ["environment:HARBORRL_NATIVE_RUNNER_WORKERS", "preflight:worker probe"],
+ "harbor.runner_transport": ["environment:HARBORRL_NATIVE_RUNNER_TRANSPORT"],
+ "harbor.environment": ["environment:HARBORRL_NATIVE_SANDBOX", "runner:environment"],
+ "gateway.audited_raw_logprobs": ["environment:HARBORRL_NATIVE_AUDITED_LOGPROBS"],
+ })
+ return usage
diff --git a/harborrl/backends/slime_v032/converter.py b/harborrl/backends/slime_v032/converter.py
index 6098332b3..075a121ae 100644
--- a/harborrl/backends/slime_v032/converter.py
+++ b/harborrl/backends/slime_v032/converter.py
@@ -131,6 +131,8 @@ def convert_samples_to_train_data(args, samples):
data["loss_masks"].append(response_mask)
data["rollout_log_probs"].append(list(span.old_logprobs))
data["rollout_mask_sums"].append(unit.token_count)
+ from harborrl.consistency.slime import record_turn
+ record_turn(ir["turns"][turn_index], list(span.tokens), list(span.old_logprobs), response_mask)
for group in batch.groups:
weight_sum = math.fsum(
1.0 / (group.group_size * unit.token_count)
diff --git a/harborrl/backends/slime_v032/launcher.py b/harborrl/backends/slime_v032/launcher.py
index c0f5c9d0d..3450782d1 100644
--- a/harborrl/backends/slime_v032/launcher.py
+++ b/harborrl/backends/slime_v032/launcher.py
@@ -4,40 +4,37 @@
import sys
from harborrl.backends.slime_v032.versions import BACKEND_CONTRACT
+from harborrl.math_registry import MATH_REGISTRY
-HOOK_PATHS = {
- "rollout": "harborrl.backends.slime_v032.rollout.generate_rollout",
- "converter": "harborrl.backends.slime_v032.converter.convert_samples_to_train_data",
- "advantage": "harborrl.backends.slime_v032.advantage.compute_advantages_and_returns",
- "loss": "harborrl.backends.slime_v032.loss.loss_function",
- "postprocess": "harborrl.backends.slime_v032.postprocess.rollout_data_postprocess",
-}
-
-def hook_arguments(contract=BACKEND_CONTRACT):
+def _hooks(contract=BACKEND_CONTRACT):
if str(contract) != BACKEND_CONTRACT:
raise ValueError(f"unsupported backend contract: {contract!r}")
+ return MATH_REGISTRY.algorithm_for_contract(str(contract)).backend_hooks
+
+
+def hook_arguments(contract=BACKEND_CONTRACT):
+ hooks = _hooks(contract)
return [
- "--rollout-function-path", HOOK_PATHS["rollout"],
- "--custom-convert-samples-to-train-data-path", HOOK_PATHS["converter"],
- "--custom-advantage-function-path", HOOK_PATHS["advantage"],
+ "--rollout-function-path", hooks["rollout"],
+ "--custom-convert-samples-to-train-data-path", hooks["converter"],
+ "--custom-advantage-function-path", hooks["advantage"],
"--loss-type", "custom_loss",
- "--custom-loss-function-path", HOOK_PATHS["loss"],
- "--rollout-data-postprocess-path", HOOK_PATHS["postprocess"],
+ "--custom-loss-function-path", hooks["loss"],
+ "--rollout-data-postprocess-path", hooks["postprocess"],
]
def validate_hook_assignment(assignment, *, contract=BACKEND_CONTRACT):
- if str(contract) != BACKEND_CONTRACT:
- raise ValueError(f"unsupported backend contract: {contract!r}")
- observed = {key: value for key, value in assignment.items() if key in HOOK_PATHS}
- if observed != HOOK_PATHS:
+ hooks = _hooks(contract)
+ observed = {key: value for key, value in assignment.items() if key in hooks}
+ if observed != hooks:
raise ValueError(f"Slime hook assignment must exactly match {BACKEND_CONTRACT}")
return observed
def hook_modules():
- return {name: path.rsplit(".", 1)[0] for name, path in HOOK_PATHS.items()}
+ return {name: path.rsplit(".", 1)[0] for name, path in _hooks().items()}
def dry_run_command(entrypoint, base_args=(), *, contract=BACKEND_CONTRACT, python=sys.executable):
diff --git a/harborrl/backends/slime_v032/loss.py b/harborrl/backends/slime_v032/loss.py
index 72f386168..502c146eb 100644
--- a/harborrl/backends/slime_v032/loss.py
+++ b/harborrl/backends/slime_v032/loss.py
@@ -104,5 +104,12 @@ def loss_function(args, batch, logits, unused_reducer):
"native_token_count": loss_masks.sum().detach(),
"native_group_count": weight_sum.detach().round(),
}
+ from harborrl.consistency.config import environment
+ if environment()["mode"] != "off":
+ p0 = torch.cat(batch["log_probs"]).detach()
+ current = torch.cat(output["log_probs"]).detach()
+ metrics["consistency_backend_abs_sum"] = ((p0 - old_logprobs).abs() * loss_masks).sum()
+ metrics["consistency_update_abs_sum"] = ((current - p0).abs() * loss_masks).sum()
+ metrics["consistency_valid_tokens"] = loss_masks.sum().detach()
return loss * LOSS_SCALE_CORRECTION, metrics
diff --git a/harborrl/backends/slime_v032/postprocess.py b/harborrl/backends/slime_v032/postprocess.py
index d778412bc..7d4c89bb3 100644
--- a/harborrl/backends/slime_v032/postprocess.py
+++ b/harborrl/backends/slime_v032/postprocess.py
@@ -3,8 +3,6 @@
import math
-from harborrl.trajectories.native import finite
-
REQUIRED_FIELDS = (
"tokens",
"response_lengths",
@@ -34,7 +32,7 @@ def _integer_scalar(value):
return int(number) if math.isfinite(number) and number.is_integer() else None
-def rollout_data_postprocess(args, rollout_id, rollout_data):
+def _validate_rollout_data(args, rollout_id, rollout_data):
"""Slime ``--rollout-data-postprocess-path`` hook; fail closed on drift."""
missing = [field for field in REQUIRED_FIELDS if field not in rollout_data]
if missing:
@@ -51,8 +49,11 @@ def rollout_data_postprocess(args, rollout_id, rollout_data):
group_size = int(getattr(args, "n_samples_per_prompt", 0))
if group_size < 2:
raise ValueError("native training requires a complete group")
- local_weight_sum = 0.0
+ group_weights = {}
for index in range(count):
+ group_id = _integer_scalar(rollout_data["rollout_ids"][index])
+ if group_id is None or group_id < 0:
+ raise ValueError("invalid native rollout id")
response_length = _integer_scalar(rollout_data["response_lengths"][index])
mask = rollout_data["loss_masks"][index]
logprobs = rollout_data["rollout_log_probs"][index]
@@ -71,8 +72,22 @@ def rollout_data_postprocess(args, rollout_id, rollout_data):
raise ValueError("invalid native rollout mask sum")
if truncated is None or truncated not in (0, 1):
raise ValueError("invalid native truncated flag")
- local_weight_sum += sum(
- 1.0 / (group_size * mask_sum) for flag in mask if flag
+ group_weights.setdefault(group_id, []).append(
+ sum(1 for flag in mask if flag) / (group_size * mask_sum)
)
- if not finite(local_weight_sum) or local_weight_sum > 1.0 + 1e-9:
- raise ValueError(f"native local weights exceed the one-per-group budget: {local_weight_sum}")
+ # Actor shards may contain partial groups; each group has its own unit budget.
+ for group_id, weights in group_weights.items():
+ local_weight_sum = math.fsum(weights)
+ if not math.isfinite(local_weight_sum) or local_weight_sum > 1.0 + 1e-9:
+ raise ValueError(
+ f"native local weights exceed the one-per-group budget for rollout id "
+ f"{group_id}: {local_weight_sum}"
+ )
+
+
+def rollout_data_postprocess(args, rollout_id, rollout_data):
+ from harborrl.consistency.config import environment
+ if environment()["mode"] == "off":
+ return _validate_rollout_data(args, rollout_id, rollout_data)
+ from harborrl.consistency.slime import check_training_batch
+ return check_training_batch(args, rollout_id, rollout_data, _validate_rollout_data)
diff --git a/harborrl/backends/slime_v032/rollout.py b/harborrl/backends/slime_v032/rollout.py
index c232921c2..44c36e2c8 100644
--- a/harborrl/backends/slime_v032/rollout.py
+++ b/harborrl/backends/slime_v032/rollout.py
@@ -1,11 +1,28 @@
-"""Slime v0.3.2 rollout function hook for complete Native Harbor groups."""
+"""Slime v0.3.2 rollout function hook for complete Native Harbor groups.
+
+This module is the training-side adapter: it maps Slime args and Sample
+objects onto the framework-free rollout core. When the manager runs the
+RolloutService (P3), groups are requested over HTTP instead of executing in
+this process; conversion to Slime samples is identical either way.
+"""
from __future__ import annotations
import asyncio
import math
+import os
+from pathlib import Path
from harborrl.backends.slime_v032.versions import lock_policy_version
from harborrl.export.native import export_training_batch
+from harborrl.rollout.runtime import (
+ RolloutRuntimeConfig,
+ close_runtime,
+ current_policy_version,
+ digest,
+ run_group,
+ runtime,
+ task_entry,
+)
def sampling_params(args):
@@ -21,6 +38,30 @@ def sampling_params(args):
}
+def runtime_config_from_args(args):
+ """Map Slime runtime args onto the extracted rollout core config."""
+ endpoint = f"http://{args.sglang_router_ip}:{args.sglang_router_port}"
+ engine_count = int(args.rollout_num_gpus) // int(args.rollout_num_gpus_per_engine)
+ limit = max(1, int(args.sglang_server_concurrency) * engine_count)
+ return RolloutRuntimeConfig.from_environment(
+ os.environ, endpoint=endpoint, concurrency_limit=limit,
+ group_size=int(args.n_samples_per_prompt))
+
+
+def slime_tokenizer_loader(args):
+ """The single Slime dependency at the rollout boundary: the tokenizer."""
+ from slime.utils.processing_utils import load_tokenizer
+
+ return lambda: load_tokenizer(args.hf_checkpoint, trust_remote_code=True)
+
+
+def group_index_of(group):
+ indexes = {getattr(sample, "group_index", None) for sample in group}
+ if len(indexes) != 1 or type(next(iter(indexes), None)) is not int:
+ raise ValueError("Slime must provide one group identity before native generation")
+ return indexes.pop()
+
+
def assert_locked_policy_versions(ir_groups, policy_version):
"""Fail closed if any generation escaped the locked weight version."""
for group in ir_groups:
@@ -32,11 +73,128 @@ def assert_locked_policy_versions(ir_groups, policy_version):
raise ValueError("native rollout observed a stale policy version")
+async def generate_group(args, group, sampling_params_, rollout_id, evaluation=False):
+ """Run one complete native group; evaluation is intentionally unsupported."""
+ from harborrl.math_registry import NATIVE_TASK_PROVIDER
+
+ if evaluation:
+ raise ValueError("native training rollout cannot be reused as evaluation")
+ if len(group) != args.n_samples_per_prompt:
+ raise ValueError("native rollout group is incomplete before launch")
+ entries = [task_entry(sample) for sample in group]
+ if any(entry != entries[0] for entry in entries[1:]):
+ raise ValueError("native rollout group mixes task locks")
+ entry = entries[0]
+ report = NATIVE_TASK_PROVIDER.adapter.inspect(
+ entry["path"], expected_digest=entry["task_digest"]
+ )
+ if report["status"] != "NEEDS_PROBE":
+ raise ValueError(f"native task preflight failed: {report['reasons']}")
+ stop = list(sampling_params_.get("stop") or [])
+ sampling_digest = digest({
+ "temperature": sampling_params_.get("temperature"),
+ "top_p": sampling_params_.get("top_p"),
+ "top_k": sampling_params_.get("top_k"),
+ "max_new_tokens": sampling_params_.get("max_new_tokens"),
+ "stop": stop,
+ })
+ config = runtime_config_from_args(args)
+ runtime_ = await runtime(config, tokenizer_loader=slime_tokenizer_loader(args))
+ policy_version = current_policy_version(runtime_)
+ trajectories = await run_group(
+ config, runtime_, entry=entry, group_index=group_index_of(group),
+ rollout_id=rollout_id, sampling_digest=sampling_digest,
+ policy_version=policy_version,
+ )
+ return bind_group_samples(group, trajectories)
+
+
+def bind_group_samples(group, trajectories):
+ """Attach trajectory evidence to Slime samples; order defines the slots."""
+ output = []
+ for sample, ir in zip(group, trajectories):
+ sample.metadata["native_ir"] = ir
+ sample.response = "native Harbor trial"
+ sample.reward = {"score": ir["evaluation"]["training_reward"]}
+ sample.status = sample.Status.COMPLETED
+ try:
+ sample.policy_version = int(ir["identity"]["policy_version"])
+ except ValueError as exc:
+ raise ValueError("native policy version must be an integer in Slime") from exc
+ output.append(sample)
+ return output
+
+
+async def run_service_groups(args, groups, rollout_id):
+ """Request complete groups from the manager-side RolloutService (P3)."""
+ from harborrl.backends.slime_v032.rollout_service_client import (
+ RolloutServiceClient,
+ fetch_pool_states,
+ load_group_result,
+ service_group_request,
+ )
+
+ token_file = os.environ.get('HARBORRL_ROLLOUT_SERVICE_TOKEN_FILE')
+ token = Path(token_file).read_text().strip() if token_file else os.environ.get('HARBORRL_ROLLOUT_SERVICE_TOKEN', '')
+ client = RolloutServiceClient(
+ os.environ["HARBORRL_ROLLOUT_SERVICE_URL"], os.environ["RUN_ID"],
+ token)
+ router = f"http://{args.sglang_router_ip}:{args.sglang_router_port}"
+ # Policy lifecycle (§7): the GPU job publishes its serving endpoint, then
+ # the manager admits the version the complete pool actually serves. The
+ # manager never opens a version some engine has not reached yet.
+ await asyncio.to_thread(client.announce_serving, router)
+ states = await asyncio.to_thread(fetch_pool_states, router)
+ from harborrl.consistency.config import environment
+ from harborrl.consistency.sglang import admit_pool
+ if environment()['mode'] != 'off':
+ await asyncio.to_thread(admit_pool, states, os.environ["RUN_DIR"])
+ await asyncio.to_thread(client.activate_policy, states, states[0]["weight_version"])
+ # The manager's activation publishes the locked policy-pool record into
+ # the shared run directory; lock the same version before requesting.
+ policy_version = lock_policy_version(args)
+ params = sampling_params(args)
+ async def request_group(group):
+ request = service_group_request(group, params, rollout_id, policy_version)
+ payload = await asyncio.to_thread(client.submit_group, request)
+ # The HTTP layer returns a status summary; completed work carries the
+ # GroupResult itself.
+ result = payload.get("result") if isinstance(payload, dict) else None
+ if not isinstance(result, dict):
+ from harborrl.backends.slime_v032.rollout_service_client import (
+ ServiceRolloutError,
+ )
+ raise ServiceRolloutError(
+ f"group {request['group_id']} is {payload.get('status')!r}: "
+ f"{payload.get('error', '')}")
+ irs = load_group_result(client.run_id, request, result, allow_synthetic=False)
+ bind_group_samples(group, irs)
+ return irs
+
+ pending = [asyncio.create_task(request_group(group)) for group in groups]
+ try:
+ results = await asyncio.gather(*pending, return_exceptions=True)
+ # Retain independent groups' evidence, but never export a partial batch.
+ for result in results:
+ if isinstance(result, BaseException):
+ raise result
+ return results
+ except BaseException:
+ # HTTP workers keep running until the manager acknowledges cancellation.
+ try:
+ await asyncio.to_thread(client.cancel_run)
+ finally:
+ await asyncio.gather(*pending, return_exceptions=True)
+ raise
+
+
async def run_native_groups(args, groups, policy_version, rollout_id):
"""Run every group concurrently and return only complete IR evidence."""
- from harborrl.rollout.native_generate import close_runtime, generate_group
-
params = sampling_params(args)
+ if os.environ.get("HARBORRL_ROLLOUT_SERVICE_URL"):
+ ir_groups = await run_service_groups(args, groups, rollout_id)
+ assert_locked_policy_versions(ir_groups, policy_version)
+ return ir_groups
try:
results = await asyncio.gather(*(
generate_group(args, group, params, rollout_id=rollout_id) for group in groups
diff --git a/harborrl/backends/slime_v032/rollout_service_client.py b/harborrl/backends/slime_v032/rollout_service_client.py
new file mode 100644
index 000000000..4fc73e492
--- /dev/null
+++ b/harborrl/backends/slime_v032/rollout_service_client.py
@@ -0,0 +1,210 @@
+"""SlimeAdapter HTTP client (P3): complete groups from the RolloutService.
+
+The GPU job never trusts a GroupResult: every manifest digest is recomputed,
+identities are checked against the request, the group must be complete with
+at least two RL_READY slots, and the policy version must be the locked one.
+After a transport timeout the client queries status; it never resubmits.
+"""
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from urllib import error, parse, request
+
+from harborrl.rollout.runtime import task_entry
+from harborrl.trajectories.native import digest, read_json, require_ready
+
+SERVICE_SCHEMA = "harborrl-rollout-service-v1"
+
+
+def fetch_pool_states(endpoint, *, timeout=120, opener=None):
+ """Read the serving pool states directly from the SGLang router.
+
+ Mirrors SGLangBackend.pool_states without a tokenizer: the GPU job calls
+ this before asking the manager to admit the version it currently serves.
+ """
+ import json as json_module
+ from urllib import request as url_request
+
+ opener = opener or url_request.build_opener(url_request.ProxyHandler({}))
+ endpoint = endpoint.rstrip("/")
+
+ def get(url):
+ with opener.open(url_request.Request(url), timeout=timeout) as response:
+ return json_module.load(response)
+
+ workers = get(endpoint + "/workers").get("workers")
+ if not isinstance(workers, list) or not workers:
+ raise ServiceRolloutError("serving router returned no workers")
+ states = []
+ for worker in workers:
+ engine = worker.get("url") if isinstance(worker, dict) else None
+ if not isinstance(engine, str) or not engine.strip():
+ raise ServiceRolloutError("serving router returned an invalid worker URL")
+ engine = engine.rstrip("/")
+ version = get(engine + "/get_weight_version").get("weight_version")
+ if not isinstance(version, str) or not version.strip():
+ raise ServiceRolloutError("missing serving weight version")
+ states.append({"endpoint": engine, "healthy": True,
+ "weight_version": version})
+ versions = {state["weight_version"] for state in states}
+ if len(versions) != 1:
+ raise ServiceRolloutError(
+ f"serving pool is mid-update; retry after all engines agree: {sorted(versions)}")
+ return states
+
+
+class ServiceRolloutError(RuntimeError):
+ def __init__(self, message, *, status=None, code=None):
+ super().__init__(message)
+ self.status, self.code = status, code
+
+
+def service_group_request(group, sampling_params, rollout_id, policy_version):
+ """Build one GroupRequest from a Slime sample group."""
+ entries = [task_entry(sample) for sample in group]
+ if any(entry != entries[0] for entry in entries[1:]):
+ raise ValueError("native rollout group mixes task locks")
+ indexes = {getattr(sample, "group_index", None) for sample in group}
+ if len(indexes) != 1 or type(next(iter(indexes), None)) is not int:
+ raise ValueError("Slime must provide one group identity before native generation")
+ index = indexes.pop()
+ return {
+ "group_id": f"group-{index}",
+ "rollout_id": int(rollout_id),
+ "group_index": index,
+ "policy_version": str(policy_version),
+ "task": entries[0],
+ "sampling": {key: sampling_params.get(key) for key in
+ ("temperature", "top_p", "top_k", "max_new_tokens", "stop")},
+ "slots": [{"slot_id": str(slot)} for slot in range(len(group))],
+ }
+
+
+class RolloutServiceClient:
+ """Synchronous urllib client; safe to call from asyncio.to_thread."""
+
+ def __init__(self, base_url, run_id, token, *, timeout=120, poll_interval=1.0,
+ opener=None):
+ self.base_url = base_url.rstrip("/")
+ self.run_id = run_id
+ self.token = token
+ self.timeout = timeout
+ self.poll_interval = poll_interval
+ self.opener = opener or request.build_opener(request.ProxyHandler({}))
+
+ def _request(self, method, path, body=None):
+ url = f"{self.base_url}{path}"
+ data = json.dumps(body).encode() if body is not None else None
+ headers = {"Content-Type": "application/json"}
+ if self.token:
+ headers["Authorization"] = f"Bearer {self.token}"
+ req = request.Request(url, data=data, headers=headers, method=method)
+ try:
+ with self.opener.open(req, timeout=self.timeout) as response:
+ return json.load(response)
+ except error.HTTPError as exc:
+ try:
+ payload = json.load(exc)
+ except (ValueError, OSError):
+ payload = {"error": {"type": "transport", "message": str(exc)}}
+ error_payload = payload.get("error", payload)
+ raise ServiceRolloutError(
+ f"{method} {path}: {error_payload.get('type')}: "
+ f"{error_payload.get('message')}",
+ status=exc.code, code=error_payload.get("type")) from exc
+ except (error.URLError, OSError, ValueError) as exc:
+ raise ServiceRolloutError(f"{method} {path}: {exc}",
+ code="transport") from exc
+
+ def health(self):
+ return self._request("GET", "/healthz")
+
+ def register(self, manifest):
+ return self._request("POST", f"/runs/{parse.quote(self.run_id)}/register",
+ {"manifest": manifest})
+
+ def submit_group(self, group_request, *, max_attempts=None):
+ """Submit idempotently; on timeout poll status, never regenerate."""
+ path = f"/runs/{parse.quote(self.run_id)}/groups"
+ try:
+ status = self._request('POST', path, group_request)
+ except ServiceRolloutError as exc:
+ if exc.code != 'transport':
+ raise
+ status = self.group_status(group_request['group_id'])
+ polls = 0
+ while status['status'] in ('queued', 'running'):
+ if max_attempts is not None and polls >= max_attempts:
+ raise ServiceRolloutError('group polling deadline reached', code='transport')
+ polls += 1
+ time.sleep(self.poll_interval)
+ status = self.group_status(group_request["group_id"])
+ return status
+
+ def group_status(self, group_id):
+ return self._request(
+ "GET", f"/runs/{parse.quote(self.run_id)}/groups/{parse.quote(group_id)}")
+
+ def cancel_group(self, group_id):
+ return self._request(
+ "POST",
+ f"/runs/{parse.quote(self.run_id)}/groups/{parse.quote(group_id)}/cancel")
+
+ def activate_policy(self, states, expected):
+ return self._request(
+ "POST", f"/runs/{parse.quote(self.run_id)}/policy/activate",
+ {"states": states, "expected": str(expected)})
+
+ def announce_serving(self, router_url):
+ return self._request(
+ "POST", f"/runs/{parse.quote(self.run_id)}/serving/announce",
+ {"router_url": router_url})
+
+ def drain(self):
+ return self._request("POST", f"/runs/{parse.quote(self.run_id)}/drain")
+
+ def cancel_run(self):
+ return self._request("POST", f"/runs/{parse.quote(self.run_id)}/cancel")
+
+
+def load_group_result(run_id, group_request, result, *, allow_synthetic=False):
+ """Validate a GroupResult and load its trajectory manifests in slot order."""
+ if not isinstance(result, dict):
+ raise ServiceRolloutError("group result must be a mapping")
+ if result.get("status") != "complete":
+ raise ServiceRolloutError(
+ f"group is {result.get('status')!r}, not complete: {result.get('error', '')}")
+ if result.get("run_id") != run_id or result.get("group_id") != group_request["group_id"]:
+ raise ServiceRolloutError("group result identity does not match the request")
+ if result.get("request_digest") != digest(group_request):
+ raise ServiceRolloutError("group result digest does not match the request")
+ slots = result.get("slots")
+ expected = [slot["slot_id"] for slot in group_request["slots"]]
+ if (not isinstance(slots, list) or len(slots) != len(expected) or len(slots) < 2
+ or [slot.get("slot_id") for slot in slots] != expected):
+ raise ServiceRolloutError("group result is incomplete or reordered")
+ trajectories = []
+ for descriptor, slot in zip(group_request["slots"], slots):
+ path = Path(slot["manifest_path"])
+ try:
+ payload = read_json(path)
+ except (OSError, ValueError) as exc:
+ raise ServiceRolloutError(f"cannot read slot manifest: {exc}") from exc
+ if digest(payload) != slot.get("manifest_sha256"):
+ raise ServiceRolloutError(f"slot {descriptor['slot_id']} manifest digest mismatch")
+ identity = payload.get("identity", {})
+ if (identity.get("policy_version") != group_request["policy_version"]
+ or identity.get("task_digest") != group_request["task"]["task_digest"]
+ or identity.get("run_id") != run_id
+ or identity.get("group_id") != group_request["group_id"]
+ or identity.get("slot_id") != descriptor["slot_id"]):
+ raise ServiceRolloutError(
+ f"slot {descriptor['slot_id']} identity differs from the request")
+ try:
+ require_ready(payload, allow_synthetic=allow_synthetic)
+ except ValueError as exc:
+ raise ServiceRolloutError(f"slot {descriptor['slot_id']}: {exc}") from exc
+ trajectories.append(payload)
+ return trajectories
diff --git a/harborrl/cli.py b/harborrl/cli.py
index 55530e0a1..0ea67a8a0 100644
--- a/harborrl/cli.py
+++ b/harborrl/cli.py
@@ -4,18 +4,35 @@
import argparse
import json
import sys
-
-ALGORITHM_CONTRACTS = {"grpo": "slime-v0.3.2-native-v1"}
+from pathlib import Path
def main(argv=None):
argv = list(sys.argv[1:] if argv is None else argv)
+ if argv and argv[0] == "web":
+ from harborrl.webui.cli import main as web_main
+
+ return web_main(argv[1:])
+ if argv and argv[0] in {"options", "validate", "init"}:
+ from harborrl.options.cli import main as options_main
+
+ return options_main(argv)
+ if argv and argv[0] == "list":
+ from harborrl.math_registry import list_components
+
+ choices = {"models", "algorithms", "tasks", "harnesses"}
+ if len(argv) != 2 or argv[1] not in choices:
+ parser = argparse.ArgumentParser(prog="harborrl list")
+ parser.error("choose one of: " + ", ".join(sorted(choices)))
+ print(json.dumps(list_components()[argv[1]], indent=2))
+ return 0
if argv and argv[0] == "hub":
from harborrl.tasks.cli import main as hub_main
return hub_main(argv[1:])
parser = argparse.ArgumentParser(
- prog="harborrl", description=__doc__, allow_abbrev=False
+ prog="harborrl", description=__doc__, allow_abbrev=False,
+ epilog="Configuration tools: harborrl options --help; harborrl validate --help; harborrl init --help; harborrl web --help."
)
parser.add_argument("-model", help="Slime model preset to select")
parser.add_argument("-harness", help="agent harness to select")
@@ -24,10 +41,13 @@ def main(argv=None):
parser.add_argument("command", choices=["train", "doctor"])
parser.add_argument("config_path", nargs="?", help="YAML configuration file")
parser.add_argument("--config", dest="config_option", help="YAML configuration file")
+ parser.add_argument("--deployment", help="deployment YAML describing the manager, GPU target and rollout workers")
parser.add_argument("--set", action="append", default=[])
parser.add_argument("--dry-run", action="store_true")
args = parser.parse_args(argv)
from harborrl.config import launch_plan, load_config
+ from harborrl.deployment.schema import embedded_local, load_deployment
+ from harborrl.deployment.schema import summary as deployment_summary
if args.config_path and args.config_option:
parser.error(
@@ -46,20 +66,36 @@ def main(argv=None):
if args.harness is not None:
overrides.append(f"harness.name={args.harness.replace('-', '_')}")
if args.alg is not None:
- contract = ALGORITHM_CONTRACTS.get(args.alg)
- if contract is None:
+ from harborrl.math_registry import MATH_REGISTRY
+
+ matches = [
+ spec for spec in MATH_REGISTRY.algorithms.items()
+ if args.alg in (spec.id,)
+ ]
+ if not matches:
parser.error(
f"unsupported algorithm: {args.alg}; "
- f"supported algorithms: {', '.join(sorted(ALGORITHM_CONTRACTS))}"
+ f"supported algorithms: "
+ f"{', '.join(spec.id for spec in MATH_REGISTRY.algorithms.items())}"
)
+ contract = next(iter(matches[0].training_backend_contracts))
overrides.append(f"training.backend_contract={contract}")
try:
- plan = launch_plan(load_config(
+ config = load_config(
config_path,
overrides,
task_ids=None if args.task is None else [args.task],
- ))
+ )
+ deployment, deployment_source = load_deployment(args.deployment)
+ if deployment_source == "embedded-local":
+ deployment = embedded_local(
+ run_root=str(Path(config["output"]["root"]).resolve()),
+ workers=config["harbor"]["workers"],
+ expected_gpus=config["deployment"]["num_gpus"],
+ )
+ plan = launch_plan(config)
+ plan["deployment"] = deployment_summary(deployment, deployment_source)
except (TypeError, ValueError, OSError) as exc:
parser.error(str(exc))
if args.dry_run:
diff --git a/harborrl/config/native.py b/harborrl/config/native.py
index 227f6e4d3..50b3ddb86 100644
--- a/harborrl/config/native.py
+++ b/harborrl/config/native.py
@@ -1,19 +1,18 @@
"""Strict native schema 2; local paths resolve relative to the config file."""
-import json
import os
import re
-import sys
from pathlib import Path
from urllib.parse import urlsplit
import yaml
-from harborrl.rollout.harbor_job.bindings import claude_config
-from harborrl.trajectories.native import RewardProfile
+from harborrl.consistency.config import DEFAULTS as CONSISTENCY_DEFAULTS
+from harborrl.consistency.config import validate as validate_consistency
FIELDS = {
+ 'consistency': set(CONSISTENCY_DEFAULTS),
'execution': {'backend'}, 'tasks': {'catalog'}, 'harness': {'name', 'profile'},
- 'harbor': {'python', 'version', 'workers'},
+ 'harbor': {'python', 'version', 'workers', 'runner_transport', 'environment'},
'gateway': {'host', 'port', 'advertised_url', 'tokenizer_digest', 'template_digest', 'audited_raw_logprobs'},
'model': {'checkpoint', 'reference', 'args_file'},
'training': {'num_rollout', 'learning_rate', 'save_interval', 'backend_contract'},
@@ -21,6 +20,7 @@
'deployment': {'layout', 'num_gpus', 'actor_gpus', 'rollout_gpus', 'actor_tensor_parallel_size', 'rollout_gpus_per_engine'},
'output': {'root'},
}
+OPTIONAL = {'harbor': {'runner_transport', 'environment'}}
class SelectedConfig(dict):
@@ -32,7 +32,10 @@ def __init__(self, config, task_ids):
def load_config(path, overrides=None, task_ids=None):
"""Load one schema-2 YAML file and apply strict ``section.key=value`` overrides."""
path = Path(path).resolve()
- config = yaml.safe_load(path.read_text())
+ try:
+ config = yaml.safe_load(path.read_text())
+ except yaml.YAMLError as exc:
+ raise ValueError(f'configuration YAML: {exc}') from exc
if not isinstance(config, dict):
raise TypeError('configuration must be a mapping')
if config.get('schema_version') != 2:
@@ -45,8 +48,12 @@ def load_config(path, overrides=None, task_ids=None):
if (not separator or field not in FIELDS.get(section_name, ())
or not isinstance(section, dict)):
raise ValueError(f'unknown native override: {key}')
- section[field] = yaml.safe_load(value)
+ try:
+ section[field] = yaml.safe_load(value)
+ except yaml.YAMLError as exc:
+ raise ValueError(f'{key}: invalid YAML override') from exc
config = validate(config, path)
+ validate_files(config)
if task_ids is None:
return config
if (not isinstance(task_ids, (list, tuple)) or not task_ids
@@ -57,16 +64,19 @@ def load_config(path, overrides=None, task_ids=None):
def validate(config, path):
- from harborrl.trajectories.native import read_json
+ if not isinstance(config, dict):
+ raise TypeError('configuration must be a mapping')
+ config['consistency'] = validate_consistency(config.get('consistency'))
if set(config) != set(FIELDS) | {'schema_version'} or type(config['schema_version']) is not int or config['schema_version'] != 2:
raise ValueError('native schema requires exactly the documented schema 2 sections')
+ if not isinstance(config['training'], dict):
+ raise TypeError('training must be a mapping')
config['training'].setdefault(
'backend_contract', 'slime-v0.3.2-native-v1')
for section, fields in FIELDS.items():
- if not isinstance(config[section], dict) or set(config[section]) != fields:
+ actual = set(config[section]) if isinstance(config[section], dict) else set()
+ if not isinstance(config[section], dict) or not fields - OPTIONAL.get(section, set()) <= actual <= fields:
raise ValueError(f'{section} requires exactly {sorted(fields)}')
- if config['execution']['backend'] != 'harbor_job' or config['harness']['name'] != 'claude_code' or config['harbor']['version'] != '0.23.0':
- raise ValueError('native requires harbor_job / claude_code / Harbor 0.23.0')
for section, key in [('tasks','catalog'), ('harness','profile'), ('harbor','python'),
('model','checkpoint'), ('model','reference'), ('output','root')]:
value = config[section][key]
@@ -82,133 +92,90 @@ def validate(config, path):
raise ValueError(f'{section}.{key} must be a positive integer')
from harborrl.backends.slime_v032.versions import CONFIG_CONTRACTS
from harborrl.trajectories.native import finite
- if config['training']['backend_contract'] not in CONFIG_CONTRACTS:
+ if (not isinstance(config['training']['backend_contract'], str)
+ or config['training']['backend_contract'] not in CONFIG_CONTRACTS):
raise ValueError(f"training.backend_contract must be one of {sorted(CONFIG_CONTRACTS)}")
if not finite(config['training']['learning_rate']) or config['training']['learning_rate'] <= 0:
- raise ValueError('positive finite learning_rate required')
+ raise ValueError('training.learning_rate must be positive and finite')
g, d, s = config['gateway'], config['deployment'], config['sampling']
if type(g['audited_raw_logprobs']) is not bool or not 0 < g['port'] < 65536:
- raise ValueError('invalid gateway audit flag or port')
+ raise ValueError('gateway.audited_raw_logprobs must be boolean and gateway.port must be in 1..65535')
for key in ('host','tokenizer_digest','template_digest'):
if not isinstance(g[key], str) or not g[key].strip():
raise ValueError(f'gateway.{key} required')
+ if not isinstance(g['advertised_url'], str):
+ raise TypeError('gateway.advertised_url must be a string')
advertised = urlsplit(g['advertised_url'])
if (advertised.scheme not in ('http', 'https') or not advertised.hostname
or advertised.username or advertised.password or advertised.query or advertised.fragment
or advertised.path not in ('', '/') or advertised.port != g['port']):
raise ValueError('advertised gateway must be a plain origin and match listener port')
- if s['group_size'] < 2 or s['max_tokens'] >= s['max_context']:
- raise ValueError('invalid group or context budget')
- if d['layout'] not in ('split','colocate'):
+ if s['group_size'] < 2:
+ raise ValueError('sampling.group_size must be at least 2')
+ if s['max_tokens'] >= s['max_context']:
+ raise ValueError('sampling.max_tokens must be below sampling.max_context')
+ if not isinstance(d['layout'], str) or d['layout'] not in ('split','colocate'):
raise ValueError('layout must be split or colocate')
if d['layout'] == 'split' and d['actor_gpus'] + d['rollout_gpus'] > d['num_gpus']:
- raise ValueError('GPU budget exceeded')
+ raise ValueError('GPU budget exceeded: deployment.actor_gpus + deployment.rollout_gpus > deployment.num_gpus')
if d['layout'] == 'colocate' and not d['actor_gpus'] == d['rollout_gpus'] == d['num_gpus']:
raise ValueError('colocate GPU counts must match')
if d['actor_gpus'] % d['actor_tensor_parallel_size'] or d['rollout_gpus'] % d['rollout_gpus_per_engine']:
- raise ValueError('GPU counts must be divisible by TP')
+ raise ValueError('deployment GPU counts must be divisible by their tensor parallel sizes')
preset = config['model']['args_file']
if not isinstance(preset,str) or not re.fullmatch(r'[A-Za-z0-9_-]+',preset):
raise ValueError('model.args_file must name a Slime model preset; SLIME_DIR is checked by doctor')
for key in ('tokenizer_digest', 'template_digest'):
if not re.fullmatch(r'[0-9a-f]{64}', g[key]):
raise ValueError(f'gateway.{key} must be a SHA-256 digest')
- workers = config['harbor']['workers']
- if (not isinstance(workers, list) or not workers or any(
+ harbor = config['harbor']
+ transport = harbor.get('runner_transport', 'ssh')
+ if transport not in ('ssh', 'local'):
+ raise ValueError('harbor.runner_transport must be ssh or local')
+ environment = harbor.get('environment', {'runtime': 'docker'})
+ if not isinstance(environment, dict) or environment.get('runtime') not in ('docker', 'sandbox'):
+ raise ValueError('harbor.environment.runtime must be docker or sandbox')
+ expected = {'runtime', 'config'} if environment['runtime'] == 'sandbox' else {'runtime'}
+ if set(environment) != expected:
+ raise ValueError('invalid harbor.environment fields')
+ if environment['runtime'] == 'sandbox':
+ if not isinstance(environment['config'], str) or not environment['config']:
+ raise ValueError('sandbox config path required')
+ environment['config'] = str((Path(path).parent / environment['config']).resolve())
+ workers = harbor['workers']
+ if (not isinstance(workers, list) or (transport == 'ssh' and not workers) or any(
not isinstance(worker, str) or not re.fullmatch(r'[A-Za-z0-9_.:+@-]+', worker) or worker.startswith('-')
for worker in workers)):
raise ValueError('harbor.workers must contain nonempty SSH destinations')
- claude_config(read_json(config['harness']['profile']), g['advertised_url'], 'validation-only')
+ if transport == 'local' and workers:
+ raise ValueError('local runner requires empty harbor.workers')
+ from harborrl.math_registry import select_math, validate_combination
+ validate_combination(select_math(config), config)
return config
-def catalog(config):
- from harborrl.data.harbor.native_inspector import inspect_native
+def validate_files(config):
+ """Check harness profile files separately from pure schema validation."""
+ from harborrl.math_registry import select_math
from harborrl.trajectories.native import read_json
- path = Path(config['tasks']['catalog'])
- entries = read_json(path)
- if not isinstance(entries,list) or not entries:
- raise ValueError('catalog must be a nonempty JSON list')
- seen = set()
- for entry in entries:
- if (set(entry) != {'id','revision','path','task_digest','reward_profile'}
- or not isinstance(entry['id'], str) or not entry['id'].strip()
- or not isinstance(entry['revision'], str) or not entry['revision'].strip()
- or entry['id'] in seen):
- raise ValueError('invalid or duplicate catalog identity')
- if (not isinstance(entry['path'], str) or not entry['path'].strip()
- or not isinstance(entry['task_digest'], str)
- or not re.fullmatch(r'[0-9a-f]{64}', entry['task_digest'])
- or not isinstance(entry['reward_profile'], dict)):
- raise ValueError(f'invalid native task lock: {entry["id"]}')
- seen.add(entry['id'])
- entry['path'] = str((path.parent / entry['path']).resolve())
- report = inspect_native(entry['path'], expected_digest=entry['task_digest'])
- if report['status'] != 'NEEDS_PROBE':
- raise ValueError(f"task preflight failed: {entry['id']}: {report['reasons']}")
- RewardProfile(**entry['reward_profile'])
- selected = set(getattr(config, 'task_ids', ()))
- if selected:
- missing = selected - {entry['id'] for entry in entries}
- if missing:
- raise ValueError(f'task selector not found in catalog: {min(missing)}')
- entries = [entry for entry in entries if entry['id'] in selected]
- return entries
+ harness = select_math(config).harness
+ try:
+ harness.adapter.validate_profile(
+ read_json(config['harness']['profile']),
+ config['gateway']['advertised_url'],
+ )
+ except (TypeError, ValueError, KeyError) as exc:
+ raise ValueError(f'harness.profile: {exc}') from exc
+ return config
+
+
+def catalog(config):
+ """Compatibility entry point for task lock validation."""
+ from harborrl.tasks.catalog import catalog as inspect_catalog
+ return inspect_catalog(config)
def launch_plan(config):
- from harborrl.backends.slime_v032.versions import runtime_contract_id
- from harborrl.config import ROOT
- catalog(config)
- c = config
- worker_overrides = os.environ.get('HARBORRL_NATIVE_TASK_WORKER_OVERRIDES', '{}')
- try:
- overrides = json.loads(worker_overrides)
- valid_workers = set(c['harbor']['workers'])
- if (not isinstance(overrides, dict) or any(not isinstance(key, str) or value not in valid_workers
- for key, value in overrides.items())):
- raise ValueError
- except (ValueError, json.JSONDecodeError) as exc:
- raise ValueError('invalid HARBORRL_NATIVE_TASK_WORKER_OVERRIDES') from exc
- environment = {
- 'DATASET': 'native', 'ALGO': 'grpo', 'HARNESS_OPTION': 'native-claude-code',
- 'HF_CKPT': c['model']['checkpoint'], 'REF_LOAD': c['model']['reference'],
- 'MODEL_ARGS_FILE': c['model']['args_file'], 'MODEL_TAG': c['model']['args_file'],
- 'NUM_ROLLOUT': c['training']['num_rollout'], 'SAVE_INTERVAL': c['training']['save_interval'],
- 'TRAIN_PYTHON': sys.executable,
- 'ROLLOUT_BATCH_SIZE': c['sampling']['groups_per_batch'],
- 'N_SAMPLES': c['sampling']['group_size'],
- 'ROLLOUT_MAX_RESPONSE_LEN': c['sampling']['max_tokens'],
- 'ROLLOUT_MAX_CONTEXT_LEN': c['sampling']['max_context'],
- 'NUM_GPUS': c['deployment']['num_gpus'], 'ACTOR_GPUS': c['deployment']['actor_gpus'],
- 'ROLLOUT_GPUS': c['deployment']['rollout_gpus'],
- 'TP_SIZE': c['deployment']['actor_tensor_parallel_size'],
- 'ROLLOUT_NUM_GPUS_PER_ENGINE': c['deployment']['rollout_gpus_per_engine'],
- 'HARBORRL_GPU_LAYOUT': c['deployment']['layout'],
- 'HARBORRL_STRUCTURED_LAUNCH': '1', 'HARBORRL_VERIFY_POLICY_POOL': '1',
- 'HARBORRL_SKIP_GLOBAL_CLEANUP': '1', 'SLIME_RAY_PLACEMENT_GPU_PROBE': '1',
- 'PYTORCH_CUDA_ALLOC_CONF': ('max_split_size_mb:2048' if c['deployment']['layout'] == 'colocate'
- else 'max_split_size_mb:2048,expandable_segments:True'),
- 'HARBORRL_NATIVE_ROLLOUT': '1',
- 'HARBORRL_NATIVE_SLIME_CONTRACT': runtime_contract_id(c['training']['backend_contract']),
- 'HARBORRL_NATIVE_RUNNER_PYTHON': c['harbor']['python'],
- 'HARBORRL_NATIVE_RUNNER_WORKERS': json.dumps(c['harbor']['workers']),
- 'HARBORRL_NATIVE_TASK_WORKER_OVERRIDES': json.dumps(overrides, sort_keys=True),
- 'HARBORRL_NATIVE_PROFILE': c['harness']['profile'],
- 'HARBORRL_NATIVE_CATALOG': c['tasks']['catalog'],
- 'HARBORRL_NATIVE_GATEWAY_HOST': c['gateway']['host'],
- 'HARBORRL_NATIVE_GATEWAY_PORT': c['gateway']['port'],
- 'HARBORRL_NATIVE_GATEWAY_URL': c['gateway']['advertised_url'],
- 'HARBORRL_NATIVE_TOKENIZER_DIGEST': c['gateway']['tokenizer_digest'],
- 'HARBORRL_NATIVE_TEMPLATE_DIGEST': c['gateway']['template_digest'],
- 'HARBORRL_NATIVE_AUDITED_LOGPROBS': str(int(c['gateway']['audited_raw_logprobs'])),
- 'HARBORRL_NATIVE_MAX_ATTEMPTS': c['sampling']['max_attempts'],
- 'HARBORRL_NATIVE_DRAIN_TIMEOUT': '30',
- 'RUNS_ROOT': c['output']['root'], 'WANDB_ENABLE': '0',
- }
- return {'config': config, 'command': [sys.executable, '-m', 'harborrl.platform.native_train'],
- 'environment': {key: str(value) for key, value in environment.items()},
- 'training_command': ['bash', str(ROOT / 'harborrl/platform/slime_train.sh')],
- 'required_services': ['GPU Ray / SGLang / Messages Gateway', 'external CPU Harbor Docker workers'],
- 'launcher': 'native Claude Code → Messages Gateway → Harbor verifier → Slime GRPO',
- 'layout': {'mode': config['deployment']['layout'], 'offload': config['deployment']['layout']=='colocate'}}
+ """Compatibility entry point for backend launch mapping."""
+ from harborrl.backends.slime_v032.config import launch_plan as build_plan
+ return build_plan(config)
diff --git a/harborrl/consistency/__init__.py b/harborrl/consistency/__init__.py
new file mode 100644
index 000000000..8031995e5
--- /dev/null
+++ b/harborrl/consistency/__init__.py
@@ -0,0 +1 @@
+"""Backend consistency evidence, independent of optimization objectives."""
diff --git a/harborrl/consistency/actor.py b/harborrl/consistency/actor.py
new file mode 100644
index 000000000..e332fc4d1
--- /dev/null
+++ b/harborrl/consistency/actor.py
@@ -0,0 +1,88 @@
+"""Small Slime actor extension: snapshot trained-model canaries before weight sync."""
+import os
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+from slime.backends.megatron_utils.actor import MegatronTrainRayActor
+from slime.backends.megatron_utils.data import DataIterator
+from slime.backends.megatron_utils.loss import get_log_probs_and_entropy
+from slime.backends.megatron_utils.model import forward_only
+from slime.backends.megatron_utils.update_weight.common import named_params_and_buffers
+from slime.utils.distributed_utils import get_gloo_group
+from slime.utils.reloadable_process_group import (
+ destroy_process_groups,
+ reload_process_groups,
+)
+
+from harborrl.trajectories.native import digest, publish
+
+
+class ConsistencyActor(MegatronTrainRayActor):
+ def update_weights(self):
+ if self.args.offload_train:
+ self.wake_up()
+ self._switch_model("actor")
+ from harborrl.rollout.runtime import tokenizer_fingerprint
+ error = None
+ try:
+ actual = tokenizer_fingerprint(self.tokenizer)
+ expected = (os.environ['HARBORRL_NATIVE_TOKENIZER_DIGEST'], os.environ['HARBORRL_NATIVE_TEMPLATE_DIGEST'])
+ if actual != expected:
+ raise ValueError('Trainer tokenizer/template differs from rollout launch identity')
+ except (ValueError, TypeError, KeyError) as exc:
+ error = str(exc)
+ errors = [None] * dist.get_world_size()
+ dist.all_gather_object(errors, error, group=get_gloo_group())
+ if any(errors):
+ raise ValueError(f'Invalid trainer identity: {errors}')
+ cases = []
+ # Literal token fixtures intentionally bypass all serving-side text templating.
+ for prompt, answer in (("Compute 1 + 1 =", " 2.\n"), ("def square(x):\n", " return x * x\n")):
+ p = self.tokenizer.encode(prompt, add_special_tokens=True)
+ r = self.tokenizer.encode(answer, add_special_tokens=False)
+ if not p or not r:
+ raise ValueError("Tokenizer produced an empty canary")
+ cases.append({"prompt_ids": p, "response_ids": r})
+ device = next(self.model[0].parameters()).device
+ data = {"tokens": [torch.tensor(c["prompt_ids"] + c["response_ids"], device=device) for c in cases],
+ "loss_masks": [torch.ones(len(c["response_ids"]), dtype=torch.int, device=device) for c in cases],
+ "total_lengths": [len(c["prompt_ids"]) + len(c["response_ids"]) for c in cases],
+ "response_lengths": [len(c["response_ids"]) for c in cases]}
+ output = forward_only(get_log_probs_and_entropy, self.args, self.model,
+ [DataIterator(data, [[i] for i in range(len(cases))])], [len(cases)])
+ for case, values in zip(cases, output["log_probs"], strict=True):
+ case["logprobs"] = values.detach().float().cpu().tolist()
+ fingerprints = []
+ for name, parameter in named_params_and_buffers(self.args, self.model):
+ flat = parameter.detach().reshape(-1)
+ indices = torch.linspace(0, max(0, flat.numel() - 1), min(16, flat.numel()), device=flat.device).long()
+ fingerprints.append({"name": name, "shape": list(parameter.shape), "dtype": str(parameter.dtype),
+ "sample": flat[indices].float().cpu().tolist()})
+ local = {"rank": dist.get_rank(), "fingerprint": digest(fingerprints), "tensor_count": len(fingerprints)}
+ shards = [None] * dist.get_world_size()
+ dist.all_gather_object(shards, local, group=get_gloo_group())
+ config = self.hf_config.to_dict()
+ reference = {"schema_version": "harborrl-sync-reference-v1", "cases": cases,
+ "model_config_digest": digest(config), "model_config": config,
+ "tokenizer_vocabulary_digest": digest(sorted(self.tokenizer.get_vocab().items())),
+ "parameter_shards": shards, "fingerprint_semantics": "16 deterministic values per tensor; not a full checksum"}
+ if self.args.offload_train:
+ self.sleep()
+ super().update_weights()
+ if self.args.offload_train:
+ reload_process_groups()
+ reference["weight_version"] = str(self.weight_updater.weight_version)
+ reference["digest"] = digest(reference)
+ error = None
+ if dist.get_rank() == 0:
+ try:
+ publish(Path(os.environ["RUN_DIR"]) / "consistency" / f"sync-{reference['weight_version']}.json", reference)
+ except (OSError, ValueError) as exc:
+ error = str(exc)
+ errors = [None] * dist.get_world_size()
+ dist.all_gather_object(errors, error, group=get_gloo_group())
+ if self.args.offload_train:
+ destroy_process_groups()
+ if any(errors):
+ raise ValueError(f"Failed to publish synchronized model evidence: {errors}")
diff --git a/harborrl/consistency/config.py b/harborrl/consistency/config.py
new file mode 100644
index 000000000..f2317e49a
--- /dev/null
+++ b/harborrl/consistency/config.py
@@ -0,0 +1,51 @@
+"""Explicit deployment-specific tolerances; no universal numeric threshold."""
+import json
+import os
+
+from harborrl.trajectories.native import finite
+
+DEFAULTS = {"mode": "off", "max_abs_delta": None, "max_mean_abs_delta": None,
+ "bad_token_delta": None, "max_bad_fraction": 0.0, "worst_tokens": 8}
+
+
+def validate(value=None):
+ if value is None:
+ value = {}
+ if not isinstance(value, dict) or set(value) - set(DEFAULTS):
+ raise ValueError("Unknown consistency configuration fields")
+ result = {**DEFAULTS, **value}
+ if result["mode"] not in ("off", "audit", "enforce"):
+ raise ValueError("consistency.mode must be off, audit or enforce")
+ for key in ("max_abs_delta", "max_mean_abs_delta", "bad_token_delta"):
+ if result[key] is not None and (not finite(result[key]) or result[key] < 0):
+ raise ValueError(f"consistency.{key} must be nonnegative and finite")
+ if result["mode"] == "enforce" and result["max_abs_delta"] is None:
+ raise ValueError("enforce requires an explicit max_abs_delta for this deployment")
+ if not finite(result["max_bad_fraction"]) or not 0 <= result["max_bad_fraction"] <= 1:
+ raise ValueError("max_bad_fraction must be in [0, 1]")
+ if type(result["worst_tokens"]) is not int or not 0 <= result["worst_tokens"] <= 32:
+ raise ValueError("worst_tokens must be an integer in [0, 32]")
+ return result
+
+
+def environment():
+ return validate(json.loads(os.environ.get("HARBORRL_CONSISTENCY", "{}")))
+
+
+def require_raw_sampling(sampling):
+ allowed = {"temperature", "top_p", "top_k", "max_new_tokens", "stop", "raw_logprobs"}
+ if (not isinstance(sampling, dict) or set(sampling) - allowed
+ or sampling.get("temperature") != 1 or sampling.get("top_p") != 1
+ or sampling.get("top_k", -1) not in (-1, 0)
+ or sampling.get("raw_logprobs", True) is not True):
+ raise ValueError("Consistency v1 requires untransformed raw-policy sampling")
+
+
+def violations(metrics, config):
+ errors = []
+ for option, field in (("max_abs_delta", "max_abs_delta"), ("max_mean_abs_delta", "mean_abs_delta")):
+ if config[option] is not None and metrics[field] > config[option]:
+ errors.append(f"{field}={metrics[field]:.8g} exceeds {config[option]}")
+ if config["bad_token_delta"] is not None and metrics["bad_fraction"] > config["max_bad_fraction"]:
+ errors.append(f"bad_fraction={metrics['bad_fraction']:.8g} exceeds {config['max_bad_fraction']}")
+ return errors
diff --git a/harborrl/consistency/metrics.py b/harborrl/consistency/metrics.py
new file mode 100644
index 000000000..4057e8c97
--- /dev/null
+++ b/harborrl/consistency/metrics.py
@@ -0,0 +1,67 @@
+"""Mergeable token statistics with bounded-memory percentile upper bounds."""
+import bisect
+import math
+
+from harborrl.trajectories.native import finite
+
+# Logarithmic buckets retain sub-micro precision near zero, with no token all-gather.
+EDGES = [0.0, *[2 ** (-24 + i / 8) for i in range(321)]]
+
+
+def statistics(rows, *, threshold=None, worst=8):
+ result = {"count": 0, "signed_sum": 0.0, "abs_sum": 0.0, "max_abs_delta": 0.0,
+ "min_log_ratio": None, "max_log_ratio": None, "bad_count": 0,
+ "histogram": [0] * (len(EDGES) + 1), "worst": []}
+ for row_index, (current, behavior, mask) in enumerate(rows):
+ if len(current) != len(behavior) or len(mask) != len(current) or not len(current):
+ raise ValueError("Probability/mask length mismatch")
+ if any(flag not in (0, 1) for flag in mask):
+ raise ValueError("Loss mask must be binary")
+ for position, (p, q, flag) in enumerate(zip(current, behavior, mask, strict=True)):
+ if not finite(p) or not finite(q) or p > 0 or q > 0:
+ raise ValueError("Log probabilities must be finite and nonpositive")
+ if not flag:
+ continue
+ delta = float(p) - float(q)
+ absolute = abs(delta)
+ if not math.isfinite(delta):
+ raise ValueError("Nonfinite log-probability difference")
+ result["count"] += 1
+ result["signed_sum"] += delta
+ result["abs_sum"] += absolute
+ result["max_abs_delta"] = max(result["max_abs_delta"], absolute)
+ result["min_log_ratio"] = delta if result["min_log_ratio"] is None else min(result["min_log_ratio"], delta)
+ result["max_log_ratio"] = delta if result["max_log_ratio"] is None else max(result["max_log_ratio"], delta)
+ result["bad_count"] += int(threshold is not None and absolute > threshold)
+ result["histogram"][bisect.bisect_left(EDGES, absolute)] += 1
+ if worst:
+ result["worst"].append({"row": row_index, "position": position, "delta": delta})
+ result["worst"] = sorted(result["worst"], key=lambda x: abs(x["delta"]), reverse=True)[:worst]
+ if not result["count"]:
+ raise ValueError("No valid response tokens")
+ if not math.isfinite(result["abs_sum"]) or not math.isfinite(result["signed_sum"]):
+ raise ValueError("Probability statistics overflow")
+ return result
+
+
+def merge(parts):
+ if not parts:
+ raise ValueError("No contributing ranks")
+ count = sum(p["count"] for p in parts)
+ bins = [sum(p["histogram"][i] for p in parts) for i in range(len(EDGES) + 1)]
+ maximum = max(p["max_abs_delta"] for p in parts)
+ def percentile(fraction):
+ target, seen = math.ceil(count * fraction), 0
+ for index, n in enumerate(bins):
+ seen += n
+ if seen >= target:
+ return min(EDGES[index], maximum) if index < len(EDGES) else maximum
+ raise ValueError("Corrupt histogram")
+ return {"token_count": count, "signed_mean_delta": math.fsum(p["signed_sum"] for p in parts) / count,
+ "mean_abs_delta": math.fsum(p["abs_sum"] for p in parts) / count,
+ "max_abs_delta": maximum, "p95_abs_delta_upper": percentile(.95), "p99_abs_delta_upper": percentile(.99),
+ "min_log_ratio": min(p["min_log_ratio"] for p in parts),
+ "max_log_ratio": max(p["max_log_ratio"] for p in parts),
+ "bad_fraction": sum(p["bad_count"] for p in parts) / count,
+ "percentile_semantics": "logarithmic-histogram upper bound; not exact quantiles",
+ "gap_semantics": "fixed-prefix token log-ratio; not full-sequence KL"}
diff --git a/harborrl/consistency/probe.py b/harborrl/consistency/probe.py
new file mode 100644
index 000000000..a218337b1
--- /dev/null
+++ b/harborrl/consistency/probe.py
@@ -0,0 +1,35 @@
+"""Replay a Megatron fixed-token reference against an explicit engine pool."""
+import argparse
+import json
+
+from harborrl.trajectories.native import digest, publish, read_json
+
+from .config import validate
+from .sglang import check_pool, http_json
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument('--reference', required=True)
+ parser.add_argument('--engine', action='append', required=True)
+ parser.add_argument('--config', required=True, help='JSON consistency mapping with explicit tolerances')
+ parser.add_argument('--output', required=True)
+ args = parser.parse_args(argv)
+ settings = validate(read_json(args.config))
+ if settings['mode'] == 'off':
+ parser.error('probe mode must be audit or enforce')
+ reference = read_json(args.reference)
+ if reference.get('digest') != digest({k: v for k, v in reference.items() if k != 'digest'}):
+ raise ValueError('Corrupt sync reference')
+ states = [{'endpoint': endpoint.rstrip('/'), 'healthy': True,
+ 'weight_version': http_json(endpoint.rstrip('/') + '/get_weight_version')['weight_version']}
+ for endpoint in args.engine]
+ report = check_pool(states, reference, settings)
+ report['reference_digest'] = reference['digest']
+ publish(args.output, report)
+ print(json.dumps(report, indent=2))
+ return int(report['status'] == 'failed')
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
diff --git a/harborrl/consistency/report.py b/harborrl/consistency/report.py
new file mode 100644
index 000000000..0b3962894
--- /dev/null
+++ b/harborrl/consistency/report.py
@@ -0,0 +1,51 @@
+"""Collect errors before raising so every participating rank makes the same decision."""
+from pathlib import Path
+
+from harborrl.trajectories.native import publish
+
+from .config import violations
+from .metrics import merge, statistics
+
+
+def assess(rows, config, *, metadata=None):
+ try:
+ return {"statistics": statistics(rows, threshold=config["bad_token_delta"], worst=config["worst_tokens"]),
+ "error": None, "metadata": metadata or {}}
+ except (ValueError, TypeError, OverflowError) as exc:
+ return {"statistics": None, "error": f"{type(exc).__name__}: {exc}", "metadata": metadata or {}}
+
+
+def decide(parts, config, *, phase):
+ structural = [p["error"] for p in parts if p["error"]]
+ contributing = [p for p in parts if p.get("contribute", True)]
+ metrics = None if structural else merge([p["statistics"] for p in contributing])
+ numeric = violations(metrics, config) if metrics else []
+ return {"schema_version": "harborrl-consistency-v1", "phase": phase, "mode": config["mode"],
+ "status": "failed" if structural or (numeric and config["mode"] == "enforce") else "warning" if numeric else "passed",
+ "structural_errors": structural, "numeric_violations": numeric, "metrics": metrics,
+ "config": config, "ranks": [{"metadata": p["metadata"], "error": p["error"],
+ "contribute": p.get("contribute", True),
+ "worst": p["statistics"]["worst"] if p["statistics"] else []} for p in parts]}
+
+
+def distributed_gate(part, config, path, *, phase, group=None):
+ import torch.distributed as dist
+ active = dist.is_available() and dist.is_initialized()
+ parts = [part]
+ if active:
+ parts = [None] * dist.get_world_size(group)
+ dist.all_gather_object(parts, part, group=group)
+ report = decide(parts, config, phase=phase)
+ write_error = None
+ if not active or dist.get_rank(group) == 0:
+ try:
+ publish(Path(path), report)
+ except (OSError, ValueError) as exc:
+ write_error = str(exc)
+ if active:
+ errors = [None] * dist.get_world_size(group)
+ dist.all_gather_object(errors, write_error, group=group)
+ write_error = next((error for error in errors if error), None)
+ if write_error or report["status"] == "failed":
+ raise ValueError(f"Consistency {phase} rejected: {write_error or report['structural_errors'] or report['numeric_violations']}")
+ return report
diff --git a/harborrl/consistency/sglang.py b/harborrl/consistency/sglang.py
new file mode 100644
index 000000000..d9a5ac654
--- /dev/null
+++ b/harborrl/consistency/sglang.py
@@ -0,0 +1,92 @@
+"""Exact-token teacher-forcing probes against every synchronized inference engine."""
+import json
+from pathlib import Path
+from urllib import request
+
+from harborrl.trajectories.native import digest, finite, publish, read_json
+
+from .config import environment, violations
+from .metrics import merge
+from .report import assess, decide
+
+
+def http_json(url, payload=None, timeout=120):
+ opener = request.build_opener(request.ProxyHandler({}))
+ req = request.Request(url, data=json.dumps(payload).encode() if payload is not None else None,
+ headers={"Content-Type": "application/json"})
+ with opener.open(req, timeout=timeout) as response:
+ return json.load(response)
+
+
+def score(endpoint, prompt, response, version, *, transport=http_json):
+ tokens = prompt + response
+ payload = {"input_ids": tokens, "sampling_params": {"temperature": 1.0, "top_p": 1.0,
+ "top_k": -1, "max_new_tokens": 0}, "return_logprob": True, "logprob_start_len": 0}
+ output = transport(endpoint.rstrip("/") + "/generate", payload)
+ meta = output.get("meta_info", {})
+ if str(meta.get("weight_version", "")) != str(version):
+ raise ValueError("Canary generation-time weight version mismatch")
+ rows = meta.get("input_token_logprobs")
+ if (not isinstance(rows, list) or len(rows) != len(tokens)
+ or any(not isinstance(row, (tuple, list)) or len(row) < 2 for row in rows)
+ or [row[1] for row in rows] != tokens):
+ raise ValueError("Canary input logprobs must preserve every exact token and position")
+ values = [row[0] for row in rows[len(prompt):]]
+ if any(not finite(value) or value > 0 for value in values):
+ raise ValueError("Invalid canary raw logprob")
+ return values
+
+
+def check_pool(states, reference, config, *, transport=http_json):
+ expected = reference["weight_version"]
+ endpoints = [s["endpoint"] for s in states]
+ if not states or len(set(endpoints)) != len(endpoints):
+ raise ValueError("Canary requires a nonempty unique engine pool")
+ if any(not s.get("healthy") or str(s.get("weight_version")) != str(expected) for s in states):
+ raise ValueError("Canary pool contains stale or unhealthy engines")
+ parts = []
+ for endpoint in endpoints:
+ try:
+ rows = [(case["logprobs"], score(endpoint, case["prompt_ids"], case["response_ids"], expected,
+ transport=transport), [1] * len(case["response_ids"])) for case in reference["cases"]]
+ part = assess(rows, config, metadata={"engine": endpoint, "weight_version": expected})
+ # Detect a version transition across scoring, in addition to each response's version.
+ if str(transport(endpoint + "/get_weight_version").get("weight_version")) != str(expected):
+ raise ValueError("Engine changed weights during canary scoring")
+ except (OSError, ValueError, TypeError, KeyError) as exc:
+ part = {"statistics": None, "error": str(exc), "metadata": {"engine": endpoint}}
+ parts.append(part)
+ report = decide(parts, config, phase="synchronized-pool")
+ # Mean/fraction limits apply independently to every engine.
+ engine_errors = [f"{p['metadata']['engine']}: {error}" for p in parts if p['statistics']
+ for error in violations(merge([p['statistics']]), config)]
+ report['numeric_violations'] = engine_errors
+ if engine_errors and report['status'] != 'failed':
+ report['status'] = 'failed' if config['mode'] == 'enforce' else 'warning'
+ return report
+
+
+def admit_pool(states, run_dir):
+ config = environment()
+ if config["mode"] == "off":
+ return
+ versions = {str(s["weight_version"]) for s in states}
+ if len(versions) != 1:
+ raise ValueError("Cannot admit a mixed-version pool")
+ version = versions.pop()
+ if not version.isdecimal():
+ raise ValueError("Invalid numeric serving version")
+ root = Path(run_dir) / "consistency"
+ reference = read_json(root / f"sync-{version}.json")
+ if reference["weight_version"] != version or reference["digest"] != digest({k: v for k, v in reference.items() if k != "digest"}):
+ raise ValueError("Canary reference identity mismatch")
+ admission = digest({'states': states, 'config': config, 'reference': reference['digest']})
+ path = root / f"pool-{version}-{admission}.json"
+ if path.exists():
+ report = read_json(path)
+ else:
+ report = check_pool(states, reference, config)
+ report["reference_digest"] = reference["digest"]
+ publish(path, report)
+ if report["status"] == "failed":
+ raise ValueError(f"Serving canary rejected version {version}")
diff --git a/harborrl/consistency/slime.py b/harborrl/consistency/slime.py
new file mode 100644
index 000000000..6493e5ea1
--- /dev/null
+++ b/harborrl/consistency/slime.py
@@ -0,0 +1,74 @@
+"""Integration using the locked Slime precomputed log_probs and postprocess hook."""
+import os
+import struct
+from pathlib import Path
+
+from harborrl.trajectories.native import digest, publish, read_json
+
+from .config import environment, require_raw_sampling
+from .report import assess, distributed_gate
+
+
+def evidence_key(tokens, logprobs, mask):
+ q = [struct.unpack("!f", struct.pack("!f", value))[0] for value in logprobs]
+ return digest({"tokens": tokens, "q": q, "mask": mask})
+
+
+def record_turn(turn, tokens, logprobs, mask):
+ config = environment()
+ if config["mode"] == "off":
+ return
+ require_raw_sampling(turn["sampling"])
+ if turn["logprob_semantics"] != "raw_model" or turn["evidence_kind"] != "serving":
+ raise ValueError("Consistency requires observed raw serving evidence")
+ key = evidence_key(tokens, logprobs, mask)
+ version = str(turn["policy_version"])
+ if not version.isdecimal():
+ raise ValueError("Invalid serving version")
+ payload = {"key": key, "weight_version": version, "sampling": turn["sampling"],
+ "tokenizer_digest": turn["tokenizer_digest"], "template_digest": turn["template_digest"],
+ "logprob_semantics": "raw_model_equals_sampling", "evidence_kind": "serving"}
+ publish(Path(os.environ["RUN_DIR"]) / "consistency" / "turns" / version / f"{key}.json", payload)
+
+
+def identity_tis(*args, **kwargs):
+ raise RuntimeError("Consistency is metrics-only; invoking a TIS correction would change the Native objective")
+
+
+def check_training_batch(args, rollout_id, data, validator):
+ import torch.distributed as dist
+ from megatron.core import mpu
+ from slime.utils.distributed_utils import get_gloo_group
+
+ from harborrl.backends.slime_v032.versions import read_locked_policy_version
+
+ config = environment()
+ root = Path(os.environ["RUN_DIR"])
+ metadata = {"rank": dist.get_rank(), "rollout_id": rollout_id, "backend": "megatron",
+ "scope": "pre-update actual packed rollout batch"}
+ try:
+ validator(args, rollout_id, data)
+ if "log_probs" not in data or len(data["log_probs"]) != len(data["tokens"]):
+ raise ValueError("Missing pre-update Megatron log_probs; --get-mismatch-metrics is required")
+ version = read_locked_policy_version(root)
+ metadata["weight_version"] = version
+ cases = []
+ for tokens, q, mask, p0 in zip(data["tokens"], data["rollout_log_probs"], data["loss_masks"], data["log_probs"], strict=True):
+ tokens, q, mask, p0 = (x.detach().cpu().tolist() if hasattr(x, "detach") else list(x) for x in (tokens, q, mask, p0))
+ # Slime stores rollout probabilities as float32. Normalize the lookup identically.
+ key = evidence_key(tokens, q, mask)
+ evidence = read_json(root / "consistency" / "turns" / version / f"{key}.json")
+ if evidence["key"] != key or evidence["weight_version"] != version:
+ raise ValueError("Frozen serving evidence does not match the training batch")
+ for field, env in (("tokenizer_digest", "HARBORRL_NATIVE_TOKENIZER_DIGEST"),
+ ("template_digest", "HARBORRL_NATIVE_TEMPLATE_DIGEST")):
+ if evidence[field] != os.environ[env]:
+ raise ValueError(f"Training batch {field} differs from launch identity")
+ require_raw_sampling(evidence["sampling"])
+ cases.append((p0, q, mask))
+ part = assess(cases, config, metadata=metadata)
+ except (ValueError, TypeError, KeyError, OSError) as exc:
+ part = {"statistics": None, "error": str(exc), "metadata": metadata}
+ part["contribute"] = mpu.get_tensor_model_parallel_rank() == 0
+ distributed_gate(part, config, root / "consistency" / f"batch-{rollout_id}.json",
+ phase="pre-update-backend-gap", group=get_gloo_group())
diff --git a/harborrl/consistency/train.py b/harborrl/consistency/train.py
new file mode 100644
index 000000000..caaea02d9
--- /dev/null
+++ b/harborrl/consistency/train.py
@@ -0,0 +1,76 @@
+"""Run the locked upstream training loop with its supported actor-class factory."""
+import functools
+import importlib.util
+import json
+import os
+from pathlib import Path
+
+from .config import environment
+
+
+class CheckedActorGroup:
+ """Check every sync, including the final one, before returning to upstream."""
+ def __init__(self, actor, manager, args):
+ self.actor, self.manager, self.args = actor, manager, args
+
+ def __getattr__(self, name):
+ return getattr(self.actor, name)
+
+ def update_weights(self):
+ import ray
+
+ from harborrl.backends.slime_v032.rollout_service_client import (
+ fetch_pool_states,
+ )
+
+ from .sglang import admit_pool
+ result = self.actor.update_weights()
+ if self.args.offload_rollout:
+ ray.get(self.manager.onload_kv.remote())
+ endpoint = f"http://{self.args.sglang_router_ip}:{self.args.sglang_router_port}"
+ admit_pool(fetch_pool_states(endpoint), os.environ['RUN_DIR'])
+ return result
+
+
+def main():
+ import ray
+ from slime.ray.placement_group import create_training_models
+ from slime.utils.arguments import parse_args
+
+ from .actor import ConsistencyActor
+
+ args = parse_args()
+ for name, expected in (("pipeline_model_parallel_size", 1), ("context_parallel_size", 1),
+ ("update_weights_interval", 1)):
+ if getattr(args, name) != expected:
+ raise ValueError(f"Consistency v1 requires {name}={expected}")
+ if any(getattr(args, key, False) for key in ('use_critic', 'keep_old_actor', 'use_tis', 'release_train', 'use_rollout_routing_replay', 'num_experts')):
+ raise ValueError("Consistency v1 supports synchronous Native actor training without routing replay")
+ if args.update_weight_mode != "full" or args.update_weight_transport != "nccl":
+ raise ValueError("Consistency v1 requires full tensor/NCCL weight synchronization")
+ settings = environment()
+ rollout_env = {key: value for key, value in os.environ.items()
+ if key.startswith('HARBORRL_') or key in ('RUN_DIR', 'RUN_ID', 'MODEL_TAG', 'MODEL_ARGS_FILE',
+ 'ROLLOUT_MAX_RESPONSE_LEN', 'ROLLOUT_MAX_CONTEXT_LEN', 'PYTHONPATH')}
+ if not ray.is_initialized():
+ ray.init(runtime_env={'env_vars': rollout_env})
+ args.get_mismatch_metrics = True
+ args.custom_tis_function_path = "harborrl.consistency.slime.identity_tis"
+ args.train_env_vars = dict(args.train_env_vars or {})
+ args.train_env_vars.update({"HARBORRL_CONSISTENCY": json.dumps(settings), "RUN_DIR": os.environ["RUN_DIR"],
+ "HARBORRL_NATIVE_TOKENIZER_DIGEST": os.environ["HARBORRL_NATIVE_TOKENIZER_DIGEST"],
+ "HARBORRL_NATIVE_TEMPLATE_DIGEST": os.environ["HARBORRL_NATIVE_TEMPLATE_DIGEST"]})
+ spec = importlib.util.spec_from_file_location("harborrl_upstream_train", Path(os.environ["SLIME_DIR"]) / "train.py")
+ module = importlib.util.module_from_spec(spec)
+ spec.loader.exec_module(module)
+ # Only the factory in this private entrypoint module is replaced; upstream files are untouched.
+ factory = functools.partial(create_training_models, actor_cls=ConsistencyActor)
+ def checked_factory(args, pgs, manager):
+ actor, critic = factory(args, pgs, manager)
+ return CheckedActorGroup(actor, manager, args), critic
+ module.create_training_models = checked_factory
+ module.train(args)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/harborrl/data/harbor/__main__.py b/harborrl/data/harbor/__main__.py
index 99d90d197..291463cbd 100644
--- a/harborrl/data/harbor/__main__.py
+++ b/harborrl/data/harbor/__main__.py
@@ -1,3 +1,4 @@
+"""Legacy TerminalEnv compatibility CLI; use harborrl hub inspect for native tasks."""
import argparse
import json
from pathlib import Path
diff --git a/harborrl/data/harbor/inspector.py b/harborrl/data/harbor/inspector.py
index 4a426fd4f..3499bdbd2 100644
--- a/harborrl/data/harbor/inspector.py
+++ b/harborrl/data/harbor/inspector.py
@@ -1,35 +1,16 @@
-"""Conservative inspection of pinned local Harbor tasks."""
+"""Legacy TerminalEnv compatibility tooling; native training does not use this path."""
-import hashlib
from dataclasses import asdict, dataclass
from pathlib import Path
-from .receipt import parse_reward
+from harborrl.tasks.content import tomllib, tree_digest # compatibility exports
-try:
- import tomllib
-except ModuleNotFoundError: # Python 3.10
- import tomli as tomllib
+from .receipt import parse_reward
PROFILE = "terminal_text_v1"
REWARD_PROFILE = "harbor_reward_txt_v1"
-def tree_digest(root: Path, *, exclude: tuple[str, ...] = ()) -> str:
- h = hashlib.sha256()
- for path in sorted(Path(root).rglob("*")):
- if str(path.relative_to(root)) in exclude:
- continue
- if path.is_symlink():
- raise ValueError("symlinks are not supported")
- if path.is_file():
- data = path.read_bytes()
- h.update(str(path.relative_to(root)).encode() + b"\0")
- h.update(str(path.stat().st_mode & 0o777).encode() + b"\0")
- h.update(str(len(data)).encode() + b"\0" + data)
- return h.hexdigest()
-
-
@dataclass(frozen=True)
class TaskRef:
dataset: str
diff --git a/harborrl/data/harbor/materializer.py b/harborrl/data/harbor/materializer.py
index 95346c414..ce0c59cec 100644
--- a/harborrl/data/harbor/materializer.py
+++ b/harborrl/data/harbor/materializer.py
@@ -1,4 +1,4 @@
-"""Publish a task without changing its verifier or Docker build context."""
+"""Legacy TerminalEnv compatibility tooling; native training does not use this path."""
import json
import shutil
diff --git a/harborrl/data/harbor/native_inspector.py b/harborrl/data/harbor/native_inspector.py
index de0a311b5..729f3c726 100644
--- a/harborrl/data/harbor/native_inspector.py
+++ b/harborrl/data/harbor/native_inspector.py
@@ -1,7 +1,7 @@
"""Native preflight: task metadata, not TerminalEnv materialization rules."""
from pathlib import Path
-from .inspector import tomllib, tree_digest
+from harborrl.tasks.content import tomllib, tree_digest
def inspect_native(path, *, expected_digest=None):
@@ -14,7 +14,7 @@ def inspect_native(path, *, expected_digest=None):
if expected_digest is not None and checksum != expected_digest:
raise ValueError("task content changed since catalog lock")
spec = tomllib.loads((root / "task.toml").read_text())
- for name in ("instruction.md", "environment/Dockerfile", "tests/test.sh"):
+ for name in ("instruction.md", "tests/test.sh"):
if not (root / name).is_file():
raise ValueError("missing " + name)
if not (root / "instruction.md").read_text().strip():
@@ -22,6 +22,8 @@ def inspect_native(path, *, expected_digest=None):
env, verifier = spec.get("environment", {}), spec.get("verifier", {})
if not isinstance(env, dict) or not isinstance(verifier, dict):
raise TypeError("invalid environment/verifier configuration")
+ if not (root / 'environment/Dockerfile').is_file() and not env.get('docker_image'):
+ raise ValueError('missing environment/Dockerfile or prebuilt docker_image')
if not isinstance(spec.get("agent", {}), dict):
raise TypeError("invalid agent configuration")
if spec.get("user_agent") or spec.get("bridge"):
diff --git a/harborrl/data/harbor/probe.py b/harborrl/data/harbor/probe.py
index 650941bb9..d3e11d948 100644
--- a/harborrl/data/harbor/probe.py
+++ b/harborrl/data/harbor/probe.py
@@ -1,11 +1,4 @@
-"""Probe a Harbor task with native Docker verifier semantics.
-
-The legacy TerminalEnv comparison path is intentionally not part of the public
-release. This probe runs the untouched task verifier twice in isolated
-containers and requires the same scalar reward, which keeps the check aligned
-with the Native Harbor Runner boundary without importing the old environment
-stack.
-"""
+"""Legacy TerminalEnv compatibility tooling; native training does not use this path."""
from __future__ import annotations
import asyncio
diff --git a/harborrl/data/harbor/receipt.py b/harborrl/data/harbor/receipt.py
index 425a8be4f..ad0476ab1 100644
--- a/harborrl/data/harbor/receipt.py
+++ b/harborrl/data/harbor/receipt.py
@@ -1,4 +1,4 @@
-"""Strict scalar receipt parsing: a missing/invalid score is an infra failure."""
+"""Legacy TerminalEnv compatibility tooling; native training does not use this path."""
import math
diff --git a/harborrl/deployment/__init__.py b/harborrl/deployment/__init__.py
new file mode 100644
index 000000000..cfae85518
--- /dev/null
+++ b/harborrl/deployment/__init__.py
@@ -0,0 +1 @@
+"""Deployment topology: machine roles, preflight and remote training targets."""
diff --git a/harborrl/deployment/controller.py b/harborrl/deployment/controller.py
new file mode 100644
index 000000000..0ee3700e3
--- /dev/null
+++ b/harborrl/deployment/controller.py
@@ -0,0 +1,207 @@
+"""Service-mode dispatch: the manager-side controller flow (P1 + P3).
+
+An independent CPU manager runs the same preflight graph, materializes the
+run on shared storage, starts the RolloutService control plane, launches the
+existing Slime job on the GPU target over SSH, and waits for the shared
+exit-code record. Rollout execution itself moves to the manager only through
+the service; training stays on the GPU.
+"""
+from __future__ import annotations
+
+import contextlib
+import json
+import os
+import signal
+import sys
+import threading
+from pathlib import Path
+
+from harborrl.config import ROOT
+from harborrl.deployment import remote
+from harborrl.deployment.schema import destination
+from harborrl.deployment.training_target import SshTrainingTarget
+from harborrl.platform.evidence import (
+ _materialize,
+ _source_audit,
+ effective_config,
+ new_run_root,
+ run_environment,
+)
+from harborrl.rollout.service import RolloutService, make_control_server
+from harborrl.trajectories.native import publish
+
+TOKEN_MODE = 0o600
+
+
+def check_gpu_control(deployment):
+ manager, gpu = deployment['manager'], deployment['gpu_target']
+ timeout = deployment['connectivity']['http_timeout_s']
+ result = remote.run_ssh(gpu['ssh'] or {'host': 'localhost'},
+ f"curl --noproxy '*' -fsS --max-time {timeout} " + remote.quote(manager['control_url'] + '/healthz'),
+ timeout=timeout + 15)
+ if result.returncode != 0:
+ raise RuntimeError('GPU cannot reach manager control service')
+ payload = json.loads(result.stdout)
+ if payload != {'ok': True, 'service': 'harborrl-rollout-service-v1'}:
+ raise RuntimeError('GPU reached an unexpected control service')
+ return {'gpu_to_controller': True, 'control_url': manager['control_url']}
+
+
+def _ensure_token(token_file):
+ """Load the run token, creating a fresh one on first use."""
+ path = Path(token_file)
+ if path.is_file():
+ token = path.read_text().strip()
+ if token:
+ return token
+ import secrets
+
+ token = secrets.token_urlsafe(32)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_EXCL, TOKEN_MODE)
+ with os.fdopen(fd, "w") as stream:
+ stream.write(token + "\n")
+ return token
+
+
+def _rollout_environment(plan, deployment, environment):
+ """Environment overlay that points the GPU job at the manager service."""
+ manager = deployment["manager"]
+ workers = deployment["rollout"]["workers"]
+ overlay = dict(environment)
+ overlay.update({
+ "HARBORRL_ROLLOUT_MODE": "service",
+ "HARBORRL_ROLLOUT_SERVICE_URL": manager["control_url"],
+ "HARBORRL_ROLLOUT_SERVICE_TOKEN_FILE": manager['run_token_file'],
+ "TRAIN_PYTHON": deployment['gpu_target']['train_python'],
+ "HARBORRL_NATIVE_GATEWAY_HOST": manager["bind"],
+ "HARBORRL_NATIVE_GATEWAY_PORT": str(manager["gateway_port"]),
+ "HARBORRL_NATIVE_GATEWAY_URL": manager["gateway_url"],
+ "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(
+ [destination(worker["ssh"]) for worker in workers]),
+ "HARBORRL_NATIVE_RUNNER_PYTHON": (plan['config']['harbor']['python']
+ if deployment['rollout']['provider'] == 'sandbox'
+ else workers[0]['runner_python']),
+ })
+ if deployment['rollout']['provider'] == 'sandbox':
+ if 'HARBORRL_NATIVE_SANDBOX' not in overlay:
+ raise ValueError('sandbox deployment requires sandbox training config')
+ transport = plan['config']['harbor'].get('runner_transport', 'ssh')
+ if (transport == 'local' and workers) or (transport == 'ssh' and not workers):
+ raise ValueError('sandbox runner transport and deployment workers disagree')
+ overlay['HARBORRL_NATIVE_RUNNER_TRANSPORT'] = transport
+ return overlay
+
+
+def _runtime_factory(deployment, environment, *, concurrency_limit, group_size, tokenizer=None):
+ """Build the per-run rollout runtime when the GPU job announces serving."""
+
+ def factory(run_id, router_url):
+ from harborrl.rollout.runtime import RolloutRuntime, RolloutRuntimeConfig
+
+ env = {**os.environ, **environment}
+ config = RolloutRuntimeConfig.from_environment(
+ env, endpoint=router_url, concurrency_limit=concurrency_limit,
+ group_size=group_size,
+ gateway_host=deployment["manager"]["bind"],
+ gateway_port=deployment["manager"]["gateway_port"],
+ gateway_url=deployment["manager"]["gateway_url"])
+ resolved = tokenizer
+ if resolved is None:
+ from transformers import AutoTokenizer
+
+ resolved = AutoTokenizer.from_pretrained(
+ env["HF_CKPT"], trust_remote_code=True)
+ return RolloutRuntime(config, resolved)
+
+ return factory
+
+
+def dispatch_service(plan, deployment, *, checks):
+ manager, gpu, storage = deployment["manager"], deployment["gpu_target"], deployment["storage"]
+ config = plan["config"]
+ root = new_run_root(storage["run_root"])
+ root.mkdir(parents=True, mode=0o700)
+ run_id = root.name
+
+ catalog_path, prompt_path = _materialize(plan, root)
+ source = _source_audit(root)
+ environment = run_environment(plan, root, catalog_path, prompt_path)
+ token = _ensure_token(manager["run_token_file"])
+ environment = _rollout_environment(plan, deployment, environment)
+
+ sampling = config["sampling"]
+ concurrency = sampling["group_size"] * sampling["groups_per_batch"]
+ service = RolloutService(
+ run_root=root,
+ runtime_factory=_runtime_factory(
+ deployment, environment,
+ concurrency_limit=concurrency, group_size=sampling["group_size"]),
+ token_file=manager["run_token_file"])
+ control = make_control_server(service, host=manager["bind"],
+ port=manager["control_port"])
+ control_thread = None
+ target = None
+ exit_code = 1
+ try:
+ service.register_run(run_id, {
+ "run_id": run_id, "deployment_source": plan["deployment"]["source"],
+ "source_commit": source["commit"],
+ "config_digest": plan["deployment"]["digest"],
+ "group_size": sampling["group_size"],
+ "workers": [worker["id"] for worker in deployment["rollout"]["workers"]],
+ }, token=token)
+ control_thread = threading.Thread(
+ target=control.serve_forever, name="harborrl-control-plane", daemon=True)
+ control_thread.start()
+ publish(root / 'control-readiness.json', check_gpu_control(deployment))
+
+ publish(root / "launch.json", {
+ "plan": plan, "run_dir": str(root), "source": source,
+ "materialized_catalog": str(catalog_path), "prompt_data": str(prompt_path),
+ "control_url": manager["control_url"]})
+ publish(root / "effective-config.json",
+ effective_config(plan, environment, dict(environment), source, checks))
+
+ target = SshTrainingTarget(
+ gpu["ssh"] or {"host": "localhost"}, source_dir=gpu["source_dir"], run_dir=root,
+ environment_file=gpu.get("environment_file"),
+ command_timeout=deployment["connectivity"]["command_timeout_s"] + 30,
+ ssh_timeout=deployment["connectivity"]["ssh_timeout_s"])
+ # The launcher executes inside the GPU checkout; manager-side absolute
+ # paths are rewritten so shared-storage artifacts keep one spelling.
+ target.write_launcher(
+ ["bash", str(ROOT / "harborrl/platform/slime_train.sh")], environment,
+ path_rewrites=[(str(manager["source_dir"]), str(gpu["source_dir"]))])
+ target.launch()
+
+ def forward_signal(signum, frame):
+ if target is not None:
+ target.cancel()
+
+ previous = {sig: signal.signal(sig, forward_signal)
+ for sig in (signal.SIGINT, signal.SIGTERM)}
+ try:
+ exit_code = target.wait()
+ finally:
+ for sig, handler in previous.items():
+ signal.signal(sig, handler)
+ except BaseException:
+ import traceback
+
+ traceback.print_exc()
+ if target is not None:
+ # Best-effort teardown; the run record keeps the failure evidence.
+ with contextlib.suppress(Exception):
+ target.cancel()
+ raise
+ finally:
+ with contextlib.suppress(Exception):
+ service.drain_run(run_id)
+ service.close()
+ if control_thread is not None:
+ control.shutdown()
+ control_thread.join(5)
+ control.server_close()
+ print(json.dumps({"run_dir": str(root), "exit_code": exit_code}), file=sys.stderr)
+ return exit_code
diff --git a/harborrl/deployment/preflight.py b/harborrl/deployment/preflight.py
new file mode 100644
index 000000000..94cb6fd6f
--- /dev/null
+++ b/harborrl/deployment/preflight.py
@@ -0,0 +1,340 @@
+"""Role-layered preflight graph shared by train, doctor and quickstart.
+
+Every check names the role and machine it ran against, the outcome, and a
+user-executable fix. The graph never raises: failures are data, and any
+failed mandatory check keeps a run from being created.
+"""
+from __future__ import annotations
+
+import json
+import socket
+import subprocess
+import time
+from pathlib import Path
+from urllib.parse import urlsplit
+
+from harborrl.deployment import remote
+from harborrl.deployment.schema import destination
+
+
+def _check(role, machine, name, ok, detail="", hint=None, duration_ms=None):
+ record = {"role": role, "machine": machine, "check": name, "ok": bool(ok),
+ "detail": str(detail)}
+ if hint:
+ record["hint"] = str(hint)
+ if duration_ms is not None:
+ record["duration_ms"] = round(duration_ms, 3)
+ return record
+
+
+def _timed(record_factory):
+ started = time.monotonic()
+ record = record_factory()
+ record["duration_ms"] = (time.monotonic() - started) * 1000
+ return record
+
+
+def _bindable(host, port):
+ """Probe with the same address reuse semantics as our HTTP servers."""
+ with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as server:
+ try:
+ server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+ server.bind((host, port))
+ server.listen()
+ return True, "port is free"
+ except OSError as exc:
+ return False, f"cannot bind manager listener on {host}:{port}: {exc}"
+
+
+def _manager_checks(deployment):
+ manager, storage = deployment["manager"], deployment["storage"]
+ checks = []
+ run_root = Path(storage["run_root"])
+ exists = run_root.is_dir()
+ writable = exists and _touch_ok(run_root) if storage["manager_writable"] else exists
+ checks.append(_timed(lambda: _check(
+ "manager", manager["id"], "storage.run_root", exists and writable, str(run_root),
+ hint="create the shared run root and grant the manager write access")))
+
+ if manager["mode"] == "service":
+ token = Path(manager["run_token_file"])
+ token_ok = token.is_file()
+ detail = str(token)
+ if token_ok:
+ mode = token.stat().st_mode & 0o777
+ token_ok = mode & 0o077 == 0
+ detail = f"{token} mode {oct(mode)}"
+ checks.append(_timed(lambda: _check(
+ "manager", manager["id"], "manager.run_token_file", token_ok, detail,
+ hint="install the run-scoped token with chmod 600")))
+
+ for name, port in (("manager.control_port", manager["control_port"]),
+ ("manager.gateway_port", manager["gateway_port"])):
+ started = time.monotonic()
+ ok, detail = _bindable(manager["bind"], port)
+ checks.append(_check(
+ "manager", manager["id"], name, ok, detail,
+ hint=f"free the port or stop the conflicting listener on {manager['bind']}:{port}",
+ duration_ms=(time.monotonic() - started) * 1000))
+ return checks
+
+
+def _touch_ok(directory):
+ probe = directory / ".harborrl-preflight-probe"
+ try:
+ probe.write_text("write probe\n")
+ probe.unlink()
+ return True
+ except OSError:
+ return False
+
+
+def _local_runner():
+ def run(command, *, timeout=30):
+ try:
+ return subprocess.run(command, capture_output=True, text=True,
+ timeout=timeout, check=False, shell=isinstance(command, str))
+ except (subprocess.TimeoutExpired, OSError) as exc:
+ return subprocess.CompletedProcess(command, 255, "", f"{type(exc).__name__}: {exc}")
+
+ def test(expression):
+ result = run(f"test {expression} && echo ok")
+ return result.returncode == 0 and result.stdout.strip() == "ok", result
+
+ return run, test
+
+
+def _gpu_checks(plan, deployment):
+ gpu, connectivity = deployment["gpu_target"], deployment["connectivity"]
+ config = plan["config"]
+ machine = gpu["id"]
+ checks = []
+ if gpu["transport"] == "ssh":
+ spec = gpu["ssh"]
+ started = time.monotonic()
+ probe = remote.run_ssh(spec, "true", timeout=connectivity["ssh_timeout_s"] + 5,
+ connect_timeout=connectivity["ssh_timeout_s"])
+ checks.append(_check(
+ "gpu_target", machine, "gpu_target.ssh", probe.returncode == 0,
+ probe.stderr.strip()[-300:] or "non-interactive ssh ok",
+ hint="the SSH config alias, agent or identity file must allow non-interactive login",
+ duration_ms=(time.monotonic() - started) * 1000))
+ if probe.returncode != 0:
+ checks.append(_check(
+ "gpu_target", machine, "gpu_target.further_probes", True,
+ "skipped: fix gpu_target.ssh first, then rerun for path, model and GPU checks"))
+ return checks
+
+ def execute(command):
+ return remote.run_ssh(spec, command, timeout=connectivity["command_timeout_s"],
+ connect_timeout=connectivity["ssh_timeout_s"])
+
+ def shell_test(expression):
+ ok, result = remote.test_remote(spec, expression,
+ timeout=connectivity["command_timeout_s"],
+ connect_timeout=connectivity["ssh_timeout_s"])
+ return ok, result
+ else:
+ run, test = _local_runner()
+
+ def execute(command):
+ return run(command, timeout=connectivity["command_timeout_s"])
+
+ def shell_test(expression):
+ return test(expression)
+
+ def path_check(name, expression, target, hint):
+ started = time.monotonic()
+ ok, _result = shell_test(expression)
+ checks.append(_check("gpu_target", machine, name, ok, target,
+ hint=hint, duration_ms=(time.monotonic() - started) * 1000))
+
+ path_check("gpu_target.source_dir", f"-d {remote.quote(gpu['source_dir'])}", gpu["source_dir"],
+ "mount or clone the HarborRL checkout at gpu_target.source_dir")
+ path_check("gpu_target.train_python", f"-x {remote.quote(gpu['train_python'])}", gpu["train_python"],
+ "provision the training interpreter at gpu_target.train_python")
+ if gpu.get("environment_file"):
+ path_check("gpu_target.environment_file",
+ f"-f {remote.quote(gpu['environment_file'])}", gpu["environment_file"],
+ "provide the controlled environment variable map")
+ for key in ("checkpoint", "reference"):
+ target = config["model"][key]
+ path_check(f"model.{key}", f"-d {remote.quote(target)}", target,
+ "the model checkpoint must exist in GPU-machine path semantics")
+
+ started = time.monotonic()
+ gpu_list = execute("nvidia-smi --query-gpu=index --format=csv,noheader")
+ visible = len([line for line in gpu_list.stdout.splitlines() if line.strip()])
+ checks.append(_check(
+ "gpu_target", machine, "gpu_target.expected_gpus",
+ gpu_list.returncode == 0 and visible >= gpu["expected_gpus"],
+ f"{visible} visible GPUs (returncode {gpu_list.returncode})",
+ hint="check CUDA visibility and the GPU allocation before training",
+ duration_ms=(time.monotonic() - started) * 1000))
+ return checks
+
+
+def _worker_checks(plan, deployment):
+ workers, connectivity = deployment["rollout"]["workers"], deployment["connectivity"]
+ expected_harbor = plan["config"]["harbor"]["version"]
+ checks = []
+ for worker in workers:
+ spec, machine = worker["ssh"], worker["id"]
+ started = time.monotonic()
+ probe = remote.run_ssh(spec, "true", timeout=connectivity["ssh_timeout_s"] + 5,
+ connect_timeout=connectivity["ssh_timeout_s"])
+ checks.append(_check(
+ "rollout_worker", machine, "worker.ssh", probe.returncode == 0,
+ probe.stderr.strip()[-300:] or "non-interactive ssh ok",
+ hint=f"SSH access to {destination(spec)} must work without a password prompt",
+ duration_ms=(time.monotonic() - started) * 1000))
+ if probe.returncode != 0:
+ continue
+
+ def worker_test(spec, machine, name, expression, target, hint):
+ started = time.monotonic()
+ ok, _result = remote.test_remote(spec, expression,
+ timeout=connectivity["command_timeout_s"],
+ connect_timeout=connectivity["ssh_timeout_s"])
+ checks.append(_check("rollout_worker", machine, name, ok, target,
+ hint=hint, duration_ms=(time.monotonic() - started) * 1000))
+
+ worker_test(spec, machine, "worker.source_dir",
+ f"-d {remote.quote(worker['source_dir'])}",
+ worker["source_dir"], "mount the HarborRL checkout on the worker")
+ worker_test(spec, machine, "worker.runner_python",
+ f"-f {remote.quote(worker['runner_python'])}",
+ worker["runner_python"], "provision the runner interpreter on the worker")
+ for index, task_root in enumerate(worker["task_roots"]):
+ worker_test(spec, machine, f"worker.task_roots[{index}]",
+ f"-d {remote.quote(task_root)}",
+ task_root, "mount the task tree on the worker")
+
+ started = time.monotonic()
+ probe_command = (f"cd {remote.quote(worker['source_dir'])} && "
+ f"{remote.quote(worker['runner_python'])} -m "
+ "harborrl.rollout.harbor_job.runner --probe")
+ result = remote.run_ssh(spec, probe_command, timeout=connectivity["command_timeout_s"],
+ connect_timeout=connectivity["ssh_timeout_s"])
+ runtime_ok = harbor_ok = False
+ detail = result.stderr.strip()[-300:] or result.stdout.strip()[-300:]
+ try:
+ payload = json.loads(result.stdout)
+ parts = payload.get("python", "").split(".")[:2]
+ runtime_ok = (result.returncode == 0
+ and len(parts) == 2 and all(p.isdigit() for p in parts)
+ and tuple(map(int, parts)) >= (3, 12))
+ harbor_ok = payload.get("harbor_version") == expected_harbor
+ if result.returncode == 0:
+ detail = f"python {payload.get('python')}, harbor {payload.get('harbor_version')}"
+ except (ValueError, AttributeError, TypeError):
+ pass
+ checks.append(_check(
+ "rollout_worker", machine, "worker.runner_runtime", runtime_ok, detail,
+ hint="runner Python must be >= 3.12 with the pinned Harbor installation",
+ duration_ms=(time.monotonic() - started) * 1000))
+ checks.append(_check(
+ "rollout_worker", machine, "worker.harbor_version", harbor_ok, detail,
+ hint=f"pin Harbor {expected_harbor} in the runner environment",
+ duration_ms=(time.monotonic() - started) * 1000))
+
+ started = time.monotonic()
+ docker = remote.run_ssh(spec, "docker info --format '{{.ServerVersion}}'",
+ timeout=connectivity["command_timeout_s"],
+ connect_timeout=connectivity["ssh_timeout_s"])
+ checks.append(_check(
+ "rollout_worker", machine, "worker.docker", docker.returncode == 0,
+ docker.stdout.strip()[:120] or docker.stderr.strip()[-200:],
+ hint="start the Docker daemon; Harbor trials require container execution",
+ duration_ms=(time.monotonic() - started) * 1000))
+ return checks
+
+
+def _control_loop_checks(deployment):
+ manager, connectivity = deployment["manager"], deployment["connectivity"]
+ checks = []
+ if manager["mode"] != "service":
+ return checks
+ dials = (("connectivity.gpu_to_controller", manager["control_url"], connectivity["gpu_to_controller"]),
+ ("connectivity.worker_to_gateway", manager["gateway_url"], connectivity["worker_to_gateway"]))
+ for name, url, enabled in dials:
+ if not enabled:
+ continue
+ parts = urlsplit(url)
+ started = time.monotonic()
+ try:
+ socket.getaddrinfo(parts.hostname, parts.port)
+ ok, detail = True, "address resolves from the manager"
+ except socket.gaierror as exc:
+ ok, detail = False, f"{exc}"
+ checks.append(_check("control_loop", manager["id"], name, ok, detail,
+ hint="fix DNS or firewall so the address resolves for every role",
+ duration_ms=(time.monotonic() - started) * 1000))
+ started = time.monotonic()
+ serving = False
+ try:
+ with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as client:
+ client.settimeout(min(connectivity["http_timeout_s"], 5))
+ serving = client.connect_ex((parts.hostname, parts.port)) == 0
+ except OSError:
+ serving = False # unreachable now; the post-start dial retries
+ checks.append(_check(
+ "control_loop", manager["id"], name + ".dial", True,
+ "listener already serving" if serving
+ else "no listener yet; the live dial reruns after components start",
+ duration_ms=(time.monotonic() - started) * 1000))
+ if connectivity["manager_to_gpu_inference"]:
+ checks.append(_check(
+ "control_loop", manager["id"], "connectivity.manager_to_gpu_inference", True,
+ "deferred: the GPU job publishes its inference endpoint after serving is healthy"))
+ return checks
+
+
+def preflight(plan, deployment, *, live=True):
+ """Run every deployment layer; remote probes are skipped when live=False."""
+ if not live:
+ return [_check("deployment", deployment["manager"]["id"], "deployment.config",
+ True, "dry-run: parsed and validated, remote probes skipped")]
+ checks = []
+ checks.extend(_manager_checks(deployment))
+ checks.extend(_gpu_checks(plan, deployment))
+ if deployment["rollout"]["provider"] == "ssh_docker":
+ checks.extend(_worker_checks(plan, deployment))
+ checks.extend(_control_loop_checks(deployment))
+ return checks
+
+
+def readiness_checks(plan, deployment, *, from_specs=()):
+ """Post-start dials: cross-machine health after components are listening.
+
+ ``from_specs`` maps a role to the ssh spec that should perform the dial;
+ callers pass the GPU target and rollout workers of a live deployment.
+ """
+ manager, connectivity = deployment["manager"], deployment["connectivity"]
+ checks = []
+ dials = (("gpu_target", "gpu_to_controller.ready", manager["control_url"],
+ connectivity["gpu_to_controller"]),
+ ("rollout_worker", "worker_to_gateway.ready", manager["gateway_url"],
+ connectivity["worker_to_gateway"]))
+ specs = dict(from_specs)
+ for role, name, url, enabled in dials:
+ if not enabled:
+ continue
+ spec = specs.get(role)
+ if spec is None:
+ checks.append(_check("control_loop", manager["id"], name, True,
+ "deferred: no machine mapped for this dial"))
+ continue
+ path = "/healthz" if "control" in name else "/readyz"
+ command = (f"python3 -c "
+ f"\"import urllib.request;urllib.request.urlopen('{url}{path}',"
+ f"timeout={connectivity['http_timeout_s']})\"")
+ started = time.monotonic()
+ result = remote.run_ssh(spec, command, timeout=connectivity["command_timeout_s"],
+ connect_timeout=connectivity["ssh_timeout_s"])
+ checks.append(_check("control_loop", destination(spec), name,
+ result.returncode == 0,
+ result.stderr.strip()[-300:] or "health dial ok",
+ hint=f"open the route from {destination(spec)} to {url}{path}",
+ duration_ms=(time.monotonic() - started) * 1000))
+ return checks
diff --git a/harborrl/deployment/remote.py b/harborrl/deployment/remote.py
new file mode 100644
index 000000000..e9ddd276c
--- /dev/null
+++ b/harborrl/deployment/remote.py
@@ -0,0 +1,55 @@
+"""Non-interactive SSH transport for deployment preflight and launch.
+
+Every invocation runs BatchMode: a password prompt is a failed check, never an
+interactive hang. User, port and identity are inherited from the invoking
+user's SSH config unless the deployment spec narrows them.
+"""
+from __future__ import annotations
+
+import shlex
+import subprocess
+
+SSH_OPTIONS = ("BatchMode=yes", "ClearAllForwardings=yes", "StrictHostKeyChecking=accept-new")
+
+
+def ssh_command(spec, remote_command, *, connect_timeout=15):
+ """Assemble one non-interactive ssh argv for a normalized spec."""
+ argv = ["ssh"]
+ for option in SSH_OPTIONS:
+ argv += ["-o", option]
+ argv += ["-o", f"ConnectTimeout={int(connect_timeout)}"]
+ if spec.get("port"):
+ argv += ["-p", str(spec["port"])]
+ if spec.get("identity"):
+ argv += ["-i", str(spec["identity"])]
+ from harborrl.deployment.schema import destination
+
+ argv += [destination(spec), remote_command]
+ return argv
+
+
+def run_ssh(spec, command, *, timeout=30, connect_timeout=15):
+ """Run one remote command; transport failures surface as returncode 255."""
+ argv = ssh_command(spec, command, connect_timeout=connect_timeout)
+ try:
+ return subprocess.run(argv, capture_output=True, text=True,
+ timeout=timeout, check=False)
+ except (subprocess.TimeoutExpired, OSError) as exc:
+ return subprocess.CompletedProcess(argv, 255, "", f"{type(exc).__name__}: {exc}")
+
+
+def test_remote(spec, expression, *, timeout=30, connect_timeout=15):
+ """Evaluate a shell test expression on the target; True when it passes."""
+ result = run_ssh(spec, f"test {expression} && echo ok", timeout=timeout,
+ connect_timeout=connect_timeout)
+ return result.returncode == 0 and result.stdout.strip() == "ok", result
+
+
+def remote_read(spec, command, *, timeout=30, connect_timeout=15):
+ """Return (returncode, stdout, stderr) for a remote command."""
+ result = run_ssh(spec, command, timeout=timeout, connect_timeout=connect_timeout)
+ return result.returncode, result.stdout, result.stderr
+
+
+def quote(value):
+ return shlex.quote(str(value))
diff --git a/harborrl/deployment/schema.py b/harborrl/deployment/schema.py
new file mode 100644
index 000000000..3cc7bf7c8
--- /dev/null
+++ b/harborrl/deployment/schema.py
@@ -0,0 +1,330 @@
+"""Deployment schema v1: machine roles and access, never training semantics.
+
+A deployment config describes WHERE the manager, GPU trainer and rollout
+workers live and HOW they reach each other. It deliberately excludes every
+training-side field (learning rate, sampling, task catalog) and never stores
+secrets: SSH targets must resolve through the invoking user's SSH config,
+agent or a restricted identity file.
+"""
+from __future__ import annotations
+
+import re
+import sys
+from pathlib import Path
+from urllib.parse import urlsplit
+
+import yaml
+
+from harborrl.config import ROOT
+
+DEPLOYMENT_SCHEMA_VERSION = 1
+DEPLOYMENT_SECTIONS = {"manager", "gpu_target", "storage", "rollout", "connectivity"}
+
+SLUG = re.compile(r"[A-Za-z0-9][A-Za-z0-9_-]*")
+LOOPBACK_HOSTS = {"localhost", "127.0.0.1", "::1", "0.0.0.0", "[::1]"}
+
+MANAGER_FIELDS = {"id", "mode", "bind", "control_port", "control_url", "gateway_port",
+ "gateway_url", "run_token_file", "source_dir", "controller_python"}
+SSH_FIELDS = {"host", "user", "port", "identity"}
+GPU_FIELDS = {"id", "transport", "ssh", "source_dir", "train_python", "environment_file", "expected_gpus"}
+STORAGE_FIELDS = {"mode", "run_root", "manager_writable", "gpu_writable", "worker_writable"}
+WORKER_FIELDS = {"id", "ssh", "source_dir", "runner_python", "docker", "task_roots"}
+ROLLOUT_FIELDS = {"provider", "workers"}
+CONNECTIVITY_FIELDS = {"command_timeout_s", "ssh_timeout_s", "http_timeout_s",
+ "gpu_to_controller", "worker_to_gateway", "manager_to_gpu_inference"}
+TRANSPORTS = ("local", "ssh")
+ROLLOUT_PROVIDERS = ("ssh_docker", "embedded", "sandbox")
+STORAGE_MODES = ("shared_fs",)
+
+
+class DeploymentError(ValueError):
+ """Raised for any malformed or unsafe deployment configuration."""
+
+
+def _slug(value, label):
+ if not isinstance(value, str) or not SLUG.fullmatch(value):
+ raise DeploymentError(f"{label} must be a short machine identifier")
+ return value
+
+
+def _positive_int(value, label):
+ if type(value) is not int or value <= 0 or type(value) is bool:
+ raise DeploymentError(f"{label} must be a positive integer")
+ return value
+
+
+def _nonempty_str(value, label):
+ if not isinstance(value, str) or not value.strip():
+ raise DeploymentError(f"{label} must be a nonempty string")
+ return value
+
+
+def _bool(value, label):
+ if type(value) is not bool:
+ raise DeploymentError(f"{label} must be a boolean")
+ return value
+
+
+def _absolute_path(value, label):
+ value = _nonempty_str(value, label)
+ if not Path(value).is_absolute():
+ raise DeploymentError(f"{label} must be an absolute path in target-machine semantics")
+ return value
+
+
+def _plain_origin(url, label, *, port=None, allow_loopback=False):
+ parts = urlsplit(_nonempty_str(url, label))
+ if (parts.scheme not in ("http", "https") or not parts.hostname
+ or parts.username or parts.password or parts.query or parts.fragment
+ or parts.path not in ("", "/")
+ or (port is not None and parts.port != port)):
+ raise DeploymentError(f"{label} must be a plain http(s) origin matching its declared port")
+ if not allow_loopback and parts.hostname.lower() in LOOPBACK_HOSTS:
+ raise DeploymentError(f"{label} must be reachable from other machines; loopback is forbidden")
+ return f"{parts.scheme}://{parts.netloc}".rstrip("/")
+
+
+def _ssh_spec(value, label, *, require_host=True):
+ if value is None:
+ value = {}
+ if not isinstance(value, dict) or set(value) - SSH_FIELDS:
+ raise DeploymentError(
+ f"{label} supports only {sorted(SSH_FIELDS)}; passwords and private keys are never stored")
+ host = value.get("host")
+ if host is not None:
+ host = _nonempty_str(host, f"{label}.host")
+ if host.startswith("-") or host.count("@") > 1:
+ raise DeploymentError(f"{label}.host must be an SSH destination, not a flag")
+ if require_host and not host:
+ raise DeploymentError(f"{label}.host is required")
+ user = value.get("user")
+ if user is not None:
+ user = _nonempty_str(user, f"{label}.user")
+ if user.startswith("-"):
+ raise DeploymentError(f"{label}.user must not look like a flag")
+ port = value.get("port")
+ if port is not None:
+ port = _positive_int(port, f"{label}.port")
+ identity = value.get("identity")
+ if identity is not None:
+ identity = _absolute_path(identity, f"{label}.identity")
+ spec = {"host": host}
+ if user:
+ spec["user"] = user
+ if port:
+ spec["port"] = port
+ if identity:
+ spec["identity"] = identity
+ return spec
+
+
+def destination(spec):
+ """Render an ssh destination from a normalized spec (user@host)."""
+ return f"{spec['user']}@{spec['host']}" if spec.get("user") else spec["host"]
+
+
+def _manager(config):
+ if not isinstance(config, dict) or set(config) != MANAGER_FIELDS:
+ raise DeploymentError(f"manager requires exactly {sorted(MANAGER_FIELDS)}")
+ mode = config["mode"]
+ if mode not in ("embedded", "service"):
+ raise DeploymentError("manager.mode must be embedded or service")
+ service = mode == "service"
+ control_port = _positive_int(config["control_port"], "manager.control_port")
+ gateway_port = _positive_int(config["gateway_port"], "manager.gateway_port")
+ if control_port == gateway_port:
+ raise DeploymentError("manager.control_port and manager.gateway_port must differ")
+ if not 0 < control_port < 65536 or not 0 < gateway_port < 65536:
+ raise DeploymentError("manager ports must be in 1..65535")
+ control_url = _plain_origin(config["control_url"], "manager.control_url",
+ port=control_port, allow_loopback=not service)
+ gateway_url = _plain_origin(config["gateway_url"], "manager.gateway_url",
+ port=gateway_port, allow_loopback=not service)
+ return {
+ "id": _slug(config["id"], "manager.id"), "mode": mode,
+ "bind": _nonempty_str(config["bind"], "manager.bind"),
+ "control_port": control_port, "control_url": control_url,
+ "gateway_port": gateway_port, "gateway_url": gateway_url,
+ "run_token_file": _absolute_path(config["run_token_file"], "manager.run_token_file"),
+ "source_dir": _absolute_path(config["source_dir"], "manager.source_dir"),
+ "controller_python": _absolute_path(config["controller_python"], "manager.controller_python"),
+ }
+
+
+def _gpu_target(config):
+ if not isinstance(config, dict) or set(config) != GPU_FIELDS:
+ raise DeploymentError(f"gpu_target requires exactly {sorted(GPU_FIELDS)}")
+ transport = config["transport"]
+ if transport not in TRANSPORTS:
+ raise DeploymentError(f"gpu_target.transport must be one of {TRANSPORTS}")
+ ssh = config["ssh"]
+ if transport == "ssh":
+ ssh = _ssh_spec(ssh, "gpu_target.ssh")
+ elif ssh is not None:
+ raise DeploymentError("gpu_target.ssh must be null for local transport")
+ target = {
+ "id": _slug(config["id"], "gpu_target.id"), "transport": transport, "ssh": ssh,
+ "source_dir": _absolute_path(config["source_dir"], "gpu_target.source_dir"),
+ "train_python": _absolute_path(config["train_python"], "gpu_target.train_python"),
+ "expected_gpus": _positive_int(config["expected_gpus"], "gpu_target.expected_gpus"),
+ }
+ environment_file = config["environment_file"]
+ if environment_file is not None:
+ target["environment_file"] = _absolute_path(environment_file, "gpu_target.environment_file")
+ return target
+
+
+def _storage(config):
+ if not isinstance(config, dict) or set(config) != STORAGE_FIELDS:
+ raise DeploymentError(f"storage requires exactly {sorted(STORAGE_FIELDS)}")
+ if config["mode"] not in STORAGE_MODES:
+ raise DeploymentError(f"storage.mode must be one of {STORAGE_MODES}")
+ return {
+ "mode": config["mode"],
+ "run_root": _absolute_path(config["run_root"], "storage.run_root"),
+ "manager_writable": _bool(config["manager_writable"], "storage.manager_writable"),
+ "gpu_writable": _bool(config["gpu_writable"], "storage.gpu_writable"),
+ "worker_writable": _bool(config["worker_writable"], "storage.worker_writable"),
+ }
+
+
+def _rollout(config):
+ if not isinstance(config, dict) or set(config) != ROLLOUT_FIELDS:
+ raise DeploymentError(f"rollout requires exactly {sorted(ROLLOUT_FIELDS)}")
+ if config["provider"] not in ROLLOUT_PROVIDERS:
+ raise DeploymentError(f"rollout.provider must be one of {ROLLOUT_PROVIDERS}")
+ workers = config["workers"]
+ # ssh_docker needs explicit machines; the embedded provider falls back to
+ # the training config's worker list, so it may start empty.
+ if (not isinstance(workers, list)
+ or (not workers and config["provider"] not in ("embedded", "sandbox"))):
+ raise DeploymentError("rollout.workers must be a nonempty list")
+ normalized, seen = [], set()
+ for worker in workers:
+ if not isinstance(worker, dict) or set(worker) != WORKER_FIELDS:
+ raise DeploymentError(f"rollout worker requires exactly {sorted(WORKER_FIELDS)}")
+ worker_id = _slug(worker["id"], "rollout worker id")
+ if worker_id in seen:
+ raise DeploymentError(f"duplicate rollout worker id: {worker_id}")
+ seen.add(worker_id)
+ task_roots = worker["task_roots"]
+ if (not isinstance(task_roots, list) or not task_roots
+ or any(not isinstance(root, str) or not root.strip() for root in task_roots)):
+ raise DeploymentError(f"worker {worker_id} requires nonempty task_roots")
+ normalized.append({
+ "id": worker_id,
+ "ssh": _ssh_spec(worker["ssh"], f"rollout worker {worker_id} ssh"),
+ "source_dir": _absolute_path(worker["source_dir"], f"worker {worker_id} source_dir"),
+ "runner_python": _absolute_path(worker["runner_python"], f"worker {worker_id} runner_python"),
+ "docker": _bool(worker["docker"], f"worker {worker_id} docker"),
+ "task_roots": [_absolute_path(root, f"worker {worker_id} task_root") for root in task_roots],
+ })
+ return {"provider": config["provider"], "workers": normalized}
+
+
+def _connectivity(config):
+ if not isinstance(config, dict) or set(config) != CONNECTIVITY_FIELDS:
+ raise DeploymentError(f"connectivity requires exactly {sorted(CONNECTIVITY_FIELDS)}")
+ return {
+ "command_timeout_s": _positive_int(config["command_timeout_s"], "connectivity.command_timeout_s"),
+ "ssh_timeout_s": _positive_int(config["ssh_timeout_s"], "connectivity.ssh_timeout_s"),
+ "http_timeout_s": _positive_int(config["http_timeout_s"], "connectivity.http_timeout_s"),
+ "gpu_to_controller": _bool(config["gpu_to_controller"], "connectivity.gpu_to_controller"),
+ "worker_to_gateway": _bool(config["worker_to_gateway"], "connectivity.worker_to_gateway"),
+ "manager_to_gpu_inference": _bool(config["manager_to_gpu_inference"], "connectivity.manager_to_gpu_inference"),
+ }
+
+
+def validate(config):
+ """Strictly validate and normalize a deployment mapping."""
+ if not isinstance(config, dict):
+ raise DeploymentError("deployment config must be a mapping")
+ if set(config) != DEPLOYMENT_SECTIONS | {"schema_version"}:
+ raise DeploymentError(
+ "deployment config requires exactly schema_version and "
+ + ", ".join(sorted(DEPLOYMENT_SECTIONS)))
+ if config["schema_version"] != DEPLOYMENT_SCHEMA_VERSION or type(config["schema_version"]) is not int:
+ raise DeploymentError(f"deployment schema_version must be {DEPLOYMENT_SCHEMA_VERSION}")
+ return {
+ "schema_version": DEPLOYMENT_SCHEMA_VERSION,
+ "manager": _manager(config["manager"]),
+ "gpu_target": _gpu_target(config["gpu_target"]),
+ "storage": _storage(config["storage"]),
+ "rollout": _rollout(config["rollout"]),
+ "connectivity": _connectivity(config["connectivity"]),
+ }
+
+
+def embedded_local(*, run_root=None, workers=(), expected_gpus=1):
+ """Default same-machine deployment; behavior matches the legacy pipeline."""
+ return validate({
+ "schema_version": 1,
+ "manager": {"id": "embedded-local", "mode": "embedded", "bind": "127.0.0.1",
+ "control_port": 49200, "control_url": "http://127.0.0.1:49200",
+ "gateway_port": 49184, "gateway_url": "http://127.0.0.1:49184",
+ "run_token_file": str(ROOT / "runs" / "run-token"),
+ "source_dir": str(ROOT), "controller_python": sys.executable},
+ "gpu_target": {"id": "local", "transport": "local", "ssh": None,
+ "source_dir": str(ROOT), "train_python": sys.executable,
+ "environment_file": None, "expected_gpus": expected_gpus},
+ "storage": {"mode": "shared_fs", "run_root": run_root or str(ROOT / "runs"),
+ "manager_writable": True, "gpu_writable": True, "worker_writable": True},
+ "rollout": {"provider": "embedded", "workers": [
+ {"id": f"worker-{index}", "ssh": {"host": worker}, "source_dir": str(ROOT),
+ "runner_python": sys.executable, "docker": True, "task_roots": [str(ROOT)]}
+ for index, worker in enumerate(workers)]},
+ "connectivity": {"command_timeout_s": 30, "ssh_timeout_s": 15, "http_timeout_s": 10,
+ "gpu_to_controller": True, "worker_to_gateway": True,
+ "manager_to_gpu_inference": True},
+ })
+
+
+DEFAULT_FILENAME = "deployment.yaml"
+
+
+def load_deployment(path=None, *, default_directory="."):
+ """Load an explicit, default-discovered or embedded-local deployment config.
+
+ Returns ``(config, source)`` where source describes where the config came
+ from; absent any file the embedded-local topology keeps legacy behavior.
+ """
+ if path is not None:
+ source = str(Path(path).expanduser())
+ candidates = [source]
+ else:
+ candidates = [str(Path(default_directory) / DEFAULT_FILENAME), DEFAULT_FILENAME]
+ source = candidates[0]
+ for candidate in candidates:
+ file = Path(candidate)
+ if not file.is_file():
+ continue
+ try:
+ text = file.read_text()
+ except OSError as exc:
+ raise DeploymentError(f"cannot read deployment config {candidate}: {exc}") from exc
+ try:
+ raw = yaml.safe_load(text)
+ except yaml.YAMLError as exc:
+ raise DeploymentError(f"deployment YAML {candidate}: {exc}") from exc
+ return validate(raw), str(file.resolve())
+ if path is not None:
+ raise DeploymentError(f"deployment config not found: {path}")
+ return embedded_local(), "embedded-local"
+
+
+def summary(config, source):
+ """Plan-embedded deployment digest; the full config travels with it."""
+ from harborrl.trajectories.native import digest
+ return {
+ "schema_version": DEPLOYMENT_SCHEMA_VERSION,
+ "source": source,
+ "digest": digest(config),
+ "mode": config["manager"]["mode"],
+ "manager_id": config["manager"]["id"],
+ "control_url": config["manager"]["control_url"],
+ "gateway_url": config["manager"]["gateway_url"],
+ "gpu": {"id": config["gpu_target"]["id"], "transport": config["gpu_target"]["transport"]},
+ "worker_ids": [worker["id"] for worker in config["rollout"]["workers"]],
+ "run_root": config["storage"]["run_root"],
+ "config": config,
+ }
diff --git a/harborrl/deployment/training_target.py b/harborrl/deployment/training_target.py
new file mode 100644
index 000000000..4de5d2468
--- /dev/null
+++ b/harborrl/deployment/training_target.py
@@ -0,0 +1,217 @@
+"""TrainingTarget (P1): launch, query and cancel one Slime training job.
+
+``local`` keeps the legacy in-process launcher byte-for-byte. ``ssh`` runs the
+same launcher script on the GPU machine through shared storage: the run
+directory is written by the manager, executed by the GPU target, and polled
+without holding an interactive session open.
+"""
+from __future__ import annotations
+
+import os
+import shlex
+import signal
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+from harborrl.deployment import remote
+from harborrl.deployment.schema import destination
+
+LAUNCHER_NAME = "gpu-launcher.sh"
+EXIT_CODE_NAME = "exit-code"
+PID_NAME = "launcher.pid"
+STDOUT_NAME = "train-stdout.log"
+
+
+class TrainingTargetError(RuntimeError):
+ pass
+
+
+class LocalTrainingTarget:
+ """Legacy target: the Slime job runs as a child of this process."""
+
+ def __init__(self, command, *, cwd, env):
+ self.command = [str(part) for part in command]
+ self.cwd = str(cwd)
+ self.env = env
+ self.child = None
+
+ def launch(self):
+ self.child = subprocess.Popen(self.command, cwd=self.cwd, env=self.env,
+ start_new_session=True)
+ return self.child.pid
+
+ def wait(self):
+ if self.child is None:
+ raise TrainingTargetError("local target was never launched")
+
+ def forward_signal(signum, frame):
+ if self.child.poll() is None:
+ try:
+ os.killpg(os.getpgid(self.child.pid), signum)
+ except ProcessLookupError:
+ pass
+
+ previous = {sig: signal.signal(sig, forward_signal)
+ for sig in (signal.SIGINT, signal.SIGTERM)}
+ try:
+ return self.child.wait()
+ finally:
+ for sig, handler in previous.items():
+ signal.signal(sig, handler)
+
+ def query(self):
+ if self.child is None:
+ return {"state": "not-launched"}
+ return {"state": "running" if self.child.poll() is None else "exited",
+ "exit_code": self.child.returncode}
+
+ def cancel(self):
+ if self.child is None or self.child.poll() is not None:
+ return False
+ try:
+ os.killpg(os.getpgid(self.child.pid), signal.SIGINT)
+ except (ProcessLookupError, PermissionError):
+ self.child.terminate()
+ return True
+
+
+class SshTrainingTarget:
+ """Remote target over shared storage; the launcher script is the contract."""
+
+ def __init__(self, spec, *, source_dir, run_dir, environment_file=None,
+ command_timeout=60, ssh_timeout=15, poll_interval=2.0):
+ self.spec = dict(spec)
+ self.source_dir = str(source_dir)
+ self.run_dir = Path(run_dir).resolve()
+ self.environment_file = environment_file
+ self.command_timeout = command_timeout
+ self.ssh_timeout = ssh_timeout
+ self.poll_interval = poll_interval
+ self._launched = False
+
+ def write_launcher(self, command, environment, *, path_rewrites=()):
+ """Materialize the launcher; env values and paths are quoted, never interpolated."""
+ command = [str(part) for part in command]
+ for manager_path, target_path in path_rewrites:
+ command = [str(part).replace(str(manager_path), str(target_path), 1)
+ if str(part).startswith(str(manager_path)) else part for part in command]
+ lines = ["#!/usr/bin/env bash",
+ "# Generated by the HarborRL manager; executed on the GPU target.",
+ "set -uo pipefail",
+ f"cd {shlex.quote(self.source_dir)}",
+ f"export RUN_DIR={shlex.quote(str(self.run_dir))}"]
+ if self.environment_file:
+ # A controlled JSON mapping only; never an arbitrary remote shell.
+ quoted_file = shlex.quote(str(self.environment_file))
+ lines.append(
+ "eval \"$(HARBORRL_ENV_FILE=" + quoted_file + " python3 - <<'HARBORRL_ENV_JSON'\n"
+ "import json, os, shlex\n"
+ "with open(os.environ['HARBORRL_ENV_FILE']) as stream:\n"
+ " for key, value in sorted(json.load(stream).items()):\n"
+ " print(f'export {key}={shlex.quote(str(value))}')\n"
+ "HARBORRL_ENV_JSON\n"
+ ")\"")
+ for key in sorted(environment):
+ lines.append(f"export {key}={shlex.quote(str(environment[key]))}")
+ lines.append("status=0")
+ # The command is a full argv (its first word is already the
+ # interpreter); execute it as-is and capture the real exit code.
+ lines.append(" ".join(shlex.quote(part) for part in command)
+ + " || status=$?")
+ lines.append(f"printf '%s\\n' \"$status\" > {shlex.quote(str(self.run_dir / EXIT_CODE_NAME))}")
+ lines.append("exit \"$status\"")
+ launcher = self.run_dir / LAUNCHER_NAME
+ launcher.write_text("\n".join(lines) + "\n")
+ launcher.chmod(0o755)
+ return launcher
+
+ def launch(self):
+ if self._launched:
+ raise TrainingTargetError("ssh target was already launched")
+ # The background job records its own session-leader pid: setsid execs
+ # the launcher, so the recorded pid heads the cancellable process group.
+ bootstrap = (f"cd {shlex.quote(str(self.run_dir))} && "
+ f"(setsid nohup bash {LAUNCHER_NAME} > {STDOUT_NAME} 2>&1"
+ f" < /dev/null & echo $! > {PID_NAME})")
+ result = remote.run_ssh(self.spec, bootstrap, timeout=self.command_timeout,
+ connect_timeout=self.ssh_timeout)
+ if result.returncode != 0:
+ raise TrainingTargetError(
+ f"remote launch failed on {destination(self.spec)}: "
+ f"{result.stderr.strip()[-500:]}")
+ self._launched = True
+ return self.run_dir / PID_NAME
+
+ def _pid(self):
+ try:
+ return int((self.run_dir / PID_NAME).read_text().strip())
+ except (OSError, ValueError):
+ return None
+
+ def exit_code(self):
+ try:
+ return int((self.run_dir / EXIT_CODE_NAME).read_text().strip())
+ except (OSError, ValueError):
+ return None
+
+ def alive(self):
+ pid = self._pid()
+ if pid is None:
+ return False
+ result = remote.run_ssh(self.spec, f"kill -0 {pid} 2>/dev/null && echo ok",
+ timeout=self.command_timeout, connect_timeout=self.ssh_timeout)
+ return result.returncode == 0 and result.stdout.strip() == "ok"
+
+ def wait(self, *, timeout=None):
+ """Poll the shared exit-code record; never resubmit or restart."""
+ deadline = None if timeout is None else time.monotonic() + timeout
+ while True:
+ code = self.exit_code()
+ if code is not None:
+ return code
+ if deadline is not None and time.monotonic() >= deadline:
+ raise TimeoutError(f"GPU job in {self.run_dir} did not finish in time")
+ time.sleep(self.poll_interval)
+
+ def query(self):
+ code = self.exit_code()
+ return {"state": "exited" if code is not None else
+ ("running" if self.alive() else "unknown"),
+ "exit_code": code, "pid": self._pid(),
+ "run_dir": str(self.run_dir)}
+
+ def cancel(self, *, grace_seconds=30):
+ pid = self._pid()
+ if pid is None:
+ return False
+ for signame in ("INT", "TERM", "KILL"):
+ remote.run_ssh(self.spec, f"kill -{signame} -- -{pid} 2>/dev/null || "
+ f"kill -{signame} {pid} 2>/dev/null || true",
+ timeout=self.command_timeout, connect_timeout=self.ssh_timeout)
+ deadline = time.monotonic() + grace_seconds
+ while time.monotonic() < deadline:
+ if self.exit_code() is not None:
+ return True
+ time.sleep(min(2.0, self.poll_interval))
+ return self.exit_code() is not None
+
+
+def local_target(plan, *, environment):
+ """Build the legacy in-process target from a resolved plan."""
+ allowed = {"PATH", "HOME", "USER", "LANG", "LC_ALL", "TMPDIR", "LD_LIBRARY_PATH",
+ "CUDA_VISIBLE_DEVICES", "HTTP_PROXY", "HTTPS_PROXY", "NO_PROXY",
+ "http_proxy", "https_proxy", "no_proxy", "PYTHONPATH",
+ "SLIME_DIR", "MEGATRON_DIR", "SGLANG_IMAGE"}
+ child_env = {key: value for key, value in os.environ.items() if key in allowed}
+ child_env["PATH"] = os.pathsep.join(
+ [str(Path(sys.executable).parent), child_env.get("PATH", os.defpath)])
+ child_env["PYTHONPATH"] = os.pathsep.join(filter(None, [
+ child_env.get("SLIME_DIR", ""), child_env.get("MEGATRON_DIR", ""),
+ str(Path(__file__).resolve().parents[2]), child_env.get("PYTHONPATH", ""),
+ ]))
+ child_env.update(environment)
+ from harborrl.config import ROOT
+
+ return LocalTrainingTarget(plan["training_command"], cwd=ROOT, env=child_env)
diff --git a/harborrl/gateway/messages.py b/harborrl/gateway/messages.py
index 479e764eb..9c474817b 100644
--- a/harborrl/gateway/messages.py
+++ b/harborrl/gateway/messages.py
@@ -58,7 +58,7 @@ def convert(payload, *, model, max_output_tokens=8192, count_only=False):
raise ProtocolError("unsupported context management control")
effort = payload.get("output_config")
if effort is not None and (not isinstance(effort, dict) or set(effort) != {"effort"}
- or effort["effort"] not in ("low", "medium", "high")):
+ or effort["effort"] not in ("low", "medium", "high", "xhigh")):
raise ProtocolError("unsupported output configuration")
if not isinstance(payload.get("tools", []), list):
raise ProtocolError("tools must be a list")
@@ -138,7 +138,13 @@ def parse_qwen_output(text, *, response_id, tools):
"""Deterministic Qwen tool markers; malformed model output is never repaired."""
if "" in text or " " in text:
raise ProtocolError("thinking output is not enabled by this profile")
- names = {tool["function"]["name"] for tool in tools}
+ names = {}
+ for tool in tools:
+ name = tool["function"]["name"]
+ alias = name.casefold()
+ if alias in names:
+ raise ProtocolError("ambiguous generated tool alias")
+ names[alias] = name
blocks, offset = [], 0
matches = list(re.finditer(r"\s*(.*?)\s* ", text, re.DOTALL))
remainder = re.sub(r"\s*(.*?)\s* ", "", text, flags=re.DOTALL)
@@ -152,9 +158,17 @@ def parse_qwen_output(text, *, response_id, tools):
except ValueError as exc:
raise ProtocolError("invalid generated tool JSON") from exc
keys(call, ("name", "arguments"), "generated tool")
- if call.get("name") not in names or not isinstance(call.get("arguments"), dict):
- raise ProtocolError("unknown generated tool or invalid arguments")
- blocks.append({"type": "tool_use", "id": f"toolu_{response_id}_{index}", "name": call["name"], "input": call["arguments"]})
+ generated_name = call.get("name")
+ if generated_name == "finish" and call.get("arguments") == {}:
+ if text[match.end():]:
+ raise ProtocolError("finish marker must be the final output")
+ return blocks
+ canonical_name = names.get(generated_name.casefold()) if isinstance(generated_name, str) else None
+ if canonical_name is None:
+ raise ProtocolError(f"unknown generated tool: {generated_name!r}")
+ if not isinstance(call.get("arguments"), dict):
+ raise ProtocolError("generated tool arguments are not an object")
+ blocks.append({"type": "tool_use", "id": f"toolu_{response_id}_{index}", "name": canonical_name, "input": call["arguments"]})
offset = match.end()
if text[offset:] or not blocks:
blocks.append({"type": "text", "text": text[offset:]})
diff --git a/harborrl/gateway/server.py b/harborrl/gateway/server.py
index 282a95e26..fd011c505 100644
--- a/harborrl/gateway/server.py
+++ b/harborrl/gateway/server.py
@@ -110,7 +110,7 @@ def do_POST(self):
gateway.registry.delivery(aid, message["id"], "delivery_unknown")
status = 401 if isinstance(exc, PermissionError) else 400 if isinstance(exc, (ProtocolError, json.JSONDecodeError)) else 502
# Do not echo backend errors or request contents (credentials may appear there).
- self.reply(status, {"type": "error", "error": {"type": "authentication_error" if status == 401 else "invalid_request_error" if status == 400 else "api_error", "message": type(exc).__name__}})
+ self.reply(status, {"type": "error", "error": {"type": "authentication_error" if status == 401 else "invalid_request_error" if status == 400 else "api_error", "message": f"{type(exc).__name__}: {exc}"}})
server = ThreadingHTTPServer((host, port), Handler)
server.daemon_threads = True
diff --git a/harborrl/gateway/sglang_backend.py b/harborrl/gateway/sglang_backend.py
index f37f594d8..d2efe5e63 100644
--- a/harborrl/gateway/sglang_backend.py
+++ b/harborrl/gateway/sglang_backend.py
@@ -9,24 +9,23 @@
from harborrl.trajectories.native import digest, finite
-from .messages import ProtocolError, parse_qwen_output
+from .messages import ProtocolError
class SGLangBackend:
- def __init__(self, endpoint, tokenizer, *, tokenizer_digest, template_digest,
- timeout=120, max_context=32768, audited_raw_logprobs=False):
+ def __init__(self, endpoint, tokenizer, model_adapter, *, tokenizer_digest,
+ template_digest, timeout=120, max_context=32768,
+ audited_raw_logprobs=False):
self.endpoint = endpoint.rstrip("/")
self.tokenizer = tokenizer
+ self.model_adapter = model_adapter
self.tokenizer_digest, self.template_digest = tokenizer_digest, template_digest
self.timeout, self.max_context = timeout, max_context
self.audited_raw_logprobs = audited_raw_logprobs
self.opener = request.build_opener(request.ProxyHandler({}))
def prepare(self, converted):
- ids = self.tokenizer.apply_chat_template(converted["messages"], tools=converted["tools"] or None,
- tokenize=True, add_generation_prompt=True, enable_thinking=False)
- if not isinstance(ids, list) or not ids or any(type(i) is not int or i < 0 for i in ids):
- raise ProtocolError("tokenizer did not produce actual input IDs")
+ ids = self.model_adapter.render_chat(self.tokenizer, converted)
if len(ids) + converted["sampling"]["max_new_tokens"] > self.max_context:
raise ProtocolError("context budget exceeded; implicit truncation is forbidden")
return ids
@@ -97,7 +96,9 @@ def decode(self, output, payload, converted, identity, response_id):
finish = meta.get("finish_reason", {})
if finish.get("type") not in ("stop", "length"):
raise ValueError("unsupported serving finish reason")
- blocks = parse_qwen_output(text, response_id=response_id, tools=converted["tools"])
+ blocks = self.model_adapter.parse_output(
+ text, response_id=response_id, tools=converted["tools"]
+ )
has_tools = any(b["type"] == "tool_use" for b in blocks)
stop_sequence = finish.get("matched") if isinstance(finish.get("matched"), str) else None
if stop_sequence not in converted["sampling"]["stop"]:
diff --git a/harborrl/gateway/trace.py b/harborrl/gateway/trace.py
index a5a5d3118..5f5ca7c6a 100644
--- a/harborrl/gateway/trace.py
+++ b/harborrl/gateway/trace.py
@@ -94,6 +94,19 @@ def delivery(self, aid, response_id, status):
state["pending"].discard(matches[0]["request_id"])
self.changed.notify_all()
+ def wait_delivered(self, aid, timeout):
+ deadline = time.monotonic() + timeout
+ with self.changed:
+ state = self.attempts[aid]
+ while state["pending"]:
+ remaining = deadline - time.monotonic()
+ if remaining <= 0:
+ raise TimeoutError("policy delivery acknowledgement still pending")
+ self.changed.wait(remaining)
+ if any(turn.get("delivery") not in ("sent", "consumed_confirmed")
+ for turn in state["turns"]):
+ raise ValueError("policy response delivery was not confirmed")
+
def consume(self, aid, response_id, content, evidence_ref):
"""Called only by trusted native-session audit, never by an HTTP policy client."""
with self.lock:
@@ -109,6 +122,13 @@ def consume(self, aid, response_id, content, evidence_ref):
publish(state["root"] / "consumption" / f"{response_id}.json", {"response_id": response_id, "content_digest": digest(content), "evidence_ref": evidence_ref})
turn.update(delivery="consumed_confirmed", consumption_ref=evidence_ref)
+ def unconsumed_responses(self, aid):
+ with self.lock:
+ state = self.attempts[aid]
+ return [{"response_id": turn["response_id"], "content": deepcopy(turn["response"]["content"]),
+ "finish_reason": turn.get("finish_reason"), "is_final": index == len(state["turns"]) - 1}
+ for index, turn in enumerate(state["turns"]) if turn.get("delivery") != "consumed_confirmed"]
+
def close(self, aid):
with self.lock:
state = self.attempts[aid]
diff --git a/harborrl/harnesses/__init__.py b/harborrl/harnesses/__init__.py
new file mode 100644
index 000000000..53da87865
--- /dev/null
+++ b/harborrl/harnesses/__init__.py
@@ -0,0 +1 @@
+"""Agent harness adapters and their protocol boundaries."""
diff --git a/harborrl/harnesses/claude_code.py b/harborrl/harnesses/claude_code.py
new file mode 100644
index 000000000..fb5c27519
--- /dev/null
+++ b/harborrl/harnesses/claude_code.py
@@ -0,0 +1,53 @@
+"""Claude Code harness adapter with explicit credentials and session audit."""
+from __future__ import annotations
+
+import hashlib
+import json
+from pathlib import Path
+
+from harborrl.rollout.harbor_job.audit import audit_session
+from harborrl.rollout.harbor_job.bindings import (
+ claude_config,
+ sanitized_runner_env,
+)
+
+
+class ClaudeCodeHarnessAdapter:
+ harness_id = "claude_code"
+
+ def validate_profile(self, profile, gateway_url):
+ return claude_config(profile, gateway_url, "validation-only")
+
+ def agent_config(self, profile, gateway_url, credential, max_output_tokens=8192):
+ return claude_config(profile, gateway_url, credential, max_output_tokens)
+
+ def runner_environment(self, environment):
+ return sanitized_runner_env(environment)
+
+ def audit_session(self, path, registry, attempt_id):
+ return audit_session(path, registry, attempt_id)
+
+ def audit_empty_final(self, path, registry, attempt_id, audit):
+ """Claude 2.1.141 omits empty final messages from persisted sessions."""
+ missing = registry.unconsumed_responses(attempt_id)
+ if not missing:
+ return
+ if len(missing) != 1 or missing[0] != {
+ "response_id": missing[0]["response_id"], "content": [],
+ "finish_reason": "end_turn", "is_final": True,
+ }:
+ raise ValueError("only one empty final end_turn may use terminal evidence")
+ path = Path(path)
+ raw = path.read_bytes()
+ events = [json.loads(line) for line in raw.splitlines() if line.strip()]
+ results = [event for event in events if event.get("type") == "result"]
+ if len(results) != 1 or events[-1] != results[0]:
+ raise ValueError("expected one final Claude result")
+ result = results[0]
+ expected = {"subtype": "success", "is_error": False, "result": "",
+ "stop_reason": "end_turn", "session_id": audit["session_id"],
+ "terminal_reason": "completed"}
+ if any(result.get(key) != value for key, value in expected.items()) or result.get("is_error") is not False:
+ raise ValueError("empty final response lacks successful matching session result")
+ evidence = f"{path}#sha256={hashlib.sha256(raw).hexdigest()}"
+ registry.consume(attempt_id, missing[0]["response_id"], [], evidence)
diff --git a/harborrl/harnesses/offline_claude.py b/harborrl/harnesses/offline_claude.py
new file mode 100644
index 000000000..cd44d37cd
--- /dev/null
+++ b/harborrl/harnesses/offline_claude.py
@@ -0,0 +1,61 @@
+"""Pinned native Claude executable deployment, without package-manager fallback."""
+import asyncio
+import hashlib
+import json
+import shlex
+from pathlib import Path
+
+from harbor.agents.installed.claude_code import ClaudeCode
+
+from harborrl.trajectories.native import publish
+
+
+def binary_checksum(binary):
+ with binary.open('rb') as source:
+ return hashlib.file_digest(source, 'sha256').hexdigest()
+
+
+class OfflineClaudeCode(ClaudeCode):
+ def __init__(self, *args, installation, **kwargs):
+ self.installation = installation
+ super().__init__(*args, **kwargs)
+
+ async def install(self, environment):
+ lock = self.installation
+ binary = Path(lock['binary'])
+ checksum = await asyncio.to_thread(binary_checksum, binary)
+ if checksum != lock['sha256']:
+ raise ValueError('offline Claude binary checksum mismatch')
+ # Harbor invokes Claude with this HOME-local prefix on every command.
+ probe = ('import json, os, platform; print(json.dumps(dict('
+ 'system=platform.system(), machine=platform.machine(), '
+ 'libc=os.confstr("CS_GNU_LIBC_VERSION"), home=os.environ.get("HOME", ""))))')
+ result = await environment.exec('python3 -c ' + shlex.quote(probe))
+ try:
+ system = json.loads(result.stdout or '')
+ except ValueError:
+ system = {}
+ if (result.return_code or system.get('system') != 'Linux' or system.get('machine') != 'x86_64'
+ or not str(system.get('libc', '')).startswith('glibc ')):
+ raise ValueError('offline binary requires Linux x86_64 glibc and a writable HOME')
+ home = system.get('home', '')
+ if not home.startswith('/') or home == '/':
+ raise ValueError('invalid agent HOME')
+ target = home + '/.local/bin/claude'
+ staging = target + '.harborrl-upload'
+ result = await environment.exec('mkdir -p ' + shlex.quote(home + '/.local/bin'))
+ if result.return_code:
+ raise ValueError('agent tool directory is not writable')
+ await environment.upload_file(binary, staging)
+ command = (f'printf "%s %s\\n" {shlex.quote(lock["sha256"])} {shlex.quote(staging)} | sha256sum -c -'
+ f' && chmod 755 {shlex.quote(staging)} && mv {shlex.quote(staging)} {shlex.quote(target)}')
+ result = await environment.exec(command)
+ if result.return_code:
+ raise ValueError('offline Claude installation checksum/permissions failed')
+ result = await environment.exec(f'{shlex.quote(target)} --version')
+ version = (result.stdout or '').strip().split()
+ if result.return_code or not version or version[0] != lock['version']:
+ raise ValueError('offline Claude installation/version probe failed')
+ publish(self.logs_dir / 'offline-installation.json', {
+ 'version': lock['version'], 'sha256': lock['sha256'], 'target': target,
+ 'platform': system, 'mode': lock['mode']})
diff --git a/harborrl/math_registry.py b/harborrl/math_registry.py
new file mode 100644
index 000000000..61f0a2b37
--- /dev/null
+++ b/harborrl/math_registry.py
@@ -0,0 +1,316 @@
+"""Explicit MATH component contracts and startup compatibility checks."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Protocol
+
+from harborrl.algorithms.grpo import GRPOAlgorithmAdapter
+from harborrl.backends.slime_v032.versions import BACKEND_CONTRACT
+from harborrl.harnesses.claude_code import ClaudeCodeHarnessAdapter
+from harborrl.models.qwen import QwenModelAdapter
+from harborrl.tasks.jsonl_provider import JsonlTaskProvider
+from harborrl.tasks.provider import NativeTaskProvider
+
+
+class ComponentRegistry:
+ def __init__(self, dimension):
+ self.dimension = dimension
+ self._components = {}
+
+ def register(self, spec):
+ if spec.id in self._components:
+ raise ValueError(f"duplicate {self.dimension} registration: {spec.id}")
+ self._components[spec.id] = spec
+ return spec
+
+ def get(self, component_id):
+ try:
+ return self._components[component_id]
+ except KeyError as exc:
+ raise ValueError(
+ f"unsupported {self.dimension}: {component_id}; "
+ f"registered: {', '.join(sorted(self._components))}"
+ ) from exc
+
+ def items(self):
+ return tuple(self._components[spec_id] for spec_id in sorted(self._components))
+
+
+@dataclass(frozen=True)
+class ModelSpec:
+ id: str
+ display_name: str
+ preset_prefix: str
+ serving_backend: str
+ tool_protocol: str
+ gateway_protocols: frozenset[str]
+ training_backend_contracts: frozenset[str]
+ adapter: QwenModelAdapter
+ version: str
+ evidence: tuple[str, ...]
+ limitations: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class AlgorithmSpec:
+ id: str
+ display_name: str
+ training_backend_contracts: frozenset[str]
+ minimum_group_size: int
+ requires_audited_raw_logprobs: bool
+ reduction: str
+ adapter: GRPOAlgorithmAdapter
+ backend_hooks: dict[str, str]
+ version: str
+ evidence: tuple[str, ...]
+ limitations: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class TaskProviderSpec:
+ id: str
+ display_name: str
+ execution_backends: frozenset[str]
+ task_format: str
+ adapter: TaskProviderAdapter
+ version: str
+ evidence: tuple[str, ...]
+ limitations: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class HarnessSpec:
+ id: str
+ display_name: str
+ request_protocol: str
+ tool_protocol: str
+ harbor_versions: frozenset[str]
+ adapter: ClaudeCodeHarnessAdapter
+ version: str
+ evidence: tuple[str, ...]
+ limitations: tuple[str, ...]
+
+
+class ModelAdapter(Protocol):
+ def render_chat(self, tokenizer, converted): ...
+
+ def parse_output(self, text, *, response_id, tools): ...
+
+
+class AlgorithmAdapter(Protocol):
+ def compute_advantages_and_returns(self, args, rollout_data): ...
+
+
+class TaskAdapter(Protocol):
+ def discover(self, tasks_dir): ...
+
+ def inspect(self, path, *, expected_digest=None): ...
+
+ def catalog(self, config): ...
+
+
+class TaskProviderAdapter(Protocol):
+ def discover(self, tasks_dir): ...
+
+ def inspect(self, path, *, expected_digest=None): ...
+
+ def catalog(self, config): ...
+
+ def prompt_rows(self, entries): ...
+
+
+class HarnessAdapter(Protocol):
+ def validate_profile(self, profile, gateway_url): ...
+
+ def agent_config(self, profile, gateway_url, credential, max_output_tokens=8192): ...
+
+QWEN_MODEL = ModelSpec(
+ id="qwen",
+ display_name="Qwen",
+ preset_prefix="qwen",
+ serving_backend="sglang",
+ tool_protocol="qwen-tool-call-v1",
+ gateway_protocols=frozenset({"anthropic-messages-v1"}),
+ training_backend_contracts=frozenset({BACKEND_CONTRACT}),
+ adapter=QwenModelAdapter(),
+ version="qwen3-profile-v1",
+ evidence=("examples/native/train_qwen_native.yaml",),
+ limitations=("preset must start with qwen", "tokenizer and template digests are checked live"),
+)
+GRPO_ALGORITHM = AlgorithmSpec(
+ id="grpo",
+ display_name="Slime GRPO",
+ training_backend_contracts=frozenset({BACKEND_CONTRACT}),
+ minimum_group_size=2,
+ requires_audited_raw_logprobs=True,
+ reduction="global-sum",
+ adapter=GRPOAlgorithmAdapter(),
+ backend_hooks={
+ "rollout": "harborrl.backends.slime_v032.rollout.generate_rollout",
+ "converter": "harborrl.backends.slime_v032.converter.convert_samples_to_train_data",
+ "advantage": "harborrl.backends.slime_v032.advantage.compute_advantages_and_returns",
+ "loss": "harborrl.backends.slime_v032.loss.loss_function",
+ "postprocess": "harborrl.backends.slime_v032.postprocess.rollout_data_postprocess",
+ },
+ version="native-centered-grpo-v1",
+ evidence=("harborrl/backends/slime_v032/loss.py",),
+ limitations=("requires complete reward-varied groups", "only Slime v0.3.2 hooks are accepted"),
+)
+HARBOR_JSONL_TASK_PROVIDER = TaskProviderSpec(
+ id="harbor_jsonl",
+ display_name="Harbor JSONL",
+ execution_backends=frozenset({"harbor_job"}),
+ task_format="native_single_step_jsonl_v1",
+ adapter=JsonlTaskProvider(),
+ version="jsonl-catalog-lock-v1",
+ evidence=("examples/native/catalog.jsonl",),
+ limitations=(
+ "one task lock must be encoded per JSONL line",
+ "tasks must be fetched before cataloging",
+ "task selectors use catalog IDs",
+ ),
+)
+NATIVE_TASK_PROVIDER = TaskProviderSpec(
+ id="native_harbor",
+ display_name="Harbor",
+ execution_backends=frozenset({"harbor_job"}),
+ task_format="native_single_step_text_v1",
+ adapter=NativeTaskProvider(),
+ version="catalog-lock-v1",
+ evidence=("examples/native/catalog.json",),
+ limitations=("tasks must be fetched before cataloging", "task selectors use catalog IDs"),
+)
+CLAUDE_CODE_HARNESS = HarnessSpec(
+ id="claude_code",
+ display_name="Claude Code",
+ request_protocol="anthropic-messages-v1",
+ tool_protocol="qwen-tool-call-v1",
+ harbor_versions=frozenset({"0.23.0"}),
+ adapter=ClaudeCodeHarnessAdapter(),
+ version="claude-code-session-audit-v1",
+ evidence=("harborrl/rollout/harbor_job/audit.py",),
+ limitations=("exact CLI version is required", "ambient credentials are rejected"),
+)
+class MATHRegistry:
+ def __init__(self):
+ self.models = ComponentRegistry("model")
+ self.algorithms = ComponentRegistry("algorithm")
+ self.tasks = ComponentRegistry("task provider")
+ self.harnesses = ComponentRegistry("harness")
+ self.models.register(QWEN_MODEL)
+ self.algorithms.register(GRPO_ALGORITHM)
+ self.tasks.register(NATIVE_TASK_PROVIDER)
+ self.tasks.register(HARBOR_JSONL_TASK_PROVIDER)
+ self.harnesses.register(CLAUDE_CODE_HARNESS)
+
+ def model_for_preset(self, preset):
+ for spec in self.models.items():
+ if str(preset).startswith(spec.preset_prefix):
+ return spec
+ raise ValueError(
+ f"unsupported model preset: {preset}; "
+ f"registered: {', '.join(spec.id for spec in self.models.items())}"
+ )
+
+ def algorithm_for_contract(self, contract):
+ for spec in self.algorithms.items():
+ if contract in spec.training_backend_contracts:
+ return spec
+ raise ValueError(f"no registered algorithm supports backend contract: {contract}")
+
+
+MATH_REGISTRY = MATHRegistry()
+
+
+@dataclass(frozen=True)
+class MATHSelection:
+ model: ModelSpec
+ algorithm: AlgorithmSpec
+ task: TaskProviderSpec
+ harness: HarnessSpec
+
+ def asdict(self):
+ return {
+ "model": _describe(self.model),
+ "algorithm": _describe(self.algorithm),
+ "task": _describe(self.task),
+ "harness": _describe(self.harness),
+ }
+
+
+def _describe(spec):
+ return {
+ "id": spec.id,
+ "display_name": spec.display_name,
+ "version": spec.version,
+ "evidence": list(spec.evidence),
+ "limitations": list(spec.limitations),
+ }
+
+
+def select_math(config):
+ return MATHSelection(
+ model=MATH_REGISTRY.model_for_preset(config["model"]["args_file"]),
+ algorithm=MATH_REGISTRY.algorithm_for_contract(
+ config["training"]["backend_contract"]
+ ),
+ task=MATH_REGISTRY.tasks.get(
+ "harbor_jsonl"
+ if str(config.get("tasks", {}).get("catalog", "")).endswith(".jsonl")
+ else "native_harbor"
+ ),
+ harness=MATH_REGISTRY.harnesses.get(config["harness"]["name"]),
+ )
+
+
+def validate_combination(selection, config):
+ contract = config["training"]["backend_contract"]
+ if contract not in selection.model.training_backend_contracts:
+ raise ValueError(
+ f"model {selection.model.id} does not support training backend {contract}"
+ )
+ if contract not in selection.algorithm.training_backend_contracts:
+ raise ValueError(
+ f"algorithm {selection.algorithm.id} does not support training backend {contract}"
+ )
+ if config["execution"]["backend"] not in selection.task.execution_backends:
+ raise ValueError(
+ f"task provider {selection.task.id} does not support execution backend "
+ f"{config['execution']['backend']}"
+ )
+ if config["harbor"]["version"] not in selection.harness.harbor_versions:
+ raise ValueError(
+ f"harness {selection.harness.id} does not support Harbor "
+ f"{config['harbor']['version']}"
+ )
+ if selection.model.tool_protocol != selection.harness.tool_protocol:
+ raise ValueError("model and harness tool protocols do not match")
+ if selection.harness.request_protocol not in selection.model.gateway_protocols:
+ raise ValueError(
+ f"model gateway does not support harness protocol {selection.harness.request_protocol}"
+ )
+ if config["sampling"]["group_size"] < selection.algorithm.minimum_group_size:
+ raise ValueError(
+ f"algorithm {selection.algorithm.id} requires group size >= "
+ f"{selection.algorithm.minimum_group_size}"
+ )
+ if (
+ selection.algorithm.requires_audited_raw_logprobs
+ and not config["gateway"]["audited_raw_logprobs"]
+ ):
+ raise ValueError(
+ f"algorithm {selection.algorithm.id} requires audited raw logprobs"
+ )
+ return selection
+
+
+def list_components():
+ return {
+ dimension: [_describe(spec) for spec in registry.items()]
+ for dimension, registry in (
+ ("models", MATH_REGISTRY.models),
+ ("algorithms", MATH_REGISTRY.algorithms),
+ ("tasks", MATH_REGISTRY.tasks),
+ ("harnesses", MATH_REGISTRY.harnesses),
+ )
+ }
diff --git a/harborrl/models/__init__.py b/harborrl/models/__init__.py
new file mode 100644
index 000000000..89314d47e
--- /dev/null
+++ b/harborrl/models/__init__.py
@@ -0,0 +1 @@
+"""Model family adapters used by serving and training boundaries."""
diff --git a/harborrl/models/qwen.py b/harborrl/models/qwen.py
new file mode 100644
index 000000000..cbd096dad
--- /dev/null
+++ b/harborrl/models/qwen.py
@@ -0,0 +1,39 @@
+"""Qwen tokenizer/template and generated tool-call protocol."""
+from __future__ import annotations
+
+from harborrl.gateway.messages import ProtocolError, parse_qwen_output
+
+
+def normalize_chat_template_ids(rendered):
+ """Accept both legacy lists and modern ``BatchEncoding`` results."""
+ if hasattr(rendered, "get"):
+ rendered = rendered.get("input_ids")
+ if isinstance(rendered, (list, tuple)):
+ if rendered and isinstance(rendered[0], (list, tuple)):
+ rendered = rendered[0]
+ return list(rendered)
+ return rendered
+
+
+class QwenModelAdapter:
+ model_id = "qwen"
+
+ def render_chat(self, tokenizer, converted):
+ rendered_ids = tokenizer.apply_chat_template(
+ converted["messages"],
+ tools=converted["tools"] or None,
+ tokenize=True,
+ add_generation_prompt=True,
+ enable_thinking=False,
+ )
+ token_ids = normalize_chat_template_ids(rendered_ids)
+ if (
+ not isinstance(token_ids, list)
+ or not token_ids
+ or any(type(token_id) is not int or token_id < 0 for token_id in token_ids)
+ ):
+ raise ProtocolError("tokenizer did not produce actual input IDs")
+ return token_ids
+
+ def parse_output(self, text, *, response_id, tools):
+ return parse_qwen_output(text, response_id=response_id, tools=tools)
diff --git a/harborrl/options/__init__.py b/harborrl/options/__init__.py
new file mode 100644
index 000000000..a53f7a1cc
--- /dev/null
+++ b/harborrl/options/__init__.py
@@ -0,0 +1,11 @@
+"""Offline configuration discovery and generation; no optional runtime imports."""
+
+
+def build_catalog():
+ from .catalog import build_catalog as build
+ return build()
+
+
+def build_config_bundle(answers, *, output_directory):
+ from .wizard import build_config_bundle as build
+ return build(answers, output_directory=output_directory)
diff --git a/harborrl/options/catalog.py b/harborrl/options/catalog.py
new file mode 100644
index 000000000..76ede0794
--- /dev/null
+++ b/harborrl/options/catalog.py
@@ -0,0 +1,147 @@
+"""JSON-safe option metadata. Existing validators remain authoritative."""
+from copy import deepcopy
+from importlib.resources import files
+
+import yaml
+
+from harborrl.config import ROOT
+from harborrl.config.native import FIELDS, OPTIONAL
+from harborrl.deployment import schema
+from harborrl.math_registry import MATH_REGISTRY
+
+from .presets import PRESETS
+
+TRAINING_FIELDS = {
+ "consistency.mode": ("string", "off, audit or enforce"),
+ "consistency.max_abs_delta": ("number_or_null", "Explicit deployment maximum token log-probability delta"),
+ "consistency.max_mean_abs_delta": ("number_or_null", "Explicit deployment mean token delta"),
+ "consistency.bad_token_delta": ("number_or_null", "Threshold identifying a bad token"),
+ "consistency.max_bad_fraction": ("number", "Allowed bad-token fraction between zero and one"),
+ "consistency.worst_tokens": ("integer", "Maximum diagnostic tokens, zero through 32"),
+ "execution.backend": ("string", "Task execution backend; currently harbor_job"),
+ "tasks.catalog": ("path", "Existing JSON/JSONL Harbor task lock; resolved on the controller"),
+ "harness.name": ("string", "Registered agent harness; currently claude_code"),
+ "harness.profile": ("path", "Pinned harness profile JSON on the controller"),
+ "harbor.python": ("path", "Runner interpreter on the local runner or SSH worker"),
+ "harbor.version": ("string", "Harbor version supported by the selected harness"),
+ "harbor.workers": ("array", "Ordered SSH destinations; empty with a local runner"),
+ "harbor.runner_transport": ("string", "local or ssh; local requires no workers"),
+ "harbor.environment": ("object", "Docker runtime, or Sandbox runtime with a config file reference"),
+ "gateway.host": ("string", "Gateway listener address, distinct from its advertised origin"),
+ "gateway.port": ("integer", "Gateway listener port, 1 through 65535"),
+ "gateway.advertised_url": ("string", "Plain HTTP(S) origin reachable by runners; port must match"),
+ "gateway.tokenizer_digest": ("string", "SHA-256 of the actual tokenizer fingerprint; never a placeholder"),
+ "gateway.template_digest": ("string", "SHA-256 of the actual chat-template fingerprint"),
+ "gateway.audited_raw_logprobs": ("boolean", "Raw serving probability audit, required by GRPO"),
+ "model.checkpoint": ("path", "Student model checkpoint; must be accessible on the GPU target"),
+ "model.reference": ("path", "Reference checkpoint accessible on the GPU target"),
+ "model.args_file": ("string", "Supported Slime preset name; currently the qwen prefix"),
+ "training.num_rollout": ("integer", "Positive number of rollout batches"),
+ "training.learning_rate": ("number", "Positive finite learning rate"),
+ "training.save_interval": ("integer", "Positive checkpoint interval in rollout batches"),
+ "training.backend_contract": ("string", "Pinned backend/algorithm contract"),
+ "sampling.group_size": ("integer", "Samples per prompt, at least two for GRPO"),
+ "sampling.groups_per_batch": ("integer", "Positive number of complete groups per batch"),
+ "sampling.max_attempts": ("integer", "Bounded invalid-attempt retries; not low-reward retries"),
+ "sampling.max_tokens": ("integer", "Positive response limit below max_context"),
+ "sampling.max_context": ("integer", "Total context token limit"),
+ "deployment.layout": ("string", "split or colocate; controls actor/serving GPU allocation"),
+ "deployment.num_gpus": ("integer", "Total positive GPU budget, not a memory-fit guarantee"),
+ "deployment.actor_gpus": ("integer", "Actor GPU count; must be divisible by actor TP"),
+ "deployment.rollout_gpus": ("integer", "Serving GPU count; must be divisible by engine TP"),
+ "deployment.actor_tensor_parallel_size": ("integer", "Positive actor tensor-parallel size"),
+ "deployment.rollout_gpus_per_engine": ("integer", "Positive serving tensor-parallel size"),
+ "output.root": ("path", "Run output root; service mode must match shared storage.run_root"),
+}
+
+DEPLOYMENT_GROUPS = {
+ "manager": schema.MANAGER_FIELDS, "gpu_target": schema.GPU_FIELDS,
+ "storage": schema.STORAGE_FIELDS, "rollout": schema.ROLLOUT_FIELDS,
+ "rollout.workers[]": schema.WORKER_FIELDS, "gpu_target.ssh": schema.SSH_FIELDS,
+ "rollout.workers[].ssh": schema.SSH_FIELDS, "connectivity": schema.CONNECTIVITY_FIELDS,
+}
+DEPLOYMENT_FIELDS = {
+ "id": ("string", "Short role identifier"), "mode": ("string", "Manager mode or shared storage mode"),
+ "bind": ("string", "Manager listener address"),
+ "control_port": ("integer", "Control listener port; different from gateway_port"),
+ "control_url": ("string", "Control origin reachable from the GPU target"),
+ "gateway_port": ("integer", "Gateway listener port"),
+ "gateway_url": ("string", "Gateway origin reachable from runners or the configured relay"),
+ "run_token_file": ("path", "Manager run-token file reference; never the secret value"),
+ "source_dir": ("path", "Absolute source checkout path on this role's machine"),
+ "controller_python": ("path", "Absolute interpreter path on the controller"),
+ "transport": ("string", "GPU launch transport: local or ssh"),
+ "ssh": ("object", "SSH specification; GPU local transport requires null"),
+ "train_python": ("path", "Absolute interpreter path on the GPU target"),
+ "environment_file": ("path_or_null", "Controlled JSON environment file on the GPU target"),
+ "expected_gpus": ("integer", "Expected target GPU inventory, at least the requested budget"),
+ "run_root": ("path", "Absolute shared run root visible to all participating roles"),
+ "manager_writable": ("boolean", "Declared shared-root access; verified by doctor"),
+ "gpu_writable": ("boolean", "Declared GPU shared-root access; verified by doctor"),
+ "worker_writable": ("boolean", "Declared worker shared-root access; verified by doctor"),
+ "provider": ("string", "Rollout placement/provider; not a dataset or algorithm"),
+ "workers": ("array", "Ordered worker role specifications; empty for local Sandbox runners"),
+ "runner_python": ("path", "Absolute interpreter path on the worker"),
+ "docker": ("boolean", "Worker Docker requirement"),
+ "task_roots": ("array", "Absolute task roots visible on this worker"),
+ "host": ("string", "SSH alias/destination, not an automatically verified host"),
+ "user": ("string", "Optional SSH username"), "port": ("integer", "Optional SSH port"),
+ "identity": ("path", "Optional identity-file reference; no private-key contents"),
+ "command_timeout_s": ("integer", "Positive remote command timeout"),
+ "ssh_timeout_s": ("integer", "Positive SSH connection timeout"),
+ "http_timeout_s": ("integer", "Positive HTTP check timeout"),
+ "gpu_to_controller": ("boolean", "Declared GPU-to-controller connectivity requirement"),
+ "worker_to_gateway": ("boolean", "Declared runner-to-gateway connectivity requirement"),
+ "manager_to_gpu_inference": ("boolean", "Declared controller-to-serving connectivity requirement"),
+}
+
+
+def _describe(spec, attributes):
+ record = {}
+ for key in ("id", "display_name", "version", "evidence", "limitations", *attributes):
+ value = getattr(spec, key)
+ record[key] = sorted(value) if isinstance(value, frozenset) else list(value) if isinstance(value, tuple) else value
+ return record
+
+
+def dataset_metadata():
+ return yaml.safe_load(files("harborrl.options").joinpath("resources/datasets.yaml").read_text(encoding="utf-8"))
+
+
+def build_catalog():
+ training = {}
+ for section, fields in sorted(FIELDS.items()):
+ for name in sorted(fields):
+ key = f"{section}.{name}"
+ kind, description = TRAINING_FIELDS[key]
+ training[key] = {"type": kind, "description": description,
+ "required": name not in OPTIONAL.get(section, set())}
+ deployment = {}
+ for section, fields in sorted(DEPLOYMENT_GROUPS.items()):
+ for name in sorted(fields):
+ kind, description = DEPLOYMENT_FIELDS[name]
+ deployment[f"{section}.{name}"] = {"type": kind, "description": description}
+ for key, values in {"manager.mode": ("embedded", "service"), "storage.mode": schema.STORAGE_MODES,
+ "rollout.provider": schema.ROLLOUT_PROVIDERS, "gpu_target.transport": schema.TRANSPORTS}.items():
+ deployment[key]["values"] = list(values)
+ datasets = deepcopy(dataset_metadata()["datasets"])
+ for item in datasets:
+ path = ROOT / item["task_root"]
+ item["available"] = item["status"] != "planned" and item["source"] == "bundled" and path.is_dir()
+ item["availability_note"] = "Provide an existing inspected task catalog; no download is performed"
+ registry = MATH_REGISTRY
+ return {
+ "catalog_version": 1, "schema_versions": {"training": 2, "deployment": 1},
+ "models": [_describe(s, ("preset_prefix", "serving_backend", "tool_protocol", "gateway_protocols", "training_backend_contracts")) for s in registry.models.items()],
+ "algorithms": [_describe(s, ("training_backend_contracts", "minimum_group_size", "requires_audited_raw_logprobs", "reduction")) for s in registry.algorithms.items()],
+ "task_providers": [_describe(s, ("execution_backends", "task_format")) for s in registry.tasks.items()],
+ "harnesses": [_describe(s, ("request_protocol", "tool_protocol", "harbor_versions")) for s in registry.harnesses.items()],
+ "datasets": datasets, "deployment_presets": deepcopy(list(PRESETS)),
+ "fields": {"training": training, "deployment": deployment},
+ "compatibility": {"scope": "component_metadata", "status": "unknown_until_configuration_validated",
+ "constraints": ["Model and harness tool/request protocols must match",
+ "Algorithm and model must support the selected backend contract",
+ "Task provider must support the execution backend",
+ "Harbor version must be supported by the harness"],
+ "validator": "harborrl.math_registry.validate_combination"},
+ }
diff --git a/harborrl/options/cli.py b/harborrl/options/cli.py
new file mode 100644
index 000000000..7438628dc
--- /dev/null
+++ b/harborrl/options/cli.py
@@ -0,0 +1,184 @@
+"""Terminal interface for the offline catalog, validation and bundle builder."""
+import argparse
+import json
+import shlex
+import sys
+from pathlib import Path
+
+import yaml
+
+from .catalog import build_catalog
+from .presets import PRESETS, worker_defaults
+from .validation import read_mapping, validate_paths
+from .wizard import build_config_bundle, publish_bundle
+
+DIMENSIONS = {"models": "models", "algorithms": "algorithms", "tasks": "task_providers",
+ "harnesses": "harnesses", "datasets": "datasets", "deployments": "deployment_presets",
+ "fields": "fields", "compat": "compatibility"}
+
+
+def ask(label, default=None, convert=str):
+ while True:
+ value = input(f"{label}" + (f" [{default}]" if default is not None else "") + ": ").strip()
+ if not value and default is not None:
+ return default
+ try:
+ if not value:
+ raise ValueError("A value is required")
+ return convert(value)
+ except ValueError as exc:
+ print(str(exc), file=sys.stderr)
+
+
+def choose(label, choices):
+ for index, value in enumerate(choices, 1):
+ print(f" {index}. {value}")
+ while True:
+ index = ask(label, 1, int)
+ if 1 <= index <= len(choices):
+ return choices[index - 1]
+ print("Choose a listed number", file=sys.stderr)
+
+
+def interactive_answers():
+ preset = choose("Deployment", [p["id"] for p in PRESETS])
+ t, d = {}, {}
+ service = preset != "embedded-local"
+ print("Components: Qwen / GRPO / Harbor tasks / Claude Code. Advanced fields use an answers file.")
+ if service:
+ d["manager"] = {
+ "source_dir": ask("Controller source directory"),
+ "controller_python": ask("Controller Python", sys.executable),
+ "control_url": ask("Controller origin (include port 49200)"),
+ "gateway_url": ask("Gateway origin (include port 49184)"),
+ }
+ d["storage"] = {"run_root": ask("Shared run root (absolute)")}
+ d["gpu_target"] = {"ssh": {"host": ask("GPU SSH alias/destination")},
+ "source_dir": ask("GPU source directory"), "train_python": ask("GPU training Python")}
+ else:
+ d["gpu_target"] = {"train_python": ask("Training Python", sys.executable)}
+ if preset == "service-ssh":
+ workers = []
+ count = ask("Number of SSH runners", 1, int)
+ if count < 1:
+ raise ValueError("At least one SSH runner is required")
+ for index in range(count):
+ worker = worker_defaults()
+ worker.update(id=f"worker-{index}", ssh={"host": ask(f"Runner {index} SSH destination")},
+ source_dir=ask("Runner source directory"), runner_python=ask("Runner Python"),
+ task_roots=[ask("Runner task root")])
+ workers.append(worker)
+ d["rollout"] = {"workers": workers}
+ else:
+ t["harbor"] = {"python": ask("Local Harbor runner Python", sys.executable)}
+ t["tasks"] = {"catalog": ask("Existing task catalog JSON/JSONL")}
+ t["harness"] = {"profile": ask("Existing harness profile JSON")}
+ t["model"] = {"checkpoint": ask("GPU checkpoint directory"), "reference": ask("GPU reference directory"),
+ "args_file": ask("Qwen model args preset", "qwen3-8B")}
+ t["gateway"] = {"tokenizer_digest": ask("Actual tokenizer SHA-256"),
+ "template_digest": ask("Actual template SHA-256")}
+ if not service:
+ t["gateway"].update(host=ask("Gateway bind address", "127.0.0.1"),
+ advertised_url=ask("Gateway origin reachable by tasks", "http://127.0.0.1:49184"))
+ layout = choose("GPU layout", ["colocate", "split"])
+ t["deployment"] = {"layout": layout, "num_gpus": ask("Total GPU budget", 1, int)}
+ if layout == "split":
+ t["deployment"].update(actor_gpus=ask("Actor GPUs", 1, int), rollout_gpus=ask("Serving GPUs", 1, int))
+ t["training"] = {"num_rollout": ask("Rollout batches", 2, int)}
+ t["sampling"] = {"group_size": ask("Samples per group", 2, int),
+ "groups_per_batch": ask("Groups per batch", 1, int)}
+ answers = {"answers_version": 1, "preset": preset, "training": t, "deployment": d}
+ if preset == "service-sandbox-local-runner":
+ answers["sandbox_config"] = ask("Existing Sandbox configuration")
+ return answers
+
+
+def render_validation(result):
+ print(f"{result['status']} (scope: {result['scope']})")
+ for row in result["checks"]:
+ print(f" {row['status']}: {row['path']}: {row['message']}")
+
+
+def render_catalog(result):
+ print("HarborRL options (catalog 1; training schema 2; deployment schema 1)")
+ for name, value in result.items():
+ if name in {"catalog_version", "schema_versions"}:
+ continue
+ if name == "fields" and len(result) > 3:
+ print("fields: use 'harborrl options fields' for field descriptions")
+ elif isinstance(value, list):
+ print(f"{name}:")
+ for row in value:
+ description = row.get("description", row.get("display_name", ""))
+ if "available" in row:
+ description += f" (status={row['status']}, locally_available={row['available']})"
+ print(f" {row.get('id', row.get('name'))}: {description}")
+ else:
+ print(yaml.safe_dump({name: value}, sort_keys=False).rstrip())
+
+
+def main(argv):
+ parser = argparse.ArgumentParser(prog="harborrl")
+ commands = parser.add_subparsers(dest="command", required=True)
+ options = commands.add_parser("options", help="List supported configuration options")
+ options.add_argument("dimension", nargs="?", choices=DIMENSIONS)
+ options.add_argument("--json", action="store_true")
+ validate = commands.add_parser("validate", help="Validate offline; no runtime is started")
+ validate.add_argument("config")
+ validate.add_argument("--deployment")
+ validate.add_argument("--check-files", action="store_true")
+ validate.add_argument("--json", action="store_true")
+ init = commands.add_parser("init", help="Create a configuration bundle in a new directory")
+ init.add_argument("--out-dir", required=True)
+ init.add_argument("--answers")
+ init.add_argument("--probe-local", action="store_true")
+ args = parser.parse_args(argv)
+ try:
+ if args.command == "options":
+ result = build_catalog()
+ if args.dimension:
+ key = DIMENSIONS[args.dimension]
+ result = {k: result[k] for k in ("catalog_version", "schema_versions", key)}
+ if args.json:
+ print(json.dumps(result, indent=2, sort_keys=True))
+ else:
+ render_catalog(result)
+ return 0
+ if args.command == "validate":
+ result = validate_paths(args.config, args.deployment, check_files=args.check_files)
+ if args.json:
+ print(json.dumps(result, indent=2, sort_keys=True))
+ else:
+ render_validation(result)
+ return int(result["status"] == "invalid")
+ if not args.answers and not sys.stdin.isatty():
+ parser.error("init requires --answers when stdin is not a terminal")
+ output = Path(args.out_dir).expanduser().absolute()
+ if output.exists() or output.is_symlink():
+ raise ValueError(f"Output directory already exists: {output}")
+ if args.probe_local:
+ from .probe import probe_local
+ print(json.dumps(probe_local(output), indent=2))
+ answers = read_mapping(args.answers) if args.answers else interactive_answers()
+ bundle = build_config_bundle(answers, output_directory=output)
+ render_validation(bundle["validation"])
+ if not args.answers:
+ print(bundle["files"]["config.yaml"])
+ print(bundle["files"]["deployment.yaml"])
+ if ask("Publish these files? (yes/no)", "no").lower() != "yes":
+ print("Cancelled; no files written")
+ return 0
+ publish_bundle(bundle, output)
+ print(f"Created configuration bundle: {output}")
+ config = shlex.quote(str(output / "config.yaml"))
+ deployment = shlex.quote(str(output / "deployment.yaml"))
+ for command, suffix in (("validate", " --check-files"), ("train", " --dry-run"), ("doctor", "")):
+ print(f"harborrl {command} {config} --deployment {deployment}{suffix}")
+ print("doctor checks the live environment and connectivity; training requires a separate command.")
+ return 0
+ except (ValueError, TypeError, KeyError, AttributeError, OSError, yaml.YAMLError) as exc:
+ print(f"error: {exc}", file=sys.stderr)
+ return 1
+ except (EOFError, KeyboardInterrupt):
+ print("Cancelled; no configuration published", file=sys.stderr)
+ return 130
diff --git a/harborrl/options/presets.py b/harborrl/options/presets.py
new file mode 100644
index 000000000..6cc7ef8b5
--- /dev/null
+++ b/harborrl/options/presets.py
@@ -0,0 +1,153 @@
+"""Small configuration recipes; final validity belongs to existing schemas."""
+import sys
+from copy import deepcopy
+from pathlib import Path
+
+from harborrl.config import ROOT
+from harborrl.consistency.config import DEFAULTS as CONSISTENCY_DEFAULTS
+from harborrl.deployment.schema import destination, embedded_local
+
+PRESETS = (
+ {"id": "embedded-local", "description": "Local training and a local Harbor runner"},
+ {"id": "service-ssh", "description": "Controller, SSH GPU target and SSH Docker runners"},
+ {"id": "service-sandbox-local-runner", "description": "Controller and runner here; GPU target over SSH; Sandbox execution"},
+)
+
+
+def merge(base, override, path=""):
+ if not isinstance(override, dict):
+ raise TypeError(f"{path or 'answers'} must be a mapping")
+ result = deepcopy(base)
+ for key, value in override.items():
+ if key not in base:
+ raise ValueError(f"unknown field: {path}{key}")
+ result[key] = (merge(base[key], value, f"{path}{key}.")
+ if isinstance(base[key], dict) and base[key] else deepcopy(value))
+ return result
+
+
+def defaults(preset, output_directory):
+ if preset not in {item["id"] for item in PRESETS}:
+ raise ValueError(f"unknown preset: {preset}")
+ output = Path(output_directory).resolve()
+ service = preset != "embedded-local"
+ sandbox = preset == "service-sandbox-local-runner"
+ training = {
+ "consistency": deepcopy(CONSISTENCY_DEFAULTS),
+ "schema_version": 2,
+ "execution": {"backend": "harbor_job"}, "tasks": {"catalog": None},
+ "harness": {"name": "claude_code", "profile": None},
+ "harbor": {"python": None, "version": "0.23.0", "workers": [],
+ "runner_transport": "ssh" if preset == "service-ssh" else "local",
+ "environment": {"runtime": "sandbox", "config": None} if sandbox else {"runtime": "docker"}},
+ "gateway": {"host": "0.0.0.0" if service else "127.0.0.1", "port": 49184,
+ "advertised_url": None if service else "http://127.0.0.1:49184",
+ "tokenizer_digest": None, "template_digest": None, "audited_raw_logprobs": True},
+ "model": {"checkpoint": None, "reference": None, "args_file": "qwen3-8B"},
+ "training": {"num_rollout": 2, "learning_rate": 1e-6, "save_interval": 1,
+ "backend_contract": "slime-v0.3.2-native-v1"},
+ "sampling": {"group_size": 2, "groups_per_batch": 1, "max_attempts": 1,
+ "max_tokens": 2048, "max_context": 32768},
+ "deployment": {"layout": "colocate", "num_gpus": 1, "actor_gpus": 1,
+ "rollout_gpus": 1, "actor_tensor_parallel_size": 1, "rollout_gpus_per_engine": 1},
+ "output": {"root": str(output / "runs")},
+ }
+ deployment = embedded_local(run_root=str(output / "runs"))
+ deployment["gpu_target"]["environment_file"] = None
+ deployment["manager"]["run_token_file"] = str(output / "run-token")
+ if service:
+ deployment["manager"].update(id="controller", mode="service", bind="0.0.0.0",
+ control_url=None, gateway_url=None)
+ deployment["gpu_target"].update(id="gpu", transport="ssh", ssh={"host": None, "user": None, "port": None, "identity": None},
+ source_dir=None, train_python=None)
+ deployment["rollout"]["provider"] = "sandbox" if sandbox else "ssh_docker"
+ return training, deployment
+
+
+def build_configs(answers, output_directory):
+ if not isinstance(answers, dict) or set(answers) - {
+ "answers_version", "preset", "training", "deployment", "sandbox_config"}:
+ raise ValueError("unknown answers field or non-mapping answers")
+ if type(answers.get("answers_version")) is not int or answers["answers_version"] != 1:
+ raise ValueError("answers_version must be 1")
+ preset = answers.get("preset")
+ training, deployment = defaults(preset, output_directory)
+ t = answers.get("training", {})
+ d = answers.get("deployment", {})
+ training = merge(training, t, "training.")
+ deployment = merge(deployment, d, "deployment.")
+
+ # Repeated fields have one source unless both were explicitly supplied.
+ for section, field, other_section, other_field in (
+ ("gateway", "host", "manager", "bind"),
+ ("gateway", "port", "manager", "gateway_port"),
+ ("gateway", "advertised_url", "manager", "gateway_url"),
+ ("output", "root", "storage", "run_root"),
+ ):
+ if field in t.get(section, {}) and other_field not in d.get(other_section, {}):
+ deployment[other_section][other_field] = training[section][field]
+ elif other_field in d.get(other_section, {}) and field not in t.get(section, {}):
+ training[section][field] = deployment[other_section][other_field]
+ elif field in t.get(section, {}) and other_field in d.get(other_section, {}):
+ left, right = training[section][field], deployment[other_section][other_field]
+ if field == "root" and isinstance(left, str) and isinstance(right, str):
+ left, right = ((Path(output_directory) / value).resolve() for value in (left, right))
+ elif field == "advertised_url" and isinstance(left, str) and isinstance(right, str):
+ left, right = left.rstrip("/"), right.rstrip("/")
+ if left != right:
+ raise ValueError(f"training.{section}.{field} conflicts with deployment.{other_section}.{other_field}")
+ count = training["deployment"]["num_gpus"]
+ if "expected_gpus" not in d.get("gpu_target", {}):
+ deployment["gpu_target"]["expected_gpus"] = count
+ if training["deployment"]["layout"] == "colocate":
+ for field in ("actor_gpus", "rollout_gpus"):
+ if field not in t.get("deployment", {}):
+ training["deployment"][field] = count
+ if training["harbor"]["runner_transport"] == "ssh":
+ workers = deployment["rollout"]["workers"]
+ if workers:
+ if "workers" not in t.get("harbor", {}):
+ training["harbor"]["workers"] = [destination(w["ssh"]) for w in workers]
+ if "python" not in t.get("harbor", {}):
+ training["harbor"]["python"] = workers[0]["runner_python"]
+ if answers.get("sandbox_config") is not None:
+ if preset != "service-sandbox-local-runner":
+ raise ValueError("sandbox_config requires the Sandbox preset")
+ environment = training["harbor"]["environment"]
+ if environment.get("config") not in (None, answers["sandbox_config"]):
+ raise ValueError("sandbox_config conflicts with training.harbor.environment.config")
+ environment["config"] = answers["sandbox_config"]
+ required = [("training", section, field) for section, field in (
+ ("tasks", "catalog"), ("harness", "profile"), ("harbor", "python"),
+ ("model", "checkpoint"), ("model", "reference"),
+ ("gateway", "tokenizer_digest"), ("gateway", "template_digest"), ("gateway", "advertised_url"))]
+ if preset != "embedded-local":
+ required += [("deployment", section, field) for section, field in (
+ ("manager", "control_url"), ("manager", "gateway_url"),
+ ("gpu_target", "source_dir"), ("gpu_target", "train_python"))]
+ configs = {"training": training, "deployment": deployment}
+ missing = [".".join(keys) for keys in required if configs[keys[0]][keys[1]][keys[2]] in (None, "")]
+ if preset != "embedded-local" and not deployment["gpu_target"]["ssh"].get("host"):
+ missing.append("deployment.gpu_target.ssh.host")
+ if preset == "service-ssh" and not deployment["rollout"]["workers"]:
+ missing.append("deployment.rollout.workers")
+ if preset == "service-sandbox-local-runner" and not training["harbor"]["environment"].get("config"):
+ missing.append("sandbox_config")
+ if missing:
+ raise ValueError("Missing required answers: " + ", ".join(missing))
+ for field in ("tokenizer_digest", "template_digest"):
+ if training["gateway"][field] == "0" * 64:
+ raise ValueError(f"gateway.{field}: a placeholder digest cannot be used by init")
+ expected_mode = "embedded" if preset == "embedded-local" else "service"
+ expected_provider = {"embedded-local": "embedded", "service-ssh": "ssh_docker",
+ "service-sandbox-local-runner": "sandbox"}[preset]
+ if deployment["manager"]["mode"] != expected_mode or deployment["rollout"]["provider"] != expected_provider:
+ raise ValueError("deployment mode/provider conflicts with the selected preset")
+ # All generated path references are rooted at the final directory, not staging.
+ deployment["storage"]["run_root"] = str((Path(output_directory) / deployment["storage"]["run_root"]).resolve())
+ return training, deployment
+
+
+def worker_defaults():
+ return {"id": "worker-0", "ssh": {"host": None}, "source_dir": str(ROOT),
+ "runner_python": sys.executable, "docker": True, "task_roots": [str(ROOT)]}
diff --git a/harborrl/options/probe.py b/harborrl/options/probe.py
new file mode 100644
index 000000000..12131c5df
--- /dev/null
+++ b/harborrl/options/probe.py
@@ -0,0 +1,39 @@
+"""Explicit, bounded local hints. Never connect to a remote machine."""
+import os
+import shlex
+import subprocess
+import sys
+from pathlib import Path
+
+
+def command_hint(command):
+ try:
+ result = subprocess.run(command, capture_output=True, text=True, timeout=3, check=True)
+ return {"method": command[0], "status": "observed", "value": result.stdout.strip(), "reason": None}
+ except (OSError, subprocess.SubprocessError) as exc:
+ return {"method": command[0], "status": "unknown", "value": None, "reason": str(exc)}
+
+
+def ssh_aliases(path):
+ aliases = set()
+ for line in Path(path).read_text(encoding="utf-8").splitlines():
+ words = shlex.split(line, comments=True)
+ if words and words[0].lower() == "host":
+ aliases.update(word for word in words[1:] if not any(c in word for c in "*?!"))
+ return sorted(aliases)
+
+
+def probe_local(output_directory):
+ try:
+ aliases = {"method": "ssh_config_hosts", "status": "observed",
+ "value": ssh_aliases(Path.home() / ".ssh/config"), "reason": "Include and Match are not evaluated"}
+ except (OSError, ValueError) as exc:
+ aliases = {"method": "ssh_config_hosts", "status": "unknown", "value": None, "reason": str(exc)}
+ parent = Path(output_directory).expanduser().absolute().parent
+ return {
+ "gpu": command_hint(["nvidia-smi", "--query-gpu=uuid", "--format=csv,noheader"]),
+ "python": command_hint([sys.executable, "-I", "--version"]),
+ "ssh_aliases": aliases,
+ "output_parent": {"method": "os.access", "status": "hint", "value": os.access(parent, os.W_OK),
+ "reason": "Access hint only; no write was attempted"},
+ }
diff --git a/harborrl/options/resources/datasets.yaml b/harborrl/options/resources/datasets.yaml
new file mode 100644
index 000000000..40d8e6726
--- /dev/null
+++ b/harborrl/options/resources/datasets.yaml
@@ -0,0 +1,40 @@
+schema_version: 1
+datasets:
+ - name: hello_world
+ status: example
+ source: bundled
+ upstream_revision: v0.1-example
+ license: MIT
+ task_root: examples/native/tasks
+ expected_task_count: 1
+ digest_policy: native-inspector-tree-sha256
+ docker_base: debian:bookworm-slim
+ gpu_required: false
+ acceptance: offline-native-contract-only
+ owner: HarborRL maintainers
+
+ - name: terminal-bench-core
+ status: planned
+ source: https://github.com/laude-institute/terminal-bench
+ upstream_revision: TBD
+ license: TBD
+ task_root: TBD
+ expected_task_count: TBD
+ digest_policy: native-inspector-tree-sha256
+ docker_base: task-defined
+ gpu_required: false
+ acceptance: pending-public-worker-matrix
+ owner: HarborRL maintainers
+
+ - name: terminal-bench-test
+ status: planned
+ source: https://github.com/laude-institute/terminal-bench
+ upstream_revision: dataset/terminal-bench-core/v0.1.x
+ license: TBD
+ task_root: TBD
+ expected_task_count: TBD
+ digest_policy: native-inspector-tree-sha256
+ docker_base: task-defined
+ gpu_required: false
+ acceptance: pending-public-worker-matrix
+ owner: HarborRL maintainers
diff --git a/harborrl/options/validation.py b/harborrl/options/validation.py
new file mode 100644
index 000000000..53465d83e
--- /dev/null
+++ b/harborrl/options/validation.py
@@ -0,0 +1,169 @@
+"""Offline checks composed from the authoritative configuration validators."""
+from copy import deepcopy
+from pathlib import Path
+
+import yaml
+
+from harborrl.config import native
+from harborrl.deployment import schema
+
+
+def read_mapping(path):
+ value = yaml.safe_load(Path(path).read_text(encoding="utf-8"))
+ if not isinstance(value, dict):
+ raise TypeError("configuration must be a mapping")
+ return value
+
+
+def check(path, status, code, message):
+ return {"path": path, "status": status, "code": code, "message": message}
+
+
+def report(checks, *, check_files=False, source="explicit"):
+ states = {item["status"] for item in checks}
+ status = "invalid" if "failed" in states else (
+ "valid_with_deferred_checks" if "deferred" in states else "valid")
+ return {"validation_version": 1, "status": status,
+ "scope": "structure_and_local_files" if check_files else "structure",
+ "deployment_source": source, "checks": checks}
+
+
+def cross_checks(training, deployment):
+ """Only compare settings actually consumed together by the selected mode."""
+ checks = []
+
+ def require(condition, path, message):
+ if not condition:
+ checks.append(check(path, "failed", "inconsistent", message))
+
+ gpu = deployment["gpu_target"]
+ require(training["deployment"]["num_gpus"] <= gpu["expected_gpus"],
+ "deployment.gpu_target.expected_gpus", "Expected GPU count is below the training budget")
+ if deployment["manager"]["mode"] != "service":
+ return checks
+ manager, rollout, harbor = deployment["manager"], deployment["rollout"], training["harbor"]
+ for key, other in (("host", "bind"), ("port", "gateway_port"), ("advertised_url", "gateway_url")):
+ left, right = training["gateway"][key], manager[other]
+ if key == "advertised_url":
+ left, right = left.rstrip("/"), right.rstrip("/")
+ require(left == right, f"training.gateway.{key}", f"Must match deployment.manager.{other}")
+ require(training["output"]["root"] == deployment["storage"]["run_root"],
+ "training.output.root", "Must match deployment.storage.run_root")
+ sandbox = harbor.get("environment", {}).get("runtime") == "sandbox"
+ require(sandbox == (rollout["provider"] == "sandbox"), "deployment.rollout.provider",
+ "Sandbox provider and training environment must agree")
+ workers = rollout["workers"]
+ local = harbor.get("runner_transport", "ssh") == "local"
+ require(not workers if local else bool(workers), "deployment.rollout.workers",
+ "Local runners require no workers; SSH runners require workers")
+ if not local and workers:
+ destinations = [schema.destination(worker["ssh"]) for worker in workers]
+ require(harbor["workers"] == destinations, "training.harbor.workers",
+ "Must match deployment worker destinations in order")
+ require(harbor["python"] == workers[0]["runner_python"], "training.harbor.python",
+ "Must match the deployment's first runner_python")
+ return checks
+
+
+def _local_file(checks, path, label, *, directory=False):
+ target = Path(path)
+ exists = target.is_dir() if directory else target.is_file()
+ checks.append(check(label, "passed" if exists else "failed", "local_path",
+ "Local path exists" if exists else "Required local path is missing"))
+
+
+def _file_checks(training, deployment):
+ checks = []
+ manager = deployment["manager"]
+ _local_file(checks, manager["source_dir"], "deployment.manager.source_dir", directory=True)
+ _local_file(checks, manager["controller_python"], "deployment.manager.controller_python")
+ for label, function in (
+ ("training.harness.profile", lambda: native.validate_files(training)),
+ ("training.tasks.catalog", lambda: native.catalog(training)),
+ ):
+ try:
+ function()
+ except (ValueError, TypeError, KeyError, OSError, AttributeError) as exc:
+ checks.append(check(label, "failed", "local_reference", str(exc)))
+ else:
+ checks.append(check(label, "passed", "local_reference", "Local reference validated"))
+ gpu = deployment["gpu_target"]
+ if gpu["transport"] == "local":
+ for key in ("checkpoint", "reference"):
+ _local_file(checks, training["model"][key], f"training.model.{key}", directory=True)
+ _local_file(checks, gpu["train_python"], "deployment.gpu_target.train_python")
+ _local_file(checks, gpu["source_dir"], "deployment.gpu_target.source_dir", directory=True)
+ if gpu.get("environment_file"):
+ _local_file(checks, gpu["environment_file"], "deployment.gpu_target.environment_file")
+ else:
+ checks.append(check("deployment.gpu_target", "deferred", "remote_paths",
+ "Model, interpreter and source paths belong to the GPU target"))
+ if training["harbor"].get("runner_transport", "ssh") == "local":
+ _local_file(checks, training["harbor"]["python"], "training.harbor.python")
+ else:
+ checks.append(check("deployment.rollout.workers", "deferred", "remote_paths",
+ "Runner paths must be checked on their worker machines"))
+ environment = training["harbor"].get("environment", {})
+ if environment.get("runtime") == "sandbox":
+ from harborrl.rollout.harbor_job.sandbox_config import (
+ load_sandbox,
+ task_binding,
+ )
+ try:
+ cfg = load_sandbox(environment["config"])
+ if cfg["adapter"] == "harborrl.rollout.harbor_job.environments.opensandbox:ManagedOpenSandboxEnvironment":
+ if cfg["options"] or any(binding["options"] for binding in cfg["bindings"]):
+ raise ValueError("standard OpenSandbox does not accept platform options")
+ else:
+ checks.append(check("sandbox.adapter", "deferred", "runner_adapter",
+ "Platform adapter validation belongs to its trusted runner environment"))
+ for entry in native.catalog(training):
+ task_binding(cfg, entry["path"])
+ except (ValueError, TypeError, KeyError, OSError, AttributeError, yaml.YAMLError) as exc:
+ checks.append(check("training.harbor.environment", "failed", "sandbox_config", str(exc)))
+ else:
+ checks.append(check("training.harbor.environment", "passed", "sandbox_config",
+ "Sandbox structure and task image bindings validated; credentials not read"))
+ checks.append(check("sandbox.platform", "deferred", "platform_access",
+ "Credentials, offline binary, capacity and network need runner preflight"))
+ return checks
+
+
+def validate_mappings(training, deployment, *, config_path, check_files=False, source="explicit"):
+ checks = []
+ normalized = {}
+ for name, function in (
+ ("training", lambda: native.validate(deepcopy(training), Path(config_path).resolve())),
+ ("deployment", lambda: schema.validate(deepcopy(deployment))),
+ ):
+ try:
+ normalized[name] = function()
+ except (ValueError, TypeError, KeyError, OSError, AttributeError) as exc:
+ checks.append(check(name, "failed", "schema", str(exc)))
+ else:
+ checks.append(check(name, "passed", "schema", "Schema and semantic checks passed"))
+ if len(normalized) == 2:
+ checks.extend(cross_checks(normalized["training"], normalized["deployment"]))
+ if check_files:
+ checks.extend(_file_checks(normalized["training"], normalized["deployment"]))
+ else:
+ checks.append(check("references", "deferred", "files_not_checked", "Use --check-files for local references"))
+ checks.append(check("runtime", "deferred", "live_preflight",
+ "GPU memory, model loading, live digests and connectivity require doctor or an explicit live test"))
+ return report(checks, check_files=check_files, source=source)
+
+
+def validate_paths(config_path, deployment_path=None, *, check_files=False):
+ try:
+ training = read_mapping(config_path)
+ deployment, source = schema.load_deployment(deployment_path)
+ if source == "embedded-local":
+ normalized = native.validate(deepcopy(training), Path(config_path).resolve())
+ deployment = schema.embedded_local(run_root=normalized["output"]["root"],
+ workers=normalized["harbor"]["workers"], expected_gpus=normalized["deployment"]["num_gpus"])
+ # The legacy validator omits an optional null key from its normalized output.
+ deployment["gpu_target"].setdefault("environment_file", None)
+ return validate_mappings(training, deployment, config_path=config_path,
+ check_files=check_files, source=source)
+ except (ValueError, TypeError, KeyError, OSError, AttributeError, yaml.YAMLError) as exc:
+ return report([check("configuration", "failed", "load", str(exc))], check_files=check_files)
diff --git a/harborrl/options/wizard.py b/harborrl/options/wizard.py
new file mode 100644
index 000000000..55cba4a53
--- /dev/null
+++ b/harborrl/options/wizard.py
@@ -0,0 +1,68 @@
+"""Build a reviewed configuration bundle, then publish it to a new directory."""
+import json
+import os
+import shutil
+import tempfile
+from pathlib import Path
+
+import yaml
+
+from harborrl.config.native import validate
+from harborrl.trajectories.native import digest
+
+from .presets import build_configs
+from .validation import validate_mappings
+
+
+def build_config_bundle(answers, *, output_directory):
+ output = Path(output_directory).expanduser().resolve()
+ training, deployment = build_configs(answers, output)
+ result = validate_mappings(training, deployment, config_path=output / "config.yaml")
+ if result["status"] == "invalid":
+ raise ValueError("; ".join(f"{row['path']}: {row['message']}" for row in result["checks"] if row["status"] == "failed"))
+ training = validate(training, output / "config.yaml")
+ manifest = {"setup_version": 1, "preset": answers["preset"],
+ "schema_versions": {"training": 2, "deployment": 1},
+ "config_digest": digest(training), "deployment_digest": digest(deployment),
+ "references": {"catalog": training["tasks"]["catalog"],
+ "profile": training["harness"]["profile"],
+ "sandbox_config": training["harbor"].get("environment", {}).get("config")},
+ "validation": result}
+ return {"files": {"config.yaml": yaml.safe_dump(training, sort_keys=False),
+ "deployment.yaml": yaml.safe_dump(deployment, sort_keys=False),
+ "setup-manifest.json": json.dumps(manifest, indent=2) + "\n"},
+ "validation": result}
+
+
+def publish_bundle(bundle, output_directory):
+ """No overwrite; same-parent staging and an exclusive per-target lock."""
+ output = Path(output_directory).expanduser().absolute()
+ if output.exists() or output.is_symlink():
+ raise FileExistsError(f"Output directory already exists: {output}")
+ if not output.parent.is_dir():
+ raise ValueError("Output parent directory must already exist")
+ files = bundle["files"]
+ if set(files) != {"config.yaml", "deployment.yaml", "setup-manifest.json"}:
+ raise ValueError("Bundle must contain exactly the three documented files")
+ lock = output.parent / f".{output.name}.init.lock"
+ descriptor = os.open(lock, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o600)
+ staging = None
+ try:
+ os.close(descriptor)
+ if output.exists() or output.is_symlink():
+ raise FileExistsError(f"Output directory already exists: {output}")
+ staging = Path(tempfile.mkdtemp(prefix=f".{output.name}.init-", dir=output.parent))
+ for name, content in files.items():
+ with (staging / name).open("x", encoding="utf-8") as stream:
+ stream.write(content)
+ stream.flush()
+ os.fsync(stream.fileno())
+ if output.exists() or output.is_symlink():
+ raise FileExistsError(f"Output directory already exists: {output}")
+ staging.rename(output)
+ staging = None
+ finally:
+ if staging is not None:
+ shutil.rmtree(staging)
+ lock.unlink()
+ return output
diff --git a/harborrl/platform/evidence.py b/harborrl/platform/evidence.py
new file mode 100644
index 000000000..d988cf86f
--- /dev/null
+++ b/harborrl/platform/evidence.py
@@ -0,0 +1,126 @@
+"""Run input preparation and reproducible source evidence."""
+import hashlib
+import json
+import subprocess
+import sys
+import time
+from pathlib import Path
+from uuid import uuid4
+
+from harborrl.config import ROOT
+from harborrl.trajectories.native import publish
+
+
+def new_run_root(output_root):
+ name = time.strftime("%Y%m%d-%H%M%S") + "-" + uuid4().hex[:8]
+ return Path(output_root) / "training" / name
+
+
+def run_environment(plan, root, catalog_path, prompt_path):
+ """Shared run paths for both local and controller-managed training."""
+ return {
+ **plan["environment"],
+ "RUN_ID": root.name, "RUN_NAME": root.name, "RUN_DIR": str(root),
+ "RUNS_ROOT": str(root.parent.parent.resolve()),
+ "CKPT_ROOT": str(root / "checkpoints"), "WANDB_DIR": str(root / "metrics/wandb"),
+ "HARBORRL_NATIVE_CATALOG": str(catalog_path),
+ "ROLLOUT_PROMPT_DATA": str(prompt_path),
+ "HARBORRL_NATIVE_PROFILE": plan["config"]["harness"]["profile"],
+ "HARBOR_IR_ROOT": str(root / "ir"),
+ }
+
+
+def _materialize(plan, root):
+ from harborrl.math_registry import select_math
+
+ config = plan["config"]
+ entries = select_math(config).task.adapter.catalog(config)
+ normalized = root / "config"
+ normalized.mkdir(parents=True)
+ catalog_path = normalized / "native_catalog.json"
+ publish(catalog_path, entries)
+ prompt_path = normalized / "native_tasks.jsonl"
+ rows = select_math(config).task.adapter.prompt_rows(entries)
+ prompt_path.write_text("".join(json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n" for row in rows))
+ return catalog_path, prompt_path
+
+
+def _source_audit(root):
+ def git(*args):
+ return subprocess.check_output(["git", *args], cwd=ROOT)
+
+ files = git("ls-files", "-z", "--cached", "--others", "--exclude-standard", "--",
+ "harborrl", "scripts", "examples", "pyproject.toml").decode().split("\0")
+ untracked = set(git("ls-files", "-z", "--others", "--exclude-standard", "--",
+ "harborrl", "scripts", "examples").decode().split("\0"))
+ (root / "source.patch").write_bytes(git("diff", "--binary", "HEAD", "--",
+ "harborrl", "scripts", "examples", "pyproject.toml"))
+ hashes = {}
+ for name in files:
+ source = ROOT / name
+ if name and source.is_file():
+ hashes[name] = hashlib.sha256(source.read_bytes()).hexdigest()
+ if name in untracked:
+ target = root / "source-new" / name
+ target.parent.mkdir(parents=True, exist_ok=True)
+ target.write_bytes(source.read_bytes())
+ payload = {
+ "commit": git("rev-parse", "HEAD").decode().strip(),
+ "dirty": bool(git("status", "--porcelain")),
+ "source_hashes": hashes,
+ "patch": "source.patch",
+ }
+ publish(root / "source-manifest.json", payload)
+ return payload
+
+
+
+
+# Proxy values and provider credentials are deliberately outside this snapshot.
+DEPLOYMENT_EVIDENCE_KEYS = ("SLIME_DIR", "MEGATRON_DIR", "SGLANG_IMAGE", "CUDA_VISIBLE_DEVICES")
+
+
+def effective_config(plan, environment, child_env, source, checks):
+ from harborrl.backends.slime_v032.config import field_usage
+ config = plan["config"]
+ return {
+ "schema_version": "harborrl-effective-config-v1",
+ "components": {
+ "model_preset": config["model"]["args_file"],
+ "algorithm": plan["math"]["algorithm"]["id"],
+ "harness": plan["math"]["harness"]["id"],
+ "execution_backend": config["execution"]["backend"],
+ "training_backend_contract": config["training"]["backend_contract"],
+ },
+ "environment": environment,
+ "deployment_environment": {key: child_env[key] for key in DEPLOYMENT_EVIDENCE_KEYS if key in child_env},
+ "training_command": plan["training_command"],
+ "expanded_command_file": "effective-command.json",
+ "source": source,
+ "runtime_versions": [check for check in checks
+ if "version" in check or check.get("service") in
+ ("slime commit", "megatron commit", "sglang image")],
+ "field_usage": field_usage(),
+ "precedence": "YAML < --set in order < explicit MATH selectors; declared deployment environment only",
+ }
+
+
+def record_command(root, command, *, dry_run=False):
+ """Record argv after shell defaults and model preset expansion."""
+ payload = {
+ "schema_version": "harborrl-effective-command-v1",
+ "command": command,
+ "mode": "dry_run" if dry_run else "training",
+ }
+ target = Path(root) / "effective-command.json"
+ if dry_run:
+ checksum = hashlib.sha256(json.dumps(command).encode()).hexdigest()
+ target = Path(root) / "dry-run-commands" / f"{checksum}.json"
+ publish(target, payload)
+
+
+if __name__ == "__main__":
+ # Internal shell boundary: RUN_DIR, mode, then the exact executable argv.
+ if len(sys.argv) < 4 or sys.argv[2] not in ("0", "1"):
+ raise SystemExit("usage: evidence RUN_DIR DRY_RUN COMMAND [ARG ...]")
+ record_command(sys.argv[1], sys.argv[3:], dry_run=sys.argv[2] == "1")
diff --git a/harborrl/platform/native_train.py b/harborrl/platform/native_train.py
index a2a356dbc..0c0eedba2 100644
--- a/harborrl/platform/native_train.py
+++ b/harborrl/platform/native_train.py
@@ -2,249 +2,50 @@
The dispatcher freezes a run directory and materializes the original-task lock
into Slime prompt rows before handing lifecycle control to the existing Slime
-launcher. It never rewrites task tests or rewards.
+launcher. It never rewrites task tests or rewards. A service-mode deployment
+routes through the manager-side controller instead of a local child process.
"""
from __future__ import annotations
-import hashlib
-import importlib
-import importlib.metadata
import json
-import os
-import shutil
-import signal
-import subprocess
import sys
-import time
from pathlib import Path
-from types import SimpleNamespace
-from uuid import uuid4
-from harborrl.config import ROOT
+from harborrl.deployment.training_target import local_target
+from harborrl.platform.evidence import effective_config, new_run_root, run_environment
from harborrl.trajectories.native import publish, read_json
def _run_root(plan):
- name = time.strftime("%Y%m%d-%H%M%S") + "-" + uuid4().hex[:8]
- return Path(plan["config"]["output"]["root"]) / "training" / name
+ return new_run_root(plan["config"]["output"]["root"])
def doctor(plan):
- config = plan["config"]
- checks = []
-
- runner = Path(config["harbor"]["python"])
- checks.append({"path": "harbor.python", "ok": runner.is_file()})
- from harborrl.rollout.harbor_job.coordinator import runner_command
- if runner.is_file():
- for worker in config["harbor"]["workers"]:
- try:
- probe = subprocess.run(
- runner_command(str(runner), ROOT, worker) + ["--probe"],
- capture_output=True, text=True, timeout=30, check=False,
- )
- except (subprocess.SubprocessError, OSError) as exc:
- probe = SimpleNamespace(returncode=1, stdout="", stderr=str(exc))
- try:
- payload = json.loads(probe.stdout)
- runtime_version = tuple(payload.get("python", "").split(".")[:2])
- version_ok = payload.get("harbor_version") == config["harbor"]["version"]
- runtime_ok = len(runtime_version) == 2 and all(part.isdigit() for part in runtime_version)
- except (ValueError, AttributeError, TypeError):
- runtime_version, version_ok, runtime_ok = (), False, False
- checks.append({
- "service": "external Harbor worker",
- "worker": worker,
- "ok": probe.returncode == 0 and runtime_ok
- and tuple(map(int, runtime_version)) >= (3, 12) and version_ok,
- "detail": probe.stdout.strip() or probe.stderr.strip()[-1000:],
- })
-
- for key, path in (("tasks.catalog", config["tasks"]["catalog"]),
- ("harness.profile", config["harness"]["profile"]),
- ("model.checkpoint", config["model"]["checkpoint"]),
- ("model.reference", config["model"]["reference"])):
- checks.append({"path": key, "ok": Path(path).exists()})
-
- model_config = Path(config["model"]["checkpoint"]) / "config.json"
- if model_config.is_file():
- try:
- model = json.loads(model_config.read_text())
- heads = model.get("num_attention_heads", 0)
- kv_heads = model.get("num_key_value_heads", heads)
- for tp in {config["deployment"]["actor_tensor_parallel_size"],
- config["deployment"]["rollout_gpus_per_engine"]}:
- checks.append({"model_tp": tp, "ok": bool(
- heads and kv_heads and heads % tp == 0 and (kv_heads % tp == 0 or tp % kv_heads == 0))})
- except (OSError, ValueError, TypeError) as exc:
- checks.append({"path": "model.config", "ok": False, "detail": str(exc)})
-
- from harborrl.config.native import catalog
- try:
- entries = catalog(config)
- for entry in entries:
- from harborrl.data.harbor.native_inspector import inspect_native
- report = inspect_native(entry["path"], expected_digest=entry["task_digest"])
- checks.append({"task": entry["id"], "ok": report["status"] == "NEEDS_PROBE",
- "detail": report["reasons"]})
- except (ValueError, OSError, TypeError) as exc:
- checks.append({"service": "native catalog", "ok": False, "detail": str(exc)})
-
- for package in ("ray", "torch", "sglang", "transformers"):
- try:
- version = importlib.metadata.version(package)
- except importlib.metadata.PackageNotFoundError:
- version = None
- checks.append({"dependency": package, "version": version, "ok": version is not None})
-
- from harborrl.backends.slime_v032.versions import (
- MEGATRON_COMMIT,
- SGLANG_IMAGE_TAG,
- SLIME_COMMIT,
- )
- slime_dir = os.environ.get("SLIME_DIR", "").strip()
- slime_path = Path(slime_dir) if slime_dir else None
- checks.append({"path": "SLIME_DIR", "ok": bool(slime_path and slime_path.is_dir()),
- "detail": slime_dir or "not set"})
- if slime_path and slime_path.is_dir():
- checks.append({"path": "slime entrypoint", "ok": (slime_path / "train.py").is_file()})
- preset = slime_path / "scripts" / "models" / f"{config['model']['args_file']}.sh"
- checks.append({"path": "slime model preset", "ok": preset.is_file(), "detail": str(preset)})
- try:
- commit = subprocess.run(["git", "-C", str(slime_path), "rev-parse", "HEAD"],
- capture_output=True, text=True, timeout=15, check=True).stdout.strip()
- except (subprocess.SubprocessError, OSError) as exc:
- commit = ""
- checks.append({"service": "slime commit", "ok": False, "detail": str(exc)})
- if commit:
- checks.append({"service": "slime commit", "ok": commit == SLIME_COMMIT,
- "detail": f"{commit} != {SLIME_COMMIT}" if commit != SLIME_COMMIT else commit})
-
- megatron_dir = os.environ.get("MEGATRON_DIR", "").strip()
- if megatron_dir:
- try:
- megatron_commit = subprocess.run(["git", "-C", megatron_dir, "rev-parse", "HEAD"],
- capture_output=True, text=True, timeout=15, check=True).stdout.strip()
- except (subprocess.SubprocessError, OSError) as exc:
- megatron_commit = ""
- checks.append({"service": "megatron commit", "ok": False, "detail": str(exc)})
- if megatron_commit:
- checks.append({"service": "megatron commit", "ok": megatron_commit == MEGATRON_COMMIT,
- "detail": f"{megatron_commit} != {MEGATRON_COMMIT}" if megatron_commit != MEGATRON_COMMIT else megatron_commit})
- else:
- checks.append({"service": "megatron commit",
- "ok": True,
- "detail": "MEGATRON_DIR not set; official Slime image bundles Megatron"})
-
- runtime_env = os.environ.copy()
- runtime_pythonpath = os.pathsep.join(filter(None, [
- str(slime_path), megatron_dir, str(ROOT), runtime_env.get("PYTHONPATH", ""),
- ]))
- runtime_env["PYTHONPATH"] = runtime_pythonpath
- for module in ("slime.rollout.base_types", "slime.utils.types",
- "slime.backends.megatron_utils.loss"):
- try:
- probe = subprocess.run([sys.executable, "-c", f"import {module}"],
- capture_output=True, text=True, timeout=90, check=False,
- env=runtime_env)
- except (subprocess.SubprocessError, OSError) as exc:
- probe = SimpleNamespace(returncode=1, stdout="", stderr=str(exc))
- checks.append({"dependency": module, "ok": probe.returncode == 0,
- "detail": probe.stderr.strip()[-1000:]})
-
- sglang_image = os.environ.get("SGLANG_IMAGE", "").strip()
- if sglang_image:
- checks.append({"service": "sglang image", "ok": sglang_image.endswith(SGLANG_IMAGE_TAG),
- "detail": sglang_image})
-
- if shutil.which("nvidia-smi"):
- try:
- result = subprocess.run(["nvidia-smi", "--query-gpu=index", "--format=csv,noheader"],
- capture_output=True, text=True, timeout=10, check=True)
- visible = os.environ.get("CUDA_VISIBLE_DEVICES")
- count = (len([item for item in visible.split(",") if item.strip() and item.strip() != "-1"])
- if visible is not None else len(result.stdout.splitlines()))
- checks.append({"resource": "visible GPU budget", "available": count,
- "ok": count >= config["deployment"]["num_gpus"]})
- except (subprocess.SubprocessError, OSError) as exc:
- checks.append({"resource": "visible GPU budget", "ok": False, "detail": str(exc)})
- else:
- checks.append({"resource": "visible GPU budget", "ok": False, "detail": "nvidia-smi unavailable"})
-
- if config["training"]["backend_contract"] != "slime-legacy":
- from harborrl.backends.slime_v032.launcher import hook_modules
- for hook_name, module_name in hook_modules().items():
- try:
- importlib.import_module(module_name)
- checks.append({"service": "training backend hook", "hook": hook_name, "ok": True})
- except (ImportError, AttributeError, TypeError, ValueError) as exc:
- checks.append({
- "service": "training backend hook", "hook": hook_name,
- "ok": False, "detail": str(exc),
- })
-
- checks.append({
- "service": "raw-model logprob audit",
- "ok": True,
- "detail": ("enabled by lock; live serving semantic probe remains a GPU gate"
- if config["gateway"]["audited_raw_logprobs"] else
- "disabled; generated evidence remains EVAL_ONLY and training will fail closed"),
- })
- return checks
+ """Compatibility entry point for deployment preflight."""
+ from harborrl.platform.preflight import doctor as check
+ return check(plan)
def _materialize(plan, root):
- from harborrl.config.native import catalog
-
- config = plan["config"]
- entries = catalog(config)
- normalized = root / "config"
- normalized.mkdir(parents=True)
- catalog_path = normalized / "native_catalog.json"
- publish(catalog_path, entries)
- prompt_path = normalized / "native_tasks.jsonl"
- rows = []
- for entry in entries:
- instruction = (Path(entry["path"]) / "instruction.md").read_text()
- rows.append({"task": instruction, "metadata": {"harborrl_native": True, "native_task": entry}})
- prompt_path.write_text("".join(json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n" for row in rows))
- return catalog_path, prompt_path
+ from harborrl.platform.evidence import _materialize as prepare
+ return prepare(plan, root)
def _source_audit(root):
- def git(*args):
- return subprocess.check_output(["git", *args], cwd=ROOT)
-
- files = git("ls-files", "-z", "--cached", "--others", "--exclude-standard", "--",
- "harborrl", "scripts", "examples", "pyproject.toml").decode().split("\0")
- untracked = set(git("ls-files", "-z", "--others", "--exclude-standard", "--",
- "harborrl", "scripts", "examples").decode().split("\0"))
- (root / "source.patch").write_bytes(git("diff", "--binary", "HEAD", "--",
- "harborrl", "scripts", "examples", "pyproject.toml"))
- hashes = {}
- for name in files:
- source = ROOT / name
- if name and source.is_file():
- hashes[name] = hashlib.sha256(source.read_bytes()).hexdigest()
- if name in untracked:
- target = root / "source-new" / name
- target.parent.mkdir(parents=True, exist_ok=True)
- target.write_bytes(source.read_bytes())
- payload = {
- "commit": git("rev-parse", "HEAD").decode().strip(),
- "dirty": bool(git("status", "--porcelain")),
- "source_hashes": hashes,
- "patch": "source.patch",
- }
- publish(root / "source-manifest.json", payload)
- return payload
+ from harborrl.platform.evidence import _source_audit as audit
+ return audit(root)
def dispatch(plan, *, doctor_only=False):
+ deployment = (plan.get("deployment") or {}).get("config")
if doctor_only:
checks = doctor(plan)
+ explicit = plan.get("deployment", {}).get("source", "embedded-local") != "embedded-local"
print(json.dumps({"checks": checks,
- "scope": "native config, original tasks, isolated Harbor, training dependencies and GPU budget; protocol/token semantics require the deferred live probe"},
+ "scope": ("deployment topology, " if explicit else "")
+ + "native config, original tasks, isolated Harbor, training "
+ "dependencies and GPU budget; protocol/token semantics "
+ "require the deferred live probe"},
indent=2))
return int(any(not check["ok"] for check in checks))
@@ -254,51 +55,23 @@ def dispatch(plan, *, doctor_only=False):
print(json.dumps({"failed_checks": failed}, indent=2), file=sys.stderr)
return 1
+ if deployment is not None and deployment["manager"]["mode"] == "service":
+ from harborrl.deployment.controller import dispatch_service
+ return dispatch_service(plan, deployment, checks=checks)
+
root = _run_root(plan)
root.mkdir(parents=True)
catalog_path, prompt_path = _materialize(plan, root)
source = _source_audit(root)
- environment = dict(plan["environment"])
- environment.update({
- "RUN_ID": root.name, "RUN_NAME": root.name, "RUN_DIR": str(root),
- "RUNS_ROOT": str(Path(plan["config"]["output"]["root"]).resolve()),
- "CKPT_ROOT": str(root / "checkpoints"), "WANDB_DIR": str(root / "metrics/wandb"),
- "HARBORRL_NATIVE_CATALOG": str(catalog_path),
- "ROLLOUT_PROMPT_DATA": str(prompt_path),
- "HARBORRL_NATIVE_PROFILE": plan["config"]["harness"]["profile"],
- "HARBOR_IR_ROOT": str(root / "ir"),
- })
+ environment = run_environment(plan, root, catalog_path, prompt_path)
launch = {"plan": plan, "run_dir": str(root), "source": source,
"materialized_catalog": str(catalog_path), "prompt_data": str(prompt_path)}
publish(root / "launch.json", launch)
- allowed = {"PATH", "HOME", "USER", "LANG", "LC_ALL", "TMPDIR", "LD_LIBRARY_PATH",
- "CUDA_VISIBLE_DEVICES", "HTTP_PROXY", "HTTPS_PROXY", "NO_PROXY",
- "http_proxy", "https_proxy", "no_proxy", "PYTHONPATH",
- "SLIME_DIR", "MEGATRON_DIR", "SGLANG_IMAGE"}
- child_env = {key: value for key, value in os.environ.items() if key in allowed}
- child_env["PATH"] = os.pathsep.join(
- [str(Path(sys.executable).parent), child_env.get("PATH", os.defpath)])
- child_env["PYTHONPATH"] = os.pathsep.join(filter(None, [
- child_env.get("SLIME_DIR", ""), child_env.get("MEGATRON_DIR", ""),
- str(ROOT), child_env.get("PYTHONPATH", ""),
- ]))
- child_env.update(environment)
- child = subprocess.Popen(plan["training_command"], cwd=ROOT, env=child_env, start_new_session=True)
-
- def forward_signal(signum, frame):
- if child.poll() is None:
- try:
- child.send_signal(signum)
- except ProcessLookupError:
- pass
-
- previous = {sig: signal.signal(sig, forward_signal) for sig in (signal.SIGINT, signal.SIGTERM)}
- try:
- return child.wait()
- finally:
- for sig, handler in previous.items():
- signal.signal(sig, handler)
+ target = local_target(plan, environment=environment)
+ publish(root / "effective-config.json", effective_config(plan, environment, target.env, source, checks))
+ target.launch()
+ return target.wait()
if __name__ == "__main__":
diff --git a/harborrl/platform/policy_pool.py b/harborrl/platform/policy_pool.py
index 697ebe2d4..945e9d09c 100644
--- a/harborrl/platform/policy_pool.py
+++ b/harborrl/platform/policy_pool.py
@@ -20,6 +20,8 @@ def validate_pool(states, expected=None):
def publish_pool(states, expected=None):
version = validate_pool(states, expected)
root = Path(os.environ['RUN_DIR'])
+ from harborrl.consistency.sglang import admit_pool
+ admit_pool(states, root)
payload = {'weight_version': version, 'engines': states, 'timestamp': time.time()}
temp = root / 'policy-pool.tmp'
temp.write_text(json.dumps(payload) + '\n')
diff --git a/harborrl/platform/preflight.py b/harborrl/platform/preflight.py
new file mode 100644
index 000000000..3a35fe88f
--- /dev/null
+++ b/harborrl/platform/preflight.py
@@ -0,0 +1,220 @@
+"""Live deployment checks for the native pipeline; no training dispatch.
+
+``doctor`` composes two layers: the deployment topology graph (manager, GPU
+target, rollout workers, control-loop addresses) and the machine-local
+environment checks. The local layer only runs where it is meaningful: when
+the GPU target is remote, dependency probes belong to the GPU job startup,
+not to this machine.
+"""
+import importlib
+import importlib.metadata
+import json
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+
+from harborrl.config import ROOT
+
+
+def _deployment(plan):
+ return (plan.get("deployment") or {}).get("config")
+
+
+def doctor(plan):
+ config = plan["config"]
+ checks = []
+ deployment = _deployment(plan)
+ # Embedded-local is a compatibility record, not an opt-in: only an
+ # explicit deployment file activates the topology layers.
+ explicit = deployment is not None and plan.get("deployment", {}).get(
+ "source", "embedded-local") != "embedded-local"
+ local_gpu = deployment is None or deployment["gpu_target"]["transport"] == "local"
+ legacy_workers = (not explicit) or deployment["rollout"]["provider"] in ("embedded", "sandbox")
+
+ if explicit:
+ from harborrl.deployment.preflight import preflight as deployment_preflight
+ checks.extend(deployment_preflight(plan, deployment, live=True))
+ if not local_gpu:
+ checks.append({
+ "role": "gpu_target", "machine": deployment["gpu_target"]["id"],
+ "check": "gpu_target.training_dependencies", "ok": True,
+ "detail": "Slime/Megatron/SGlang versions are enforced fail-closed "
+ "by slime_train.sh at GPU job startup on the GPU machine",
+ })
+
+ runner = Path(config["harbor"]["python"])
+ checks.append({"path": "harbor.python", "ok": runner.is_file()})
+ from harborrl.rollout.harbor_job.coordinator import runner_command
+ if legacy_workers and runner.is_file():
+ workers = ([None] if config['harbor'].get('runner_transport') == 'local'
+ else config['harbor']['workers'])
+ for worker in workers:
+ try:
+ probe = subprocess.run(
+ runner_command(str(runner), ROOT, worker) + ["--probe"],
+ capture_output=True, text=True, timeout=30, check=False,
+ )
+ except (subprocess.SubprocessError, OSError) as exc:
+ probe = SimpleNamespace(returncode=1, stdout="", stderr=str(exc))
+ try:
+ payload = json.loads(probe.stdout)
+ runtime_version = tuple(payload.get("python", "").split(".")[:2])
+ version_ok = payload.get("harbor_version") == config["harbor"]["version"]
+ runtime_ok = len(runtime_version) == 2 and all(part.isdigit() for part in runtime_version)
+ except (ValueError, AttributeError, TypeError):
+ runtime_version, version_ok, runtime_ok = (), False, False
+ checks.append({
+ "service": "local Harbor runner" if worker is None else "external Harbor worker",
+ "worker": worker,
+ "ok": probe.returncode == 0 and runtime_ok
+ and tuple(map(int, runtime_version)) >= (3, 12) and version_ok,
+ "detail": probe.stdout.strip() or probe.stderr.strip()[-1000:],
+ })
+ if 'HARBORRL_NATIVE_SANDBOX' in plan['environment']:
+ for worker in workers:
+ try:
+ probe = subprocess.run(
+ runner_command(str(runner), ROOT, worker,
+ module='harborrl.rollout.harbor_job.sandbox_probe') + ['--stdin'],
+ input=plan['environment']['HARBORRL_NATIVE_SANDBOX'], cwd=ROOT,
+ capture_output=True, text=True, timeout=60, check=False)
+ checks.append({'service': 'sandbox offline runtime', 'worker': worker,
+ 'ok': probe.returncode == 0, 'detail': probe.stdout.strip()})
+ except (subprocess.SubprocessError, OSError) as exc:
+ checks.append({'service': 'sandbox offline runtime', 'worker': worker,
+ 'ok': False, 'detail': type(exc).__name__})
+
+ for key, path in (("tasks.catalog", config["tasks"]["catalog"]),
+ ("harness.profile", config["harness"]["profile"]),
+ ("model.checkpoint", config["model"]["checkpoint"]),
+ ("model.reference", config["model"]["reference"])):
+ checks.append({"path": key, "ok": Path(path).exists()})
+
+ model_config = Path(config["model"]["checkpoint"]) / "config.json"
+ if model_config.is_file():
+ try:
+ model = json.loads(model_config.read_text())
+ heads = model.get("num_attention_heads", 0)
+ kv_heads = model.get("num_key_value_heads", heads)
+ for tp in {config["deployment"]["actor_tensor_parallel_size"],
+ config["deployment"]["rollout_gpus_per_engine"]}:
+ checks.append({"model_tp": tp, "ok": bool(
+ heads and kv_heads and heads % tp == 0 and (kv_heads % tp == 0 or tp % kv_heads == 0))})
+ except (OSError, ValueError, TypeError) as exc:
+ checks.append({"path": "model.config", "ok": False, "detail": str(exc)})
+
+ from harborrl.tasks.catalog import catalog
+ try:
+ entries = catalog(config)
+ for entry in entries:
+ # catalog() already checks the complete task tree against its lock.
+ checks.append({"task": entry["id"], "ok": True, "detail": []})
+ except (ValueError, OSError, TypeError) as exc:
+ checks.append({"service": "native catalog", "ok": False, "detail": str(exc)})
+
+ if local_gpu:
+ for package in ("ray", "torch", "sglang", "transformers"):
+ try:
+ version = importlib.metadata.version(package)
+ except importlib.metadata.PackageNotFoundError:
+ version = None
+ checks.append({"dependency": package, "version": version, "ok": version is not None})
+
+ from harborrl.backends.slime_v032.versions import (
+ MEGATRON_COMMIT,
+ SGLANG_IMAGE_TAG,
+ SLIME_COMMIT,
+ )
+ if local_gpu:
+ slime_dir = os.environ.get("SLIME_DIR", "").strip()
+ slime_path = Path(slime_dir) if slime_dir else None
+ checks.append({"path": "SLIME_DIR", "ok": bool(slime_path and slime_path.is_dir()),
+ "detail": slime_dir or "not set"})
+ if slime_path and slime_path.is_dir():
+ checks.append({"path": "slime entrypoint", "ok": (slime_path / "train.py").is_file()})
+ preset = slime_path / "scripts" / "models" / f"{config['model']['args_file']}.sh"
+ checks.append({"path": "slime model preset", "ok": preset.is_file(), "detail": str(preset)})
+ try:
+ commit = subprocess.run(["git", "-C", str(slime_path), "rev-parse", "HEAD"],
+ capture_output=True, text=True, timeout=15, check=True).stdout.strip()
+ except (subprocess.SubprocessError, OSError) as exc:
+ commit = ""
+ checks.append({"service": "slime commit", "ok": False, "detail": str(exc)})
+ if commit:
+ checks.append({"service": "slime commit", "ok": commit == SLIME_COMMIT,
+ "detail": f"{commit} != {SLIME_COMMIT}" if commit != SLIME_COMMIT else commit})
+
+ megatron_dir = os.environ.get("MEGATRON_DIR", "").strip()
+ if megatron_dir:
+ try:
+ megatron_commit = subprocess.run(["git", "-C", megatron_dir, "rev-parse", "HEAD"],
+ capture_output=True, text=True, timeout=15, check=True).stdout.strip()
+ except (subprocess.SubprocessError, OSError) as exc:
+ megatron_commit = ""
+ checks.append({"service": "megatron commit", "ok": False, "detail": str(exc)})
+ if megatron_commit:
+ checks.append({"service": "megatron commit", "ok": megatron_commit == MEGATRON_COMMIT,
+ "detail": f"{megatron_commit} != {MEGATRON_COMMIT}" if megatron_commit != MEGATRON_COMMIT else megatron_commit})
+ else:
+ checks.append({"service": "megatron commit",
+ "ok": True,
+ "detail": "MEGATRON_DIR not set; official Slime image bundles Megatron"})
+
+ runtime_env = os.environ.copy()
+ runtime_pythonpath = os.pathsep.join(filter(None, [
+ str(slime_path), megatron_dir, str(ROOT), runtime_env.get("PYTHONPATH", ""),
+ ]))
+ runtime_env["PYTHONPATH"] = runtime_pythonpath
+ for module in ("slime.rollout.base_types", "slime.utils.types",
+ "slime.backends.megatron_utils.loss"):
+ try:
+ probe = subprocess.run([sys.executable, "-c", f"import {module}"],
+ capture_output=True, text=True, timeout=90, check=False,
+ env=runtime_env)
+ except (subprocess.SubprocessError, OSError) as exc:
+ probe = SimpleNamespace(returncode=1, stdout="", stderr=str(exc))
+ checks.append({"dependency": module, "ok": probe.returncode == 0,
+ "detail": probe.stderr.strip()[-1000:]})
+
+ sglang_image = os.environ.get("SGLANG_IMAGE", "").strip()
+ if sglang_image:
+ checks.append({"service": "sglang image", "ok": sglang_image.endswith(SGLANG_IMAGE_TAG),
+ "detail": sglang_image})
+
+ if shutil.which("nvidia-smi"):
+ try:
+ result = subprocess.run(["nvidia-smi", "--query-gpu=index", "--format=csv,noheader"],
+ capture_output=True, text=True, timeout=10, check=True)
+ visible = os.environ.get("CUDA_VISIBLE_DEVICES")
+ count = (len([item for item in visible.split(",") if item.strip() and item.strip() != "-1"])
+ if visible is not None else len(result.stdout.splitlines()))
+ checks.append({"resource": "visible GPU budget", "available": count,
+ "ok": count >= config["deployment"]["num_gpus"]})
+ except (subprocess.SubprocessError, OSError) as exc:
+ checks.append({"resource": "visible GPU budget", "ok": False, "detail": str(exc)})
+ else:
+ checks.append({"resource": "visible GPU budget", "ok": False, "detail": "nvidia-smi unavailable"})
+
+ if config["training"]["backend_contract"] != "slime-legacy":
+ from harborrl.backends.slime_v032.launcher import hook_modules
+ for hook_name, module_name in hook_modules().items():
+ try:
+ importlib.import_module(module_name)
+ checks.append({"service": "training backend hook", "hook": hook_name, "ok": True})
+ except (ImportError, AttributeError, TypeError, ValueError) as exc:
+ checks.append({
+ "service": "training backend hook", "hook": hook_name,
+ "ok": False, "detail": str(exc),
+ })
+
+ checks.append({
+ "service": "raw-model logprob audit",
+ "ok": True,
+ "detail": ("enabled by lock; live serving semantic probe remains a GPU gate"
+ if config["gateway"]["audited_raw_logprobs"] else
+ "disabled; generated evidence remains EVAL_ONLY and training will fail closed"),
+ })
+ return checks
diff --git a/harborrl/platform/slime_train.sh b/harborrl/platform/slime_train.sh
index 50377dbab..8d7972874 100644
--- a/harborrl/platform/slime_train.sh
+++ b/harborrl/platform/slime_train.sh
@@ -171,12 +171,19 @@ TRAIN_ARGS=(
export CUDA_DEVICE_MAX_CONNECTIONS=1
TRAIN_PYTHON="${TRAIN_PYTHON:-python3}"
export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
+ENTRY=("${SLIME_DIR}/train.py")
+if [[ "${HARBORRL_CONSISTENCY_MODE:-off}" != "off" ]]; then
+ ENTRY=(-m harborrl.consistency.train)
+fi
+
+"${TRAIN_PYTHON}" -m harborrl.platform.evidence "${RUN_DIR}" "${DRY_RUN:-0}" \
+ "${TRAIN_PYTHON}" -u "${ENTRY[@]}" "${TRAIN_ARGS[@]}"
if [[ "${DRY_RUN:-0}" == "1" ]]; then
printf '[dry-run] '
- printf '%q ' "${TRAIN_PYTHON}" -u "${SLIME_DIR}/train.py" "${TRAIN_ARGS[@]}"
+ printf '%q ' "${TRAIN_PYTHON}" -u "${ENTRY[@]}" "${TRAIN_ARGS[@]}"
printf '\n'
exit 0
fi
-exec "${TRAIN_PYTHON}" -u "${SLIME_DIR}/train.py" "${TRAIN_ARGS[@]}"
+exec "${TRAIN_PYTHON}" -u "${ENTRY[@]}" "${TRAIN_ARGS[@]}"
diff --git a/harborrl/rollout/exporters/native.py b/harborrl/rollout/exporters/native.py
index 55355c7aa..05fb63be8 100644
--- a/harborrl/rollout/exporters/native.py
+++ b/harborrl/rollout/exporters/native.py
@@ -1,47 +1,36 @@
-"""Trajectory-aware GRPO export, independent of Slime/Torch imports.
+"""Compatibility record view of the canonical trajectory-weighted GRPO batch.
-These records must enter a backend that honors token_weights and advantages.
-They must NOT be passed through the interactive reward/whitening builder.
+No independent advantage calculation or secondary whitening occurs here.
"""
-import math
-
-from harborrl.trajectories.native import Identity, finite, require_ready
+from harborrl.export.native import export_training_batch
+from harborrl.export.validate import token_weight
+from harborrl.trajectories.native import Identity
def export_group(trajectories, group_size, *, epsilon=1e-6, allow_synthetic=False):
if type(group_size) is not int or group_size < 2 or len(trajectories) != group_size:
raise ValueError("a complete group of at least two trajectories is required")
- if not finite(epsilon) or epsilon <= 0:
- raise ValueError("epsilon must be positive and finite")
- for ir in trajectories:
- require_ready(ir, allow_synthetic=allow_synthetic)
- ids = [Identity(**ir["identity"]) for ir in trajectories]
- if len({i.group_key for i in ids}) != 1:
- raise ValueError("mixed group, policy, task or profiles")
- for key in ("slot_id", "attempt_id", "trajectory_id"):
- if len({getattr(i, key) for i in ids}) != group_size:
- raise ValueError(f"duplicate {key}")
- rewards = [ir["evaluation"]["training_reward"] for ir in trajectories]
- mean = math.fsum(rewards) / group_size
- std = math.sqrt(math.fsum((r - mean) ** 2 for r in rewards) / group_size)
- if not finite(mean) or not finite(std):
- raise ValueError("nonfinite group statistics")
- advantages = [0.0 if std == 0 else (r - mean) / (std + epsilon) for r in rewards]
+ batch = export_training_batch(
+ [trajectories], group_size=group_size, epsilon=epsilon,
+ allow_synthetic=allow_synthetic,
+ )
+ group = batch.groups[0]
records = []
- for ir, ident, advantage in zip(trajectories, ids, advantages):
- count = sum(len(t["output_ids"]) for t in ir["turns"])
- for index, turn in enumerate(ir["turns"]):
- n = len(turn["output_ids"])
- records.append({"identity": ident.to_dict(), "turn_index": index,
- "tokens": turn["input_ids"] + turn["output_ids"],
- "prompt_length": len(turn["input_ids"]), "response_length": n,
- "loss_mask": [0] * len(turn["input_ids"]) + [1] * n,
- "old_logprobs": turn["logprobs"], "advantages": [advantage] * n,
- "token_weights": [1.0 / (group_size * count)] * n,
- "training_reward": ir["evaluation"]["training_reward"],
- "padding": False})
- return {"export_version": "native-grpo-v2", "group_key": ids[0].group_key,
- "trajectory_count": group_size, "mean": mean, "std": std,
- "advantages": advantages, "records": records,
+ for ir, unit in zip(trajectories, group.trajectories):
+ ident = Identity(**ir["identity"])
+ weight = token_weight(group_size, unit.token_count)
+ for span in unit.turns:
+ n = span.response_length
+ records.append({
+ "identity": ident.to_dict(), "turn_index": span.turn_index,
+ "tokens": list(span.tokens), "prompt_length": span.prompt_length,
+ "response_length": n, "loss_mask": [int(flag) for flag in span.loss_mask],
+ "old_logprobs": list(span.old_logprobs), "advantages": [unit.advantage] * n,
+ "token_weights": [weight] * n, "training_reward": ir["evaluation"]["training_reward"],
+ "padding": False,
+ })
+ return {"export_version": "native-grpo-v2", "group_key": group.group_key,
+ "trajectory_count": group_size, "mean": group.reward_mean, "std": group.reward_std,
+ "advantages": [unit.advantage for unit in group.trajectories], "records": records,
"synthetic": allow_synthetic,
"reduction": "sum_token_weighted_loss; no secondary whitening"}
diff --git a/harborrl/rollout/harbor_job/__main__.py b/harborrl/rollout/harbor_job/__main__.py
index 01828fd19..da10c1bb6 100644
--- a/harborrl/rollout/harbor_job/__main__.py
+++ b/harborrl/rollout/harbor_job/__main__.py
@@ -13,8 +13,10 @@ def main(argv=None):
smoke.add_argument("--output", required=True)
args = parser.parse_args(argv)
if args.command == "inspect":
- from harborrl.data.harbor.native_inspector import inspect_native
- result = inspect_native(args.task, expected_digest=args.expected_digest)
+ from harborrl.math_registry import NATIVE_TASK_PROVIDER
+ result = NATIVE_TASK_PROVIDER.adapter.inspect(
+ args.task, expected_digest=args.expected_digest
+ )
print(json.dumps(result, indent=2))
return int(result["status"] in ("INVALID", "UNSUPPORTED"))
from .offline import smoke as run_smoke
diff --git a/harborrl/rollout/harbor_job/bindings.py b/harborrl/rollout/harbor_job/bindings.py
index 4cd439689..e0e553e85 100644
--- a/harborrl/rollout/harbor_job/bindings.py
+++ b/harborrl/rollout/harbor_job/bindings.py
@@ -5,8 +5,9 @@
def claude_config(profile, gateway_url, credential, max_output_tokens=8192):
required = {"cli_version", "model", "max_turns", "agent_timeout_sec", "setup_timeout_sec", "verifier_timeout_sec"}
- if set(profile) != required:
- raise ValueError(f"Claude profile requires exactly {sorted(required)}")
+ allowed = required | {"disallowed_tools"}
+ if not required.issubset(profile) or set(profile) - allowed:
+ raise ValueError(f"Claude profile requires {sorted(required)} and optionally disallowed_tools")
if not isinstance(profile["cli_version"], str) or not re.fullmatch(r"\d+\.\d+\.\d+(?:-[A-Za-z0-9.]+)?", profile["cli_version"]):
raise ValueError("exact Claude CLI version required; latest is forbidden")
if not isinstance(profile["model"], str) or not re.fullmatch(r"[A-Za-z0-9_.:/-]+", profile["model"]):
@@ -14,6 +15,10 @@ def claude_config(profile, gateway_url, credential, max_output_tokens=8192):
for key in required - {"model", "cli_version"}:
if type(profile[key]) is not int or profile[key] <= 0:
raise ValueError(f"positive integer required: {key}")
+ disallowed_tools = profile.get("disallowed_tools", "Agent,Task")
+ if not isinstance(disallowed_tools, str) or not re.fullmatch(
+ r"[A-Za-z][A-Za-z0-9_]*(?:,[A-Za-z][A-Za-z0-9_]*)*", disallowed_tools):
+ raise ValueError("disallowed tools must be a comma-separated identifier list")
url = urlsplit(gateway_url)
if url.scheme not in ("http", "https") or not url.hostname or url.username or url.password or url.query or url.fragment or url.path not in ("", "/"):
raise ValueError("Anthropic base URL must be a plain origin (no /v1 suffix)")
@@ -27,9 +32,10 @@ def claude_config(profile, gateway_url, credential, max_output_tokens=8192):
"override_timeout_sec": profile["agent_timeout_sec"],
"override_setup_timeout_sec": profile["setup_timeout_sec"],
"kwargs": {"version": profile["cli_version"], "max_turns": profile["max_turns"],
- "max_thinking_tokens": 0, "disallowed_tools": "Agent,Task",
+ "max_thinking_tokens": 0, "disallowed_tools": disallowed_tools,
"permission_mode": "bypassPermissions"},
"env": {**client_controls(), "ANTHROPIC_API_KEY": credential,
+ "ANTHROPIC_MODEL": profile["model"],
"ANTHROPIC_BASE_URL": gateway_url.rstrip("/"),
"NO_PROXY": url.hostname, "no_proxy": url.hostname,
"CLAUDE_CODE_MAX_OUTPUT_TOKENS": str(max_output_tokens)}}
diff --git a/harborrl/rollout/harbor_job/coordinator.py b/harborrl/rollout/harbor_job/coordinator.py
index 4afa6a27d..17047cd40 100644
--- a/harborrl/rollout/harbor_job/coordinator.py
+++ b/harborrl/rollout/harbor_job/coordinator.py
@@ -12,10 +12,9 @@
from dataclasses import replace
from pathlib import Path
+from harborrl.math_registry import CLAUDE_CODE_HARNESS
from harborrl.trajectories.native import assemble, publish
-from .audit import audit_session
-from .bindings import sanitized_runner_env
from .collector import collect
@@ -66,8 +65,7 @@ def export(self):
return export_group([self.completed[slot] for slot in self.identities], len(self.identities))
-def runner_command(runner_python, project, runner_host=None):
- module = "harborrl.rollout.harbor_job.runner"
+def runner_command(runner_python, project, runner_host=None, *, module="harborrl.rollout.harbor_job.runner"):
if runner_host is None:
return [runner_python, "-m", module]
remote = f"cd {shlex.quote(str(project))} && exec {shlex.quote(runner_python)} -m {module}"
@@ -77,13 +75,13 @@ def runner_command(runner_python, project, runner_host=None):
async def run_attempt(identity, *, task_path, profile, gateway_url, runner_python,
root, registry, reward_profile, timeout=1800, cleanup_timeout=120,
- max_output_tokens=8192, runner_host=None):
+ max_output_tokens=8192, runner_host=None, sandbox=None):
"""One real trial. CPU-only tests use a separate fake worker, never this path."""
root = Path(root).resolve()
root.mkdir(parents=True, exist_ok=False)
credential = secrets.token_urlsafe(32)
project = str(Path(__file__).resolve().parents[3])
- env = sanitized_runner_env(os.environ)
+ env = CLAUDE_CODE_HARNESS.adapter.runner_environment(os.environ)
env["PYTHONPATH"] = project
stderr = (root / "runner.stderr").open("wb")
try:
@@ -100,6 +98,12 @@ async def run_attempt(identity, *, task_path, profile, gateway_url, runner_pytho
req = {"identity": identity.to_dict(), "task_path": str(Path(task_path).resolve()), "profile": profile,
"gateway_url": gateway_url, "credential": credential, "output": str(root / "runner"),
"max_output_tokens": max_output_tokens}
+ if sandbox is not None:
+ from harborrl.trajectories.native import digest
+ req.update(sandbox=sandbox, runtime_digest=digest(sandbox))
+ timeout = (sandbox['create_timeout_s'] + sandbox['cleanup_timeout_s'] * 2
+ + sum(profile[k] for k in ('setup_timeout_sec', 'agent_timeout_sec', 'verifier_timeout_sec')))
+ cleanup_timeout = sandbox['cleanup_timeout_s'] + 15
process.stdin.write(json.dumps(req).encode() + b"\n")
await process.stdin.drain()
@@ -133,16 +137,26 @@ async def lifecycle():
await asyncio.wait_for(process.wait(), cleanup_timeout)
if process.returncode != 0 or terminal["type"] != "terminal":
raise RuntimeError("native trial did not finish normally")
+ if sandbox is not None:
+ from harborrl.trajectories.native import read_json
+ lifecycle_path = root / 'runner' / 'sandbox-lifecycle.json'
+ lifecycle = read_json(lifecycle_path) if lifecycle_path.is_file() else {}
+ if lifecycle.get('state') != 'deleted' or lifecycle.get('runtime_digest') != req['runtime_digest']:
+ publish(root / 'cleanup-required.json', {'identity': identity.to_dict(), 'reason': 'Sandbox deletion unconfirmed'})
+ raise RuntimeError('sandbox deletion unconfirmed')
trial_dir = Path(terminal["trial_dir"]).resolve()
result_path = Path(terminal["result_path"]).resolve()
if not trial_dir.is_relative_to(root / "runner" / "harbor") or result_path != trial_dir / "result.json":
raise ValueError("Runner result path is outside this attempt")
- sessions = [p for p in (trial_dir / "agent" / "sessions" / "projects").rglob("*.jsonl")]
+ sessions = list((trial_dir / "agent" / "sessions" / "projects").rglob("*.jsonl"))
if len(sessions) != 1:
raise ValueError("expected exactly one native Claude session; auxiliary sessions require review")
- audit_session(sessions[0], registry, identity.attempt_id)
+ registry.wait_delivered(identity.attempt_id, 30.0)
+ audit = CLAUDE_CODE_HARNESS.adapter.audit_session(sessions[0], registry, identity.attempt_id)
+ receipt = collect(identity, trial_id, result_path, trial_dir / "verifier", reward_profile)
+ CLAUDE_CODE_HARNESS.adapter.audit_empty_final(
+ trial_dir / "agent" / "claude-code.txt", registry, identity.attempt_id, audit)
turns, seal = registry.seal(identity.attempt_id)
- receipt = collect(identity, trial_id, terminal["result_path"], trial_dir / "verifier", reward_profile)
publish(root / "evaluation.json", receipt)
ir = assemble(identity, trial_id, turns, seal, receipt)
publish(root / "trajectory.v2.json", ir)
@@ -152,6 +166,8 @@ async def lifecycle():
publish(root / "failure.json", {"identity": identity.to_dict(), "error_type": type(exc).__name__})
raise
finally:
+ if sandbox is not None and (root / 'runner' / 'cleanup-required.json').exists():
+ publish(root / 'cleanup-required.json', {'identity': identity.to_dict(), 'reason': 'Inspect runner/cleanup-required.json'})
if registered:
registry.close(identity.attempt_id)
if process.returncode is None:
diff --git a/harborrl/rollout/harbor_job/environments/__init__.py b/harborrl/rollout/harbor_job/environments/__init__.py
new file mode 100644
index 000000000..d73e6b751
--- /dev/null
+++ b/harborrl/rollout/harbor_job/environments/__init__.py
@@ -0,0 +1 @@
+"""Optional Harbor environment adapters loaded only by the isolated runner."""
diff --git a/harborrl/rollout/harbor_job/environments/opensandbox.py b/harborrl/rollout/harbor_job/environments/opensandbox.py
new file mode 100644
index 000000000..829f9dadc
--- /dev/null
+++ b/harborrl/rollout/harbor_job/environments/opensandbox.py
@@ -0,0 +1,189 @@
+"""Audited lifecycle around Harbor's OpenSandbox environment."""
+import asyncio
+import fcntl
+import json
+import os
+import tempfile
+import time
+from contextlib import asynccontextmanager
+from datetime import timedelta
+from pathlib import Path
+
+import httpx
+from harbor.environments.opensandbox import OpenSandboxEnvironment
+
+from harborrl.rollout.harbor_job.sandbox_config import read_credential
+from harborrl.trajectories.native import digest, publish
+
+
+@asynccontextmanager
+async def startup_slot(directory, count):
+ """Process-shared creation slots; kernel releases locks on runner death."""
+ directory = Path(directory)
+ directory.mkdir(parents=True, exist_ok=True)
+ held = None
+ try:
+ while held is None:
+ for index in range(count):
+ handle = (directory / f'{index}.lock').open('a')
+ try:
+ fcntl.flock(handle, fcntl.LOCK_EX | fcntl.LOCK_NB)
+ held = handle
+ break
+ except BlockingIOError:
+ handle.close()
+ if held is None:
+ await asyncio.sleep(0.2)
+ yield
+ finally:
+ if held is not None:
+ held.close()
+
+
+class ManagedOpenSandboxEnvironment(OpenSandboxEnvironment):
+ @classmethod
+ def validate_config(cls, cfg):
+ if cfg['options'] or any(b['options'] for b in cfg['bindings']):
+ raise ValueError('standard OpenSandbox adapter does not accept platform options')
+
+ @classmethod
+ def agent_gateway(cls, cfg, gateway_url):
+ return gateway_url
+
+ @classmethod
+ async def network_check(cls, cfg):
+ return {'status': 'not_probed', 'reason': 'Use the explicit sandbox smoke test for allocation checks'}
+
+ async def validate_binding(self):
+ pass
+
+ def extensions(self):
+ return {}
+
+ def api_params(self):
+ return {}
+
+ def startup_transport(self, allocated):
+ from harborrl.rollout.harbor_job.sandbox_transport import (
+ SandboxStartupTransport,
+ )
+ return SandboxStartupTransport(self.cfg['domain'].rstrip('/') + '/v1', allocated)
+
+ def __init__(self, *args, sandbox_config, binding, evidence_dir, startup_dir, gateway_url=None, **kwargs):
+ self.validate_config(sandbox_config)
+ self.cfg, self.binding = sandbox_config, binding
+ self.evidence = Path(evidence_dir)
+ self.startup_dir = startup_dir
+ self.gateway_url = gateway_url
+ self._startup_transport = None
+ self.record = {'runtime_digest': digest(sandbox_config),
+ 'image': binding['image'], 'sandbox_id': None, 'state': 'pending', 'events': []}
+ super().__init__(*args, domain=self.cfg['domain'], api_key=read_credential(self.cfg),
+ protocol=self.cfg['protocol'], use_server_proxy=True,
+ ready_timeout_sec=self.cfg['create_timeout_s'],
+ sandbox_timeout_sec=self.cfg['lifecycle_minutes'] * 60,
+ request_timeout_sec=self.cfg['request_timeout_s'], **kwargs)
+
+ def record_state(self, state):
+ self.record['state'] = state
+ self.record['events'].append({'state': state, 'time': time.time()})
+ publish(self.evidence / 'sandbox-events' / f'{len(self.record["events"]):04d}.json', self.record)
+ with tempfile.NamedTemporaryFile(mode='w', dir=self.evidence, delete=False) as stream:
+ json.dump(self.record, stream)
+ stream.flush()
+ os.fsync(stream.fileno())
+ os.replace(stream.name, self.evidence / 'sandbox-lifecycle.json')
+
+ def cleanup_required(self, reason):
+ publish(self.evidence / 'cleanup-required.json', {
+ 'sandbox_id': self.record['sandbox_id'], 'image': self.binding['image'],
+ 'reason': reason, 'runtime_digest': self.record['runtime_digest']})
+
+ async def _create_sandbox(self, sdk):
+ await self.validate_binding()
+ async with startup_slot(self.startup_dir, self.cfg['startup_concurrency']):
+ self.record_state('creating')
+ def allocated(sandbox_id):
+ self.record['sandbox_id'] = sandbox_id
+ self.record_state('allocated')
+
+ self._startup_transport = self.startup_transport(allocated)
+ connection = self._build_connection_config(sdk).model_copy(update={'transport': self._startup_transport})
+ try:
+ sandbox = await asyncio.wait_for(sdk['Sandbox'].create(
+ self.binding['image'], connection_config=connection,
+ timeout=timedelta(minutes=self.cfg['lifecycle_minutes']),
+ ready_timeout=timedelta(seconds=self.cfg['create_timeout_s']),
+ extensions=self.extensions(),
+ resource=self._build_resource(), env=dict(self._persistent_env),
+ network_policy=self._build_network_policy(sdk), skip_health_check=True),
+ self.cfg['create_timeout_s'])
+ except BaseException: # noqa: BLE001 - persist unknown allocation on cancellation too
+ if self.record['sandbox_id']:
+ await self._cleanup_allocated()
+ else:
+ self.record_state('creation_unknown')
+ self.cleanup_required('Creation outcome unknown; reconcile platform inventory before retry')
+ await self._startup_transport.aclose()
+ raise RuntimeError('Sandbox creation outcome unknown') from None
+ self._sandbox = sandbox
+ self.record['sandbox_id'] = sandbox.id
+ self.record_state('created')
+ try:
+ await asyncio.wait_for(self._wait_until_ready(sandbox), self.cfg['create_timeout_s'])
+ self.record_state('ready')
+ return sandbox
+ except BaseException: # noqa: BLE001 - cleanup before redacting SDK errors
+ await self.stop(delete=True)
+ raise RuntimeError('Sandbox readiness failed') from None
+
+ async def _cleanup_allocated(self):
+ """The SDK may already have killed an instance whose endpoints failed."""
+ self.record_state('deleting')
+ url = self.cfg['domain'].rstrip('/') + '/v1/sandboxes/' + self.record['sandbox_id']
+ try:
+ async with asyncio.timeout(self.cfg['cleanup_timeout_s']):
+ async with httpx.AsyncClient(timeout=30, headers={'OPEN-SANDBOX-API-KEY': self._api_key},
+ params=self.api_params()) as client:
+ status = await client.get(url)
+ if status.status_code != 404:
+ (await client.delete(url)).raise_for_status()
+ status = await client.get(url)
+ if status.status_code != 404:
+ raise RuntimeError('allocation deletion unconfirmed')
+ self.record_state('deleted')
+ except BaseException: # noqa: BLE001 - retain debt if cleanup is interrupted
+ self.record_state('delete_failed')
+ self.cleanup_required('Allocated Sandbox deletion was not confirmed')
+
+ async def start(self, force_build):
+ try:
+ await super().start(force_build)
+ except BaseException:
+ if self._sandbox is not None:
+ await self.stop(delete=True)
+ raise
+
+ async def stop(self, delete=True):
+ if self._sandbox is None:
+ if self._startup_transport is not None:
+ await self._startup_transport.aclose()
+ return
+ sandbox = self._sandbox
+ self.record_state('deleting')
+ try:
+ await asyncio.wait_for(sandbox.kill(), self.cfg['cleanup_timeout_s'])
+ self.record_state('deleted')
+ (self.evidence / 'cleanup-required.json').unlink(missing_ok=True)
+ self._sandbox = None
+ except BaseException: # noqa: BLE001 - preserve cleanup debt on cancellation
+ self.record_state('delete_failed')
+ self.cleanup_required('Sandbox kill was not acknowledged')
+ raise RuntimeError('Sandbox deletion failed') from None
+ finally:
+ try:
+ await asyncio.wait_for(sandbox.close(), 10)
+ except Exception: # noqa: BLE001 - close errors must not expose SAT headers
+ self.logger.warning('Sandbox SDK connection close failed')
+ if self._startup_transport is not None:
+ await self._startup_transport.aclose()
diff --git a/harborrl/rollout/harbor_job/offline.py b/harborrl/rollout/harbor_job/offline.py
index 7dfa3220e..8f5dea362 100644
--- a/harborrl/rollout/harbor_job/offline.py
+++ b/harborrl/rollout/harbor_job/offline.py
@@ -7,6 +7,7 @@
from harborrl.gateway.server import Gateway, make_server
from harborrl.gateway.trace import TraceRegistry
+from harborrl.math_registry import CLAUDE_CODE_HARNESS
from harborrl.rollout.exporters.native import export_group
from harborrl.trajectories.native import (
Identity,
@@ -17,7 +18,6 @@
require_ready,
)
-from .audit import audit_session
from .collector import collect
@@ -72,7 +72,9 @@ def smoke(output):
events.append({"sessionId": trial, "type": "assistant", "message": message})
session.write_text("".join(json.dumps(e) + "\n" for e in events))
registry.close(identity.attempt_id)
- audit_session(session, registry, identity.attempt_id)
+ CLAUDE_CODE_HARNESS.adapter.audit_session(
+ session, registry, identity.attempt_id
+ )
turns, seal = registry.seal(identity.attempt_id)
publish(directory / "result.json", {"id": trial, "finished_at": "synthetic", "verifier_environment_mode": "shared",
"verifier_result": {"rewards": {"reward": slot}}})
diff --git a/harborrl/rollout/harbor_job/runner.py b/harborrl/rollout/harbor_job/runner.py
index a5b1c9389..7aa23fe4a 100644
--- a/harborrl/rollout/harbor_job/runner.py
+++ b/harborrl/rollout/harbor_job/runner.py
@@ -14,11 +14,9 @@
import sys
from pathlib import Path
-from harborrl.data.harbor.native_inspector import inspect_native
+from harborrl.math_registry import CLAUDE_CODE_HARNESS, NATIVE_TASK_PROVIDER
from harborrl.trajectories.native import Identity, digest, publish
-from .bindings import claude_config
-
def probe():
if sys.version_info < (3, 12):
@@ -43,7 +41,9 @@ async def execute(request, emit, read_command):
identity = Identity(**request["identity"])
if digest(request["profile"]) != identity.harness_digest:
raise ValueError("harness profile differs from attempt manifest")
- inspection = inspect_native(request["task_path"], expected_digest=identity.task_digest)
+ inspection = NATIVE_TASK_PROVIDER.adapter.inspect(
+ request["task_path"], expected_digest=identity.task_digest
+ )
if inspection["status"] != "NEEDS_PROBE":
raise ValueError("native task preflight failed: " + ",".join(inspection["reasons"]))
root = Path(request["output"]).resolve()
@@ -51,12 +51,36 @@ async def execute(request, emit, read_command):
root.mkdir(parents=True, exist_ok=False)
publish(root / "manifest.json", {"identity": identity.to_dict(), "task": inspection,
"profile": request["profile"], "gateway_url": request["gateway_url"]})
+ sandbox = request.get('sandbox')
+ agent_gateway = request['gateway_url']
+ if sandbox is not None:
+ from harborrl.rollout.harbor_job.sandbox_config import environment_class
+ agent_gateway = environment_class(sandbox).agent_gateway(sandbox, agent_gateway)
+ agent = CLAUDE_CODE_HARNESS.adapter.agent_config(
+ request['profile'], agent_gateway, request['credential'], request['max_output_tokens'])
+ environment = {'type': 'docker', 'delete': True}
+ if sandbox is not None:
+ from harborrl.rollout.harbor_job.sandbox_config import task_binding
+ if digest(sandbox) != request.get('runtime_digest'):
+ raise ValueError('sandbox runtime lock mismatch')
+ if sandbox['installation'] and sandbox['installation']['version'] != request['profile']['cli_version']:
+ raise ValueError('offline harness version mismatch')
+ publish(root / 'runtime.json', {'config': sandbox, 'digest': digest(sandbox)})
+ environment = {'type': 'opensandbox', 'delete': True,
+ 'import_path': sandbox['adapter'],
+ 'kwargs': {'sandbox_config': sandbox, 'binding': task_binding(sandbox, request['task_path']),
+ 'evidence_dir': str(root),
+ 'gateway_url': request['gateway_url'],
+ 'startup_dir': str(root.parents[2] / '.sandbox-startup')}}
+ if sandbox['installation']:
+ agent.pop('name')
+ agent['import_path'] = 'harborrl.harnesses.offline_claude:OfflineClaudeCode'
+ agent['kwargs']['installation'] = sandbox['installation']
config = TrialConfig.model_validate({
"task": {"path": inspection["path"]},
"trial_name": digest(identity.to_dict()), "trials_dir": str(root / "harbor"),
- "agent": claude_config(request["profile"], request["gateway_url"], request["credential"],
- request["max_output_tokens"]),
- "environment": {"type": "docker", "delete": True},
+ "agent": agent,
+ "environment": environment,
"verifier": {"disable": False, "override_timeout_sec": request["profile"]["verifier_timeout_sec"]}})
trial = await Trial.create(config)
trial_id = str(trial.id)
diff --git a/harborrl/rollout/harbor_job/sandbox_config.py b/harborrl/rollout/harbor_job/sandbox_config.py
new file mode 100644
index 000000000..bb008e184
--- /dev/null
+++ b/harborrl/rollout/harbor_job/sandbox_config.py
@@ -0,0 +1,135 @@
+"""Non-secret Sandbox settings; no SDK imports in the training process."""
+import asyncio
+import copy
+import importlib
+import re
+from contextlib import asynccontextmanager
+from pathlib import Path
+from urllib.parse import urlsplit
+
+import yaml
+
+from harborrl.tasks.content import tomllib
+
+
+def _check_options(value):
+ if isinstance(value, dict):
+ for key, item in value.items():
+ if not isinstance(key, str):
+ raise TypeError('sandbox option names must be strings')
+ name = key.lower().replace('-', '_')
+ if name in {'token', 'credential', 'password', 'secret', 'authorization'} or name.endswith(('_api_key', '_token', '_secret')) or name == 'api_key':
+ raise ValueError('sandbox options must reference credential files, not contain secrets')
+ _check_options(item)
+ elif isinstance(value, list):
+ for item in value:
+ _check_options(item)
+
+
+def load_sandbox(path):
+ path = Path(path)
+ cfg = yaml.safe_load(path.read_text())
+ required = {'domain', 'credential_file', 'bindings'}
+ defaults = {'protocol': 'https', 'use_server_proxy': True,
+ 'request_timeout_s': 60, 'create_timeout_s': 600, 'lifecycle_minutes': 120,
+ 'cleanup_timeout_s': 120, 'max_live_instances': 2, 'startup_concurrency': 1,
+ 'adapter': 'harborrl.rollout.harbor_job.environments.opensandbox:ManagedOpenSandboxEnvironment',
+ 'options': {}, 'installation': None}
+ if not isinstance(cfg, dict) or not required <= set(cfg) or set(cfg) - required - defaults.keys():
+ raise ValueError('invalid sandbox configuration fields')
+ cfg = {**defaults, **copy.deepcopy(cfg)}
+ url = urlsplit(cfg['domain'])
+ if (url.scheme not in ('http', 'https')
+ or not url.hostname or url.username or url.password or url.query or url.fragment
+ or url.path.rstrip('/').endswith('/v1') or cfg['protocol'] != url.scheme):
+ raise ValueError('invalid sandbox domain/protocol')
+ for key in defaults.keys() - {'protocol', 'use_server_proxy', 'adapter', 'options', 'installation'}:
+ if type(cfg[key]) is not int or cfg[key] <= 0:
+ raise ValueError(f'sandbox {key} must be a positive integer')
+ if type(cfg['use_server_proxy']) is not bool or not isinstance(cfg['options'], dict):
+ raise ValueError('invalid sandbox proxy/options')
+ _check_options(cfg['options'])
+ if not isinstance(cfg['adapter'], str) or not re.fullmatch(r'[A-Za-z_]\w*(?:\.[A-Za-z_]\w*)*:[A-Za-z_]\w*', cfg['adapter']):
+ raise ValueError('sandbox adapter must be module:Class')
+ if not isinstance(cfg['credential_file'], str) or not cfg['credential_file']:
+ raise ValueError('sandbox credential_file required')
+ cfg['credential_file'] = str((path.parent / cfg['credential_file']).resolve())
+ bindings = cfg['bindings']
+ if not isinstance(bindings, list) or not bindings:
+ raise ValueError('sandbox image bindings required')
+ images = set()
+ for binding in bindings:
+ if not isinstance(binding, dict) or set(binding) - {'image', 'max_live_instances', 'options'} or not {'image', 'max_live_instances'} <= set(binding):
+ raise ValueError('invalid sandbox binding fields')
+ binding.setdefault('options', {})
+ _check_options(binding['options'])
+ if (not isinstance(binding['image'], str) or not binding['image']
+ or binding['image'] in images or not isinstance(binding['options'], dict)):
+ raise ValueError('invalid or duplicate sandbox image binding')
+ if type(binding['max_live_instances']) is not int or binding['max_live_instances'] <= 0:
+ raise ValueError('binding max_live_instances must be positive')
+ images.add(binding['image'])
+ install = cfg['installation']
+ if install is None:
+ return cfg
+ if not isinstance(install, dict) or set(install) != {'mode', 'version', 'binary', 'sha256'}:
+ raise ValueError('installation requires mode, version, binary, sha256')
+ if (install['mode'] != 'offline_binary' or not isinstance(install['version'], str)
+ or not re.fullmatch(r'\d+\.\d+\.\d+', install['version'])
+ or not isinstance(install['sha256'], str) or not re.fullmatch(r'[0-9a-f]{64}', install['sha256'])
+ or not isinstance(install['binary'], str) or not install['binary']):
+ raise ValueError('invalid pinned offline binary installation')
+ install['binary'] = str((path.parent / install['binary']).resolve())
+ return cfg
+
+
+def task_binding(cfg, task_path):
+ spec = tomllib.loads((Path(task_path) / 'task.toml').read_text())
+ image = spec.get('environment', {}).get('docker_image')
+ for binding in cfg['bindings']:
+ if image == binding['image']:
+ return binding
+ raise ValueError('sandbox task requires a prebuilt image with an exact binding')
+
+
+def environment_class(cfg):
+ """Import trusted deployment code only in the runner, never in GPU training."""
+ module, name = cfg['adapter'].split(':')
+ cls = getattr(importlib.import_module(module), name)
+ from harborrl.rollout.harbor_job.environments.opensandbox import (
+ ManagedOpenSandboxEnvironment,
+ )
+ if not isinstance(cls, type) or not issubclass(cls, ManagedOpenSandboxEnvironment):
+ raise TypeError('sandbox adapter must implement the audited OpenSandbox lifecycle')
+ cls.validate_config(cfg)
+ return cls
+
+
+def read_credential(cfg):
+ path = Path(cfg['credential_file'])
+ if path.stat().st_mode & 0o077:
+ raise ValueError('sandbox credential file must have mode 600 or stricter')
+ value = path.read_text().strip()
+ if not value or '\n' in value:
+ raise ValueError('sandbox credential file must contain one nonempty token')
+ return value
+
+
+class SandboxAdmission:
+ """One runtime owns capacity; unresolved cleanup blocks further admission."""
+ def __init__(self, cfg):
+ self.total = asyncio.Semaphore(cfg['max_live_instances'])
+ self.bindings = {b['image']: asyncio.Semaphore(b['max_live_instances']) for b in cfg['bindings']}
+ self.blocked = False
+
+ @asynccontextmanager
+ async def lease(self, binding, root):
+ # Wait for image-specific capacity before reserving a global live slot.
+ async with self.bindings[binding['image']], self.total:
+ if self.blocked:
+ raise RuntimeError('sandbox admission blocked by unresolved cleanup')
+ try:
+ yield
+ finally:
+ if (Path(root) / 'cleanup-required.json').exists():
+ self.blocked = True
diff --git a/harborrl/rollout/harbor_job/sandbox_probe.py b/harborrl/rollout/harbor_job/sandbox_probe.py
new file mode 100644
index 000000000..ebed3f47d
--- /dev/null
+++ b/harborrl/rollout/harbor_job/sandbox_probe.py
@@ -0,0 +1,63 @@
+"""Offline dependency check or explicit live Environment check (no allocation)."""
+import argparse
+import asyncio
+import hashlib
+import importlib.metadata
+import json
+import sys
+from pathlib import Path
+
+from harborrl.rollout.harbor_job.sandbox_config import (
+ environment_class,
+ load_sandbox,
+ read_credential,
+)
+
+
+def check(cfg):
+ from harborrl.rollout.harbor_job.runner import probe
+ result = probe()
+ version = importlib.metadata.version('opensandbox')
+ if version != '0.1.9':
+ raise ValueError('sandbox runner requires opensandbox==0.1.9')
+ from opensandbox.models.execd import RunCommandOpts
+
+ adapter = environment_class(cfg)
+ if not {'timeout', 'envs', 'working_directory'} <= RunCommandOpts.model_fields.keys():
+ raise ValueError('unsupported SDK command options')
+ checksum = None
+ if cfg['installation']:
+ with Path(cfg['installation']['binary']).open('rb') as source:
+ checksum = hashlib.file_digest(source, 'sha256').hexdigest()
+ if checksum != cfg['installation']['sha256']:
+ raise ValueError('offline binary checksum mismatch')
+ read_credential(cfg)
+ result.update(opensandbox_version=version, environment=adapter.__name__, binary_sha256=checksum)
+ return result
+
+
+async def network_check(cfg):
+ return await environment_class(cfg).network_check(cfg)
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument('config', nargs='?')
+ parser.add_argument('--stdin', action='store_true')
+ parser.add_argument('--network', action='store_true')
+ args = parser.parse_args()
+ try:
+ cfg = json.load(sys.stdin) if args.stdin else load_sandbox(args.config)
+ result = check(cfg)
+ if args.network:
+ result['environments'] = asyncio.run(network_check(cfg))
+ print(json.dumps(result))
+ except Exception as exc: # noqa: BLE001 - redact authenticated SDK exceptions
+ # SDK/HTTP exceptions may contain authenticated endpoint URLs.
+ print(json.dumps({'ok': False, 'error_type': type(exc).__name__}))
+ return 1
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
diff --git a/harborrl/rollout/harbor_job/sandbox_transport.py b/harborrl/rollout/harbor_job/sandbox_transport.py
new file mode 100644
index 000000000..385ad5701
--- /dev/null
+++ b/harborrl/rollout/harbor_job/sandbox_transport.py
@@ -0,0 +1,27 @@
+"""Observe SDK allocation IDs without retrying creation requests."""
+import re
+
+import httpx
+
+
+class SandboxStartupTransport(httpx.AsyncBaseTransport):
+ def __init__(self, base_url, on_created, *, transport=None):
+ self.base_url = base_url.rstrip('/')
+ self.on_created = on_created
+ self.transport = transport or httpx.AsyncHTTPTransport()
+
+ async def handle_async_request(self, request):
+ url = str(request.url).split('?', 1)[0]
+ response = await self.transport.handle_async_request(request)
+ if request.method == 'POST' and url == self.base_url + '/sandboxes' and response.is_success:
+ await response.aread()
+ try:
+ sandbox_id = response.json()['id']
+ if isinstance(sandbox_id, str) and re.fullmatch(r'[A-Za-z0-9_-]+', sandbox_id):
+ self.on_created(sandbox_id)
+ except (ValueError, KeyError, TypeError):
+ pass # Let the SDK reject malformed creation responses.
+ return response
+
+ async def aclose(self):
+ await self.transport.aclose()
diff --git a/harborrl/rollout/native_generate.py b/harborrl/rollout/native_generate.py
index 5f59276e7..b5cfeebda 100644
--- a/harborrl/rollout/native_generate.py
+++ b/harborrl/rollout/native_generate.py
@@ -1,255 +1,17 @@
-"""Slime group hook for native Harbor trials.
-
-The hook keeps one trajectory per Slime slot and stores its immutable IR in
-metadata. Turn expansion happens only at the training converter boundary.
-"""
-from __future__ import annotations
-
-import asyncio
-import json
-import os
-import secrets
-from pathlib import Path
-
-from harborrl.rollout.harbor_job.coordinator import GroupSlots, run_attempt
-from harborrl.trajectories.native import Identity, RewardProfile, digest, read_json
-
-
-class NativeRolloutRuntime:
- def __init__(self, args):
- from slime.utils.processing_utils import load_tokenizer
-
- from harborrl.gateway.server import Gateway, make_server
- from harborrl.gateway.sglang_backend import SGLangBackend
- from harborrl.gateway.trace import TraceRegistry
-
- self.args = args
- self.run_root = Path(os.environ["RUN_DIR"]).resolve()
- self.catalog = read_json(os.environ["HARBORRL_NATIVE_CATALOG"])
- self.profile = read_json(os.environ["HARBORRL_NATIVE_PROFILE"])
- self.rollout_root = self.run_root / "native-rollouts"
- self.rollout_root.mkdir(parents=True, exist_ok=True)
- self.registry = TraceRegistry(self.run_root / "native-traces")
- engine_count = int(args.rollout_num_gpus) // int(args.rollout_num_gpus_per_engine)
- limit = max(1, int(args.sglang_server_concurrency) * engine_count)
- self.semaphore = asyncio.Semaphore(limit)
- tokenizer = load_tokenizer(args.hf_checkpoint, trust_remote_code=True)
- tokenizer_digest, template_digest = tokenizer_fingerprint(tokenizer)
- expected_tokenizer = os.environ["HARBORRL_NATIVE_TOKENIZER_DIGEST"]
- expected_template = os.environ["HARBORRL_NATIVE_TEMPLATE_DIGEST"]
- if (tokenizer_digest != expected_tokenizer or template_digest != expected_template):
- raise ValueError("live tokenizer/template digest differs from the native lock")
- endpoint = f"http://{args.sglang_router_ip}:{args.sglang_router_port}"
- backend = SGLangBackend(
- endpoint,
- tokenizer,
- tokenizer_digest=tokenizer_digest,
- template_digest=template_digest,
- max_context=int(args.rollout_max_context_len),
- audited_raw_logprobs=os.environ.get("HARBORRL_NATIVE_AUDITED_LOGPROBS") == "1",
- )
- self.backend = backend
- gateway = Gateway(
- self.registry,
- backend,
- model=self.profile["model"],
- max_output_tokens=int(args.rollout_max_response_len),
- )
- self.server = make_server(
- gateway,
- host=os.environ.get("HARBORRL_NATIVE_GATEWAY_HOST", "0.0.0.0"),
- port=int(os.environ["HARBORRL_NATIVE_GATEWAY_PORT"]),
- )
- self.thread = asyncio.get_running_loop().run_in_executor(None, self.server.serve_forever)
-
- def close(self):
- self.server.shutdown()
- self.server.server_close()
-
-
-def tokenizer_fingerprint(tokenizer):
- messages = [{"role": "user", "content": "HarborRL native tokenizer/template fingerprint"}]
- kwargs = {"tokenize": True, "add_generation_prompt": True, "enable_thinking": False}
- rendered = tokenizer.apply_chat_template(messages, **kwargs)
- template = digest({"messages": messages, "kwargs": kwargs, "token_ids": rendered})
- vocabulary = tokenizer.get_vocab()
- tokenizer_digest = digest({
- "class": type(tokenizer).__name__,
- "vocab": sorted((str(key), int(value)) for key, value in vocabulary.items()),
- })
- return tokenizer_digest, template
-
-
-_RUNTIME = None
-_RUNTIME_LOCK = asyncio.Lock()
-
-
-async def runtime(args):
- global _RUNTIME
- if _RUNTIME is None:
- async with _RUNTIME_LOCK:
- if _RUNTIME is None:
- _RUNTIME = NativeRolloutRuntime(args)
- return _RUNTIME
-
-
-def publish_native_version(states, expected):
- """Open the next admission epoch only after the complete pool is healthy."""
- if _RUNTIME is None:
- return
- _RUNTIME.registry.drain(float(os.environ.get("HARBORRL_NATIVE_DRAIN_TIMEOUT", "30")))
- _RUNTIME.registry.publish_version(states, str(expected))
-
-
-def current_policy_version(runtime_):
- """Publish and lock the version currently served by the rollout pool."""
- states = runtime_.backend.pool_states()
- from harborrl.platform.policy_pool import publish_pool
- version = publish_pool(states)
- runtime_.registry.drain(float(os.environ.get("HARBORRL_NATIVE_DRAIN_TIMEOUT", "30")))
- runtime_.registry.publish_version(states, version)
- return version
-
-
-def close_runtime():
- global _RUNTIME
- if _RUNTIME is not None:
- _RUNTIME.close()
- _RUNTIME = None
-
-
-def _task_entry(sample):
- metadata = sample.metadata if isinstance(sample.metadata, dict) else {}
- entry = metadata.get("native_task")
- required = {"id", "revision", "path", "task_digest", "reward_profile"}
- if not isinstance(entry, dict) or set(entry) != required:
- raise ValueError("native rollout requires a locked task catalog entry")
- return entry
-
-
-def _identity(
- args,
- sample,
- slot,
- rollout_id,
- task_digest,
- reward_digest,
- sampling_digest,
- policy_version,
-):
- group_index = getattr(sample, "group_index", None)
- if type(group_index) is not int:
- raise ValueError("Slime must provide group identity before native generation")
- return Identity(
- run_id=os.environ["RUN_ID"],
- batch_id=f"rollout-{rollout_id}",
- group_id=f"group-{group_index}",
- slot_id=str(slot),
- attempt_id=secrets.token_hex(16),
- trajectory_id=secrets.token_hex(16),
- task_digest=task_digest,
- policy_version=str(policy_version),
- harness_digest=digest(read_json(os.environ["HARBORRL_NATIVE_PROFILE"])),
- reward_digest=reward_digest,
- sampling_digest=sampling_digest,
- )
-
-
-async def _run_slot(
- args,
- runtime_,
- sample,
- slot,
- rollout_id,
- entry,
- sampling_digest,
- policy_version,
-):
- reward_profile = RewardProfile(**entry["reward_profile"])
- identities = [
- _identity(
- args,
- sample,
- index,
- rollout_id=rollout_id,
- task_digest=entry["task_digest"],
- reward_digest=reward_profile.digest,
- sampling_digest=sampling_digest,
- policy_version=policy_version,
- )
- for index in range(args.n_samples_per_prompt)
- ]
- slots = GroupSlots(identities, max_attempts=int(os.environ["HARBORRL_NATIVE_MAX_ATTEMPTS"]))
- workers = json.loads(os.environ["HARBORRL_NATIVE_RUNNER_WORKERS"])
- task_workers = json.loads(os.environ.get("HARBORRL_NATIVE_TASK_WORKER_OVERRIDES", "{}"))
- for attempt_index in range(slots.max_attempts):
- identity = slots.start(str(slot))
- runner_host = task_workers.get(entry["id"])
- if runner_host is None:
- runner_host = workers[(slot + attempt_index) % len(workers)]
- root = runtime_.rollout_root / identity.group_id / identity.attempt_id
- try:
- async with runtime_.semaphore:
- ir = await run_attempt(
- identity,
- task_path=entry["path"],
- profile=runtime_.profile,
- gateway_url=os.environ["HARBORRL_NATIVE_GATEWAY_URL"],
- runner_python=os.environ["HARBORRL_NATIVE_RUNNER_PYTHON"],
- runner_host=runner_host,
- root=root,
- registry=runtime_.registry,
- reward_profile=reward_profile,
- max_output_tokens=int(args.rollout_max_response_len),
- )
- except (OSError, ValueError, RuntimeError, asyncio.TimeoutError) as exc:
- slots.finish(str(slot), error=f"{type(exc).__name__}: {exc}")
- continue
- slots.finish(str(slot), ir)
- return ir
- raise RuntimeError(f"native slot {slot} exhausted its retry budget")
-
-
-async def generate_group(args, group, sampling_params, rollout_id, evaluation=False):
- """Run one complete native group; evaluation is intentionally unsupported."""
- from harborrl.data.harbor.native_inspector import inspect_native
-
- if evaluation:
- raise ValueError("native training rollout cannot be reused as evaluation")
- if len(group) != args.n_samples_per_prompt:
- raise ValueError("native rollout group is incomplete before launch")
- entries = [_task_entry(sample) for sample in group]
- if any(entry != entries[0] for entry in entries[1:]):
- raise ValueError("native rollout group mixes task locks")
- entry = entries[0]
- report = inspect_native(entry["path"], expected_digest=entry["task_digest"])
- if report["status"] != "NEEDS_PROBE":
- raise ValueError(f"native task preflight failed: {report['reasons']}")
- stop = list(sampling_params.get("stop") or [])
- sampling_digest = digest({
- "temperature": sampling_params.get("temperature"),
- "top_p": sampling_params.get("top_p"),
- "top_k": sampling_params.get("top_k"),
- "max_new_tokens": sampling_params.get("max_new_tokens"),
- "stop": stop,
- })
- runtime_ = await runtime(args)
- policy_version = current_policy_version(runtime_)
- trajectories = await asyncio.gather(*[
- _run_slot(
- args, runtime_, sample, slot, rollout_id, entry, sampling_digest, policy_version
- )
- for slot, sample in enumerate(group)
- ])
- output = []
- for sample, ir in zip(group, trajectories):
- sample.metadata["native_ir"] = ir
- sample.response = "native Harbor trial"
- sample.reward = {"score": ir["evaluation"]["training_reward"]}
- sample.status = sample.Status.COMPLETED
- try:
- sample.policy_version = int(ir["identity"]["policy_version"])
- except ValueError as exc:
- raise ValueError("native policy version must be an integer in Slime") from exc
- output.append(sample)
- return output
+"""Compatibility imports for the framework-independent rollout runtime."""
+from harborrl.rollout.runtime import ( # noqa: F401
+ GroupSlots,
+ NativeRolloutRuntime,
+ RolloutRuntime,
+ RolloutRuntimeConfig,
+ close_runtime,
+ current_policy_version,
+ publish_native_version,
+ run_attempt,
+ run_group,
+ run_slot,
+ runtime,
+ slot_identity,
+ task_entry,
+ tokenizer_fingerprint,
+)
diff --git a/harborrl/rollout/runtime.py b/harborrl/rollout/runtime.py
new file mode 100644
index 000000000..558bab01d
--- /dev/null
+++ b/harborrl/rollout/runtime.py
@@ -0,0 +1,350 @@
+"""Extracted rollout core (P2): gateway, traces and complete groups.
+
+This module owns rollout execution and must not import Slime or Ray: training
+frameworks live in ``harborrl.backends.*`` adapters that build a
+``RolloutRuntimeConfig`` and hand over a tokenizer. Group semantics, worker
+scheduling, retries and the RL_READY contract stay exactly as the embedded
+pipeline enforced them.
+"""
+from __future__ import annotations
+
+import asyncio
+import json
+import secrets
+import threading
+from contextlib import nullcontext
+from dataclasses import dataclass
+from pathlib import Path
+
+from harborrl.models.qwen import normalize_chat_template_ids
+from harborrl.rollout.harbor_job.coordinator import GroupSlots, run_attempt
+from harborrl.trajectories.native import Identity, RewardProfile, digest, read_json
+
+TASK_ENTRY_KEYS = {"id", "revision", "path", "task_digest", "reward_profile"}
+
+
+@dataclass(frozen=True)
+class RolloutRuntimeConfig:
+ """Deployment-independent description of one run's rollout machinery."""
+
+ run_id: str
+ run_root: str
+ catalog_path: str
+ profile_path: str
+ gateway_host: str
+ gateway_port: int
+ gateway_url: str
+ runner_python: str
+ workers: tuple
+ task_worker_overrides: dict
+ max_attempts: int
+ max_output_tokens: int
+ max_context: int
+ concurrency_limit: int
+ group_size: int
+ expected_tokenizer_digest: str
+ expected_template_digest: str
+ audited_raw_logprobs: bool
+ drain_timeout: float
+ model_tag: str
+ endpoint: str
+ sandbox: dict = None
+ runner_transport: str = 'ssh'
+
+ @staticmethod
+ def _required(env, name):
+ value = env.get(name)
+ if not isinstance(value, str) or not value.strip():
+ raise ValueError(f"rollout runtime requires {name}")
+ return value
+
+ @classmethod
+ def from_environment(cls, env, *, endpoint, concurrency_limit, group_size,
+ gateway_host=None, gateway_port=None, gateway_url=None,
+ workers=None, runner_python=None):
+ if not isinstance(endpoint, str) or not endpoint.strip():
+ raise ValueError("rollout runtime requires a serving endpoint")
+ if type(group_size) is not int or group_size < 2:
+ raise ValueError("rollout runtime requires a group size of at least two")
+ if workers is None:
+ workers = json.loads(cls._required(env, "HARBORRL_NATIVE_RUNNER_WORKERS"))
+ if runner_python is None:
+ runner_python = cls._required(env, "HARBORRL_NATIVE_RUNNER_PYTHON")
+ try:
+ overrides = json.loads(env.get("HARBORRL_NATIVE_TASK_WORKER_OVERRIDES") or "{}")
+ except ValueError as exc:
+ raise ValueError("invalid HARBORRL_NATIVE_TASK_WORKER_OVERRIDES") from exc
+ return cls(
+ run_id=cls._required(env, "RUN_ID"),
+ run_root=cls._required(env, "RUN_DIR"),
+ catalog_path=cls._required(env, "HARBORRL_NATIVE_CATALOG"),
+ profile_path=cls._required(env, "HARBORRL_NATIVE_PROFILE"),
+ gateway_host=gateway_host or env.get("HARBORRL_NATIVE_GATEWAY_HOST", "0.0.0.0"),
+ gateway_port=int(gateway_port or cls._required(env, "HARBORRL_NATIVE_GATEWAY_PORT")),
+ gateway_url=gateway_url or cls._required(env, "HARBORRL_NATIVE_GATEWAY_URL"),
+ runner_python=runner_python,
+ workers=tuple(workers),
+ task_worker_overrides=dict(overrides),
+ max_attempts=int(env.get("HARBORRL_NATIVE_MAX_ATTEMPTS", "2")),
+ max_output_tokens=int(env.get("ROLLOUT_MAX_RESPONSE_LEN", "8192")),
+ max_context=int(env.get("ROLLOUT_MAX_CONTEXT_LEN", "32768")),
+ concurrency_limit=max(1, int(concurrency_limit)),
+ group_size=int(group_size),
+ expected_tokenizer_digest=cls._required(env, "HARBORRL_NATIVE_TOKENIZER_DIGEST"),
+ expected_template_digest=cls._required(env, "HARBORRL_NATIVE_TEMPLATE_DIGEST"),
+ audited_raw_logprobs=env.get("HARBORRL_NATIVE_AUDITED_LOGPROBS") == "1",
+ drain_timeout=float(env.get("HARBORRL_NATIVE_DRAIN_TIMEOUT", "30")),
+ model_tag=env.get("MODEL_TAG", env.get("MODEL_ARGS_FILE", "qwen3-8B")),
+ endpoint=endpoint.rstrip("/"),
+ sandbox=json.loads(env.get('HARBORRL_NATIVE_SANDBOX', 'null')),
+ runner_transport=env.get('HARBORRL_NATIVE_RUNNER_TRANSPORT', 'ssh'),
+ )
+
+ def environment(self):
+ """Render the environment mapping this config was built from."""
+ return {
+ "RUN_ID": self.run_id, "RUN_DIR": self.run_root,
+ "HARBORRL_NATIVE_CATALOG": self.catalog_path,
+ "HARBORRL_NATIVE_PROFILE": self.profile_path,
+ "HARBORRL_NATIVE_GATEWAY_HOST": self.gateway_host,
+ "HARBORRL_NATIVE_GATEWAY_PORT": str(self.gateway_port),
+ "HARBORRL_NATIVE_GATEWAY_URL": self.gateway_url,
+ "HARBORRL_NATIVE_RUNNER_PYTHON": self.runner_python,
+ "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(list(self.workers)),
+ "HARBORRL_NATIVE_TASK_WORKER_OVERRIDES": json.dumps(
+ self.task_worker_overrides, sort_keys=True),
+ "HARBORRL_NATIVE_MAX_ATTEMPTS": str(self.max_attempts),
+ "ROLLOUT_MAX_RESPONSE_LEN": str(self.max_output_tokens),
+ "ROLLOUT_MAX_CONTEXT_LEN": str(self.max_context),
+ "HARBORRL_NATIVE_TOKENIZER_DIGEST": self.expected_tokenizer_digest,
+ "HARBORRL_NATIVE_TEMPLATE_DIGEST": self.expected_template_digest,
+ "HARBORRL_NATIVE_AUDITED_LOGPROBS": str(int(self.audited_raw_logprobs)),
+ "HARBORRL_NATIVE_DRAIN_TIMEOUT": str(self.drain_timeout),
+ "MODEL_TAG": self.model_tag,
+ "HARBORRL_NATIVE_SANDBOX": json.dumps(self.sandbox),
+ "HARBORRL_NATIVE_RUNNER_TRANSPORT": self.runner_transport,
+ }
+
+
+def tokenizer_fingerprint(tokenizer):
+ messages = [{"role": "user", "content": "HarborRL native tokenizer/template fingerprint"}]
+ kwargs = {"tokenize": True, "add_generation_prompt": True, "enable_thinking": False}
+ rendered_ids = normalize_chat_template_ids(
+ tokenizer.apply_chat_template(messages, **kwargs))
+ vocabulary = tokenizer.get_vocab()
+ tokenizer_digest = digest({
+ "class": type(tokenizer).__name__,
+ "vocab": sorted((str(key), int(value)) for key, value in vocabulary.items()),
+ })
+ template = digest({"messages": messages, "kwargs": kwargs, "token_ids": rendered_ids})
+ return tokenizer_digest, template
+
+
+class RolloutRuntime:
+ """One gateway, one trace registry and one rollout root per run."""
+
+ def __init__(self, config, tokenizer, *, backend=None):
+ from harborrl.gateway.server import Gateway, make_server
+ from harborrl.gateway.sglang_backend import SGLangBackend
+ from harborrl.gateway.trace import TraceRegistry
+ from harborrl.math_registry import MATH_REGISTRY
+
+ self.config = config
+ self.run_root = Path(config.run_root).resolve()
+ self.catalog = read_json(config.catalog_path)
+ self.profile = read_json(config.profile_path)
+ self.rollout_root = self.run_root / "native-rollouts"
+ self.rollout_root.mkdir(parents=True, exist_ok=True)
+ self.registry = TraceRegistry(self.run_root / "native-traces")
+ self.semaphore = asyncio.Semaphore(config.concurrency_limit)
+ if config.sandbox:
+ from harborrl.rollout.harbor_job.sandbox_config import SandboxAdmission
+ self.sandbox_admission = SandboxAdmission(config.sandbox)
+ tokenizer_digest, template_digest = tokenizer_fingerprint(tokenizer)
+ if (tokenizer_digest != config.expected_tokenizer_digest
+ or template_digest != config.expected_template_digest):
+ raise ValueError("live tokenizer/template digest differs from the native lock")
+ model = MATH_REGISTRY.model_for_preset(config.model_tag)
+ self.backend = backend or SGLangBackend(
+ config.endpoint, tokenizer, model.adapter,
+ tokenizer_digest=tokenizer_digest, template_digest=template_digest,
+ max_context=config.max_context,
+ audited_raw_logprobs=config.audited_raw_logprobs,
+ )
+ gateway = Gateway(self.registry, self.backend, model=self.profile["model"],
+ max_output_tokens=config.max_output_tokens)
+ self.server = make_server(gateway, host=config.gateway_host,
+ port=config.gateway_port)
+ self._start_gateway()
+
+ def _start_gateway(self):
+ self.thread = threading.Thread(
+ target=self.server.serve_forever, name="harborrl-native-gateway", daemon=True
+ )
+ self.thread.start()
+
+ def close(self):
+ self.server.shutdown()
+ if getattr(self.thread, "is_alive", lambda: False)():
+ self.thread.join(5)
+ self.server.server_close()
+
+ def manifest_path(self, identity):
+ """Deterministic shared-storage path of one trajectory manifest."""
+ if isinstance(identity, dict):
+ identity = Identity(**identity)
+ return self.rollout_root / identity.group_id / identity.attempt_id / "trajectory.v2.json"
+
+
+# Historical name kept for existing hooks and evidence readers.
+NativeRolloutRuntime = RolloutRuntime
+
+_RUNTIME = None
+_RUNTIME_LOCK = asyncio.Lock()
+
+
+async def runtime(config, *, tokenizer=None, tokenizer_loader=None):
+ """Process-wide runtime cache for the embedded GPU-side rollout path."""
+ global _RUNTIME
+ if _RUNTIME is None:
+ async with _RUNTIME_LOCK:
+ if _RUNTIME is None:
+ if tokenizer is None:
+ if tokenizer_loader is None:
+ raise ValueError("runtime requires a tokenizer or a loader")
+ tokenizer = tokenizer_loader()
+ _RUNTIME = RolloutRuntime(config, tokenizer)
+ return _RUNTIME
+
+
+def publish_native_version(runtime_, states, expected):
+ """Open the next admission epoch only after the complete pool is healthy."""
+ runtime_.registry.drain(runtime_.config.drain_timeout)
+ runtime_.registry.publish_version(states, str(expected))
+
+
+def current_policy_version(runtime_):
+ """Publish and lock the version currently served by the rollout pool."""
+ states = runtime_.backend.pool_states()
+ from harborrl.platform.policy_pool import publish_pool
+ version = publish_pool(states)
+ runtime_.registry.drain(runtime_.config.drain_timeout)
+ runtime_.registry.publish_version(states, version)
+ return version
+
+
+def close_runtime():
+ global _RUNTIME
+ if _RUNTIME is not None:
+ _RUNTIME.close()
+ _RUNTIME = None
+
+
+def task_entry(sample):
+ metadata = sample.metadata if isinstance(sample.metadata, dict) else {}
+ entry = metadata.get("native_task")
+ if not isinstance(entry, dict) or set(entry) != TASK_ENTRY_KEYS:
+ raise ValueError("native rollout requires a locked task catalog entry")
+ return entry
+
+
+def slot_identity(config, group_index, slot, rollout_id, entry,
+ reward_digest, sampling_digest, policy_version, *, harness_digest=None):
+ return Identity(
+ run_id=config.run_id,
+ batch_id=f"rollout-{rollout_id}",
+ group_id=f"group-{group_index}",
+ slot_id=str(slot),
+ attempt_id=secrets.token_hex(16),
+ trajectory_id=secrets.token_hex(16),
+ task_digest=entry["task_digest"],
+ policy_version=str(policy_version),
+ harness_digest=harness_digest if harness_digest is not None else digest(read_json(config.profile_path)),
+ reward_digest=reward_digest,
+ sampling_digest=sampling_digest,
+ )
+
+
+def _group_slots(config, runtime_, group_index, rollout_id, entry,
+ sampling_digest, policy_version, size):
+ reward_profile = RewardProfile(**entry["reward_profile"])
+ harness_digest = digest(runtime_.profile)
+ identities = [
+ slot_identity(config, group_index, index, rollout_id, entry,
+ reward_profile.digest, sampling_digest, policy_version,
+ harness_digest=harness_digest)
+ for index in range(size)
+ ]
+ return GroupSlots(identities, max_attempts=config.max_attempts), reward_profile
+
+
+async def run_slot(config, runtime_, group_index, slot, rollout_id, entry,
+ sampling_digest, policy_version):
+ """Compatibility entry point for executing an individual group slot."""
+ slots, reward_profile = _group_slots(
+ config, runtime_, group_index, rollout_id, entry,
+ sampling_digest, policy_version, config.group_size)
+ return await _run_slot(config, runtime_, slots, slot, entry, reward_profile)
+
+
+async def _run_slot(config, runtime_, slots, slot, entry, reward_profile):
+ """Execute one slot with the group's shared identity and retry bookkeeping."""
+ workers = config.workers
+ overrides = config.task_worker_overrides
+ for attempt_index in range(slots.max_attempts):
+ identity = slots.start(str(slot))
+ runner_host = overrides.get(entry["id"])
+ if config.runner_transport == 'local':
+ runner_host = None
+ elif runner_host is None:
+ if not workers:
+ raise ValueError("rollout runtime requires at least one worker destination")
+ runner_host = workers[(slot + attempt_index) % len(workers)]
+ root = runtime_.rollout_root / identity.group_id / identity.attempt_id
+ admission = nullcontext()
+ if config.sandbox:
+ from harborrl.rollout.harbor_job.sandbox_config import task_binding
+ admission = runtime_.sandbox_admission.lease(task_binding(config.sandbox, entry['path']), root)
+ try:
+ async with admission, runtime_.semaphore:
+ ir = await run_attempt(
+ identity,
+ task_path=entry["path"],
+ profile=runtime_.profile,
+ gateway_url=config.gateway_url,
+ runner_python=config.runner_python,
+ runner_host=runner_host,
+ root=root,
+ registry=runtime_.registry,
+ reward_profile=reward_profile,
+ max_output_tokens=config.max_output_tokens,
+ **({'sandbox': config.sandbox} if config.sandbox else {}),
+ )
+ except (OSError, ValueError, RuntimeError, asyncio.TimeoutError) as exc:
+ slots.finish(str(slot), error=f"{type(exc).__name__}: {exc}")
+ if config.sandbox and ((root / 'cleanup-required.json').exists() or runtime_.sandbox_admission.blocked):
+ raise RuntimeError('sandbox cleanup unresolved; automatic retry blocked') from exc
+ continue
+ slots.finish(str(slot), ir)
+ return ir
+ raise RuntimeError(f"native slot {slot} exhausted its retry budget")
+
+
+async def run_group(config, runtime_, *, entry, group_index, rollout_id,
+ sampling_digest, policy_version, group_size=None):
+ """Execute one complete group and return per-slot IRs in slot order."""
+ size = config.group_size if group_size is None else int(group_size)
+ slots, reward_profile = _group_slots(
+ config, runtime_, group_index, rollout_id, entry,
+ sampling_digest, policy_version, size)
+ tasks = [asyncio.create_task(
+ _run_slot(config, runtime_, slots, slot, entry, reward_profile))
+ for slot in range(size)
+ ]
+ try:
+ return list(await asyncio.gather(*tasks))
+ except BaseException:
+ for task in tasks:
+ task.cancel()
+ await asyncio.gather(*tasks, return_exceptions=True)
+ raise
diff --git a/harborrl/rollout/service.py b/harborrl/rollout/service.py
new file mode 100644
index 000000000..e223ab05a
--- /dev/null
+++ b/harborrl/rollout/service.py
@@ -0,0 +1,562 @@
+"""RolloutService (P3): the manager-side HTTP control plane for rollout groups.
+
+First-phase contract: HTTP JSON, run-scoped tokens, idempotent group
+submission. Bulky payloads (trajectory manifests, token ids, logprobs, loss
+masks) live on the shared run directory; HTTP responses carry paths plus
+SHA-256 digests, and the SlimeAdapter recomputes every digest before
+converting anything into training samples.
+
+Rules that keep the lifecycle safe:
+- same group id and same content -> the existing state, never a second run;
+- same group id and different content -> rejected;
+- after a timeout the client must query, never resubmit;
+- a new policy version is admitted only after the whole serving pool is
+ healthy on it (drain first, publish second).
+"""
+from __future__ import annotations
+
+import asyncio
+import hashlib
+import hmac
+import json
+import secrets
+import threading
+import time
+from dataclasses import dataclass, field
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+from urllib.parse import urlsplit
+
+from harborrl.rollout.exporters.native import export_group
+from harborrl.rollout.runtime import run_group
+from harborrl.trajectories.native import digest, finite, publish, read_json
+
+SERVICE_SCHEMA = "harborrl-rollout-service-v1"
+GROUP_REQUEST_KEYS = {"group_id", "rollout_id", "group_index", "policy_version",
+ "task", "sampling", "slots"}
+SAMPLING_KEYS = {"temperature", "top_p", "top_k", "max_new_tokens", "stop"}
+TASK_ENTRY_KEYS = {"id", "revision", "path", "task_digest", "reward_profile"}
+GROUP_DIR = "rollout-service/groups"
+
+
+class ServiceError(Exception):
+ """Maps onto one HTTP status; carries a machine-readable code."""
+
+ def __init__(self, status, code, message):
+ super().__init__(message)
+ self.status, self.code = status, code
+
+
+def validate_group_request(request):
+ if not isinstance(request, dict) or set(request) != GROUP_REQUEST_KEYS:
+ raise ServiceError(400, "invalid_request",
+ f"group request requires exactly {sorted(GROUP_REQUEST_KEYS)}")
+ for key in ("group_id", "policy_version"):
+ if not isinstance(request[key], str) or not request[key].strip():
+ raise ServiceError(400, "invalid_request", f"{key} must be a nonempty string")
+ for key in ("rollout_id", "group_index"):
+ if type(request[key]) is not int or request[key] < 0:
+ raise ServiceError(400, "invalid_request", f"{key} must be a nonnegative integer")
+ task = request["task"]
+ if not isinstance(task, dict) or set(task) != TASK_ENTRY_KEYS:
+ raise ServiceError(400, "invalid_request",
+ f"task requires exactly {sorted(TASK_ENTRY_KEYS)}")
+ if not isinstance(task["reward_profile"], dict):
+ raise ServiceError(400, "invalid_request", "task.reward_profile must be a mapping")
+ sampling = request["sampling"]
+ if not isinstance(sampling, dict) or set(sampling) != SAMPLING_KEYS:
+ raise ServiceError(400, "invalid_request",
+ f"sampling requires exactly {sorted(SAMPLING_KEYS)}")
+ if not finite(sampling["temperature"]) or not finite(sampling["top_p"]):
+ raise ServiceError(400, "invalid_request", "sampling probabilities must be finite")
+ if type(sampling["top_k"]) is not int:
+ raise ServiceError(400, "invalid_request", "sampling.top_k must be an integer")
+ if type(sampling["max_new_tokens"]) is not int or sampling["max_new_tokens"] < 1:
+ raise ServiceError(400, "invalid_request", "sampling.max_new_tokens must be positive")
+ if (not isinstance(sampling["stop"], list)
+ or any(not isinstance(stop, str) or not stop for stop in sampling["stop"])):
+ raise ServiceError(400, "invalid_request", "sampling.stop must be a string list")
+ slots = request["slots"]
+ if (not isinstance(slots, list) or len(slots) < 2
+ or any(not isinstance(slot, dict) or set(slot) != {"slot_id"}
+ or not isinstance(slot["slot_id"], str) or not slot["slot_id"].strip()
+ for slot in slots)):
+ raise ServiceError(400, "invalid_request",
+ "slots must describe at least two unique slot ids")
+ if len({slot["slot_id"] for slot in slots}) != len(slots):
+ raise ServiceError(400, "invalid_request", "slot ids must be unique")
+ return request
+
+
+def request_digest(request):
+ """Canonical digest of the full request content, group id included."""
+ return digest(request)
+
+
+def manifest_path_from(run_root, identity):
+ """Deterministic trajectory manifest path for one identity."""
+ return Path(run_root) / "native-rollouts" / identity["group_id"] / \
+ identity["attempt_id"] / "trajectory.v2.json"
+
+
+def build_group_result(run_id, request, trajectories, *, runtime, export=None):
+ if len(trajectories) != len(request["slots"]):
+ raise ServiceError(500, "incomplete_group",
+ "executor returned a different slot count")
+ slots = []
+ for descriptor, ir in zip(request["slots"], trajectories):
+ identity = ir["identity"]
+ path = runtime.manifest_path(identity)
+ payload = read_json(path)
+ seal = ir["trace_seal"]
+ slots.append({
+ "slot_id": descriptor["slot_id"],
+ "trajectory_id": identity["trajectory_id"],
+ "attempt_id": identity["attempt_id"],
+ "manifest_path": str(path),
+ "manifest_sha256": digest(payload),
+ "finish_reason": ir["termination"],
+ "reward": ir["evaluation"]["training_reward"],
+ "policy_version": identity["policy_version"],
+ "task_digest": identity["task_digest"],
+ "harness_digest": identity["harness_digest"],
+ "sampling_digest": identity["sampling_digest"],
+ "readiness": ir["readiness"],
+ "integrity_errors": ir["integrity_errors"],
+ "trace_seal": {"status": seal["status"], "errors": seal["errors"],
+ "digest": digest(seal)},
+ "verifier_digest": ir["evaluation"].get("source_result_digest"),
+ })
+ result = {
+ "schema_version": SERVICE_SCHEMA,
+ "run_id": run_id,
+ "group_id": request["group_id"],
+ "rollout_id": request["rollout_id"],
+ "request_digest": request_digest(request),
+ "policy_version": request["policy_version"],
+ "status": "complete",
+ "slots": slots,
+ "completed_at": time.time(),
+ }
+ if export is not None:
+ result["group_export"] = export
+ return result
+
+
+async def default_executor(config, runtime_, request):
+ """The production executor: the framework-free rollout core."""
+ return await run_group(
+ config, runtime_, entry=request["task"], group_index=request["group_index"],
+ rollout_id=request["rollout_id"], sampling_digest=digest(request["sampling"]),
+ policy_version=request["policy_version"], group_size=len(request["slots"]))
+
+
+@dataclass
+class GroupState:
+ request: dict
+ request_sha: str
+ status: str = "queued"
+ result: dict = None
+ error: str = None
+ created_at: float = field(default_factory=time.time)
+ future: object = None
+
+ def summary(self, *, include_result=True):
+ payload = {"group_id": self.request["group_id"], "status": self.status,
+ "request_digest": self.request_sha,
+ "created_at": self.created_at}
+ if self.error:
+ payload["error"] = self.error
+ if include_result and self.result is not None:
+ payload["result"] = self.result
+ return payload
+
+
+@dataclass
+class RunState:
+ run_id: str
+ manifest: dict
+ manifest_sha: str
+ token_digest: str
+ config: object = None
+ runtime: object = None
+ drained: bool = False
+ serving: str = None
+ groups: dict = field(default_factory=dict)
+
+
+class RolloutService:
+ """Run registry plus a single-threaded asyncio executor for group work."""
+
+ def __init__(self, *, run_root, executor=None, runtime_factory=None,
+ token_file=None, group_wait_timeout=3600.0, poll_interval=0.05,
+ allow_synthetic=False):
+ self.run_root = Path(run_root).resolve()
+ self.executor = executor or default_executor
+ self.runtime_factory = runtime_factory
+ self.allow_synthetic = allow_synthetic
+ self.token = None
+ if token_file is not None:
+ token = Path(token_file).read_text().strip()
+ if not token:
+ raise ServiceError(500, "invalid_token_file", "run token file is empty")
+ self.token = token
+ self.group_wait_timeout = group_wait_timeout
+ self.poll_interval = poll_interval
+ self.runs = {}
+ self.lock = threading.RLock()
+ self.loop = asyncio.new_event_loop()
+ self.thread = threading.Thread(target=self._serve_loop,
+ name="harborrl-rollout-service", daemon=True)
+ self.thread.start()
+
+ def _serve_loop(self):
+ asyncio.set_event_loop(self.loop)
+ self.loop.run_forever()
+
+ def close(self):
+ with self.lock:
+ states = [state for state in self.runs.values() if state.runtime is not None]
+ for state in states:
+ try:
+ state.runtime.close()
+ except AttributeError:
+ pass
+ self.loop.call_soon_threadsafe(self.loop.stop)
+ self.thread.join(5)
+
+ # -- run lifecycle ----------------------------------------------------
+
+ def _token_digest(self, token):
+ return hashlib.sha256(token.encode()).hexdigest()
+
+ def register_run(self, run_id, manifest, *, token=None):
+ if not isinstance(run_id, str) or not run_id.strip():
+ raise ServiceError(400, "invalid_request", "run id required")
+ if not isinstance(manifest, dict) or not manifest:
+ raise ServiceError(400, "invalid_request", "manifest must be a nonempty mapping")
+ if manifest.get("run_id", run_id) != run_id:
+ raise ServiceError(400, "invalid_request", "manifest run id must match the path")
+ manifest_sha = digest(manifest)
+ with self.lock:
+ existing = self.runs.get(run_id)
+ if existing is not None:
+ if existing.manifest_sha != manifest_sha:
+ raise ServiceError(409, "conflict",
+ "run id already registered with a different manifest")
+ return {"run_id": run_id, "state": "registered", "existed": True}
+ if self.token is not None and token != self.token:
+ raise ServiceError(401, "unauthorized", "invalid manager run token")
+ generated = token is None
+ if generated:
+ token = secrets.token_urlsafe(32)
+ self.runs[run_id] = RunState(run_id=run_id, manifest=manifest,
+ manifest_sha=manifest_sha,
+ token_digest=self._token_digest(token))
+ return {"run_id": run_id, "state": "registered", "existed": False,
+ **({"run_token": token} if generated else {})}
+
+ def authenticate(self, run_id, token):
+ with self.lock:
+ state = self.runs.get(run_id)
+ if state is None:
+ raise ServiceError(404, "not_found", f"unknown run: {run_id}")
+ if not isinstance(token, str) or not token or not hmac.compare_digest(
+ state.token_digest, self._token_digest(token)):
+ raise ServiceError(401, "unauthorized", "invalid run token")
+ return state
+
+ def _run(self, run_id):
+ state = self.runs.get(run_id)
+ if state is None:
+ raise ServiceError(404, "not_found", f"unknown run: {run_id}")
+ return state
+
+ def attach_runtime(self, run_id, config, runtime):
+ """Bind the rollout runtime that executes this run's groups."""
+ with self.lock:
+ state = self._run(run_id)
+ state.config, state.runtime = config, runtime
+ return {"run_id": run_id, "state": "attached"}
+
+ def announce_serving(self, run_id, router_url):
+ parts = urlsplit(router_url or "")
+ if (parts.scheme not in ("http", "https") or not parts.hostname
+ or parts.username or parts.password or parts.query
+ or parts.fragment or parts.path not in ("", "/")):
+ raise ServiceError(400, "invalid_request", "router_url must be a plain origin")
+ with self.lock:
+ state = self._run(run_id)
+ state.serving = f"{parts.scheme}://{parts.netloc}".rstrip("/")
+ if state.runtime is None and self.runtime_factory is not None:
+ runtime = self.runtime_factory(run_id, state.serving)
+ state.runtime = runtime
+ if state.config is None:
+ # The factory-built runtime carries its own config; the
+ # executor needs both for group admission.
+ state.config = getattr(runtime, "config", None)
+ return {"run_id": run_id, "router_url": state.serving,
+ "runtime": "attached" if state.runtime is not None else "pending"}
+
+ # -- policy lifecycle (§7) ---------------------------------------------
+
+ def activate_policy(self, run_id, states, expected):
+ from harborrl.platform.policy_pool import validate_pool
+
+ with self.lock:
+ state = self._run(run_id)
+ if state.runtime is None:
+ raise ServiceError(409, "admission_closed", "run has no serving runtime yet")
+ try:
+ version = validate_pool(states, str(expected))
+ backend = getattr(state.runtime, 'backend', None)
+ if backend is not None:
+ live_states = backend.pool_states()
+ validate_pool(live_states, version)
+ if {item['endpoint'] for item in live_states} != {item['endpoint'] for item in states}:
+ raise ValueError('manager and GPU disagree on serving engine membership')
+ states = live_states
+ except (RuntimeError, TypeError, ValueError) as exc:
+ raise ServiceError(409, "pool_invalid", str(exc)) from exc
+ registry = state.runtime.registry
+ registry.drain(float(getattr(state.config, "drain_timeout", 30)))
+ registry.publish_version(states, version)
+ # Mirror policy_pool.publish_pool into the shared run directory so the
+ # GPU side can lock the same version without manager-local state; the
+ # record is replaceable because activation legitimately advances.
+ payload = {"weight_version": version, "engines": states, "timestamp": time.time()}
+ temp = self.run_root / "policy-pool.tmp"
+ temp.write_text(json.dumps(payload) + "\n")
+ temp.replace(self.run_root / "policy-pool.json")
+ return {"run_id": run_id, "policy_version": version, "admission": "open"}
+
+ # -- group lifecycle ----------------------------------------------------
+
+ def _group_dir(self, run_id, group_id):
+ return self.run_root / GROUP_DIR / run_id / group_id
+
+ def submit_group(self, run_id, request, *, wait=True):
+ request = validate_group_request(request)
+ sha = request_digest(request)
+ with self.lock:
+ state = self._run(run_id)
+ existing = state.groups.get(request["group_id"])
+ if existing is not None:
+ if existing.request_sha != sha:
+ raise ServiceError(409, "conflict",
+ "group id already submitted with different content")
+ group = existing
+ else:
+ if state.drained:
+ raise ServiceError(409, "admission_closed", "run is draining")
+ if state.runtime is None or state.config is None:
+ raise ServiceError(409, "admission_closed",
+ "run has no rollout runtime attached")
+ registry = getattr(state.runtime, "registry", None)
+ version = getattr(registry, "version", None) if registry is not None else None
+ accepting = getattr(registry, "accepting", True) if registry is not None else False
+ if not accepting or version != request["policy_version"]:
+ raise ServiceError(
+ 409, "stale_policy",
+ f"policy {request['policy_version']!r} is not the admitted version "
+ f"({version!r}, accepting={accepting}); activate first")
+ group = GroupState(request=request, request_sha=sha)
+ state.groups[request["group_id"]] = group
+ future = asyncio.run_coroutine_threadsafe(
+ self._execute(state, group), self.loop)
+ group.future = future
+ if wait:
+ return self._wait_group(run_id, group)
+ return group.summary(include_result=False)
+
+ def _wait_group(self, run_id, group):
+ deadline = time.monotonic() + self.group_wait_timeout
+ while group.status in ("queued", "running"):
+ if time.monotonic() >= deadline:
+ return {**group.summary(include_result=False),
+ "hint": "query GET /groups/{group_id}; do not resubmit"}
+ time.sleep(self.poll_interval)
+ return group.summary()
+
+ async def _execute(self, state, group):
+ # Immutable state records: each transition gets its own file, and
+ # publish() refuses to rewrite history.
+ request = group.request
+ directory = self._group_dir(state.run_id, request["group_id"])
+ publish(directory / "request.json", request)
+ group.status = "running"
+ publish(directory / "running.json", group.summary(include_result=False))
+ try:
+ trajectories = await self.executor(state.config, state.runtime, request)
+ export = export_group(
+ trajectories, len(trajectories),
+ allow_synthetic=self.allow_synthetic)
+ export_path = directory / "group-export.json"
+ publish(export_path, export)
+ group.result = build_group_result(
+ state.run_id, request, trajectories, runtime=state.runtime,
+ export={"path": str(export_path), "sha256": digest(export)})
+ group.status = "complete"
+ publish(directory / "result.json", group.result)
+ except asyncio.CancelledError:
+ group.status = "cancelled"
+ publish(directory / "cancelled.json", group.summary(include_result=False))
+ raise
+ except Exception as exc: # noqa: BLE001 — any executor failure is
+ # recorded as a failed group instead of killing the service
+ group.status = "failed"
+ group.error = f"{type(exc).__name__}: {exc}"
+ publish(directory / "failed.json", group.summary(include_result=False))
+
+ def group_status(self, run_id, group_id):
+ with self.lock:
+ state = self._run(run_id)
+ group = state.groups.get(group_id)
+ if group is None:
+ raise ServiceError(404, "not_found", f"unknown group: {group_id}")
+ return group.summary()
+
+ def cancel_group(self, run_id, group_id):
+ with self.lock:
+ state = self._run(run_id)
+ group = state.groups.get(group_id)
+ if group is None:
+ raise ServiceError(404, "not_found", f"unknown group: {group_id}")
+ self._cancel_groups([group])
+ return group.summary()
+
+ def drain_run(self, run_id):
+ with self.lock:
+ state = self._run(run_id)
+ state.drained = True
+ runtime = state.runtime
+ if runtime is not None:
+ registry = getattr(runtime, "registry", None)
+ if registry is not None:
+ timeout = float(getattr(state.config, "drain_timeout", 30))
+ try:
+ registry.drain(timeout)
+ except TimeoutError as exc:
+ raise ServiceError(409, "drain_timeout",
+ f"generation still in flight: {exc}") from exc
+ return {"run_id": run_id, "state": "drained"}
+
+ def cancel_run(self, run_id):
+ with self.lock:
+ state = self._run(run_id)
+ groups = list(state.groups.values())
+ self._cancel_groups(groups)
+ return {"run_id": run_id, "state": "cancelled",
+ "groups": {group.request["group_id"]: group.status for group in groups}}
+
+ def _cancel_groups(self, groups):
+ for group in groups:
+ if group.status in ("queued", "running") and group.future is not None:
+ group.future.cancel()
+ deadline = time.monotonic() + 30
+ while any(group.status in ("queued", "running") for group in groups) \
+ and time.monotonic() < deadline:
+ time.sleep(self.poll_interval)
+
+
+def make_control_server(service, host="127.0.0.1", port=0, *, max_body_bytes=8 * 1024 * 1024):
+ """Bounded stdlib HTTP transport for the control plane."""
+
+ class Handler(BaseHTTPRequestHandler):
+ def setup(self):
+ super().setup()
+ self.connection.settimeout(120)
+
+ def log_message(self, *args):
+ pass
+
+ def reply(self, status, payload):
+ body = json.dumps(payload).encode()
+ self.send_response(status)
+ self.send_header("Content-Type", "application/json")
+ self.send_header("Content-Length", str(len(body)))
+ self.end_headers()
+ self.wfile.write(body)
+
+ def token(self):
+ token = self.headers.get("x-api-key", "")
+ auth = self.headers.get("Authorization", "")
+ if auth:
+ if not auth.startswith("Bearer ") or token:
+ raise ServiceError(400, "invalid_request", "ambiguous authentication")
+ token = auth[7:]
+ return token
+
+ def read_json(self):
+ if self.headers.get("Transfer-Encoding"):
+ raise ServiceError(400, "invalid_request", "chunked bodies are not supported")
+ size = int(self.headers.get("Content-Length", "0"))
+ if size <= 0 or size > max_body_bytes:
+ raise ServiceError(400, "invalid_request", "invalid request body size")
+ return json.loads(self.rfile.read(size))
+
+ def dispatch(self, method):
+ parts = [part for part in urlsplit(self.path).path.split("/") if part]
+ try:
+ payload = self.route(method, parts)
+ except ServiceError as exc:
+ self.reply(exc.status, {"error": {"type": exc.code, "message": str(exc)}})
+ except (ValueError, TypeError, KeyError, OSError, json.JSONDecodeError) as exc:
+ self.reply(400, {"error": {"type": "invalid_request",
+ "message": f"{type(exc).__name__}: {exc}"}})
+ else:
+ self.reply(200, payload)
+
+ def route(self, method, parts):
+ if method == "GET" and parts == ["healthz"]:
+ return {"ok": True, "service": SERVICE_SCHEMA}
+ if method == "GET" and parts == ["readyz"]:
+ with service.lock:
+ attached = sum(1 for state in service.runs.values()
+ if state.runtime is not None)
+ return {"ok": True, "runs": len(service.runs), "attached_runs": attached}
+ if len(parts) < 2 or parts[0] != "runs":
+ raise ServiceError(404, "not_found", "unsupported endpoint")
+ run_id = parts[1]
+ if len(parts) == 3 and method == "POST" and parts[2] == "register":
+ body = self.read_json()
+ if not isinstance(body, dict) or set(body) != {"manifest"}:
+ raise ServiceError(400, "invalid_request", "body must be {manifest}")
+ return service.register_run(run_id, body["manifest"], token=self.token())
+ service.authenticate(run_id, self.token())
+ if len(parts) == 3 and method == "POST" and parts[2] == "groups":
+ return service.submit_group(run_id, self.read_json())
+ if (len(parts) == 4 and parts[2] == "groups"
+ and method == "GET"):
+ return service.group_status(run_id, parts[3])
+ if (len(parts) == 5 and parts[2] == "groups" and parts[4] == "cancel"
+ and method == "POST"):
+ return service.cancel_group(run_id, parts[3])
+ if len(parts) == 4 and method == "POST" and parts[2] == "policy" \
+ and parts[3] == "activate":
+ body = self.read_json()
+ if not isinstance(body, dict) or set(body) != {"states", "expected"}:
+ raise ServiceError(400, "invalid_request",
+ "body must be {states, expected}")
+ return service.activate_policy(run_id, body["states"], body["expected"])
+ if len(parts) == 4 and method == "POST" and parts[2] == "serving" \
+ and parts[3] == "announce":
+ body = self.read_json()
+ if not isinstance(body, dict) or set(body) != {"router_url"}:
+ raise ServiceError(400, "invalid_request", "body must be {router_url}")
+ return service.announce_serving(run_id, body["router_url"])
+ if len(parts) == 3 and method == "POST" and parts[2] == "drain":
+ return service.drain_run(run_id)
+ if len(parts) == 3 and method == "POST" and parts[2] == "cancel":
+ return service.cancel_run(run_id)
+ raise ServiceError(404, "not_found", "unsupported endpoint")
+
+ def do_GET(self):
+ self.dispatch("GET")
+
+ def do_POST(self):
+ self.dispatch("POST")
+
+ server = ThreadingHTTPServer((host, port), Handler)
+ server.daemon_threads = True
+ return server
diff --git a/harborrl/tasks/catalog.py b/harborrl/tasks/catalog.py
new file mode 100644
index 000000000..d887e7ec6
--- /dev/null
+++ b/harborrl/tasks/catalog.py
@@ -0,0 +1,8 @@
+"""Compatibility entry point for the selected Harbor task provider."""
+from __future__ import annotations
+
+from harborrl.math_registry import select_math
+
+
+def catalog(config):
+ return select_math(config).task.adapter.catalog(config)
diff --git a/harborrl/tasks/cli.py b/harborrl/tasks/cli.py
index 8cbb3c5d3..f9d337cd3 100644
--- a/harborrl/tasks/cli.py
+++ b/harborrl/tasks/cli.py
@@ -7,7 +7,7 @@
from pathlib import Path
from uuid import uuid4
-from harborrl.data.harbor.native_inspector import inspect_native
+from harborrl.math_registry import NATIVE_TASK_PROVIDER
def main(argv=None):
@@ -23,12 +23,12 @@ def main(argv=None):
if args.command == "inspect":
if not args.tasks_dir.is_dir():
parser.error("tasks-dir must exist")
- tasks = sorted(path.parent for path in args.tasks_dir.rglob("task.toml"))
+ tasks = NATIVE_TASK_PROVIDER.adapter.discover(args.tasks_dir)
if not tasks:
parser.error("no task.toml found")
rows = []
for task in tasks:
- result = inspect_native(task)
+ result = NATIVE_TASK_PROVIDER.adapter.inspect(task)
rows.append({
**result,
"dataset": args.dataset,
diff --git a/harborrl/tasks/content.py b/harborrl/tasks/content.py
new file mode 100644
index 000000000..e108f167e
--- /dev/null
+++ b/harborrl/tasks/content.py
@@ -0,0 +1,26 @@
+"""Native task content hashing and TOML reader shared with compatibility tools."""
+import hashlib
+from pathlib import Path
+
+try:
+ import tomllib
+except ModuleNotFoundError: # Python 3.10
+ import tomli as tomllib
+
+
+__all__ = ["tomllib", "tree_digest"]
+
+
+def tree_digest(root: Path, *, exclude: tuple[str, ...] = ()) -> str:
+ h = hashlib.sha256()
+ for path in sorted(Path(root).rglob("*")):
+ if str(path.relative_to(root)) in exclude:
+ continue
+ if path.is_symlink():
+ raise ValueError("symlinks are not supported")
+ if path.is_file():
+ data = path.read_bytes()
+ h.update(str(path.relative_to(root)).encode() + b"\0")
+ h.update(str(path.stat().st_mode & 0o777).encode() + b"\0")
+ h.update(str(len(data)).encode() + b"\0" + data)
+ return h.hexdigest()
diff --git a/harborrl/tasks/jsonl_provider.py b/harborrl/tasks/jsonl_provider.py
new file mode 100644
index 000000000..4fdeae0fd
--- /dev/null
+++ b/harborrl/tasks/jsonl_provider.py
@@ -0,0 +1,92 @@
+"""JSONL Harbor task catalog provider with the same native task lock contract."""
+from __future__ import annotations
+
+import json
+import re
+from pathlib import Path
+
+from harborrl.data.harbor.native_inspector import inspect_native
+from harborrl.trajectories.native import RewardProfile
+
+
+class JsonlTaskProvider:
+ provider_id = "harbor_jsonl"
+
+ def discover(self, tasks_dir):
+ return sorted(path.parent for path in Path(tasks_dir).rglob("task.toml"))
+
+ def inspect(self, path, *, expected_digest=None):
+ return inspect_native(path, expected_digest=expected_digest)
+
+ def catalog(self, config):
+ path = Path(config["tasks"]["catalog"])
+ entries, seen = [], set()
+ with path.open(encoding="utf-8") as source:
+ for line_number, line in enumerate(source, start=1):
+ if not line.strip():
+ raise ValueError(f"blank JSONL catalog line: {line_number}")
+ try:
+ entry = json.loads(line)
+ except json.JSONDecodeError as exc:
+ raise ValueError(
+ f"invalid JSONL catalog line {line_number}: {exc.msg}"
+ ) from exc
+ if (
+ not isinstance(entry, dict)
+ or set(entry)
+ != {"id", "revision", "path", "task_digest", "reward_profile"}
+ or not isinstance(entry["id"], str)
+ or not entry["id"].strip()
+ or not isinstance(entry["revision"], str)
+ or not entry["revision"].strip()
+ or entry["id"] in seen
+ ):
+ raise ValueError(
+ f"invalid or duplicate JSONL catalog identity: {line_number}"
+ )
+ if (
+ not isinstance(entry["path"], str)
+ or not entry["path"].strip()
+ or not isinstance(entry["task_digest"], str)
+ or not re.fullmatch(r"[0-9a-f]{64}", entry["task_digest"])
+ or not isinstance(entry["reward_profile"], dict)
+ ):
+ raise ValueError(f'invalid native task lock: {entry["id"]}')
+ seen.add(entry["id"])
+ entry["path"] = str((path.parent / entry["path"]).resolve())
+ report = self.inspect(
+ entry["path"], expected_digest=entry["task_digest"]
+ )
+ if report["status"] != "NEEDS_PROBE":
+ raise ValueError(
+ f'task preflight failed: {entry["id"]}: {report["reasons"]}'
+ )
+ RewardProfile(**entry["reward_profile"])
+ entries.append(entry)
+ if not entries:
+ raise ValueError("catalog must contain at least one JSONL task lock")
+ selected = set(getattr(config, "task_ids", ()))
+ if selected:
+ missing = selected - {entry["id"] for entry in entries}
+ if missing:
+ raise ValueError(
+ f"task selector not found in catalog: {min(missing)}"
+ )
+ entries = [entry for entry in entries if entry["id"] in selected]
+ return entries
+
+ def prompt_rows(self, entries):
+ rows = []
+ for entry in entries:
+ instruction = (Path(entry["path"]) / "instruction.md").read_text()
+ rows.append(
+ {
+ "task": instruction,
+ "metadata": {
+ "harborrl_native": True,
+ "task_provider": self.provider_id,
+ "native_task": entry,
+ },
+ }
+ )
+ return rows
diff --git a/harborrl/tasks/provider.py b/harborrl/tasks/provider.py
new file mode 100644
index 000000000..239fd58ef
--- /dev/null
+++ b/harborrl/tasks/provider.py
@@ -0,0 +1,72 @@
+"""Native Harbor task discovery, locking, and prompt materialization."""
+from __future__ import annotations
+
+import re
+from pathlib import Path
+
+from harborrl.data.harbor.native_inspector import inspect_native
+from harborrl.trajectories.native import RewardProfile, read_json
+
+
+class NativeTaskProvider:
+ provider_id = "native_harbor"
+
+ def discover(self, tasks_dir):
+ return sorted(path.parent for path in Path(tasks_dir).rglob("task.toml"))
+
+ def inspect(self, path, *, expected_digest=None):
+ return inspect_native(path, expected_digest=expected_digest)
+
+ def catalog(self, config):
+ path = Path(config["tasks"]["catalog"])
+ entries = read_json(path)
+ if not isinstance(entries, list) or not entries:
+ raise ValueError("catalog must be a nonempty JSON list")
+ seen = set()
+ for entry in entries:
+ if (
+ not isinstance(entry, dict)
+ or set(entry)
+ != {"id", "revision", "path", "task_digest", "reward_profile"}
+ or not isinstance(entry["id"], str)
+ or not entry["id"].strip()
+ or not isinstance(entry["revision"], str)
+ or not entry["revision"].strip()
+ or entry["id"] in seen
+ ):
+ raise ValueError("invalid or duplicate catalog identity")
+ if (
+ not isinstance(entry["path"], str)
+ or not entry["path"].strip()
+ or not isinstance(entry["task_digest"], str)
+ or not re.fullmatch(r"[0-9a-f]{64}", entry["task_digest"])
+ or not isinstance(entry["reward_profile"], dict)
+ ):
+ raise ValueError(f'invalid native task lock: {entry["id"]}')
+ seen.add(entry["id"])
+ entry["path"] = str((path.parent / entry["path"]).resolve())
+ report = self.inspect(entry["path"], expected_digest=entry["task_digest"])
+ if report["status"] != "NEEDS_PROBE":
+ raise ValueError(
+ f'task preflight failed: {entry["id"]}: {report["reasons"]}'
+ )
+ RewardProfile(**entry["reward_profile"])
+ selected = set(getattr(config, "task_ids", ()))
+ if selected:
+ missing = selected - {entry["id"] for entry in entries}
+ if missing:
+ raise ValueError(f"task selector not found in catalog: {min(missing)}")
+ entries = [entry for entry in entries if entry["id"] in selected]
+ return entries
+
+ def prompt_rows(self, entries):
+ rows = []
+ for entry in entries:
+ instruction = (Path(entry["path"]) / "instruction.md").read_text()
+ rows.append(
+ {
+ "task": instruction,
+ "metadata": {"harborrl_native": True, "native_task": entry},
+ }
+ )
+ return rows
diff --git a/harborrl/webui/__init__.py b/harborrl/webui/__init__.py
new file mode 100644
index 000000000..765ec5b8e
--- /dev/null
+++ b/harborrl/webui/__init__.py
@@ -0,0 +1 @@
+"""Local configuration and training console; runtime imports stay lazy."""
diff --git a/harborrl/webui/cli.py b/harborrl/webui/cli.py
new file mode 100644
index 000000000..1193ce5d8
--- /dev/null
+++ b/harborrl/webui/cli.py
@@ -0,0 +1,67 @@
+"""Start a single-user loopback console, optionally opening the browser."""
+import argparse
+import contextlib
+import os
+import signal
+import webbrowser
+from pathlib import Path
+
+
+@contextlib.contextmanager
+def workspace_lock(workspace):
+ workspace.mkdir(parents=True, exist_ok=True)
+ with (workspace / ".console.lock").open("a+b") as stream:
+ try:
+ if os.name == "posix":
+ import fcntl
+ fcntl.flock(stream.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+ else:
+ import msvcrt
+ stream.seek(0)
+ if stream.read(1) == b"":
+ stream.write(b"0")
+ stream.flush()
+ stream.seek(0)
+ msvcrt.locking(stream.fileno(), msvcrt.LK_NBLCK, 1)
+ except OSError as exc:
+ raise ValueError("Another console owns this workspace") from exc
+ yield
+
+
+def main(argv):
+ parser = argparse.ArgumentParser(prog="harborrl web", description=__doc__)
+ parser.add_argument("--workspace", default="./harborrl-web", help="Configurations and job logs directory")
+ parser.add_argument("--port", type=int, default=0, help="Loopback port; 0 selects a free port")
+ parser.add_argument("--no-browser", action="store_true", help="Print the URL without opening a browser")
+ args = parser.parse_args(argv)
+ if not 0 <= args.port <= 65535:
+ parser.error("port must be in 0..65535")
+ from .server import Console, make_server
+
+ workspace = Path(args.workspace).expanduser().resolve()
+ try:
+ with workspace_lock(workspace):
+ console = Console(workspace)
+ server = make_server(console, args.port)
+ url = f"http://127.0.0.1:{server.server_port}/#token={console.token}"
+ print(f"HarborRL console: {url}\nWorkspace: {workspace}\nKeep this terminal open. Ctrl+C stops the console and requests active job cleanup.", flush=True)
+ if not args.no_browser:
+ try:
+ webbrowser.open(url)
+ except webbrowser.Error:
+ print("Browser unavailable; open the printed URL.", flush=True)
+ previous = signal.getsignal(signal.SIGTERM)
+ def terminate(_signum, _frame):
+ raise KeyboardInterrupt
+ signal.signal(signal.SIGTERM, terminate)
+ try:
+ server.serve_forever()
+ except KeyboardInterrupt:
+ pass
+ finally:
+ server.server_close()
+ console.jobs.close()
+ signal.signal(signal.SIGTERM, previous)
+ except (OSError, ValueError) as exc:
+ parser.error(str(exc))
+ return 0
diff --git a/harborrl/webui/jobs.py b/harborrl/webui/jobs.py
new file mode 100644
index 000000000..28a36df2b
--- /dev/null
+++ b/harborrl/webui/jobs.py
@@ -0,0 +1,173 @@
+"""One foreground CLI job at a time, with immutable inputs and durable logs."""
+import json
+import os
+import signal
+import subprocess
+import threading
+from datetime import datetime, timezone
+from pathlib import Path
+from uuid import uuid4
+
+import yaml
+
+from harborrl.options.wizard import build_config_bundle, publish_bundle
+
+
+def now():
+ return datetime.now(timezone.utc).isoformat()
+
+
+class Jobs:
+ def __init__(self, root):
+ self.root = Path(root)
+ self.root.mkdir(parents=True, exist_ok=True)
+ self.lock = threading.RLock()
+ self.child = None
+ self.active = None
+ self.closed = False
+ self.records = {}
+ for path in self.root.glob("*/job.json"):
+ record = json.loads(path.read_text(encoding="utf-8"))
+ if record["state"] in {"running", "stopping"}:
+ record.update(state="unknown", message="Console restarted; verify target cleanup before launching again.")
+ self.records[record["id"]] = record
+
+ def _save(self, record):
+ path = self.root / record["id"] / "job.json"
+ temporary = path.with_suffix(".tmp")
+ temporary.write_text(json.dumps(record, indent=2) + "\n", encoding="utf-8")
+ temporary.replace(path)
+
+ def _refresh(self):
+ if self.child is not None and self.child.poll() is not None:
+ record = self.records[self.active]
+ code = self.child.returncode
+ record.update(state="stopped" if record["state"] == "stopping" else "succeeded" if code == 0 else "failed",
+ exit_code=code, finished_at=now())
+ self._save(record)
+ self.child = self.active = None
+
+ def _watch(self, child):
+ child.wait()
+ with self.lock:
+ if self.child is child:
+ self._refresh()
+
+ def list(self):
+ with self.lock:
+ self._refresh()
+ return [dict(record) for record in sorted(self.records.values(), key=lambda r: r["created_at"], reverse=True)]
+
+ def start(self, configuration, bundle, mode):
+ if mode not in {"dry-run", "doctor", "train"}:
+ raise ValueError("Unknown run mode")
+ with self.lock:
+ self._refresh()
+ if self.closed or self.child is not None:
+ raise ValueError("A job is already active or the console is closing")
+ if any(record["state"] == "unknown" for record in self.records.values()):
+ raise ValueError("An interrupted job has unknown cleanup status. Reconcile it before launching again.")
+ identifier = uuid4().hex
+ root = self.root / identifier
+ root.mkdir(mode=0o700)
+ training = yaml.safe_load(bundle["files"]["config.yaml"])
+ deployment = yaml.safe_load(bundle["files"]["deployment.yaml"])
+ manifest = json.loads(bundle["files"]["setup-manifest.json"])
+ output = str(Path(training["output"]["root"]) / "web" / identifier)
+ training["output"]["root"] = deployment["storage"]["run_root"] = output
+ snapshot = build_config_bundle({"answers_version": 1, "preset": manifest["preset"],
+ "training": training, "deployment": deployment}, output_directory=root / "inputs")
+ publish_bundle(snapshot, root / "inputs")
+ manager = deployment["manager"]
+ command = [manager["controller_python"], "-u", "-m", "harborrl.cli", "doctor" if mode == "doctor" else "train",
+ str(root / "inputs/config.yaml"), "--deployment", str(root / "inputs/deployment.yaml")]
+ if mode == "dry-run":
+ command.append("--dry-run")
+ record = {"id": identifier, "configuration": configuration, "mode": mode, "state": "running",
+ "created_at": now(), "finished_at": None, "exit_code": None, "command": command,
+ "log_path": str(root / "console.log"), "snapshot": str(root / "inputs"), "output_root": output}
+ self._save(record)
+ env = {**os.environ, "PYTHONUNBUFFERED": "1"}
+ env["PYTHONPATH"] = os.pathsep.join(filter(None, [manager["source_dir"], env.get("PYTHONPATH")]))
+ try:
+ with (root / "console.log").open("wb") as log:
+ self.child = subprocess.Popen(command, cwd=manager["source_dir"], env=env, stdin=subprocess.DEVNULL,
+ stdout=log, stderr=subprocess.STDOUT, start_new_session=True)
+ except OSError as exc:
+ record.update(state="failed", message=str(exc), finished_at=now())
+ else:
+ self.active = identifier
+ record["pid"] = self.child.pid
+ self.records[identifier] = record
+ self._save(record)
+ if self.child is not None:
+ threading.Thread(target=self._watch, args=(self.child,), name="harborrl-web-job", daemon=True).start()
+ return dict(record)
+
+ def stop(self, identifier):
+ with self.lock:
+ self._refresh()
+ if identifier != self.active or self.child is None:
+ raise ValueError("This job is not active")
+ record = self.records[identifier]
+ if record["state"] != "stopping":
+ # The existing CLI forwards signals to its local/SSH training target.
+ try:
+ self.child.send_signal(signal.SIGINT) if os.name == "posix" else self.child.terminate()
+ except ProcessLookupError:
+ self._refresh()
+ return dict(record)
+ record["state"] = "stopping"
+ self._save(record)
+ return dict(record)
+
+ def reconcile(self, identifier):
+ with self.lock:
+ record = self.records[identifier]
+ if record["state"] != "unknown":
+ raise ValueError("Only an interrupted job can be reconciled")
+ record.update(state="interrupted", finished_at=now(), message="Target cleanup confirmed by operator.")
+ self._save(record)
+ return dict(record)
+
+ def log(self, identifier):
+ with self.lock:
+ self._refresh()
+ record = self.records[identifier]
+ path = self.root / identifier / "console.log"
+ with path.open("rb") as stream:
+ size = stream.seek(0, os.SEEK_END)
+ stream.seek(max(0, size - 65536))
+ tail = stream.read(65536).decode("utf-8", errors="replace")
+ result = {"job": dict(record), "text": tail, "bytes": size, "truncated": size > 65536,
+ "training_text": "", "training_log_path": None, "training_truncated": False}
+ output = Path(record["output_root"])
+ candidates = sorted(output.glob("training/*/train-stdout.log"))
+ if candidates:
+ training_log = candidates[-1]
+ if training_log.resolve().is_relative_to(output.resolve()):
+ with training_log.open("rb") as stream:
+ length = stream.seek(0, os.SEEK_END)
+ stream.seek(max(0, length - 65536))
+ result.update(training_text=stream.read(65536).decode("utf-8", errors="replace"),
+ training_log_path=str(training_log), training_truncated=length > 65536)
+ return result
+
+ def close(self):
+ with self.lock:
+ self.closed = True
+ self._refresh()
+ child = self.child
+ if child:
+ self.stop(self.active)
+ if child:
+ try:
+ child.wait(timeout=30)
+ except subprocess.TimeoutExpired:
+ with self.lock:
+ record = self.records[self.active]
+ record.update(state="unknown", message="Graceful shutdown timed out; target cleanup must be checked.")
+ self._save(record)
+ else:
+ with self.lock:
+ self._refresh()
diff --git a/harborrl/webui/server.py b/harborrl/webui/server.py
new file mode 100644
index 000000000..442699175
--- /dev/null
+++ b/harborrl/webui/server.py
@@ -0,0 +1,189 @@
+"""Loopback-only HTTP boundary for the existing configuration services."""
+import hmac
+import json
+import os
+import re
+import secrets
+import threading
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from importlib.resources import files
+from pathlib import Path
+from urllib.parse import urlsplit
+
+import yaml
+
+from harborrl.options.catalog import build_catalog
+from harborrl.options.presets import PRESETS, defaults
+from harborrl.options.validation import read_mapping, validate_mappings
+from harborrl.options.wizard import build_config_bundle, publish_bundle
+
+from .jobs import Jobs
+
+ASSETS = {"/": ("index.html", "text/html; charset=utf-8"),
+ "/app.js": ("app.js", "text/javascript; charset=utf-8"),
+ "/style.css": ("style.css", "text/css; charset=utf-8"),
+ "/lucide.min.js": ("lucide.min.js", "text/javascript; charset=utf-8"),
+ "/brand.jpg": ("brand.jpg", "image/jpeg")}
+NAME = re.compile(r"[a-zA-Z0-9][a-zA-Z0-9_-]{0,63}")
+
+
+class Console:
+ def __init__(self, workspace):
+ self.workspace = Path(workspace).expanduser().resolve()
+ self.configurations = self.workspace / "configurations"
+ self.configurations.mkdir(parents=True, exist_ok=True)
+ self.jobs = Jobs(self.workspace / "jobs")
+ self.token = secrets.token_urlsafe(32)
+ self.lock = threading.RLock()
+
+ def directory(self, name):
+ if not isinstance(name, str) or not NAME.fullmatch(name):
+ raise ValueError("Name must be 1-64 letters, digits, underscores or hyphens")
+ path = self.configurations / name
+ if path.is_symlink() or path.resolve().parent != self.configurations:
+ raise ValueError("Configuration must stay inside the workspace")
+ return path
+
+ def bootstrap(self):
+ return {"workspace": str(self.workspace), "catalog": build_catalog(),
+ "live_supported": os.name == "posix",
+ "defaults": {item["id"]: dict(zip(("training", "deployment"),
+ defaults(item["id"], self.configurations / "experiment"))) for item in PRESETS},
+ "configurations": self.list_configurations(), "jobs": self.jobs.list()}
+
+ def list_configurations(self):
+ result = []
+ for path in sorted(self.configurations.iterdir()):
+ if path.is_dir() and not path.is_symlink() and NAME.fullmatch(path.name) and (path / "setup-manifest.json").is_file():
+ result.append({"id": path.name, "path": str(path)})
+ return result
+
+ def preview(self, body):
+ output = self.directory(body["name"])
+ bundle = build_config_bundle(body["answers"], output_directory=output)
+ if body.get("check_files") is True:
+ bundle["validation"] = validate_mappings(yaml.safe_load(bundle["files"]["config.yaml"]),
+ yaml.safe_load(bundle["files"]["deployment.yaml"]), config_path=output / "config.yaml", check_files=True)
+ return bundle
+
+ def save(self, body):
+ with self.lock:
+ output = self.directory(body["name"])
+ bundle = build_config_bundle(body["answers"], output_directory=output)
+ publish_bundle(bundle, output)
+ return {"id": output.name, "path": str(output), "validation": bundle["validation"]}
+
+ def configuration(self, identifier):
+ root = self.directory(identifier)
+ manifest = json.loads((root / "setup-manifest.json").read_text(encoding="utf-8"))
+ answers = {"answers_version": 1, "preset": manifest["preset"],
+ "training": read_mapping(root / "config.yaml"), "deployment": read_mapping(root / "deployment.yaml")}
+ return {"id": identifier, "answers": answers,
+ "files": {name: (root / name).read_text(encoding="utf-8") for name in
+ ("config.yaml", "deployment.yaml", "setup-manifest.json")}}
+
+ def start(self, body):
+ mode = body["mode"]
+ if mode not in {"dry-run", "doctor", "train"}:
+ raise ValueError("Unknown run mode")
+ if mode != "dry-run" and (body.get("confirmed") is not True or os.name != "posix"):
+ raise ValueError("Live jobs require explicit confirmation and a Linux/POSIX controller")
+ with self.lock:
+ identifier = body["configuration"]
+ root = self.directory(identifier)
+ config = self.configuration(identifier)
+ result = validate_mappings(yaml.safe_load(config["files"]["config.yaml"]),
+ yaml.safe_load(config["files"]["deployment.yaml"]), config_path=root / "config.yaml", check_files=True)
+ if result["status"] == "invalid":
+ raise ValueError("; ".join(row["message"] for row in result["checks"] if row["status"] == "failed"))
+ return self.jobs.start(identifier, config, mode)
+
+
+def make_server(console, port=0):
+ class Handler(BaseHTTPRequestHandler):
+ def setup(self):
+ super().setup()
+ self.connection.settimeout(15)
+
+ def log_message(self, *_args):
+ pass
+
+ def respond(self, status, payload, content_type="application/json; charset=utf-8"):
+ data = payload if isinstance(payload, bytes) else json.dumps(payload).encode("utf-8")
+ self.send_response(status)
+ for name, value in {"Content-Type": content_type, "Content-Length": str(len(data)),
+ "Cache-Control": "no-store", "X-Content-Type-Options": "nosniff",
+ "Referrer-Policy": "no-referrer", "X-Frame-Options": "DENY",
+ "Content-Security-Policy": "default-src 'self'; script-src 'self'; style-src 'self'; img-src 'self'; connect-src 'self'; frame-ancestors 'none'; base-uri 'none'"}.items():
+ self.send_header(name, value)
+ self.end_headers()
+ self.wfile.write(data)
+
+ def dispatch(self, method):
+ try:
+ host = self.headers.get("Host", "")
+ if urlsplit("http://" + host).hostname not in {"127.0.0.1", "localhost"}:
+ return self.respond(403, {"error": "Loopback Host required"})
+ path = urlsplit(self.path).path
+ if method == "GET" and path in ASSETS:
+ name, mime = ASSETS[path]
+ return self.respond(200, files("harborrl.webui").joinpath("static", name).read_bytes(), mime)
+ if not hmac.compare_digest(self.headers.get("Authorization", ""), "Bearer " + console.token):
+ return self.respond(403, {"error": "Console session expired or not authorized. Reopen the CLI URL."})
+ if method == "GET":
+ if path == "/api/bootstrap":
+ return self.respond(200, console.bootstrap())
+ if path == "/api/jobs":
+ return self.respond(200, console.jobs.list())
+ if path.startswith("/api/jobs/"):
+ return self.respond(200, console.jobs.log(path.removeprefix("/api/jobs/")))
+ if path.startswith("/api/configurations/"):
+ return self.respond(200, console.configuration(path.removeprefix("/api/configurations/")))
+ if method == "POST":
+ if self.headers.get("Origin") != "http://" + host:
+ return self.respond(403, {"error": "Same-origin request required"})
+ if self.headers.get("Content-Type", "").split(";")[0] != "application/json":
+ return self.respond(415, {"error": "JSON body required"})
+ size = int(self.headers.get("Content-Length", "0"))
+ if not 0 < size <= 1048576:
+ return self.respond(413, {"error": "Request must be between 1 byte and 1 MiB"})
+ body = json.loads(self.rfile.read(size))
+ if not isinstance(body, dict):
+ raise TypeError("Request must be an object")
+ if path == "/api/preview":
+ return self.respond(200, console.preview(body))
+ if path == "/api/configurations":
+ return self.respond(201, console.save(body))
+ if path == "/api/jobs":
+ return self.respond(201, console.start(body))
+ if path == "/api/stop":
+ return self.respond(200, console.jobs.stop(body["id"]))
+ if path == "/api/reconcile":
+ if body.get("confirmed") is not True:
+ raise ValueError("Confirm target cleanup before reconciling")
+ return self.respond(200, console.jobs.reconcile(body["id"]))
+ if path == "/api/import":
+ answers = yaml.safe_load(body["text"])
+ if (not isinstance(answers, dict) or type(answers.get("answers_version")) is not int
+ or answers.get("answers_version") != 1):
+ raise ValueError("Import an answers_version: 1 YAML/JSON mapping")
+ if set(answers) - {"answers_version", "preset", "training", "deployment", "sandbox_config"}:
+ raise ValueError("Unknown answer fields")
+ if any(not isinstance(answers.get(key, {}), dict) for key in ("training", "deployment")):
+ raise ValueError("training and deployment answers must be objects")
+ if answers.get("preset") not in {p["id"] for p in PRESETS}:
+ raise ValueError("Unknown deployment preset")
+ return self.respond(200, {"answers": answers})
+ self.respond(404, {"error": "Not found"})
+ except (ValueError, TypeError, KeyError, OSError, AttributeError, yaml.YAMLError) as exc:
+ self.respond(400, {"error": str(exc)})
+
+ def do_GET(self):
+ self.dispatch("GET")
+
+ def do_POST(self):
+ self.dispatch("POST")
+
+ server = ThreadingHTTPServer(("127.0.0.1", port), Handler)
+ server.daemon_threads = True
+ return server
diff --git a/harborrl/webui/static/app.js b/harborrl/webui/static/app.js
new file mode 100644
index 000000000..0a6a5dc0d
--- /dev/null
+++ b/harborrl/webui/static/app.js
@@ -0,0 +1,884 @@
+/* The form edits answers; Python remains the authoritative builder and validator. */
+"use strict";
+const $ = (selector) => document.querySelector(selector);
+const $$ = (selector) => [...document.querySelectorAll(selector)];
+const escapeHtml = (value) =>
+ String(value ?? "").replace(
+ /[&<>"']/g,
+ (c) =>
+ ({ "&": "&", "<": "<", ">": ">", '"': """, "'": "'" })[
+ c
+ ],
+ );
+const icon = (name) => `${options.choices.map((item) => `${item} `).join("")} `;
+ else if (type === "checkbox")
+ control = `
${type === "checkbox" ? control : ""}${label} ${type === "checkbox" ? "" : control}${options.hint ? `${escapeHtml(options.hint)} ` : ""}
`;
+}
+const section = (title, content) =>
+ ``;
+function renderForm() {
+ const preset = state.answers.preset,
+ service = preset !== "embedded-local",
+ sandbox = preset === "service-sandbox-local-runner";
+ let html = "";
+ if (state.step === 0) {
+ const presets = [
+ ["embedded-local", "Local", "One machine", "monitor"],
+ ["service-ssh", "SSH workers", "Distributed Docker", "network"],
+ ["service-sandbox-local-runner", "Sandbox", "Controller + GPU", "box"],
+ ];
+ html = `
Deployment topology ${service ? "Service" : "Embedded"} ${presets.map(([id, label, detail, ico]) => `
${icon(ico)}${label}${detail}
`).join("")}
Worker ${index + 1} ${icon("trash-2")} ${field(`deployment.rollout.workers.${index}.id`, "ID")}${field(`deployment.rollout.workers.${index}.ssh.host`, "SSH destination")}${field(`deployment.rollout.workers.${index}.source_dir`, "Source directory", "text", { full: true })}${field(`deployment.rollout.workers.${index}.runner_python`, "Runner Python", "text", { full: true })}${field(`deployment.rollout.workers.${index}.task_roots.0`, "Task root", "text", { full: true })}
No workers configured.
';
+ $$("[data-remove-worker]").forEach((button) =>
+ button.addEventListener("click", () => {
+ workers.splice(Number(button.dataset.removeWorker), 1);
+ deriveWorkers();
+ changed();
+ renderForm();
+ }),
+ );
+}
+function renderSummary() {
+ const service = state.answers.preset !== "embedded-local",
+ sandbox = state.answers.preset === "service-sandbox-local-runner";
+ const nodes = [
+ ["monitor", "Controller", service ? "Service mode" : "Embedded mode"],
+ [
+ "cpu",
+ "GPU target",
+ service
+ ? read("deployment.gpu_target.ssh.host") || "Not configured"
+ : "Local machine",
+ ],
+ [
+ sandbox ? "box" : "network",
+ "Rollout",
+ sandbox
+ ? "Sandbox environment"
+ : service
+ ? `${state.answers.deployment.rollout?.workers?.length || 0} SSH workers`
+ : "Local Docker",
+ ],
+ ];
+ $("#topology").innerHTML = nodes
+ .map(
+ ([ico, title, detail], index) =>
+ `${index ? '
' : ""}${icon(ico)}
${title} ${escapeHtml(detail)}
${rows.map(([key, value]) => `${key} ${escapeHtml(value)} `).join("")} `;
+ $("#summary-status").textContent = state.saved
+ ? "Saved configuration"
+ : "Unsaved configuration";
+ icons();
+}
+function updateActions() {
+ $("#previous").disabled = state.step === 0 || state.busy;
+ $("#next").hidden = state.step === 3;
+ $("#save").hidden = state.step !== 3;
+ $("#next").disabled = state.busy;
+ $("#save").disabled =
+ state.busy ||
+ !state.preview ||
+ state.preview.validation.status === "invalid" ||
+ !!state.saved;
+ $("#step-description").textContent = `Step ${state.step + 1} of 4`;
+ $$("[data-step]").forEach((button) => {
+ button.classList.toggle(
+ "active",
+ Number(button.dataset.step) === state.step,
+ );
+ button.setAttribute(
+ "aria-current",
+ Number(button.dataset.step) === state.step ? "step" : "false",
+ );
+ });
+ const active = state.jobs.some((job) =>
+ ["running", "stopping", "unknown"].includes(job.state),
+ );
+ $("#dry-run").disabled = !state.saved || state.busy || active;
+ $("#doctor").disabled =
+ !state.saved || state.busy || active || !state.bootstrap?.live_supported;
+ $("#train").disabled =
+ !state.saved || state.busy || active || !state.bootstrap?.live_supported;
+}
+async function preview(checkFiles = false) {
+ state.preview = null;
+ $("#validation-result").textContent = "Checking configuration...";
+ $("#config-preview").textContent = "";
+ try {
+ state.preview = await api("/api/preview", {
+ name: $("#config-name").value,
+ answers: state.answers,
+ check_files: checkFiles,
+ });
+ const result = state.preview.validation;
+ $("#validation-result").innerHTML =
+ `${result.status === "invalid" ? "Changes required" : "Structure valid · runtime checks deferred"}
${result.checks.map((row) => `${icon(row.status === "passed" ? "circle-check" : row.status === "failed" ? "circle-x" : "clock-3")}
${escapeHtml(row.path)} ${escapeHtml(row.message)}
${icon("file-code-2")}${escapeHtml(config.id)} `,
+ )
+ .join("")
+ : 'No configurations yet ';
+ $$("[data-config]").forEach((button) =>
+ button.addEventListener("click", () =>
+ action(async () => {
+ if (
+ !state.saved &&
+ !(await confirmAction(
+ "Open saved configuration?",
+ "The current draft will be replaced.",
+ "Open configuration",
+ ))
+ )
+ return;
+ const config = await api(
+ `/api/configurations/${encodeURIComponent(button.dataset.config)}`,
+ );
+ state.answers = config.answers;
+ state.saved = config.id;
+ $("#config-name").value = config.id;
+ $("#draft-state").textContent = "Saved";
+ $("#draft-state").className = "badge saved";
+ showView("configuration");
+ await goStep(3);
+ renderSaved();
+ }),
+ ),
+ );
+ icons();
+}
+function download(name, text, type = "text/plain") {
+ const url = URL.createObjectURL(new Blob([text], { type }));
+ const anchor = document.createElement("a");
+ anchor.href = url;
+ anchor.download = name;
+ anchor.click();
+ setTimeout(() => URL.revokeObjectURL(url), 1000);
+}
+function confirmAction(title, message, label = "Confirm") {
+ const dialog = $("#confirm-dialog");
+ $("#confirm-title").textContent = title;
+ $("#confirm-message").textContent = message;
+ $("#confirm-action").textContent = label;
+ dialog.returnValue = "cancel";
+ return new Promise((resolve) => {
+ dialog.addEventListener(
+ "close",
+ () => resolve(dialog.returnValue === "confirm"),
+ { once: true },
+ );
+ dialog.showModal();
+ });
+}
+async function startJob(mode) {
+ if (
+ mode !== "dry-run" &&
+ !(await confirmAction(
+ mode === "train" ? "Start training?" : "Run live environment checks?",
+ mode === "train"
+ ? `Configuration: ${state.saved}\nGPU target: ${read("deployment.gpu_target.ssh.host") || "Local machine"}\nGPU budget: ${read("training.deployment.num_gpus")}\n\nTraining runs preflight, then allocates the configured GPU and rollout resources.`
+ : "Doctor executes environment and network checks on the configured machines.",
+ mode === "train" ? "Start training" : "Run doctor",
+ ))
+ )
+ return;
+ const job = await api("/api/jobs", {
+ configuration: state.saved,
+ mode,
+ confirmed: mode !== "dry-run",
+ });
+ state.selectedJob = job.id;
+ showView("runs");
+ await refreshJobs();
+}
+function renderJobs() {
+ $("#run-count").textContent = state.jobs.length;
+ $("#jobs-list").innerHTML = state.jobs.length
+ ? `CONFIGURATION MODE STARTED STATUS EXIT ${state.jobs.map((job) => `${escapeHtml(job.configuration)}
${job.id.slice(0, 8)}
${escapeHtml(job.mode)} ${new Date(job.created_at).toLocaleString()} ${escapeHtml(job.state)} ${job.exit_code ?? "—"}
${icon("activity")}
No runs yet Your dry-runs, environment checks and training jobs appear here.
HarborRL · Training console
+
+ Training console
+
+ Connecting
+
+
+
+
+
+
+
+
+
+
EXPERIMENT SETUP
+
Training configuration
+
+
+
+
+
+
+
+
+ Configuration name Draft
+
+
+
+ 1 Deployment 2 Model & tasks3 Training4 Review & launch
+
+
+
+
+
+
Configuration checks
+
+
+
+
+ No checks run
+
+
+
Generated files
+
+
+
+
+
+ config.yaml deployment.yaml manifest
+
+
+Complete required fields to preview.
+
+
+
Launch
+
+ Live runs use the controller environment and configured
+ GPU target.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Execution plan
+
+
+
+
+
+
+
+
+
+
+ Log source
+ Controller
+ GPU training
+
+
+
+
+
+
+
+
+ Confirm cleanup
+
+
+
+
+
+
+
+
+
+
+
+
Advanced answers
+
+
+
+
+
+
+
+ Apply answers
+
+
+
+
diff --git a/harborrl/webui/static/lucide-LICENSE.txt b/harborrl/webui/static/lucide-LICENSE.txt
new file mode 100644
index 000000000..718bb3f0e
--- /dev/null
+++ b/harborrl/webui/static/lucide-LICENSE.txt
@@ -0,0 +1,43 @@
+ISC License
+
+Copyright (c) 2026 Lucide Icons and Contributors
+
+Permission to use, copy, modify, and/or distribute this software for any
+purpose with or without fee is hereby granted, provided that the above
+copyright notice and this permission notice appear in all copies.
+
+THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
+WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
+ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
+OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+---
+
+The following Lucide icons are derived from the Feather project:
+
+airplay, alert-circle, alert-octagon, alert-triangle, aperture, arrow-down-circle, arrow-down-left, arrow-down-right, arrow-down, arrow-left-circle, arrow-left, arrow-right-circle, arrow-right, arrow-up-circle, arrow-up-left, arrow-up-right, arrow-up, at-sign, calendar, cast, check, chevron-down, chevron-left, chevron-right, chevron-up, chevrons-down, chevrons-left, chevrons-right, chevrons-up, circle, clipboard, clock, code, columns, command, compass, corner-down-left, corner-down-right, corner-left-down, corner-left-up, corner-right-down, corner-right-up, corner-up-left, corner-up-right, crosshair, database, divide-circle, divide-square, dollar-sign, download, external-link, feather, frown, hash, headphones, help-circle, info, italic, key, layout, life-buoy, link-2, link, loader, lock, log-in, log-out, maximize, meh, minimize, minimize-2, minus-circle, minus-square, minus, monitor, moon, more-horizontal, more-vertical, move, music, navigation-2, navigation, octagon, pause-circle, percent, plus-circle, plus-square, plus, power, radio, rss, search, server, share, shopping-bag, sidebar, smartphone, smile, square, table-2, tablet, target, terminal, trash-2, trash, triangle, tv, type, upload, x-circle, x-octagon, x-square, x, zoom-in, zoom-out
+
+The MIT License (MIT) (for the icons listed above)
+
+Copyright (c) 2013-present Cole Bemis
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/harborrl/webui/static/lucide.min.js b/harborrl/webui/static/lucide.min.js
new file mode 100644
index 000000000..e3e5586e9
--- /dev/null
+++ b/harborrl/webui/static/lucide.min.js
@@ -0,0 +1,12 @@
+/**
+ * @license lucide v1.61.0 - ISC
+ *
+ * This source code is licensed under the ISC license.
+ * See the LICENSE file in the root directory of this source tree.
+ */
+
+(function(a,i){typeof exports=="object"&&typeof module<"u"?i(exports):typeof define=="function"&&define.amd?define(["exports"],i):(a=typeof globalThis<"u"?globalThis:a||self,i(a.lucide={}))})(this,(function(a){"use strict";const i={xmlns:"http://www.w3.org/2000/svg",width:24,height:24,viewBox:"0 0 24 24",fill:"none",stroke:"currentColor","stroke-width":2,"stroke-linecap":"round","stroke-linejoin":"round"},Ka=([h,t,c])=>{const d=document.createElementNS("http://www.w3.org/2000/svg",h);return Object.keys(t).forEach(M=>{d.setAttribute(M,String(t[M]))}),c?.length&&c.forEach(M=>{const p=Ka(M);d.appendChild(p)}),d},Ja=(h,t={})=>{const c={...i,...t};return Ka(["svg",c,h])},FS=(...h)=>h.filter((t,c,d)=>!!t&&t.trim()!==""&&d.indexOf(t)===c).join(" ").trim(),RS=h=>{for(const t in h)if(t.startsWith("aria-")||t==="role"||t==="title")return!0;return!1},bS=h=>{let t="",c=!1;for(const d of h){if(d==="-"||d==="_"||d<=" "){c=t.length>0;continue}t.length===0?t+=d.toLowerCase():t+=c?d.toUpperCase():d,c=!1}return t},TS=h=>{const t=bS(h);return t.charAt(0).toUpperCase()+t.slice(1)},qS=h=>Array.from(h.attributes).reduce((t,c)=>(t[c.name]=c.value,t),{}),Qa=h=>typeof h=="string"?h:!h||!h.class?"":h.class&&typeof h.class=="string"?h.class.split(" "):h.class&&Array.isArray(h.class)?h.class:"",ja=(h,{nameAttr:t,icons:c,attrs:d})=>{const M=h.getAttribute(t);if(M==null)return;const p=TS(M),Xa=c[p];if(!Xa)return console.warn(`${h.outerHTML} icon name was not found in the provided icons object.`);const Na=qS(h),US=RS(Na)?{}:{"aria-hidden":"true"},zS={...i,"data-lucide":M,...US,...d,...Na},OS=Qa(Na),GS=Qa(d),DS=FS("lucide",`lucide-${M}`,...OS,...GS);DS&&Object.assign(zS,{class:DS});const ZS=Ja(Xa,zS);return h.parentNode?.replaceChild(ZS,h)},Ya=[["path",{d:"m14 12 4 4 4-4"}],["path",{d:"M18 16V7"}],["path",{d:"m2 16 4.039-9.69a.5.5 0 0 1 .923 0L11 16"}],["path",{d:"M3.304 13h6.392"}]],_a=[["path",{d:"m14 11 4-4 4 4"}],["path",{d:"M18 16V7"}],["path",{d:"m2 16 4.039-9.69a.5.5 0 0 1 .923 0L11 16"}],["path",{d:"M3.304 13h6.392"}]],xa=[["path",{d:"m15 16 2.536-7.328a1.02 1.02 1 0 1 1.928 0L22 16"}],["path",{d:"M15.697 14h5.606"}],["path",{d:"m2 16 4.039-9.69a.5.5 0 0 1 .923 0L11 16"}],["path",{d:"M3.304 13h6.392"}]],ah=[["circle",{cx:"16",cy:"4",r:"1"}],["path",{d:"m18 19 1-7-6 1"}],["path",{d:"m5 8 3-3 5.5 3-2.36 3.5"}],["path",{d:"M4.24 14.5a5 5 0 0 0 6.88 6"}],["path",{d:"M13.76 17.5a5 5 0 0 0-6.88-6"}]],hh=[["path",{d:"M22 12h-2.48a2 2 0 0 0-1.93 1.46l-2.35 8.36a.25.25 0 0 1-.48 0L9.24 2.18a.25.25 0 0 0-.48 0l-2.35 8.36A2 2 0 0 1 4.49 12H2"}]],th=[["path",{d:"M10 13H6"}],["path",{d:"M10 15v-4a2 2 0 0 0-4 0v4"}],["path",{d:"M14 14.5a.5.5 0 0 0 .5.5h1a2.5 2.5 0 0 0 2.5-2.5v-1A2.5 2.5 0 0 0 15.5 9h-1a.5.5 0 0 0-.5.5z"}],["rect",{x:"2",y:"5",width:"20",height:"14",rx:"2"}]],dh=[["path",{d:"M18 17.5a2.5 2.5 0 1 1-4 2.03V12"}],["path",{d:"M6 12H4a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h16a2 2 0 0 1 2 2v5a2 2 0 0 1-2 2h-2"}],["path",{d:"M6 8h12"}],["path",{d:"M6.6 15.572A2 2 0 1 0 10 17v-5"}]],ch=[["path",{d:"M5 17H4a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h16a2 2 0 0 1 2 2v10a2 2 0 0 1-2 2h-1"}],["path",{d:"m12 15 5 6H7Z"}]],C=[["circle",{cx:"12",cy:"13",r:"8"}],["path",{d:"M5 3 2 6"}],["path",{d:"m22 6-3-3"}],["path",{d:"M6.38 18.7 4 21"}],["path",{d:"M17.64 18.67 20 21"}],["path",{d:"m9 13 2 2 4-4"}]],u=[["circle",{cx:"12",cy:"13",r:"8"}],["path",{d:"M5 3 2 6"}],["path",{d:"m22 6-3-3"}],["path",{d:"M6.38 18.7 4 21"}],["path",{d:"M17.64 18.67 20 21"}],["path",{d:"M9 13h6"}]],Mh=[["path",{d:"M6.87 6.87a8 8 0 1 0 11.26 11.26"}],["path",{d:"M19.9 14.25a8 8 0 0 0-9.15-9.15"}],["path",{d:"m22 6-3-3"}],["path",{d:"M6.26 18.67 4 21"}],["path",{d:"m2 2 20 20"}],["path",{d:"M4 4 2 6"}]],g=[["circle",{cx:"12",cy:"13",r:"8"}],["path",{d:"M5 3 2 6"}],["path",{d:"m22 6-3-3"}],["path",{d:"M6.38 18.7 4 21"}],["path",{d:"M17.64 18.67 20 21"}],["path",{d:"M12 10v6"}],["path",{d:"M9 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Wheat=WV,a.WheatOff=ZV,a.Whistle=IV,a.WholeWord=XV,a.Wifi=aS,a.WifiCog=NV,a.WifiHigh=KV,a.WifiLock=JV,a.WifiLow=QV,a.WifiOff=jV,a.WifiPen=YV,a.WifiSync=_V,a.WifiZero=xV,a.Wind=tS,a.WindArrowDown=dS,a.WindArrowUp=hS,a.Wine=MS,a.WineOff=cS,a.Workflow=pS,a.Worm=lS,a.WrapText=Sa,a.Wrench=nS,a.WrenchOff=iS,a.X=rS,a.XCircle=w1,a.XLineTop=eS,a.XOctagon=K2,a.XSquare=Ca,a.Zap=oS,a.ZapOff=vS,a.ZodiacAquarius=$S,a.ZodiacAries=mS,a.ZodiacCancer=yS,a.ZodiacCapricorn=sS,a.ZodiacGemini=CS,a.ZodiacLeo=uS,a.ZodiacLibra=AS,a.ZodiacOphiuchus=gS,a.ZodiacPisces=HS,a.ZodiacSagittarius=VS,a.ZodiacScorpio=SS,a.ZodiacTaurus=wS,a.ZodiacVirgo=LS,a.ZoomIn=fS,a.ZoomOut=kS,a.createElement=Ja,a.createIcons=BS,a.icons=PS}));
+//# sourceMappingURL=lucide.min.js.map
diff --git a/harborrl/webui/static/style.css b/harborrl/webui/static/style.css
new file mode 100644
index 000000000..122b593ca
--- /dev/null
+++ b/harborrl/webui/static/style.css
@@ -0,0 +1,1094 @@
+:root {
+ font-family: Inter, "Segoe UI", Arial, sans-serif;
+ color: #262a29;
+ background: #fff;
+ font-synthesis: none;
+ font-size: 14px;
+ letter-spacing: 0;
+ --green: #13745b;
+ --green-light: #edf7f2;
+ --border: #e3e7e4;
+ --muted: #727a75;
+ --red: #b83c3c;
+}
+* {
+ box-sizing: border-box;
+}
+body {
+ margin: 0;
+}
+button,
+input,
+select,
+textarea {
+ font: inherit;
+ letter-spacing: 0;
+}
+button {
+ display: inline-flex;
+ align-items: center;
+ justify-content: center;
+ gap: 8px;
+ border: 1px solid transparent;
+ border-radius: 6px;
+ padding: 9px 14px;
+ min-height: 38px;
+ cursor: pointer;
+ background: none;
+ color: inherit;
+ font-weight: 550;
+ transition:
+ background 0.12s,
+ border-color 0.12s;
+}
+button:hover {
+ background: #f1f4f2;
+}
+button:disabled {
+ opacity: 0.45;
+ cursor: not-allowed;
+}
+button:focus-visible,
+a:focus-visible {
+ outline: 2px solid #13745b;
+ outline-offset: 3px;
+}
+svg {
+ width: 17px;
+ height: 17px;
+ flex-shrink: 0;
+ stroke-width: 1.7;
+}
+input,
+select,
+textarea {
+ border: 1px solid #cdd5cf;
+ border-radius: 5px;
+ min-width: 0;
+ width: 100%;
+ background: #fff;
+ color: #282e2a;
+ padding: 10px 11px;
+ min-height: 40px;
+}
+input:focus,
+select:focus,
+textarea:focus {
+ outline: 2px solid #d1e9dd;
+ border-color: var(--green);
+}
+input:disabled {
+ background: #f5f7f5;
+ color: #666;
+}
+input[type="checkbox"] {
+ width: 16px;
+ height: 16px;
+ min-height: 16px;
+ accent-color: var(--green);
+}
+textarea {
+ resize: vertical;
+}
+label {
+ display: block;
+ font-size: 12px;
+ font-weight: 600;
+ margin-bottom: 7px;
+ color: #535d55;
+}
+h1,
+h2,
+h3,
+p {
+ margin: 0;
+}
+h1 {
+ font-size: 25px;
+ font-weight: 620;
+ line-height: 1.25;
+}
+h2 {
+ font-size: 16px;
+ font-weight: 620;
+}
+h3 {
+ font-size: 13px;
+ font-weight: 650;
+}
+p {
+ line-height: 1.6;
+}
+a {
+ color: var(--green);
+}
+pre {
+ white-space: pre-wrap;
+ overflow-wrap: anywhere;
+}
+button.primary {
+ background: var(--green);
+ color: #fff;
+}
+button.primary:hover {
+ background: #0f614c;
+}
+button.secondary {
+ background: #fff;
+ border-color: #d9dfda;
+}
+button.secondary:hover {
+ background: #f6f8f6;
+ border-color: #b7c4bb;
+}
+button.danger {
+ color: var(--red);
+ background: #fff5f3;
+ border-color: #efd4cf;
+}
+.icon-button {
+ width: 36px;
+ height: 36px;
+ min-height: 36px;
+ padding: 8px;
+ flex-shrink: 0;
+}
+.muted {
+ color: var(--muted);
+ font-size: 12px;
+ line-height: 1.6;
+}
+.topbar {
+ height: 70px;
+ display: flex;
+ align-items: center;
+ border-bottom: 1px solid var(--border);
+ padding: 0 30px;
+ gap: 24px;
+ background: white;
+}
+.brand {
+ width: 173px;
+ display: flex;
+ flex-shrink: 0;
+}
+.brand img {
+ width: 100%;
+ height: auto;
+}
+.app-title {
+ font-size: 13px;
+ border-left: 1px solid var(--border);
+ padding-left: 24px;
+ color: #667168;
+}
+.connection {
+ display: flex;
+ align-items: center;
+ gap: 8px;
+ margin-left: auto;
+ font-size: 12px;
+ color: #606c63;
+}
+.dot,
+.status-dot {
+ width: 7px;
+ height: 7px;
+ background: #299474;
+ border-radius: 50%;
+ flex-shrink: 0;
+}
+.connection.offline .dot {
+ background: #c4863b;
+}
+.shell {
+ display: grid;
+ grid-template-columns: 228px minmax(0, 1fr);
+ min-height: calc(100vh - 70px);
+}
+.sidebar {
+ background: #f8faf8;
+ border-right: 1px solid var(--border);
+ padding: 28px 14px 20px;
+ display: flex;
+ flex-direction: column;
+ min-width: 0;
+}
+.nav-caption {
+ font-size: 10px;
+ font-weight: 650;
+ color: #879088;
+ letter-spacing: 1.1px;
+ padding: 0 14px;
+}
+.sidebar nav {
+ margin-top: 12px;
+ display: grid;
+ gap: 5px;
+}
+.nav-item {
+ justify-content: flex-start;
+ width: 100%;
+ padding: 10px 13px;
+ color: #677068;
+ font-size: 13px;
+}
+.nav-item.active {
+ background: #eaf2ec;
+ color: #176d51;
+}
+.nav-item svg {
+ margin-right: 2px;
+}
+.count {
+ margin-left: auto;
+ font-size: 10px;
+ min-width: 20px;
+ border-radius: 4px;
+ background: #e4eae5;
+ padding: 2px 5px;
+ text-align: center;
+}
+.saved-heading {
+ margin-top: 37px;
+ display: flex;
+ align-items: center;
+ justify-content: space-between;
+}
+.saved-heading .nav-caption {
+ font-size: 9px;
+ padding-right: 0;
+}
+.saved-list {
+ display: grid;
+ gap: 3px;
+ margin-top: 10px;
+}
+.saved-list > .muted {
+ padding: 0 14px;
+}
+.saved-item {
+ display: flex;
+ justify-content: flex-start;
+ min-width: 0;
+ font-size: 12px;
+ font-weight: 450;
+ padding: 9px 13px;
+ text-align: left;
+}
+.saved-item span {
+ overflow: hidden;
+ text-overflow: ellipsis;
+ white-space: nowrap;
+}
+.saved-item.selected {
+ color: var(--green);
+ background: #eef4ef;
+}
+.sidebar-bottom {
+ margin-top: auto;
+ padding: 40px 13px 0;
+ color: #89928b;
+ display: flex;
+ align-items: flex-start;
+ gap: 8px;
+ font-size: 10px;
+}
+.sidebar-bottom span {
+ overflow-wrap: anywhere;
+ line-height: 1.6;
+}
+.sidebar-bottom svg {
+ width: 14px;
+ height: 14px;
+}
+main {
+ padding: 35px 38px 40px;
+ min-width: 0;
+ max-width: 1590px;
+ width: 100%;
+ margin: 0 auto;
+}
+.page-heading {
+ display: flex;
+ justify-content: space-between;
+ align-items: center;
+ gap: 18px;
+ margin-bottom: 27px;
+}
+.eyebrow {
+ color: #89938b;
+ font-size: 10px;
+ font-weight: 650;
+ letter-spacing: 1.2px;
+ margin-bottom: 9px;
+}
+.toolbar {
+ display: flex;
+ gap: 8px;
+ align-items: center;
+}
+.configuration-name {
+ display: flex;
+ align-items: center;
+ gap: 14px;
+ margin-bottom: 25px;
+}
+.configuration-name label {
+ margin: 0;
+ white-space: nowrap;
+}
+.configuration-name input {
+ max-width: 255px;
+ font-size: 13px;
+ height: 36px;
+ min-height: 36px;
+}
+.badge {
+ padding: 4px 7px;
+ background: #f0f2ef;
+ color: #667166;
+ font-size: 10px;
+ border-radius: 4px;
+ white-space: nowrap;
+}
+.badge.saved,
+.badge.succeeded {
+ color: #176d51;
+ background: #e6f3eb;
+}
+.badge.failed {
+ color: #b43d3d;
+ background: #fbeceb;
+}
+.badge.running {
+ color: #176d51;
+ background: #e5f4eb;
+}
+.badge.stopping,
+.badge.unknown {
+ color: #986719;
+ background: #fbf2dd;
+}
+.steps {
+ display: grid;
+ grid-template-columns: repeat(4, minmax(0, 1fr));
+ border-bottom: 1px solid var(--border);
+ gap: 6px;
+ margin-bottom: 31px;
+}
+.steps button {
+ font-size: 12px;
+ border-radius: 0;
+ border-bottom: 2px solid transparent;
+ padding: 10px 2px 17px;
+ justify-content: flex-start;
+ min-width: 0;
+ color: #868c86;
+ gap: 9px;
+}
+.steps button > span {
+ height: 22px;
+ width: 22px;
+ border-radius: 50%;
+ border: 1px solid #d8ded9;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ flex-shrink: 0;
+ font-size: 10px;
+}
+.steps button.active {
+ border-bottom-color: var(--green);
+ color: var(--green);
+}
+.steps button.active > span {
+ background: var(--green);
+ border-color: var(--green);
+ color: white;
+}
+.editor-layout {
+ display: grid;
+ grid-template-columns: minmax(0, 1fr) 242px;
+ gap: 38px;
+}
+.editor {
+ min-width: 0;
+}
+.section-heading {
+ display: flex;
+ align-items: center;
+ justify-content: space-between;
+ gap: 14px;
+ margin-bottom: 20px;
+}
+.form-section {
+ margin-bottom: 27px;
+}
+.form-section + .form-section {
+ border-top: 1px solid var(--border);
+ padding-top: 25px;
+}
+.form-grid {
+ display: grid;
+ grid-template-columns: repeat(2, minmax(0, 1fr));
+ gap: 20px 18px;
+}
+.field.full {
+ grid-column: 1/-1;
+}
+.field small {
+ color: #8a938c;
+ font-size: 10px;
+ display: block;
+ margin-top: 5px;
+ line-height: 1.5;
+}
+.field.required label:after {
+ content: " *";
+ color: #b9915a;
+}
+.checkbox-field {
+ display: flex;
+ align-items: center;
+ gap: 9px;
+ padding-top: 12px;
+}
+.checkbox-field label {
+ margin: 0;
+}
+.preset-options {
+ display: grid;
+ grid-template-columns: repeat(3, minmax(0, 1fr));
+ gap: 9px;
+ margin-bottom: 26px;
+}
+.preset {
+ border: 1px solid var(--border);
+ border-radius: 6px;
+ min-height: 104px;
+ flex-direction: column;
+ align-items: flex-start;
+ justify-content: center;
+ text-align: left;
+ padding: 14px;
+ gap: 12px;
+ font-size: 12px;
+}
+.preset svg {
+ color: #839087;
+ width: 21px;
+ height: 21px;
+}
+.preset.active {
+ border-color: #72a88c;
+ background: #f2f8f3;
+ color: #116a4e;
+}
+.preset.active svg {
+ color: #13745b;
+}
+.preset small {
+ font-size: 10px;
+ font-weight: 400;
+ color: #859085;
+ display: block;
+ margin-top: 4px;
+}
+.summary {
+ border-left: 1px solid var(--border);
+ padding-left: 25px;
+ align-self: start;
+ position: sticky;
+ top: 20px;
+ min-width: 0;
+}
+.summary-heading {
+ display: flex;
+ gap: 8px;
+ align-items: center;
+ color: #607264;
+ margin-bottom: 20px;
+}
+.summary-heading h2 {
+ font-size: 12px;
+ color: #546659;
+}
+.topology-node {
+ display: grid;
+ grid-template-columns: 30px 1fr;
+ gap: 10px;
+ align-items: center;
+ padding: 10px 0;
+}
+.topology-node > .node-icon {
+ width: 30px;
+ height: 30px;
+ background: #f0f4ef;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ color: #617769;
+ border-radius: 5px;
+}
+.topology-node span {
+ display: block;
+ font-size: 10px;
+ color: #8a958c;
+ margin-top: 3px;
+ overflow-wrap: anywhere;
+}
+.topology-node strong {
+ font-size: 11px;
+ font-weight: 550;
+}
+.topology-connector {
+ height: 20px;
+ margin-left: 14px;
+ border-left: 1px dashed #c7d6ca;
+}
+.summary dl {
+ border-top: 1px solid var(--border);
+ margin: 21px 0 0;
+ padding: 19px 0 0;
+ display: grid;
+ grid-template-columns: 1fr auto;
+ gap: 13px;
+ font-size: 11px;
+}
+.summary dt {
+ color: #869087;
+}
+.summary dd {
+ margin: 0;
+ text-align: right;
+ font-weight: 550;
+ overflow-wrap: anywhere;
+ max-width: 110px;
+}
+.summary-footer {
+ border-top: 1px solid var(--border);
+ display: flex;
+ align-items: center;
+ gap: 7px;
+ margin-top: 21px;
+ padding-top: 17px;
+ font-size: 10px;
+ color: #929a93;
+}
+.summary-footer .status-dot {
+ background: #b2bbb0;
+ width: 5px;
+ height: 5px;
+}
+.editor-footer {
+ display: flex;
+ gap: 12px;
+ align-items: center;
+ padding-top: 23px;
+ border-top: 1px solid var(--border);
+ margin-top: 24px;
+}
+.editor-footer .muted {
+ margin-left: auto;
+ font-size: 10px;
+}
+.notice {
+ padding: 12px 15px;
+ margin-bottom: 22px;
+ background: #fbf5e8;
+ border: 1px solid #eadbbc;
+ border-radius: 5px;
+ color: #875e24;
+ font-size: 13px;
+ overflow-wrap: anywhere;
+}
+.notice.success {
+ background: #edf7ef;
+ border-color: #cfe5d4;
+ color: #296b42;
+}
+.validation-result {
+ margin-bottom: 24px;
+}
+.validation-row {
+ display: flex;
+ gap: 10px;
+ padding: 10px 0;
+ border-bottom: 1px solid #edf0ed;
+ font-size: 12px;
+ align-items: flex-start;
+}
+.validation-row svg {
+ width: 15px;
+ height: 15px;
+ margin-top: 2px;
+}
+.validation-row.passed svg {
+ color: var(--green);
+}
+.validation-row.failed svg {
+ color: var(--red);
+}
+.validation-row.deferred svg {
+ color: #b28943;
+}
+.validation-row strong {
+ display: block;
+ font-size: 11px;
+ font-weight: 600;
+ overflow-wrap: anywhere;
+ margin-bottom: 3px;
+}
+.validation-row p {
+ font-size: 11px;
+ color: #7c867e;
+}
+.check-status {
+ font-size: 12px;
+ font-weight: 600;
+ margin-bottom: 12px;
+ color: #5f6c63;
+}
+.file-tabs {
+ display: flex;
+ gap: 3px;
+ border: 1px solid var(--border);
+ border-bottom: 0;
+ background: #fafbf9;
+ padding: 5px 7px;
+ border-radius: 5px 5px 0 0;
+ overflow: auto;
+}
+.file-tabs button {
+ font-size: 10px;
+ min-height: 29px;
+ padding: 5px 9px;
+ white-space: nowrap;
+}
+.file-tabs button.active {
+ background: #eaf0e8;
+ color: #3e684b;
+}
+.code-panel {
+ font:
+ 11px/1.8 Consolas,
+ "SFMono-Regular",
+ monospace;
+ padding: 18px;
+ background: #fafbf9;
+ border: 1px solid var(--border);
+ margin: 0;
+ min-height: 130px;
+ max-height: 380px;
+ overflow: auto;
+ border-radius: 0 0 5px 5px;
+ color: #586758;
+}
+.preview-heading {
+ margin-bottom: 12px;
+}
+.launch-section {
+ margin-top: 26px;
+}
+.launch-section p {
+ font-size: 11px;
+ margin: 8px 0 15px;
+}
+.launch-actions {
+ display: flex;
+ gap: 9px;
+ flex-wrap: wrap;
+}
+.jobs-table {
+ width: 100%;
+ border-collapse: collapse;
+ font-size: 12px;
+ margin-top: 20px;
+}
+.jobs-table th {
+ text-align: left;
+ color: #8a938b;
+ font-size: 10px;
+ font-weight: 500;
+ padding: 14px 12px;
+ background: #f6f8f5;
+ border-bottom: 1px solid var(--border);
+}
+.jobs-table td {
+ padding: 16px 12px;
+ border-bottom: 1px solid var(--border);
+}
+.jobs-table tr[data-job] {
+ cursor: pointer;
+}
+.jobs-table tr[data-job]:hover,
+.jobs-table tr.selected {
+ background: #f5f9f4;
+}
+.jobs-table .job-name {
+ font-weight: 550;
+}
+.jobs-table .job-id {
+ font-size: 10px;
+ color: #90998f;
+ margin-top: 3px;
+}
+.table-wrap {
+ overflow: auto;
+}
+.empty-state {
+ padding: 70px 20px;
+ text-align: center;
+ border-block: 1px solid var(--border);
+ color: #94a18f;
+}
+.empty-state svg {
+ width: 30px;
+ height: 30px;
+ margin-bottom: 16px;
+}
+.empty-state h2 {
+ color: #60735e;
+ margin-bottom: 8px;
+}
+.empty-state p {
+ font-size: 12px;
+}
+#job-detail {
+ margin-top: 35px;
+}
+#job-meta {
+ font-size: 11px;
+ overflow-wrap: anywhere;
+ margin-top: 8px;
+}
+.log-panel {
+ background: #1e2824;
+ color: #c3d3c8;
+ border-color: #1e2824;
+ border-radius: 5px;
+ min-height: 260px;
+ max-height: 510px;
+ font-size: 11px;
+}
+#log-note {
+ font-size: 10px;
+ margin-top: 10px;
+}
+dialog {
+ border: 1px solid #dce2da;
+ border-radius: 8px;
+ padding: 26px;
+ width: min(540px, calc(100% - 32px));
+ box-shadow: 0 20px 90px #15291d25;
+ color: #354237;
+}
+dialog::backdrop {
+ background: #172a1c55;
+}
+dialog p {
+ font-size: 13px;
+ white-space: pre-wrap;
+ overflow-wrap: anywhere;
+ margin-top: 18px;
+}
+.dialog-heading {
+ display: flex;
+ justify-content: space-between;
+ align-items: center;
+ gap: 12px;
+}
+.dialog-actions {
+ display: flex;
+ gap: 10px;
+ justify-content: flex-end;
+ margin-top: 25px;
+}
+#answers-dialog {
+ width: min(850px, calc(100% - 32px));
+}
+#answers-editor {
+ font:
+ 12px/1.6 Consolas,
+ monospace;
+ height: 50vh;
+ margin-top: 18px;
+}
+.worker-heading {
+ display: flex;
+ justify-content: space-between;
+ align-items: center;
+ margin: 20px 0 12px;
+}
+.worker-group {
+ border-bottom: 1px solid var(--border);
+ padding-bottom: 20px;
+}
+.error-field {
+ border-color: #bf7770;
+}
+.small-select {
+ max-width: 130px;
+}
+.visually-hidden {
+ position: absolute;
+ width: 1px;
+ height: 1px;
+ overflow: hidden;
+ clip: rect(0, 0, 0, 0);
+}
+[hidden] {
+ display: none !important;
+}
+.mobile-only {
+ display: none;
+}
+.nav-caption,
+.eyebrow {
+ letter-spacing: 0;
+}
+.log-selector {
+ display: flex;
+ align-items: center;
+ gap: 12px;
+ margin-top: 22px;
+}
+.log-selector label {
+ margin: 0;
+}
+@media (min-width: 1600px) {
+ main {
+ padding-left: 55px;
+ padding-right: 55px;
+ }
+ .editor-layout {
+ grid-template-columns: minmax(0, 1fr) 280px;
+ gap: 50px;
+ }
+ .summary {
+ padding-left: 32px;
+ }
+}
+@media (max-width: 1150px) {
+ .shell {
+ grid-template-columns: 195px minmax(0, 1fr);
+ }
+ main {
+ padding: 28px 24px;
+ }
+ .editor-layout {
+ grid-template-columns: minmax(0, 1fr) 195px;
+ gap: 23px;
+ }
+ .summary {
+ padding-left: 19px;
+ }
+ .preset {
+ padding: 11px;
+ }
+ .steps button {
+ font-size: 10px;
+ gap: 6px;
+ }
+ .saved-heading .nav-caption {
+ font-size: 8px;
+ padding-left: 10px;
+ }
+}
+@media (max-width: 900px) {
+ .editor-layout {
+ grid-template-columns: minmax(0, 1fr);
+ }
+ .summary {
+ display: none;
+ }
+ .shell {
+ grid-template-columns: 185px minmax(0, 1fr);
+ }
+ .page-heading .secondary {
+ font-size: 11px;
+ padding: 7px 9px;
+ }
+ .configuration-name {
+ gap: 10px;
+ }
+ .configuration-name label {
+ font-size: 10px;
+ }
+}
+@media (max-width: 650px) {
+ .mobile-only {
+ display: inline-flex;
+ }
+ .topbar {
+ height: 61px;
+ padding: 0 17px;
+ gap: 14px;
+ }
+ .brand {
+ width: 139px;
+ }
+ .app-title {
+ display: none;
+ }
+ .connection {
+ font-size: 10px;
+ }
+ .shell {
+ display: block;
+ min-height: calc(100vh - 61px);
+ }
+ .sidebar {
+ padding: 10px 14px;
+ border-right: 0;
+ border-bottom: 1px solid var(--border);
+ display: block;
+ }
+ .sidebar > .nav-caption,
+ .saved-heading,
+ .sidebar-bottom {
+ display: none;
+ }
+ .sidebar nav {
+ margin: 0;
+ display: flex;
+ gap: 10px;
+ }
+ .nav-item {
+ width: auto;
+ padding: 6px 12px;
+ font-size: 12px;
+ min-height: 34px;
+ }
+ .saved-list {
+ display: flex;
+ overflow: auto;
+ margin-top: 8px;
+ gap: 5px;
+ }
+ .saved-item {
+ padding: 5px 9px;
+ min-height: 30px;
+ max-width: 200px;
+ flex-shrink: 0;
+ }
+ .saved-list > .muted {
+ font-size: 10px;
+ padding-left: 12px;
+ }
+ main {
+ padding: 25px 18px;
+ }
+ .page-heading {
+ align-items: flex-start;
+ margin-bottom: 24px;
+ gap: 10px;
+ }
+ h1 {
+ font-size: 21px;
+ }
+ .eyebrow {
+ font-size: 9px;
+ }
+ .page-heading .secondary {
+ font-size: 0;
+ gap: 0;
+ width: 36px;
+ padding: 8px;
+ }
+ .page-heading .toolbar {
+ gap: 2px;
+ }
+ .configuration-name {
+ flex-wrap: wrap;
+ gap: 8px;
+ }
+ .configuration-name label {
+ width: 100%;
+ font-size: 11px;
+ }
+ .configuration-name input {
+ max-width: calc(100% - 65px);
+ }
+ .steps {
+ gap: 3px;
+ margin-bottom: 25px;
+ }
+ .steps button {
+ flex-direction: column;
+ align-items: center;
+ justify-content: flex-start;
+ font-size: 10px;
+ gap: 8px;
+ text-align: center;
+ line-height: 1.4;
+ padding: 8px 0 12px;
+ }
+ .steps button > span {
+ width: 20px;
+ height: 20px;
+ }
+ .form-grid {
+ grid-template-columns: minmax(0, 1fr);
+ gap: 17px;
+ }
+ .preset-options {
+ gap: 6px;
+ }
+ .preset {
+ min-height: 110px;
+ font-size: 10px;
+ gap: 8px;
+ padding: 10px 7px;
+ }
+ .preset small {
+ font-size: 9px;
+ line-height: 1.5;
+ }
+ .preset svg {
+ height: 18px;
+ width: 18px;
+ }
+ .section-heading h2 {
+ font-size: 14px;
+ }
+ .section-heading .secondary {
+ font-size: 10px;
+ padding: 7px 9px;
+ }
+ .editor-footer {
+ gap: 8px;
+ }
+ .editor-footer button {
+ font-size: 11px;
+ padding: 8px 10px;
+ }
+ .editor-footer .muted {
+ font-size: 9px;
+ }
+ .launch-actions {
+ gap: 7px;
+ }
+ .launch-actions button {
+ font-size: 11px;
+ padding: 8px 10px;
+ }
+ .jobs-table th,
+ .jobs-table td {
+ padding: 12px 8px;
+ }
+ .jobs-table th:nth-child(3),
+ .jobs-table td:nth-child(3) {
+ display: none;
+ }
+ .jobs-table {
+ font-size: 11px;
+ }
+ .code-panel {
+ font-size: 10px;
+ }
+ .dialog-actions {
+ flex-wrap: wrap;
+ }
+}
diff --git a/practices/h_cluster/README.md b/practices/h_cluster/README.md
new file mode 100644
index 000000000..36fcd7cd3
--- /dev/null
+++ b/practices/h_cluster/README.md
@@ -0,0 +1,100 @@
+# H-cluster deployment practice
+
+This optional package adapts HarborRL to Brainbox and restricted Sandbox egress.
+It shares the core Controller, scheduling, Harbor Runner and trajectory audit;
+it does not implement another training loop. Installing HarborRL alone does not
+install or import this package. All example addresses and paths are placeholders.
+
+## Topology
+
+```text
+GPU training adapter -> developer Controller / RolloutService
+ -> local Harbor Runner -> Sandbox platform
+Sandbox Claude -> loopback mailbox <- developer relay pump
+ -> developer Gateway -> GPU SGLang
+```
+
+The developer initiates authenticated platform-proxy requests. Sandbox needs
+no direct route to the GPU or developer private listener. The developer must
+reach the platform proxy and every advertised inference endpoint; the GPU
+must reach the controller. Relay responses, including SSE, are fully buffered.
+
+## Install and configure
+
+Use Python 3.12+ for the runner. From the repository root:
+
+```bash
+python -m pip install -e . -e practices/h_cluster
+```
+
+Install the practice in the runner environment. The controller can use a separate
+Python environment with the dependencies required for its tokenizer and service.
+Start with `configs/deployment.example.yaml` and `configs/sandbox.example.yaml`.
+Use service placement, a local developer runner and no SSH rollout workers.
+Set the training config's `harbor.environment.runtime` to `sandbox`, its
+`config` to your Sandbox config, and `harbor.runner_transport` to `local`.
+
+For each task image create a matching platform Environment. Expose command
+port 44772 and the configured relay port. Its capacity must cover the binding
+limit; use prewarm 0 for an idle test deployment. Pin images and offline Claude
+binary hashes. The all-zero example hash must be replaced. Put AK:SK credentials
+in the referenced mode-600 file; never commit credentials or private site files.
+Tasks and verifiers needing external packages must have those dependencies
+prepared offline. The model relay is not a general internet proxy.
+
+The core only interprets common lifecycle settings. This package validates
+project, Environment IDs, mounts and relay options. Whole-Gi memory values are
+expressed using Gi. Endpoint GET 502/503/504 responses are retried within the
+creation deadline; the allocation POST is never retried. Platform Pending can
+still time out and must not be mistaken for ready capacity.
+
+## Verify and run
+
+No GPU or platform credentials are needed for the package's local HTTP/mock tests:
+
+```bash
+python -m pip install -e '.[dev]' -e practices/h_cluster
+python -m pytest practices/h_cluster/tests
+python scripts/sandbox_sdk_contract.py
+```
+
+After filling in private configuration, use the runner interpreter:
+
+```bash
+python -m harborrl.rollout.harbor_job.sandbox_probe /srv/harborrl/site/sandbox.yaml
+python -m harborrl.rollout.harbor_job.sandbox_probe /srv/harborrl/site/sandbox.yaml --network
+harborrl train /srv/harborrl/site/train.yaml --deployment /srv/harborrl/site/deployment.yaml --dry-run
+```
+
+The first command is local. The second performs authenticated, read-only
+Environment checks and does not create instances. The dry run parses the launch
+plan and does not contact the GPU. Check actual routes before starting training.
+Removing `--dry-run` launches the selected GPU job and can allocate Sandbox
+instances. Use a new run directory for each test.
+
+Inspect instance lifecycle files and cleanup-required records after cancellation.
+An empty instance list can disagree with Environment capacity details; verify
+both before reusing capacity. Never delete templates or instances belonging to
+other runs. The framework records unresolved cleanup rather than assuming it
+succeeded. It does not automatically replace platform Environment templates.
+
+## Configuration migration
+
+Experimental flat Brainbox configs are intentionally not accepted as standard
+OpenSandbox settings. Replace `provider: brainbox` with
+`adapter: harborrl_h_cluster.environment:BrainboxEnvironment`; move project and
+relay settings under `options`, and Environment ID/mount requirements under
+each binding's `options`. The platform always uses command port 44772.
+Keep lifecycle limits and offline installation in their common top-level fields.
+
+## Operational limits
+
+Set concurrency within project quotas and each Environment's actual capacity.
+Measure agent execution separately from allocated or Running instances.
+Pending allocations, offline verifier dependencies, turn limits and invalid
+generated tools can reject a complete training group. Session content must
+match the recorded model response to pass consumption auditing.
+
+Start with a serial complete-group run before increasing concurrency. Inspect
+rejected groups and confirm cleanup of every allocated instance. Checkpoint
+creation and policy-version changes alone do not establish learning improvement.
diff --git a/practices/h_cluster/configs/deployment.example.yaml b/practices/h_cluster/configs/deployment.example.yaml
new file mode 100644
index 000000000..e89c2b62f
--- /dev/null
+++ b/practices/h_cluster/configs/deployment.example.yaml
@@ -0,0 +1,37 @@
+# Replace example hosts and paths; no site-specific addresses are required.
+schema_version: 1
+manager:
+ id: manager
+ mode: service
+ bind: 0.0.0.0
+ control_port: 49200
+ control_url: http://manager.example:49200
+ gateway_port: 49184
+ gateway_url: http://manager.example:49184
+ run_token_file: /srv/harborrl/site/run-token
+ source_dir: /srv/harborrl/repo
+ controller_python: /opt/harborrl/controller/bin/python
+gpu_target:
+ id: trainer
+ transport: ssh
+ ssh: {host: training-host}
+ source_dir: /srv/harborrl/repo
+ train_python: /opt/harborrl/training/bin/python
+ environment_file: /srv/harborrl/site/gpu-env.json
+ expected_gpus: 8
+storage:
+ mode: shared_fs
+ run_root: /srv/harborrl/runs
+ manager_writable: true
+ gpu_writable: true
+ worker_writable: true
+rollout:
+ provider: sandbox
+ workers: []
+connectivity:
+ command_timeout_s: 30
+ ssh_timeout_s: 15
+ http_timeout_s: 10
+ gpu_to_controller: true
+ worker_to_gateway: false
+ manager_to_gpu_inference: true
diff --git a/practices/h_cluster/configs/sandbox.example.yaml b/practices/h_cluster/configs/sandbox.example.yaml
new file mode 100644
index 000000000..6229e8659
--- /dev/null
+++ b/practices/h_cluster/configs/sandbox.example.yaml
@@ -0,0 +1,23 @@
+adapter: harborrl_h_cluster.environment:BrainboxEnvironment
+domain: https://sandbox.example/brainbox
+credential_file: /run/secrets/harborrl/sandbox-key
+max_live_instances: 8
+startup_concurrency: 1
+request_timeout_s: 180
+create_timeout_s: 600
+options:
+ project: REPLACE_PROJECT
+ gateway_transport: platform_pull
+ relay_port: 49185
+ relay_timeout_s: 180
+bindings:
+ - image: registry.example/project/task:locked-version
+ max_live_instances: 1
+ options:
+ environment_id: REPLACE_ENVIRONMENT_ID
+ required_mount_paths: []
+installation:
+ mode: offline_binary
+ version: 2.1.141
+ binary: /opt/harborrl/assets/claude
+ sha256: '0000000000000000000000000000000000000000000000000000000000000000'
diff --git a/practices/h_cluster/pyproject.toml b/practices/h_cluster/pyproject.toml
new file mode 100644
index 000000000..0e06e0611
--- /dev/null
+++ b/practices/h_cluster/pyproject.toml
@@ -0,0 +1,16 @@
+[build-system]
+requires = ["setuptools>=69", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "harborrl-h-cluster"
+version = "0.1.0"
+description = "Optional H-cluster deployment practice for HarborRL"
+requires-python = ">=3.12"
+dependencies = ["harborrl>=0.1.0", "harbor==0.23.0", "opensandbox==0.1.9", "httpx>=0.27,<1"]
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
diff --git a/practices/h_cluster/src/harborrl_h_cluster/__init__.py b/practices/h_cluster/src/harborrl_h_cluster/__init__.py
new file mode 100644
index 000000000..e8e8a73b2
--- /dev/null
+++ b/practices/h_cluster/src/harborrl_h_cluster/__init__.py
@@ -0,0 +1 @@
+"""Optional H-cluster deployment adapters; not imported by HarborRL core."""
diff --git a/practices/h_cluster/src/harborrl_h_cluster/environment.py b/practices/h_cluster/src/harborrl_h_cluster/environment.py
new file mode 100644
index 000000000..6b0b68527
--- /dev/null
+++ b/practices/h_cluster/src/harborrl_h_cluster/environment.py
@@ -0,0 +1,143 @@
+"""Brainbox-specific settings and relay on the shared audited lifecycle."""
+import asyncio
+import re
+from contextlib import suppress
+
+import httpx
+
+from harborrl.rollout.harbor_job.environments.opensandbox import (
+ ManagedOpenSandboxEnvironment,
+)
+from harborrl.rollout.harbor_job.sandbox_config import read_credential
+from harborrl.trajectories.native import publish
+from harborrl_h_cluster.transport import SandboxStartupTransport
+
+
+async def validate_environment(cfg, binding, credential):
+ options, target = cfg['options'], binding['options']
+ async with httpx.AsyncClient(timeout=cfg['request_timeout_s']) as client:
+ response = await client.get(
+ cfg['domain'].rstrip('/') + '/v1/sandbox-environments/' + target['environment_id'],
+ params={'project': options['project']}, headers={'OPEN-SANDBOX-API-KEY': credential})
+ if response.status_code != 200:
+ raise RuntimeError(f'Environment query failed: HTTP {response.status_code}')
+ environment = response.json()
+ if environment.get('image', {}).get('uri') != binding['image']:
+ raise ValueError('Environment image does not match task image')
+ required_ports = {44772}
+ if options['gateway_transport'] == 'platform_pull':
+ required_ports.add(options['relay_port'])
+ if not required_ports <= {p.get('containerPort') for p in environment.get('ports', [])}:
+ raise ValueError('Environment command or relay port missing')
+ capacity = environment.get('instanceCapacity')
+ if type(capacity) is not int or capacity < binding['max_live_instances']:
+ raise ValueError('Environment capacity below configured admission limit')
+ mounts = {v.get('mountPath') for v in environment.get('volumes', [])}
+ if set(target['required_mount_paths']) - mounts:
+ raise ValueError('Environment required mounts missing')
+ return {'environment_id': target['environment_id'], 'image': binding['image'], 'capacity': capacity}
+
+
+class BrainboxEnvironment(ManagedOpenSandboxEnvironment):
+ @classmethod
+ def validate_config(cls, cfg):
+ options = cfg['options']
+ if set(options) != {'project', 'gateway_transport', 'relay_port', 'relay_timeout_s'}:
+ raise ValueError('H-cluster options require project, gateway_transport, relay_port, relay_timeout_s')
+ if not isinstance(options['project'], str) or not options['project'].strip():
+ raise ValueError('H-cluster project required')
+ if options['gateway_transport'] not in ('direct', 'platform_pull') or cfg['use_server_proxy'] is not True:
+ raise ValueError('H-cluster requires server proxy and a supported gateway transport')
+ if (type(options['relay_port']) is not int or not 0 < options['relay_port'] < 65536
+ or options['relay_port'] == 44772 or type(options['relay_timeout_s']) is not int
+ or options['relay_timeout_s'] < 30):
+ raise ValueError('invalid H-cluster relay port/timeout')
+ seen = set()
+ for binding in cfg['bindings']:
+ target = binding['options']
+ if set(target) != {'environment_id', 'required_mount_paths'}:
+ raise ValueError('H-cluster binding requires environment_id and required_mount_paths')
+ eid = target['environment_id']
+ if not isinstance(eid, str) or not re.fullmatch(r'[A-Za-z0-9_-]+', eid) or eid in seen:
+ raise ValueError('invalid or duplicate H-cluster Environment ID')
+ seen.add(eid)
+ if not isinstance(target['required_mount_paths'], list) or any(
+ not isinstance(p, str) or not p.startswith('/') for p in target['required_mount_paths']):
+ raise ValueError('invalid required_mount_paths')
+
+ @classmethod
+ def agent_gateway(cls, cfg, gateway_url):
+ if cfg['options']['gateway_transport'] == 'platform_pull':
+ return f"http://127.0.0.1:{cfg['options']['relay_port']}"
+ return gateway_url
+
+ @classmethod
+ async def network_check(cls, cfg):
+ credential = read_credential(cfg)
+ if ':' not in credential:
+ raise ValueError('H-cluster credential must contain AK:SK')
+ return [await validate_environment(cfg, binding, credential) for binding in cfg['bindings']]
+
+ def __init__(self, *args, **kwargs):
+ self.relay_task = None
+ super().__init__(*args, **kwargs)
+ self.record['environment_id'] = self.binding['options']['environment_id']
+
+ async def validate_binding(self):
+ if ':' not in self._api_key:
+ raise ValueError('H-cluster credential must contain AK:SK')
+ await validate_environment(self.cfg, self.binding, self._api_key)
+
+ def extensions(self):
+ return {'environmentId': self.binding['options']['environment_id']}
+
+ def api_params(self):
+ return {'project': self.cfg['options']['project']}
+
+ def startup_transport(self, allocated):
+ return SandboxStartupTransport(self.cfg['domain'].rstrip('/') + '/v1', allocated,
+ timeout=self.cfg['create_timeout_s'])
+
+ def _build_resource(self):
+ resource = super()._build_resource()
+ memory = re.fullmatch(r'(\d+)Mi', resource.get('memory', ''))
+ if memory and int(memory[1]) % 1024 == 0:
+ resource['memory'] = f'{int(memory[1]) // 1024}Gi'
+ return resource
+
+ async def start(self, force_build):
+ try:
+ await super().start(force_build)
+ if self.cfg['options']['gateway_transport'] == 'platform_pull':
+ from harborrl_h_cluster.relay import start_relay
+ if not self.gateway_url:
+ raise ValueError('platform relay requires manager Gateway URL')
+ self.relay_task = await start_relay(self, self.gateway_url)
+ except BaseException:
+ if self._sandbox is not None:
+ await self.stop(delete=True)
+ raise
+
+ async def exec(self, *args, **kwargs):
+ if self.relay_task is None:
+ return await super().exec(*args, **kwargs)
+ command = asyncio.create_task(super().exec(*args, **kwargs))
+ try:
+ done, _ = await asyncio.wait((command, self.relay_task), return_when=asyncio.FIRST_COMPLETED)
+ if self.relay_task in done:
+ publish(self.evidence / 'relay-failed.json', {'reason': 'manager relay pump stopped'})
+ raise RuntimeError('manager relay pump stopped; attempt invalid')
+ return await command
+ finally:
+ if not command.done():
+ command.cancel()
+ with suppress(asyncio.CancelledError):
+ await command
+
+ async def stop(self, delete=True):
+ if self.relay_task is not None:
+ task, self.relay_task = self.relay_task, None
+ task.cancel()
+ with suppress(asyncio.CancelledError, Exception):
+ await task
+ await super().stop(delete=delete)
diff --git a/practices/h_cluster/src/harborrl_h_cluster/relay.py b/practices/h_cluster/src/harborrl_h_cluster/relay.py
new file mode 100644
index 000000000..3bf7d5b32
--- /dev/null
+++ b/practices/h_cluster/src/harborrl_h_cluster/relay.py
@@ -0,0 +1,50 @@
+"""Lifecycle of one manager-initiated relay inside a real sandbox."""
+import asyncio
+import secrets
+import tempfile
+from pathlib import Path
+
+import httpx
+
+from harborrl.trajectories.native import publish
+from harborrl_h_cluster import relay_server
+from harborrl_h_cluster.reverse_relay import pump
+
+
+async def start_relay(environment, gateway_url):
+ cfg = {**environment.cfg, **environment.cfg['options']}
+ token = secrets.token_hex(32)
+ await environment.upload_file(Path(relay_server.__file__), '/tmp/harborrl-relay.py')
+ with tempfile.NamedTemporaryFile(mode='w', encoding='ascii') as secret:
+ secret.write(token)
+ secret.flush()
+ await environment.upload_file(Path(secret.name), '/tmp/harborrl-relay-token')
+ result = await environment.exec('chmod 600 /tmp/harborrl-relay-token && '
+ 'nohup python3 /tmp/harborrl-relay.py --token-file /tmp/harborrl-relay-token '
+ f"--port {cfg['relay_port']} --timeout {cfg['relay_timeout_s']} "
+ '> /tmp/harborrl-relay.log 2>&1 < /dev/null &')
+ if result.return_code:
+ raise RuntimeError('sandbox relay process failed to start')
+ endpoint = await environment._sandbox.get_endpoint(cfg['relay_port'])
+ url = endpoint.endpoint
+ if not url.startswith(('http://', 'https://')):
+ url = cfg['protocol'] + '://' + url
+ headers = endpoint.headers or {}
+ async with httpx.AsyncClient(timeout=10, trust_env=False) as client:
+ for _ in range(20):
+ try:
+ response = await client.get(url.rstrip('/') + '/relay/health',
+ headers={**headers, 'X-Relay-Token': token})
+ if response.status_code == 200 and response.json() == {'ok': True}:
+ break
+ except (httpx.HTTPError, ValueError):
+ pass
+ await asyncio.sleep(1)
+ else:
+ raise RuntimeError('sandbox relay readiness failed')
+ publish(environment.evidence / 'relay-ready.json', {
+ 'transport': 'platform_pull', 'port': cfg['relay_port'],
+ 'upstream': gateway_url, 'sandbox_id': environment.record['sandbox_id'],
+ 'response_mode': 'buffered', 'timeout_s': cfg['relay_timeout_s']})
+ return asyncio.create_task(pump(url, headers, token, gateway_url, asyncio.Event(),
+ timeout=cfg['relay_timeout_s'] - 10))
diff --git a/practices/h_cluster/src/harborrl_h_cluster/relay_server.py b/practices/h_cluster/src/harborrl_h_cluster/relay_server.py
new file mode 100644
index 000000000..689d47c1c
--- /dev/null
+++ b/practices/h_cluster/src/harborrl_h_cluster/relay_server.py
@@ -0,0 +1,150 @@
+"""Sandbox-local HTTP mailbox. Standalone stdlib file, uploaded by the runner.
+
+The manager pulls each request once. Upstream inference is never retried after
+an ambiguous transport failure. This prototype buffers responses, including SSE.
+"""
+import argparse
+import base64
+import hmac
+import json
+import queue
+import secrets
+import threading
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+
+MAX_BODY = 16 * 1024 * 1024
+PATHS = {'/v1/messages', '/v1/messages/count_tokens'}
+REQUEST_HEADERS = {'content-type', 'x-api-key', 'authorization', 'anthropic-version', 'anthropic-beta'}
+RESPONSE_HEADERS = {'content-type', 'request-id', 'retry-after'}
+
+
+class Mailbox:
+ def __init__(self, token, *, timeout=120, capacity=8):
+ self.token, self.timeout = token, timeout
+ self.slots = threading.BoundedSemaphore(capacity)
+ self.queue = queue.Queue(maxsize=capacity)
+ self.pending = {}
+ self.lock = threading.Lock()
+
+
+def make_server(host, port, mailbox):
+ class Handler(BaseHTTPRequestHandler):
+ def log_message(self, *args):
+ pass
+
+ def reply(self, status, body, headers=None):
+ self.send_response(status)
+ for key, value in (headers or {}).items():
+ if key.lower() in RESPONSE_HEADERS:
+ self.send_header(key, value)
+ self.send_header('Content-Length', str(len(body)))
+ self.end_headers()
+ self.wfile.write(body)
+
+ def json_reply(self, status, data):
+ self.reply(status, json.dumps(data).encode(), {'content-type': 'application/json'})
+
+ def authorized(self):
+ return hmac.compare_digest(self.headers.get('X-Relay-Token', ''), mailbox.token)
+
+ def read_body(self):
+ length = int(self.headers.get('Content-Length', '0'))
+ if not 0 <= length <= MAX_BODY or self.headers.get('Transfer-Encoding'):
+ raise ValueError('invalid body length')
+ return self.rfile.read(length)
+
+ def do_GET(self):
+ if not self.authorized():
+ self.json_reply(403, {'error': 'forbidden'})
+ return
+ if self.path == '/relay/health':
+ self.json_reply(200, {'ok': True})
+ return
+ if self.path != '/relay/next':
+ self.json_reply(404, {'error': 'not_found'})
+ return
+ while True:
+ try:
+ item = mailbox.queue.get_nowait()
+ except queue.Empty:
+ self.reply(204, b'')
+ return
+ with mailbox.lock:
+ active = item['id'] in mailbox.pending
+ if active:
+ self.json_reply(200, item)
+ return
+
+ def do_POST(self):
+ try:
+ self.handle_post()
+ except (ValueError, KeyError, TypeError):
+ self.json_reply(400, {'error': 'invalid_request'})
+
+ def handle_post(self):
+ if self.path.startswith('/relay/result/'):
+ if not self.authorized():
+ self.json_reply(403, {'error': 'forbidden'})
+ return
+ data = json.loads(self.read_body())
+ if type(data['status']) is not int or not 200 <= data['status'] <= 599:
+ raise ValueError('invalid status')
+ body = base64.b64decode(data['body'], validate=True)
+ headers = data['headers']
+ if not isinstance(headers, dict) or any(
+ not isinstance(k, str) or not isinstance(v, str) or '\r' in v or '\n' in v
+ for k, v in headers.items()
+ ):
+ raise ValueError('invalid response headers')
+ with mailbox.lock:
+ pending = mailbox.pending.get(self.path.rsplit('/', 1)[-1])
+ if pending is None or pending['event'].is_set():
+ self.json_reply(409, {'error': 'request_expired_or_completed'})
+ return
+ pending['result'] = (data['status'], body, headers)
+ pending['event'].set()
+ self.json_reply(200, {'ok': True})
+ return
+ if self.path.split('?', 1)[0] not in PATHS:
+ self.json_reply(404, {'error': 'not_found'})
+ return
+ body = self.read_body()
+ if not mailbox.slots.acquire(blocking=False):
+ self.json_reply(503, {'error': 'relay_capacity'})
+ return
+ identity = secrets.token_hex(16)
+ pending = {'event': threading.Event()}
+ try:
+ with mailbox.lock:
+ mailbox.pending[identity] = pending
+ try:
+ mailbox.queue.put_nowait({'id': identity, 'path': self.path, 'method': 'POST',
+ 'headers': {k: v for k, v in self.headers.items() if k.lower() in REQUEST_HEADERS},
+ 'body': base64.b64encode(body).decode()})
+ except queue.Full:
+ self.json_reply(503, {'error': 'relay_queue_full'})
+ return
+ if not pending['event'].wait(mailbox.timeout):
+ self.json_reply(504, {'error': 'relay_deadline'})
+ return
+ self.reply(*pending['result'])
+ finally:
+ with mailbox.lock:
+ mailbox.pending.pop(identity, None)
+ mailbox.slots.release()
+
+ server = ThreadingHTTPServer((host, port), Handler)
+ server.daemon_threads = True
+ return server
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument('--token-file', required=True)
+ parser.add_argument('--port', type=int, default=49185)
+ parser.add_argument('--timeout', type=float, default=120)
+ args = parser.parse_args()
+ server = make_server('0.0.0.0', args.port,
+ Mailbox(Path(args.token_file).read_text().strip(), timeout=args.timeout))
+ server.serve_forever()
diff --git a/practices/h_cluster/src/harborrl_h_cluster/reverse_relay.py b/practices/h_cluster/src/harborrl_h_cluster/reverse_relay.py
new file mode 100644
index 000000000..86316dbac
--- /dev/null
+++ b/practices/h_cluster/src/harborrl_h_cluster/reverse_relay.py
@@ -0,0 +1,49 @@
+"""Manager-initiated sandbox relay over the authenticated platform proxy.
+
+Experimental transport: HTTP/SSE bodies are buffered and no upstream POST is
+retried. Credentials and request bodies must never enter diagnostic logs.
+"""
+import asyncio
+import base64
+
+import httpx
+
+from .relay_server import MAX_BODY, PATHS, REQUEST_HEADERS, RESPONSE_HEADERS
+
+
+async def pump(proxy_url, proxy_headers, relay_token, gateway_url, stop, *, timeout=110):
+ headers = {**proxy_headers, 'X-Relay-Token': relay_token}
+ async with httpx.AsyncClient(timeout=timeout, trust_env=False) as upstream, \
+ httpx.AsyncClient(timeout=30, trust_env=False, headers=headers) as platform:
+ while not stop.is_set():
+ pending = await platform.get(proxy_url.rstrip('/') + '/relay/next')
+ if pending.status_code == 204:
+ await asyncio.sleep(0.25)
+ continue
+ pending.raise_for_status()
+ item = pending.json()
+ if (item['method'] != 'POST' or item['path'].split('?', 1)[0] not in PATHS
+ or not isinstance(item['id'], str)
+ or len(item['id']) != 32 or any(c not in '0123456789abcdef' for c in item['id'])):
+ raise ValueError('invalid relay request')
+ body = base64.b64decode(item['body'], validate=True)
+ if len(body) > MAX_BODY:
+ raise ValueError('relay body too large')
+ request_headers = {k: v for k, v in item['headers'].items() if k.lower() in REQUEST_HEADERS}
+ try:
+ async with upstream.stream('POST', gateway_url.rstrip('/') + item['path'],
+ content=body, headers=request_headers) as response:
+ content = bytearray()
+ async for chunk in response.aiter_bytes():
+ content.extend(chunk)
+ if len(content) > MAX_BODY // 2:
+ raise ValueError('relay response too large')
+ result = {'status': response.status_code,
+ 'headers': {k: v for k, v in response.headers.items()
+ if k.lower() in RESPONSE_HEADERS},
+ 'body': base64.b64encode(content).decode()}
+ except (httpx.HTTPError, ValueError):
+ result = {'status': 502, 'headers': {'content-type': 'application/json'},
+ 'body': base64.b64encode(b'{"error":"upstream_failed_no_retry"}').decode()}
+ delivered = await platform.post(proxy_url.rstrip('/') + '/relay/result/' + item['id'], json=result)
+ delivered.raise_for_status()
diff --git a/practices/h_cluster/src/harborrl_h_cluster/transport.py b/practices/h_cluster/src/harborrl_h_cluster/transport.py
new file mode 100644
index 000000000..b72151681
--- /dev/null
+++ b/practices/h_cluster/src/harborrl_h_cluster/transport.py
@@ -0,0 +1,28 @@
+"""Bounded retries for H-platform endpoint discovery, never allocation POSTs."""
+import asyncio
+import re
+import time
+
+from harborrl.rollout.harbor_job.sandbox_transport import (
+ SandboxStartupTransport as AllocationTransport,
+)
+
+
+class SandboxStartupTransport(AllocationTransport):
+ def __init__(self, base_url, on_created, *, timeout, transport=None, retry_delay=2):
+ super().__init__(base_url, on_created, transport=transport)
+ self.deadline = time.monotonic() + timeout
+ self.retry_delay = retry_delay
+
+ async def handle_async_request(self, request):
+ url = str(request.url).split('?', 1)[0]
+ endpoint_get = request.method == 'GET' and re.fullmatch(
+ re.escape(self.base_url) + r'/sandboxes/[A-Za-z0-9_-]+/endpoints/\d+', url)
+ while True:
+ response = await super().handle_async_request(request)
+ if not endpoint_get or response.status_code not in (502, 503, 504):
+ return response
+ if self.deadline - time.monotonic() <= self.retry_delay:
+ return response
+ await response.aclose()
+ await asyncio.sleep(self.retry_delay)
diff --git a/practices/h_cluster/tests/test_environment.py b/practices/h_cluster/tests/test_environment.py
new file mode 100644
index 000000000..95ea0dda3
--- /dev/null
+++ b/practices/h_cluster/tests/test_environment.py
@@ -0,0 +1,69 @@
+import asyncio
+
+import httpx
+import pytest
+from harbor.environments.opensandbox import OpenSandboxEnvironment
+from harborrl_h_cluster.environment import BrainboxEnvironment, validate_environment
+
+
+def settings():
+ return {'domain': 'https://sandbox.example', 'request_timeout_s': 1, 'use_server_proxy': True,
+ 'options': {'project': 'test', 'gateway_transport': 'platform_pull', 'relay_port': 49185,
+ 'relay_timeout_s': 180},
+ 'bindings': [{'image': 'image', 'max_live_instances': 2,
+ 'options': {'environment_id': 'env-test', 'required_mount_paths': ['/data']}}]}
+
+
+@pytest.mark.parametrize('memory,normalized', [('1024Mi', '1Gi'), ('2048Mi', '2Gi'), ('512Mi', '512Mi')])
+def test_resource_units(monkeypatch, memory, normalized):
+ monkeypatch.setattr(OpenSandboxEnvironment, '_build_resource', lambda _: {'cpu': '2', 'memory': memory})
+ assert object.__new__(BrainboxEnvironment)._build_resource() == {'cpu': '2', 'memory': normalized}
+
+
+@pytest.mark.parametrize('change', ['port', 'project', 'binding', 'duplicate'])
+def test_options_reject_invalid_platform_configuration(change):
+ cfg = settings()
+ if change == 'port':
+ cfg['options']['relay_port'] = 44772
+ elif change == 'project':
+ cfg['options']['project'] = ''
+ elif change == 'binding':
+ cfg['bindings'][0]['options']['environment_id'] = '../other'
+ else:
+ cfg['bindings'].append(cfg['bindings'][0])
+ with pytest.raises(ValueError):
+ BrainboxEnvironment.validate_config(cfg)
+
+
+def test_platform_routing_and_extensions():
+ cfg = settings()
+ BrainboxEnvironment.validate_config(cfg)
+ assert BrainboxEnvironment.agent_gateway(cfg, 'http://gateway') == 'http://127.0.0.1:49185'
+ env = object.__new__(BrainboxEnvironment)
+ env.cfg, env.binding = cfg, cfg['bindings'][0]
+ assert env.extensions() == {'environmentId': 'env-test'}
+ assert env.api_params() == {'project': 'test'}
+ cfg['options']['gateway_transport'] = 'direct'
+ assert BrainboxEnvironment.agent_gateway(cfg, 'http://gateway') == 'http://gateway'
+
+
+@pytest.mark.parametrize('ports,capacity,mounts,accepted', [
+ ([44772, 49185], 2, ['/data'], True), ([44772], 2, ['/data'], False),
+ ([44772, 49185], 1, ['/data'], False), ([44772, 49185], 2, [], False),
+])
+def test_environment_preflight_checks_platform_contract(monkeypatch, ports, capacity, mounts, accepted):
+ cfg = settings()
+
+ def handler(request):
+ assert request.method == 'GET'
+ assert request.url.params['project'] == 'test'
+ return httpx.Response(200, json={'image': {'uri': 'image'}, 'instanceCapacity': capacity,
+ 'ports': [{'containerPort': p} for p in ports], 'volumes': [{'mountPath': p} for p in mounts]})
+
+ client = httpx.AsyncClient
+ monkeypatch.setattr(httpx, 'AsyncClient', lambda **kw: client(**kw, transport=httpx.MockTransport(handler)))
+ if accepted:
+ assert asyncio.run(validate_environment(cfg, cfg['bindings'][0], 'test-only'))['capacity'] == 2
+ else:
+ with pytest.raises(ValueError):
+ asyncio.run(validate_environment(cfg, cfg['bindings'][0], 'test-only'))
diff --git a/practices/h_cluster/tests/test_relay.py b/practices/h_cluster/tests/test_relay.py
new file mode 100644
index 000000000..b8e98007b
--- /dev/null
+++ b/practices/h_cluster/tests/test_relay.py
@@ -0,0 +1,137 @@
+import asyncio
+import base64
+import contextlib
+import json
+import threading
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+
+import httpx
+import pytest
+from harborrl_h_cluster.relay_server import Mailbox, make_server
+from harborrl_h_cluster.reverse_relay import pump
+
+
+@contextlib.contextmanager
+def serving(server):
+ thread = threading.Thread(target=server.serve_forever, daemon=True)
+ thread.start()
+ try:
+ yield f'http://127.0.0.1:{server.server_port}'
+ finally:
+ server.shutdown()
+ server.server_close()
+ thread.join(5)
+
+
+def test_relay_auth_timeout_and_no_redelivery():
+ async def run(url):
+ async with httpx.AsyncClient(trust_env=False) as client:
+ assert (await client.get(url + '/relay/next')).status_code == 403
+ assert (await client.post(url + '/unexpected', content=b'x')).status_code == 404
+ pending = asyncio.create_task(client.post(url + '/v1/messages', content=b'body'))
+ headers = {'X-Relay-Token': 'secret'}
+ for _ in range(100):
+ item = await client.get(url + '/relay/next', headers=headers)
+ if item.status_code == 200:
+ break
+ await asyncio.sleep(.01)
+ assert item.status_code == 200
+ assert (await client.get(url + '/relay/next', headers=headers)).status_code == 204
+ assert (await pending).status_code == 504
+ delivered = await client.post(url + '/relay/result/' + item.json()['id'],
+ headers=headers, json={'status': 200, 'headers': {}, 'body': base64.b64encode(b'late').decode()})
+ assert delivered.status_code == 409
+ with serving(make_server('127.0.0.1', 0, Mailbox('secret', timeout=.5))) as url:
+ asyncio.run(run(url))
+
+
+@pytest.mark.parametrize('status,content_type,body', [
+ (200, 'text/event-stream', b'event: message_start\ndata: {"id":"test"}\n\n'),
+ (429, 'application/json', b'{"error":"rate_limited"}'),
+])
+def test_reverse_roundtrip_preserves_bytes_and_headers(status, content_type, body):
+ calls = []
+ class Upstream(BaseHTTPRequestHandler):
+ def log_message(self, *args):
+ pass
+ def do_POST(self):
+ calls.append((self.path, self.headers.get('x-api-key'),
+ self.rfile.read(int(self.headers['Content-Length']))))
+ self.send_response(status)
+ self.send_header('Content-Type', content_type)
+ self.send_header('Content-Length', str(len(body)))
+ self.end_headers()
+ self.wfile.write(body)
+ async def run(relay, gateway):
+ stop = asyncio.Event()
+ task = asyncio.create_task(pump(relay, {}, 'secret', gateway, stop))
+ try:
+ async with httpx.AsyncClient(trust_env=False) as client:
+ response = await client.post(relay + '/v1/messages?beta=true',
+ headers={'x-api-key': 'attempt-token'}, content=b'{"stream":true}')
+ assert response.status_code == status
+ assert response.content == body
+ assert response.headers['content-type'] == content_type
+ finally:
+ stop.set()
+ task.cancel()
+ with contextlib.suppress(asyncio.CancelledError):
+ await task
+ with serving(make_server('127.0.0.1', 0, Mailbox('secret'))) as relay, \
+ serving(ThreadingHTTPServer(('127.0.0.1', 0), Upstream)) as gateway:
+ asyncio.run(run(relay, gateway))
+ assert calls == [('/v1/messages?beta=true', 'attempt-token', b'{"stream":true}')]
+
+
+def test_real_gateway_trace_and_session_audit_through_relay(tmp_path):
+ from harborrl.gateway.server import Gateway
+ from harborrl.gateway.server import make_server as make_gateway
+ from harborrl.gateway.trace import TraceRegistry
+ from harborrl.rollout.harbor_job.audit import audit_session
+ from harborrl.trajectories.native import Identity, RewardProfile, digest
+
+ class Backend:
+ def count_tokens(self, converted):
+ return 2
+
+ def generate(self, converted, ident, response_id):
+ return {'input_ids': [1, 2], 'output_ids': [3], 'logprobs': [-.2], 'logprob_semantics': 'raw_model',
+ 'policy_version': ident.policy_version, 'tokenizer_digest': 'tok', 'template_digest': 'tpl',
+ 'engine_id': 'synthetic', 'serving_input': {'input_ids': [1, 2]}, 'sampling': converted['sampling'],
+ 'finish_reason': 'end_turn', 'evidence_kind': 'synthetic', 'content': [{'type': 'text', 'text': 'done'}]}
+
+ sampling = {'temperature': 1.0, 'top_p': 1.0, 'top_k': -1, 'max_new_tokens': 64, 'stop': []}
+ identity = Identity(run_id='run', batch_id='batch', group_id='group', slot_id='0', attempt_id='attempt',
+ trajectory_id='trajectory', task_digest='task', policy_version='1', harness_digest='claude',
+ reward_digest=RewardProfile().digest, sampling_digest=digest(sampling))
+ registry = TraceRegistry(tmp_path / 'traces')
+ credential = registry.register(identity, 'trial')
+ gateway = Gateway(registry, Backend(), model='policy')
+
+ async def run(relay, upstream):
+ stop = asyncio.Event()
+ task = asyncio.create_task(pump(relay, {}, 'secret', upstream, stop))
+ try:
+ async with httpx.AsyncClient(trust_env=False) as client:
+ response = await client.post(relay + '/v1/messages',
+ headers={'x-api-key': credential}, json={'model': 'policy', 'max_tokens': 64,
+ 'messages': [{'role': 'user', 'content': 'hello'}]})
+ assert response.status_code == 200
+ message = response.json()
+ session = tmp_path / 'session.jsonl'
+ session.write_text(json.dumps({'sessionId': 'relay-session', 'type': 'assistant',
+ 'message': message}) + '\n')
+ audit_session(session, registry, identity.attempt_id)
+ registry.close(identity.attempt_id)
+ turns, seal = registry.seal(identity.attempt_id)
+ assert seal['errors'] == []
+ assert len(turns) == 1
+ assert turns[0]['delivery'] == 'consumed_confirmed'
+ finally:
+ stop.set()
+ task.cancel()
+ with contextlib.suppress(asyncio.CancelledError):
+ await task
+ with serving(make_server('127.0.0.1', 0, Mailbox('secret'))) as relay, \
+ serving(make_gateway(gateway)) as upstream:
+ asyncio.run(run(relay, upstream))
diff --git a/practices/h_cluster/tests/test_transport.py b/practices/h_cluster/tests/test_transport.py
new file mode 100644
index 000000000..9e56c93df
--- /dev/null
+++ b/practices/h_cluster/tests/test_transport.py
@@ -0,0 +1,66 @@
+import asyncio
+
+import httpx
+from harborrl_h_cluster.transport import SandboxStartupTransport
+
+
+def test_transient_endpoint_retries_do_not_recreate_sandbox():
+ async def run():
+ calls, ids = [], []
+
+ async def serve(request):
+ calls.append((request.method, request.url.path))
+ if request.method == 'POST':
+ return httpx.Response(201, json={'id': 'sbx-one'})
+ if len(calls) < 4:
+ return httpx.Response(503)
+ return httpx.Response(200, json={'endpoint': 'ready'})
+
+ transport = SandboxStartupTransport('https://platform.test/v1', ids.append, timeout=10,
+ transport=httpx.MockTransport(serve), retry_delay=0)
+ async with httpx.AsyncClient(transport=transport) as client:
+ await client.post('https://platform.test/v1/sandboxes', json={})
+ response = await client.get('https://platform.test/v1/sandboxes/sbx-one/endpoints/44772')
+ assert response.status_code == 200
+ assert ids == ['sbx-one']
+ assert sum(method == 'POST' for method, _ in calls) == 1
+ assert len(calls) == 4
+
+ asyncio.run(run())
+
+
+def test_mutations_proxy_requests_and_auth_failures_are_not_retried():
+ async def run():
+ calls = []
+
+ async def serve(request):
+ calls.append(request)
+ return httpx.Response(401 if '/endpoints/' in request.url.path else 503)
+
+ transport = SandboxStartupTransport('https://platform.test/v1', lambda _: None, timeout=10,
+ transport=httpx.MockTransport(serve), retry_delay=0)
+ async with httpx.AsyncClient(transport=transport) as client:
+ await client.post('https://platform.test/v1/sandboxes')
+ await client.get('https://platform.test/v1/sandboxes/sbx-one/proxy/44772')
+ await client.get('https://platform.test/v1/sandboxes/sbx-one/endpoints/44772')
+ assert len(calls) == 3
+
+ asyncio.run(run())
+
+
+def test_endpoint_retry_deadline_returns_final_failure():
+ async def run():
+ calls = []
+
+ async def serve(request):
+ calls.append(request)
+ return httpx.Response(503)
+
+ transport = SandboxStartupTransport('https://platform.test/v1', lambda _: None, timeout=0,
+ transport=httpx.MockTransport(serve), retry_delay=0)
+ async with httpx.AsyncClient(transport=transport) as client:
+ response = await client.get('https://platform.test/v1/sandboxes/sbx-one/endpoints/44772')
+ assert response.status_code == 503
+ assert len(calls) == 1
+
+ asyncio.run(run())
diff --git a/pyproject.toml b/pyproject.toml
index ed654f6bb..1cbdca1ad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,10 +16,16 @@ dependencies = [
harborrl = "harborrl.cli:main"
[project.optional-dependencies]
+sandbox = [
+ "harbor==0.23.0; python_version >= '3.12'",
+ "opensandbox==0.1.9; python_version >= '3.12'",
+ "httpx>=0.27,<1",
+]
dev = [
"pytest>=8.0",
"pytest-asyncio>=0.24",
"ruff>=0.8",
+ "httpx>=0.27,<1",
]
[tool.uv]
@@ -31,6 +37,8 @@ exclude = ["runs*", ".venv*", "workspace*", "local*", "docs*", "tests*"]
[tool.setuptools.package-data]
"harborrl.platform" = ["*.sh"]
+"harborrl.options" = ["resources/*.yaml"]
+"harborrl.webui" = ["static/*"]
[tool.pytest.ini_options]
testpaths = ["tests"]
diff --git a/scripts/audit_public_tree.sh b/scripts/audit_public_tree.sh
index 7f80ce653..b9c044203 100755
--- a/scripts/audit_public_tree.sh
+++ b/scripts/audit_public_tree.sh
@@ -4,11 +4,40 @@ set -euo pipefail
ROOT="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/.." && pwd)"
cd "$ROOT"
-python3 - <<'PY'
+python3 - "$@" <<'PY'
+import argparse
from pathlib import Path
import re
import subprocess
+parser = argparse.ArgumentParser(description="Audit public files and optional new-commit document history.")
+parser.add_argument("--history-base", help="Check every commit after this ancestor, including deleted documents.")
+args = parser.parse_args()
+
+public_documents = {
+ "CONTRIBUTING.md", "NOTICE.md", "README.md", "README_zh.md", "SECURITY.md",
+ "configs/harbor_hub/public_compatibility_summary.md",
+ "configs/harbor_hub/public_compatibility_summary_zh.md",
+ "docs/agents/environment-check.md", "docs/configuration-wizard.md",
+ "docs/deployment.md", "docs/sandbox-rollout.md",
+ "docs/train-inference-consistency.md", "docs/web-console.md",
+ "practices/h_cluster/README.md", "harborrl/webui/static/lucide-LICENSE.txt",
+}
+
+
+def audit_documents(names, location):
+ for name in names:
+ path = Path(name)
+ if path.suffix.lower() not in {".md", ".rst", ".adoc", ".txt"}:
+ continue
+ task_instruction = (
+ path.parts[:3] == ("examples", "native", "tasks")
+ and path.name == "instruction.md"
+ )
+ if name not in public_documents and not task_instruction:
+ raise SystemExit(f"document is not approved for publication ({location}): {name}")
+
+
root = Path(".")
files = subprocess.check_output(
["git", "ls-files", "-z"], text=True
@@ -16,6 +45,23 @@ files = subprocess.check_output(
files = [Path(name) for name in files if name]
if not files:
raise SystemExit("tracked tree is empty")
+audit_documents([path.as_posix() for path in files], "index")
+
+if args.history_base:
+ if subprocess.run(
+ ["git", "merge-base", "--is-ancestor", args.history_base, "HEAD"],
+ stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
+ ).returncode:
+ raise SystemExit("history base must resolve to an ancestor of HEAD")
+ commits = subprocess.check_output(
+ ["git", "rev-list", f"{args.history_base}..HEAD"], text=True
+ ).splitlines()
+ for commit in commits:
+ names = subprocess.check_output(
+ ["git", "ls-tree", "-r", "--name-only", "-z", commit], text=True
+ ).split("\0")
+ audit_documents([name for name in names if name], commit[:12])
+ print(f"public document history audit passed: {len(commits)} new commits")
forbidden_directories = {"backends", "benchmarks", "deploy", "runs", "tools"}
for path in files:
@@ -36,10 +82,11 @@ internal_patterns = {
"personal path": re.compile(rb"/(mnt/shared-storage-user|home/[A-Za-z0-9_.-]+|root)/"),
"internal DNS": re.compile(rb"(?i)[a-z0-9.-]+\.(local|internal|svc)\b"),
"private registry": re.compile(rb"(?i)(registry\.[a-z0-9.-]+/(?:[a-z0-9_.-]+/){2,})"),
+ "cluster DNS": re.compile(rb"(?i)[a-z0-9.-]+\.(?:pjlab|p\x6a-lab)\.org\.cn\b"),
}
text_suffixes = {
".cfg", ".env", ".github", ".in", ".json", ".md", ".py", ".sh", ".toml",
- ".txt", ".yaml", ".yml",
+ ".txt", ".yaml", ".yml", ".lock",
}
for path in files:
if path.suffix.lower() not in text_suffixes and path.name != "Dockerfile":
diff --git a/scripts/collect_service_evidence.py b/scripts/collect_service_evidence.py
new file mode 100755
index 000000000..fa55d4add
--- /dev/null
+++ b/scripts/collect_service_evidence.py
@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+"""Collect service-mode run evidence for the controller-placement validation.
+
+Reads one run directory and reports: exit code, policy versions, checkpoints,
+rollout groups/slots (from the RolloutService artifacts), RL_READY counts,
+rewards, trace seals, integrity errors, and cleanup records. Machine-readable
+JSON goes to stdout; exit 0 only when the mandatory gates hold.
+"""
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+
+def read_json(path):
+ return json.loads(Path(path).read_text())
+
+
+def main(run_dir):
+ run_dir = Path(run_dir)
+ report = {"run_dir": str(run_dir), "gates": {}}
+
+ exit_code = int((run_dir / "exit-code").read_text().strip())
+ report["exit_code"] = exit_code
+ report["gates"]["exit_code"] = exit_code == 0
+
+ # Policy versions: the manager's activation publishes policy-pool.json
+ # (snapshot) and each completed group records its admitted version.
+ versions = []
+ snapshot = run_dir / "policy-pool.json"
+ final_version = None
+ if snapshot.is_file():
+ final_version = read_json(snapshot).get("weight_version")
+ for result in sorted(run_dir.glob("rollout-service/groups/*/group-*/result.json")):
+ versions.append(read_json(result)["policy_version"])
+ report["policy_versions"] = {"groups": versions, "final_pool": final_version}
+ report["gates"]["policy_advanced"] = len(set(versions)) >= 2 and (
+ final_version is not None and str(final_version) == str(versions[-1]))
+
+ checkpoints = sorted(p.name for p in run_dir.glob("checkpoints/*/iter_*")) \
+ if run_dir.joinpath("checkpoints").is_dir() else []
+ report["checkpoints"] = checkpoints
+ report["gates"]["checkpoints_written"] = len(checkpoints) >= 2
+
+ # Service artifacts: groups executed by the manager.
+ groups_root = run_dir / "rollout-service" / "groups"
+ groups = {}
+ if groups_root.is_dir():
+ for run_id_dir in groups_root.iterdir():
+ for group_dir in run_id_dir.iterdir():
+ summary = {"group_id": group_dir.name, "states": [],
+ "has_result": (group_dir / "result.json").is_file()}
+ for state in ("running", "result", "cancelled", "failed"):
+ if (group_dir / f"{state}.json").is_file():
+ summary["states"].append(state)
+ groups[group_dir.name] = summary
+ report["service_groups"] = groups
+ report["gates"]["service_groups_complete"] = bool(groups) and all(
+ g["states"] and "result" in g["states"] and g["has_result"]
+ for g in groups.values())
+
+ # Native rollouts + traces.
+ attempts = list((run_dir / "native-rollouts").rglob("trajectory.v2.json")) \
+ if (run_dir / "native-rollouts").is_dir() else []
+ readiness, rewards, integrity, seals = {}, [], 0, {"sealed": 0, "unsealed": 0}
+ turns_total = 0
+ for path in attempts:
+ ir = read_json(path)
+ readiness[ir["readiness"]] = readiness.get(ir["readiness"], 0) + 1
+ integrity += len(ir.get("integrity_errors", []))
+ rewards.append(ir["evaluation"]["training_reward"])
+ turns_total += len(ir["turns"])
+ if ir["trace_seal"]["status"] == "sealed" and not ir["trace_seal"]["errors"]:
+ seals["sealed"] += 1
+ else:
+ seals["unsealed"] += 1
+ report["trajectories"] = {
+ "count": len(attempts), "readiness": readiness, "rewards": rewards,
+ "turns_total": turns_total, "integrity_errors": integrity, "seals": seals}
+ report["gates"]["all_rl_ready"] = len(attempts) >= 6 and readiness.get("RL_READY") == len(attempts)
+ report["gates"]["zero_integrity_errors"] = integrity == 0
+ report["gates"]["zero_unsealed"] = seals["unsealed"] == 0
+
+ cleanup = [p.name for p in run_dir.rglob("*cleanup-required.json")]
+ unsealed = [p.name for p in run_dir.rglob("*trace-unsealed.json")]
+ failed = [p.name for p in run_dir.rglob("*failure.json")]
+ report["cleanup_records"] = {"cleanup_required": cleanup, "trace_unsealed": unsealed,
+ "failures": failed}
+ report["gates"]["clean_run"] = not (cleanup or unsealed or failed)
+
+ passed = int(exit_code) == 0 and all(report["gates"].values())
+ report["summary"] = ("PASS" if passed else "FAIL") + \
+ f" gates={report['gates']}"
+ print(json.dumps(report, indent=2, ensure_ascii=False))
+ return 0 if passed else 1
+
+
+if __name__ == "__main__":
+ if len(sys.argv) != 2:
+ raise SystemExit("usage: collect_service_evidence.py RUN_DIR")
+ raise SystemExit(main(sys.argv[1]))
diff --git a/scripts/readiness_dial.py b/scripts/readiness_dial.py
new file mode 100755
index 000000000..7268c4563
--- /dev/null
+++ b/scripts/readiness_dial.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""Live cross-machine readiness dials for the controller placement validation.
+
+Starts a temporary control plane + a /readyz listener on the manager, then
+dials them from the GPU target and the rollout worker over SSH — the same
+hops the real deployment requires (gpu_to_controller, worker_to_gateway).
+"""
+from __future__ import annotations
+
+import ipaddress
+import json
+import subprocess
+import sys
+import threading
+import time
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+
+from harborrl.rollout.service import RolloutService, make_control_server
+
+
+def ssh(spec, command, timeout=20):
+ result = subprocess.run(
+ ["ssh", "-o", "BatchMode=yes", "-o", "ConnectTimeout=10",
+ "-o", "StrictHostKeyChecking=accept-new", spec, command],
+ capture_output=True, text=True, timeout=timeout, check=False)
+ return result.returncode == 0, (result.stdout + result.stderr).strip()[-200:]
+
+
+def main(gpu_spec, worker_spec, manager_address):
+ manager_address = str(ipaddress.IPv4Address(manager_address))
+ # 1) a real control plane on the manager's control port
+ service = RolloutService(run_root="/tmp/harborrl-readiness")
+ control = make_control_server(service, host="0.0.0.0", port=49200)
+ control_thread = threading.Thread(target=control.serve_forever, daemon=True)
+ control_thread.start()
+ # 2) a plain /readyz listener standing in for the gateway port
+ class Gateway(ThreadingHTTPServer):
+ daemon_threads = True
+
+ class Handler(BaseHTTPRequestHandler):
+ def log_message(self, *args):
+ pass
+
+ def do_GET(self):
+ body = b'{"ok": true}'
+ self.send_response(200)
+ self.send_header("Content-Type", "application/json")
+ self.send_header("Content-Length", str(len(body)))
+ self.end_headers()
+ self.wfile.write(body)
+
+ gateway = Gateway(("0.0.0.0", 49184), Handler)
+ gateway_thread = threading.Thread(target=gateway.serve_forever, daemon=True)
+ gateway_thread.start()
+ time.sleep(1)
+
+ checks = []
+ checks.append(("manager.control_plane_listening",
+ *ssh("localhost", "true"))) # manager-local sanity
+ # Direct local health check of the control plane.
+ local = subprocess.run(
+ ["python3", "-c",
+ "import urllib.request;urllib.request.urlopen('http://127.0.0.1:49200/healthz',timeout=5)"],
+ capture_output=True, text=True, timeout=10, check=False)
+ checks.append(("manager.control_healthz_local", local.returncode == 0,
+ "healthz answered on the manager"))
+ checks.append(("gpu_to_controller",
+ *ssh(gpu_spec,
+ "python3 -c \"import urllib.request;"
+ f"urllib.request.urlopen('http://{manager_address}:49200/healthz',"
+ "timeout=10)\"")))
+ checks.append(("worker_to_gateway",
+ *ssh(worker_spec,
+ "python3 -c \"import urllib.request;"
+ f"urllib.request.urlopen('http://{manager_address}:49184/readyz',"
+ "timeout=10)\"")))
+
+ control.shutdown()
+ gateway.shutdown()
+ report = [{"check": name, "ok": ok, "detail": detail}
+ for name, ok, detail in checks]
+ passed = all(item["ok"] for item in report)
+ print(json.dumps({"readiness": report,
+ "summary": "PASS" if passed else "FAIL"}, indent=2))
+ return 0 if passed else 1
+
+
+if __name__ == "__main__":
+ if len(sys.argv) != 4:
+ raise SystemExit("usage: readiness_dial.py GPU_SSH_SPEC WORKER_SSH_SPEC MANAGER_IPV4")
+ raise SystemExit(main(sys.argv[1], sys.argv[2], sys.argv[3]))
diff --git a/scripts/rollout_service_smoke.py b/scripts/rollout_service_smoke.py
new file mode 100755
index 000000000..c0de2e1a9
--- /dev/null
+++ b/scripts/rollout_service_smoke.py
@@ -0,0 +1,263 @@
+#!/usr/bin/env python3
+"""CPU smoke for the RolloutService control plane (no GPU, no Harbor, no SSH).
+
+Starts a real control server on loopback with a synthetic executor, then
+drives the full lifecycle through the production HTTP client: register,
+policy gate, activate, idempotent submit, conflict rejection, digest
+validation, cancel, drain. Exits 0 only when every contract holds.
+"""
+from __future__ import annotations
+
+import asyncio
+import json
+import tempfile
+import threading
+from pathlib import Path
+from types import SimpleNamespace
+
+from harborrl.backends.slime_v032.rollout_service_client import (
+ RolloutServiceClient,
+ ServiceRolloutError,
+ load_group_result,
+)
+from harborrl.rollout.harbor_job.collector import collect
+from harborrl.rollout.service import (
+ RolloutService,
+ ServiceError,
+ make_control_server,
+ manifest_path_from,
+)
+from harborrl.trajectories.native import (
+ Identity,
+ RewardProfile,
+ assemble,
+ digest,
+ publish,
+)
+
+SAMPLING = {"temperature": 1.0, "top_p": 1.0, "top_k": -1,
+ "max_new_tokens": 64, "stop": []}
+TASK = {"id": "smoke-task", "revision": "1", "path": "/task",
+ "task_digest": "d" * 64,
+ "reward_profile": {"key": "reward", "scale": 1.0, "offset": 0.0,
+ "raw_range": [0.0, 1.0]}}
+RUN_ID = "smoke-run-1"
+
+
+def step(name, ok, detail=""):
+ status = "PASS" if ok else "FAIL"
+ print(f"[{status}] {name}" + (f" — {detail}" if detail else ""))
+ return ok
+
+
+def identity(slot, *, group_id, policy_version):
+ return Identity(run_id=RUN_ID, batch_id="rollout-0", group_id=group_id,
+ slot_id=slot, attempt_id=f"attempt-{policy_version}-{slot}",
+ trajectory_id=f"trajectory-{policy_version}-{slot}",
+ task_digest=TASK["task_digest"],
+ policy_version=policy_version, harness_digest="claude",
+ reward_digest=RewardProfile().digest,
+ sampling_digest=digest(SAMPLING))
+
+
+def synthetic_ir(root, slot, reward, *, group_id, policy_version):
+ ident = identity(slot, group_id=group_id, policy_version=policy_version)
+ attempt_root = root / "receipts" / ident.attempt_id
+ attempt_root.mkdir(parents=True, exist_ok=True)
+ result = {"id": "trial-" + ident.attempt_id,
+ "finished_at": "2026-10-21T00:00:00Z",
+ "verifier_environment_mode": "shared",
+ "verifier_result": {"rewards": {"reward": reward}}}
+ (attempt_root / "result.json").write_text(json.dumps(result))
+ (attempt_root / "reward.json").write_text(json.dumps({"reward": reward}))
+ evaluation = collect(ident, result["id"], attempt_root / "result.json",
+ attempt_root, RewardProfile(), evidence_kind="synthetic")
+ trial = evaluation["harbor_trial_id"]
+ turn = {"identity": ident.to_dict(), "harbor_trial_id": trial,
+ "request_id": f"r-{slot}", "response_id": f"m-{slot}",
+ "engine_id": "engine", "input_ids": [1, 2], "output_ids": [3, 4],
+ "logprobs": [-0.2, -0.3], "logprob_semantics": "raw_model",
+ "policy_version": policy_version, "tokenizer_digest": "tok",
+ "template_digest": "template", "delivery": "consumed_confirmed",
+ "consumption_ref": "session#digest", "role": "policy",
+ "sampling": SAMPLING, "client_request": {"model": "policy"},
+ "serving_input": {"input_ids": [1, 2]},
+ "response": {"content": [{"type": "text", "text": "done"}]},
+ "finish_reason": "end_turn", "evidence_kind": "synthetic"}
+ for key in ("client_request", "serving_input", "response"):
+ turn[key + "_digest"] = digest(turn[key])
+ seal = {"identity": ident.to_dict(), "harbor_trial_id": trial,
+ "pending_requests": 0, "status": "sealed",
+ "request_ids": [turn["request_id"]], "turns_digest": digest([turn]),
+ "errors": []}
+ return assemble(ident, trial, [turn], seal, evaluation)
+
+
+class Registry:
+ def __init__(self):
+ self.version, self.accepting = None, False
+
+ def drain(self, timeout):
+ self.accepting = False
+
+ def publish_version(self, states, expected):
+ self.version, self.accepting = str(expected), True
+
+
+class Runtime:
+ def __init__(self, root):
+ self.registry, self.rollout_root = Registry(), root
+
+ def manifest_path(self, ident):
+ return manifest_path_from(self.rollout_root, ident)
+
+ def close(self):
+ pass
+
+
+def group_request(group_index, *, policy_version="1", slots=2):
+ return {"group_id": f"group-{group_index}", "rollout_id": 0,
+ "group_index": group_index, "policy_version": policy_version,
+ "task": TASK, "sampling": dict(SAMPLING),
+ "slots": [{"slot_id": str(slot)} for slot in range(slots)]}
+
+
+def main():
+ root = Path(tempfile.mkdtemp(prefix="harborrl-service-smoke-"))
+ executions = []
+
+ async def executor(config, runtime_, request):
+ executions.append(request["group_id"])
+ if request["group_index"] >= 1:
+ while True: # later groups block until they are cancelled
+ await asyncio.sleep(0.05)
+ irs = []
+ for descriptor, reward in zip(request["slots"], (1.0, 0.0)):
+ ir = synthetic_ir(root, descriptor["slot_id"], reward,
+ group_id=request["group_id"],
+ policy_version=request["policy_version"])
+ publish(runtime_.manifest_path(ir["identity"]), ir)
+ irs.append(ir)
+ return irs
+
+ runtime = Runtime(root)
+ service = RolloutService(run_root=root, executor=executor, allow_synthetic=True,
+ group_wait_timeout=10)
+ # The smoke owns the service process, so it registers with its own token
+ # instead of consuming the one-time generated token.
+ service.register_run(RUN_ID, {"run_id": RUN_ID, "group_size": 2,
+ "purpose": "cpu service smoke"},
+ token="smoke-token")
+ service.attach_runtime(RUN_ID, SimpleNamespace(drain_timeout=1), runtime)
+ control = make_control_server(service, host="127.0.0.1", port=0)
+ thread = threading.Thread(target=control.serve_forever, daemon=True)
+ thread.start()
+ url = f"http://127.0.0.1:{control.server_address[1]}"
+ client = RolloutServiceClient(url, RUN_ID, "smoke-token", timeout=10,
+ poll_interval=0.05)
+
+ results = []
+ try:
+ results.append(step("healthz answers", client.health()["ok"]))
+ # Register is idempotent by manifest digest.
+ replay = service.register_run(RUN_ID, {"run_id": RUN_ID,
+ "group_size": 2,
+ "purpose": "cpu service smoke"})
+ results.append(step("register replay is idempotent", replay["existed"]))
+ try:
+ service.register_run(RUN_ID, {"run_id": RUN_ID, "other": 1})
+ results.append(step("manifest conflict rejected", False))
+ except ServiceError as exc:
+ results.append(step("manifest conflict rejected", exc.status == 409))
+
+ # Auth: a wrong token never reaches group endpoints.
+ bad = RolloutServiceClient(url, RUN_ID, "wrong", timeout=10)
+ try:
+ bad.group_status("group-0")
+ results.append(step("bad token rejected", False))
+ except ServiceRolloutError as exc:
+ results.append(step("bad token rejected", exc.status == 401))
+
+ # Policy gate: nothing runs before activation.
+ try:
+ client.submit_group(group_request(0))
+ results.append(step("submit before activate rejected", False))
+ except ServiceRolloutError as exc:
+ results.append(step("submit before activate rejected",
+ exc.code == "stale_policy"))
+
+ states = [{"endpoint": f"http://engine-{i}", "healthy": True,
+ "weight_version": "1"} for i in range(2)]
+ activation = client.activate_policy(states, "1")
+ results.append(step("policy activation opens admission",
+ activation["policy_version"] == "1"
+ and runtime.registry.accepting))
+ pool = json.loads((root / "policy-pool.json").read_text())
+ results.append(step("policy pool published to shared run dir",
+ pool["weight_version"] == "1"))
+
+ request = group_request(0)
+ first = client.submit_group(request)
+ results.append(step("group completes with two slots",
+ first["status"] == "complete"
+ and len(first["result"]["slots"]) == 2))
+ assert client.group_status("group-0")["status"] == "complete"
+ again = client.submit_group(json.loads(json.dumps(request)))
+ results.append(step("resubmit is idempotent",
+ again["result"]["request_digest"]
+ == first["result"]["request_digest"]
+ and executions.count("group-0") == 1,
+ f"executions={executions}"))
+ conflicting = group_request(0)
+ conflicting["sampling"]["temperature"] = 0.5
+ try:
+ client.submit_group(conflicting)
+ results.append(step("same id different content rejected", False))
+ except ServiceRolloutError as exc:
+ results.append(step("same id different content rejected",
+ exc.code == "conflict"))
+
+ trajectories = load_group_result(
+ RUN_ID, request, first["result"],
+ allow_synthetic=any(slot["readiness"] != "RL_READY"
+ for slot in first["result"]["slots"]))
+ results.append(step("client validates manifests and digests",
+ [ir["identity"]["slot_id"] for ir in trajectories]
+ == ["0", "1"]))
+ export_path = Path(first["result"]["group_export"]["path"])
+ export = json.loads(export_path.read_text())
+ results.append(step("group export lands on shared storage",
+ export["trajectory_count"] == 2
+ and export["mean"] == 0.5, str(export_path.relative_to(root))))
+
+ # Cancel a group that is still running, then stop admitting.
+ slow = group_request(1)
+ service.group_wait_timeout = 0.2
+ pending = service.submit_group(RUN_ID, slow)
+ results.append(step("timeout answer says running and points at status",
+ pending["status"] in ("queued", "running")
+ and "hint" in pending))
+ cancelled = client.cancel_group("group-1")
+ results.append(step("running group cancels",
+ cancelled["status"] == "cancelled"))
+
+ client.drain()
+ try:
+ client.submit_group(group_request(2))
+ results.append(step("drain closes admission", False))
+ except ServiceRolloutError as exc:
+ results.append(step("drain closes admission",
+ exc.code == "admission_closed"))
+ finally:
+ control.shutdown()
+ control.server_close()
+ thread.join(5)
+ service.close()
+
+ print(f"\nrun root: {root}")
+ print(f"{sum(results)}/{len(results)} checks passed")
+ return 0 if all(results) else 1
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/sandbox_sdk_contract.py b/scripts/sandbox_sdk_contract.py
new file mode 100644
index 000000000..9957d6ee7
--- /dev/null
+++ b/scripts/sandbox_sdk_contract.py
@@ -0,0 +1,60 @@
+"""Check the actual optional SDK/Harbor imports without allocating resources."""
+import asyncio
+import inspect
+import json
+import tempfile
+from pathlib import Path
+from types import SimpleNamespace
+
+from harbor.models.trial.config import TrialConfig
+from opensandbox import Sandbox
+
+from harborrl.harnesses.offline_claude import OfflineClaudeCode
+from harborrl.rollout.harbor_job.environments.opensandbox import (
+ ManagedOpenSandboxEnvironment,
+)
+from harborrl.trajectories.native import read_json
+
+sdk = ManagedOpenSandboxEnvironment._load_opensandbox(None)
+assert {'extensions', 'skip_health_check', 'ready_timeout'} <= inspect.signature(Sandbox.create).parameters.keys()
+assert 'import_path' in TrialConfig.model_fields['environment'].annotation.model_fields
+assert not inspect.isabstract(OfflineClaudeCode)
+
+
+async def cleanup_contract():
+ with tempfile.TemporaryDirectory() as directory:
+ env = object.__new__(ManagedOpenSandboxEnvironment)
+ env._startup_transport = None
+ env.cfg = {'cleanup_timeout_s': 1}
+ env.evidence = Path(directory)
+ env.binding = {'image': 'image-test'}
+ env.record = {'sandbox_id': 'sandbox-test', 'runtime_digest': 'test', 'events': []}
+ calls = []
+
+ async def kill():
+ calls.append('kill')
+
+ async def close():
+ calls.append('close')
+
+ env._sandbox = SimpleNamespace(kill=kill, close=close)
+ await env.stop()
+ assert calls == ['kill', 'close']
+ assert read_json(env.evidence / 'sandbox-lifecycle.json')['state'] == 'deleted'
+
+ async def failed_kill():
+ raise RuntimeError('secret-must-not-appear')
+
+ env._sandbox = SimpleNamespace(kill=failed_kill, close=close)
+ try:
+ await env.stop()
+ except RuntimeError as exc:
+ assert str(exc) == 'Sandbox deletion failed'
+ else:
+ raise AssertionError('deletion failure was swallowed')
+ assert 'secret-must-not-appear' not in (env.evidence / 'cleanup-required.json').read_text()
+ assert read_json(env.evidence / 'sandbox-lifecycle.json')['state'] == 'delete_failed'
+
+
+asyncio.run(cleanup_contract())
+print(json.dumps({'sdk_symbols': sorted(sdk), 'agent': OfflineClaudeCode.__name__, 'status': 'passed'}))
diff --git a/scripts/sandbox_smoke.py b/scripts/sandbox_smoke.py
new file mode 100644
index 000000000..c297c381d
--- /dev/null
+++ b/scripts/sandbox_smoke.py
@@ -0,0 +1,65 @@
+"""Explicit single-instance live probe. Uses real quota and always attempts deletion."""
+import argparse
+import asyncio
+import json
+import shlex
+from pathlib import Path
+from urllib.parse import urlsplit
+
+from harbor.models.task.config import EnvironmentConfig
+from harbor.models.trial.paths import TrialPaths
+
+from harborrl.rollout.harbor_job.sandbox_config import environment_class, load_sandbox
+from harborrl.rollout.harbor_job.sandbox_probe import check
+from harborrl.trajectories.native import publish
+
+
+async def run(args):
+ cfg = load_sandbox(args.config)
+ check(cfg)
+ root = Path(args.output).resolve()
+ root.mkdir(parents=True, exist_ok=False)
+ task = root / 'environment'
+ task.mkdir()
+ binding = cfg['bindings'][0]
+ adapter = environment_class(cfg)
+ env = adapter(
+ environment_dir=task, environment_name='sandbox-smoke', session_id=root.name,
+ trial_paths=TrialPaths(root / 'trial'), task_env_config=EnvironmentConfig(docker_image=binding['image']),
+ sandbox_config=cfg, binding=binding, evidence_dir=str(root), startup_dir=str(root / 'startup'),
+ gateway_url=args.gateway)
+ results = {}
+ try:
+ await env.start(False)
+ ok = await env.exec('printf sandbox-ok')
+ assert ok.return_code == 0 and ok.stdout == 'sandbox-ok', 'positive command failed'
+ negative = await env.exec('exit 7')
+ assert negative.return_code == 7, 'negative command exit code lost'
+ source = root / 'source.bin'
+ source.write_bytes(b'harborrl\x00sandbox\xff\n')
+ await env.upload_file(source, '/tmp/harborrl-smoke.bin')
+ await env.download_file('/tmp/harborrl-smoke.bin', root / 'download.bin')
+ assert source.read_bytes() == (root / 'download.bin').read_bytes(), 'file roundtrip failed'
+ agent_gateway = adapter.agent_gateway(cfg, args.gateway)
+ gateway = urlsplit(agent_gateway)
+ if gateway.scheme not in ('http', 'https') or not gateway.hostname or gateway.path not in ('', '/'):
+ raise ValueError('gateway must be an HTTP origin')
+ command = f'curl -sS -o /dev/null --connect-timeout 10 --max-time 20 {shlex.quote(agent_gateway)}'
+ connected = await env.exec(command)
+ publish(root / 'gateway-probe.json', {'exit_code': connected.return_code,
+ 'stdout': connected.stdout, 'stderr': connected.stderr})
+ assert connected.return_code == 0, 'Sandbox cannot reach Gateway HTTP endpoint'
+ results.update(command=True, negative_exit=True, files=True, gateway_http=True)
+ finally:
+ await env.stop(True)
+ results['deleted'] = True
+ publish(root / 'smoke-result.json', results)
+ print(json.dumps(results))
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument('config')
+ parser.add_argument('--output', required=True)
+ parser.add_argument('--gateway', required=True)
+ asyncio.run(run(parser.parse_args()))
diff --git a/scripts/sync_options_manifest.py b/scripts/sync_options_manifest.py
new file mode 100644
index 000000000..ea0370ef3
--- /dev/null
+++ b/scripts/sync_options_manifest.py
@@ -0,0 +1,22 @@
+"""Generate the repository compatibility manifest from packaged metadata."""
+import argparse
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--check", action="store_true")
+ args = parser.parse_args()
+ source = ROOT / "harborrl/options/resources/datasets.yaml"
+ target = ROOT / "configs/harbor_hub/manifests.yaml"
+ expected = source.read_text(encoding="utf-8")
+ if args.check:
+ return int(target.read_text(encoding="utf-8") != expected)
+ target.write_text(expected, encoding="utf-8")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/tests/harborrl/test_delivery_barrier.py b/tests/harborrl/test_delivery_barrier.py
new file mode 100644
index 000000000..e6c0f0955
--- /dev/null
+++ b/tests/harborrl/test_delivery_barrier.py
@@ -0,0 +1,41 @@
+"""Delivery acknowledgements must precede native-session consumption audit."""
+import threading
+
+import pytest
+
+from harborrl.gateway.trace import TraceRegistry
+
+
+def test_delivery_barrier_waits_and_wakes(tmp_path):
+ registry = TraceRegistry(tmp_path)
+ registry.attempts["attempt"] = {"pending": {"request"}, "turns": []}
+ entered = threading.Event()
+ finished = threading.Event()
+
+ def wait():
+ entered.set()
+ registry.wait_delivered("attempt", 2)
+ finished.set()
+
+ worker = threading.Thread(target=wait)
+ worker.start()
+ assert entered.wait(1)
+ assert not finished.is_set()
+ with registry.changed:
+ registry.attempts["attempt"]["pending"].clear()
+ registry.attempts["attempt"]["turns"].append({"delivery": "sent"})
+ registry.changed.notify_all()
+ worker.join(3)
+ assert finished.is_set()
+ assert not worker.is_alive()
+
+
+def test_delivery_barrier_rejects_pending_and_unknown(tmp_path):
+ registry = TraceRegistry(tmp_path)
+ registry.attempts["attempt"] = {"pending": {"request"}, "turns": []}
+ with pytest.raises(TimeoutError, match="pending"):
+ registry.wait_delivered("attempt", 0)
+ registry.attempts["attempt"]["pending"].clear()
+ registry.attempts["attempt"]["turns"].append({"delivery": "delivery_unknown"})
+ with pytest.raises(ValueError, match="not confirmed"):
+ registry.wait_delivered("attempt", 0)
diff --git a/tests/harborrl/test_deployment_config.py b/tests/harborrl/test_deployment_config.py
new file mode 100644
index 000000000..8e6eb9f46
--- /dev/null
+++ b/tests/harborrl/test_deployment_config.py
@@ -0,0 +1,176 @@
+"""Deployment schema v1: parsing, strictness, safety and defaults."""
+from __future__ import annotations
+
+import json
+
+import pytest
+
+from harborrl.deployment.schema import (
+ DeploymentError,
+ embedded_local,
+ load_deployment,
+ summary,
+ validate,
+)
+
+
+def deployment_mapping(tmp_path, **overrides):
+ values = {
+ "schema_version": 1,
+ "manager": {
+ "id": "manager-a", "mode": "service", "bind": "0.0.0.0",
+ "control_port": 49200, "control_url": "http://manager-a:49200",
+ "gateway_port": 49184, "gateway_url": "http://manager-a:49184",
+ "run_token_file": str(tmp_path / "secrets" / "run-token"),
+ "source_dir": str(tmp_path / "manager-source"),
+ "controller_python": str(tmp_path / "manager-venv" / "bin" / "python"),
+ },
+ "gpu_target": {
+ "id": "gpu-8x-h200", "transport": "ssh",
+ "ssh": {"host": "gpu-cluster-a"},
+ "source_dir": str(tmp_path / "gpu-source"),
+ "train_python": str(tmp_path / "gpu-venv" / "bin" / "python"),
+ "environment_file": str(tmp_path / "gpu-env.json"),
+ "expected_gpus": 8,
+ },
+ "storage": {
+ "mode": "shared_fs", "run_root": str(tmp_path / "runs"),
+ "manager_writable": True, "gpu_writable": True, "worker_writable": True,
+ },
+ "rollout": {
+ "provider": "ssh_docker",
+ "workers": [
+ {"id": "worker-a", "ssh": {"host": "worker-a"},
+ "source_dir": str(tmp_path / "worker-source"),
+ "runner_python": str(tmp_path / "runner-venv" / "bin" / "python"),
+ "docker": True, "task_roots": [str(tmp_path / "tasks")]},
+ {"id": "worker-b", "ssh": {"host": "worker-b", "port": 2222},
+ "source_dir": str(tmp_path / "worker-source"),
+ "runner_python": str(tmp_path / "runner-venv" / "bin" / "python"),
+ "docker": True, "task_roots": [str(tmp_path / "tasks")]},
+ ],
+ },
+ "connectivity": {
+ "command_timeout_s": 30, "ssh_timeout_s": 15, "http_timeout_s": 10,
+ "gpu_to_controller": True, "worker_to_gateway": True,
+ "manager_to_gpu_inference": True,
+ },
+ }
+ for section, fields in overrides.items():
+ if section in values and isinstance(values[section], dict) and isinstance(fields, dict):
+ values[section].update(fields)
+ else:
+ values[section] = fields
+ return values
+
+
+def test_valid_deployment_normalizes_and_summarizes(tmp_path):
+ config = validate(deployment_mapping(tmp_path))
+ assert config["manager"]["control_url"] == "http://manager-a:49200"
+ assert config["rollout"]["workers"][1]["ssh"] == {"host": "worker-b", "port": 2222}
+ assert config["gpu_target"]["ssh"] == {"host": "gpu-cluster-a"}
+ plan_summary = summary(config, "deployment.yaml")
+ assert plan_summary["mode"] == "service"
+ assert plan_summary["gpu"]["transport"] == "ssh"
+ assert plan_summary["worker_ids"] == ["worker-a", "worker-b"]
+ assert plan_summary["config"] == config
+ assert summary(config, "deployment.yaml")["digest"] == plan_summary["digest"]
+
+
+@pytest.mark.parametrize("mutation, message", [
+ ({"manager": {"mode": "service", "control_url": "http://127.0.0.1:49200"}}, "loopback"),
+ ({"manager": {"control_url": "http://manager-a:49300"}}, "matching its declared port"),
+ ({"manager": {"gateway_port": 49200}}, "must differ"),
+ ({"manager": {"bind": ""}}, "bind"),
+ ({"gpu_target": {"transport": "local", "ssh": {"host": "gpu"}}}, "null for local"),
+ ({"gpu_target": {"transport": "rdma"}}, "transport"),
+ ({"gpu_target": {"expected_gpus": 0}}, "expected_gpus"),
+ ({"rollout": {"workers": []}}, "nonempty list"),
+ ({"connectivity": {"ssh_timeout_s": 0}}, "ssh_timeout_s"),
+ ({"storage": {"run_root": "relative/runs"}}, "absolute path"),
+])
+def test_invalid_deployments_fail_closed(tmp_path, mutation, message):
+ with pytest.raises(DeploymentError, match=message):
+ validate(deployment_mapping(tmp_path, **mutation))
+
+
+def test_duplicate_worker_ids_and_secret_fields_rejected(tmp_path):
+ values = deployment_mapping(tmp_path)
+ values["rollout"]["workers"][1]["id"] = "worker-a"
+ with pytest.raises(DeploymentError, match="duplicate"):
+ validate(values)
+ values = deployment_mapping(tmp_path)
+ values["gpu_target"]["ssh"]["password"] = "hunter2"
+ with pytest.raises(DeploymentError, match="never stored"):
+ validate(values)
+
+
+def test_service_mode_requires_reachable_urls_but_embedded_allows_loopback(tmp_path):
+ values = deployment_mapping(tmp_path, manager={"mode": "embedded"})
+ values["manager"]["control_url"] = "http://127.0.0.1:49200"
+ values["manager"]["gateway_url"] = "http://127.0.0.1:49184"
+ assert validate(values)["manager"]["mode"] == "embedded"
+
+
+def test_embedded_local_default_is_valid():
+ config = embedded_local(run_root="/tmp/harborrl-runs", workers=("worker-a",))
+ assert config["manager"]["mode"] == "embedded"
+ assert config["gpu_target"]["transport"] == "local"
+ assert config["rollout"]["provider"] == "embedded"
+ assert [worker["id"] for worker in config["rollout"]["workers"]] == ["worker-0"]
+ # Training-config SSH destinations are addresses, not ids: the embedded
+ # topology keeps slug ids while preserving the destination for access.
+ config = embedded_local(workers=("root@192.0.2.10", "worker-b"))
+ assert [worker["id"] for worker in config["rollout"]["workers"]] == [
+ "worker-0", "worker-1"]
+ assert config["rollout"]["workers"][0]["ssh"]["host"] == "root@192.0.2.10"
+
+
+def test_load_deployment_explicit_default_and_embedded(tmp_path, monkeypatch):
+ monkeypatch.chdir(tmp_path)
+ config, source = load_deployment()
+ assert source == "embedded-local"
+ assert config["manager"]["mode"] == "embedded"
+
+ (tmp_path / "deployment.yaml").write_text(
+ json.dumps(deployment_mapping(tmp_path)))
+ config, source = load_deployment()
+ assert source == str(tmp_path / "deployment.yaml")
+ assert config["manager"]["mode"] == "service"
+ with pytest.raises(DeploymentError, match="not found"):
+ load_deployment(tmp_path / "missing.yaml")
+
+
+def test_cli_dry_run_embeds_deployment_summary(tmp_path, capsys, monkeypatch):
+ from pathlib import Path
+
+ from test_native_training import native_config
+
+ config_path, task = native_config(tmp_path)
+ from harborrl.data.harbor.native_inspector import inspect_native
+
+ catalog_path = Path(
+ json.loads(config_path.read_text().split("tasks: ", 1)[1].split("\n", 1)[0])["catalog"])
+ catalog = json.loads(catalog_path.read_text())
+ catalog[0]["task_digest"] = inspect_native(task)["task_digest"]
+ catalog_path.write_text(json.dumps(catalog))
+
+ deployment_file = tmp_path / "deployment.yaml"
+ deployment_file.write_text(json.dumps(deployment_mapping(tmp_path)))
+
+ from harborrl.cli import main
+
+ assert main(["train", str(config_path), "--deployment", str(deployment_file),
+ "--dry-run"]) == 0
+ plan = json.loads(capsys.readouterr().out)
+ assert plan["deployment"]["mode"] == "service"
+ assert plan["deployment"]["gpu"]["transport"] == "ssh"
+ assert plan["deployment"]["source"] == str(deployment_file)
+
+ # Without a deployment file the embedded-local topology is attached.
+ monkeypatch.chdir(tmp_path)
+ deployment_file.unlink()
+ assert main(["train", str(config_path), "--dry-run"]) == 0
+ plan = json.loads(capsys.readouterr().out)
+ assert plan["deployment"]["mode"] == "embedded"
+ assert plan["deployment"]["source"] == "embedded-local"
diff --git a/tests/harborrl/test_deployment_preflight.py b/tests/harborrl/test_deployment_preflight.py
new file mode 100644
index 000000000..d7906c9be
--- /dev/null
+++ b/tests/harborrl/test_deployment_preflight.py
@@ -0,0 +1,241 @@
+"""Deployment preflight graph: role-layered checks with faked SSH transport."""
+from __future__ import annotations
+
+import json
+import socket
+import subprocess
+from pathlib import Path
+
+from test_deployment_config import deployment_mapping
+from test_native_training import native_config
+
+from harborrl.deployment import preflight as deployment_preflight
+from harborrl.deployment.schema import embedded_local, validate
+
+
+def test_bindable_rejects_existing_listener():
+ with socket.socket() as listener:
+ listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+ listener.bind(('127.0.0.1', 0))
+ listener.listen()
+ ok, _ = deployment_preflight._bindable(*listener.getsockname())
+ assert not ok
+
+
+def test_bindable_allows_http_server_restart_after_connection_close():
+ with socket.socket() as listener:
+ listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+ listener.bind(('127.0.0.1', 0))
+ listener.listen()
+ address = listener.getsockname()
+ with socket.create_connection(address, timeout=2) as client:
+ accepted, _ = listener.accept()
+ with accepted:
+ accepted.settimeout(2)
+ accepted.shutdown(socket.SHUT_WR)
+ assert client.recv(1) == b''
+ client.shutdown(socket.SHUT_WR)
+ assert accepted.recv(1) == b''
+ ok, detail = deployment_preflight._bindable(*address)
+ assert ok, detail
+
+
+def completed(returncode=0, stdout="", stderr=""):
+ return subprocess.CompletedProcess([], returncode, stdout, stderr)
+
+
+def fake_remote(responses):
+ """Responses keyed by command prefix; unmatched shell tests succeed."""
+ calls = []
+ probe = json.dumps({"harbor_version": "0.23.0", "python": "3.12.13"})
+
+ def run_ssh(spec, command, *, timeout=30, connect_timeout=15):
+ calls.append((dict(spec), command))
+ for prefix, result in responses.items():
+ if command.startswith(prefix):
+ return completed(**result) if isinstance(result, dict) else result
+ if "runner --probe" in command:
+ return completed(stdout=probe)
+ if command.startswith("test "):
+ return completed(stdout="ok\n") # shell test protocol: && echo ok
+ return completed()
+
+ return run_ssh, calls
+
+
+def resolvable(deployment):
+ """Point service URLs at loopback so preflight resolution checks pass."""
+ manager = deployment["manager"]
+ manager["control_url"] = f"http://127.0.0.1:{manager['control_port']}"
+ manager["gateway_url"] = f"http://127.0.0.1:{manager['gateway_port']}"
+ return deployment
+
+
+def service_deployment(tmp_path):
+ return validate(deployment_mapping(tmp_path))
+
+
+def training_plan(tmp_path):
+ config_path, task = native_config(tmp_path)
+ from harborrl.config import load_config
+ from harborrl.data.harbor.native_inspector import inspect_native
+
+ catalog_path = Path(
+ json.loads(config_path.read_text().split("tasks: ", 1)[1].split("\n", 1)[0])["catalog"])
+ catalog = json.loads(catalog_path.read_text())
+ catalog[0]["task_digest"] = inspect_native(task)["task_digest"]
+ catalog_path.write_text(json.dumps(catalog))
+ return {"config": load_config(config_path)}
+
+
+def by_name(checks, name):
+ return [check for check in checks if check.get("check") == name or check.get("path") == name]
+
+
+def test_gpu_layer_probes_remote_machine_semantics(tmp_path, monkeypatch):
+ plan, deployment = training_plan(tmp_path), resolvable(service_deployment(tmp_path))
+ (tmp_path / "runs").mkdir()
+ (tmp_path / "secrets").mkdir()
+ token = tmp_path / "secrets" / "run-token"
+ token.write_text("token\n")
+ token.chmod(0o600)
+ run_ssh, calls = fake_remote({
+ "nvidia-smi": completed(stdout="0\n1\n2\n3\n4\n5\n6\n7\n"),
+ })
+ monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh)
+ checks = deployment_preflight.preflight(plan, deployment)
+ assert all(check["ok"] for check in checks), checks
+ gpu_checks = [check for check in checks if check["role"] == "gpu_target"]
+ assert {check["check"] for check in gpu_checks} >= {
+ "gpu_target.ssh", "gpu_target.source_dir", "gpu_target.train_python",
+ "gpu_target.expected_gpus"}
+ assert all(check["machine"] == "gpu-8x-h200" for check in gpu_checks)
+ # Model paths are evaluated on the GPU machine, not from the manager.
+ tested = [command for _, command in calls if "test -d" in command]
+ assert any("/model" in command for command in tested)
+
+
+def test_gpu_layer_reports_shortage_and_unreachable_ssh(tmp_path, monkeypatch):
+ plan, deployment = training_plan(tmp_path), service_deployment(tmp_path)
+ (tmp_path / "runs").mkdir()
+ token = tmp_path / "secrets" / "run-token"
+ token.parent.mkdir()
+ token.write_text("token\n")
+ token.chmod(0o600)
+ run_ssh, _ = fake_remote({"nvidia-smi": completed(stdout="0\n1\n")})
+ monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh)
+ checks = deployment_preflight.preflight(plan, deployment)
+ shortage = by_name(checks, "gpu_target.expected_gpus")
+ assert shortage and not shortage[0]["ok"]
+ assert "visible GPUs" in shortage[0]["detail"]
+
+ run_ssh, _ = fake_remote({"": completed(returncode=255, stderr="Permission denied")})
+ monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh)
+ checks = deployment_preflight.preflight(plan, deployment)
+ unreachable = by_name(checks, "gpu_target.ssh")
+ assert unreachable and not unreachable[0]["ok"]
+ assert unreachable[0]["role"] == "gpu_target"
+ assert "hint" in unreachable[0]
+
+
+def test_worker_layer_validates_runner_runtime_and_docker(tmp_path, monkeypatch):
+ plan, deployment = training_plan(tmp_path), service_deployment(tmp_path)
+ (tmp_path / "runs").mkdir()
+ token = tmp_path / "secrets" / "run-token"
+ token.parent.mkdir()
+ token.write_text("token\n")
+ token.chmod(0o600)
+ probe = json.dumps({"harbor_version": "0.23.0", "python": "3.12.13"})
+ run_ssh, _ = fake_remote({
+ "docker info": completed(returncode=1, stderr="Cannot connect to the Docker daemon"),
+ "cd ": completed(stdout=probe),
+ })
+ monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh)
+ checks = deployment_preflight.preflight(plan, deployment)
+ docker = [check for check in checks if check["check"] == "worker.docker"]
+ assert len(docker) == 2 and not any(check["ok"] for check in docker)
+ assert all("Docker daemon" in check["hint"] for check in docker)
+ harbor = [check for check in checks if check["check"] == "worker.harbor_version"]
+ assert all(check["ok"] for check in harbor)
+
+ run_ssh, _ = fake_remote({"cd ": completed(stdout=json.dumps(
+ {"harbor_version": "0.22.0", "python": "3.12.13"}))})
+ monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh)
+ checks = deployment_preflight.preflight(plan, deployment)
+ harbor = [check for check in checks if check["check"] == "worker.harbor_version"]
+ assert not any(check["ok"] for check in harbor)
+ # a wrong python version is also a failed runtime gate
+ run_ssh, _ = fake_remote({"cd ": completed(stdout=json.dumps(
+ {"harbor_version": "0.23.0", "python": "3.11.4"}))})
+ monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh)
+ checks = deployment_preflight.preflight(plan, deployment)
+ runtime = [check for check in checks if check["check"] == "worker.runner_runtime"]
+ assert not any(check["ok"] for check in runtime)
+
+
+def test_manager_layer_checks_storage_token_and_ports(tmp_path):
+ plan = training_plan(tmp_path)
+ deployment = resolvable(service_deployment(tmp_path))
+ checks = deployment_preflight.preflight(plan, deployment)
+ assert not by_name(checks, "storage.run_root")[0]["ok"] # run root missing
+
+ (tmp_path / "runs").mkdir()
+ token = tmp_path / "secrets" / "run-token"
+ token.parent.mkdir()
+ token.write_text("token\n")
+ token.chmod(0o644)
+ checks = deployment_preflight.preflight(plan, deployment)
+ assert not by_name(checks, "manager.run_token_file")[0]["ok"]
+ token.chmod(0o600)
+ checks = deployment_preflight.preflight(plan, deployment)
+ assert by_name(checks, "manager.run_token_file")[0]["ok"]
+ assert by_name(checks, "manager.control_port")[0]["ok"]
+
+
+def test_control_loop_layer_defers_live_dials_and_inference(tmp_path, monkeypatch):
+ plan, deployment = training_plan(tmp_path), service_deployment(tmp_path)
+ (tmp_path / "runs").mkdir()
+ token = tmp_path / "secrets" / "run-token"
+ token.parent.mkdir()
+ token.write_text("token\n")
+ token.chmod(0o600)
+ run_ssh, _ = fake_remote({})
+ monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh)
+ monkeypatch.setattr(deployment_preflight.socket, "getaddrinfo",
+ lambda host, port: [(None, None, None, None, (host, port))])
+ checks = deployment_preflight.preflight(plan, deployment)
+ inference = by_name(checks, "connectivity.manager_to_gpu_inference")
+ assert inference and inference[0]["ok"] and "deferred" in inference[0]["detail"]
+ dials = [check for check in checks if check["check"].endswith(".dial")]
+ assert dials and all("deferred" not in check["detail"] or check["ok"] for check in dials)
+
+
+def test_doctor_gates_local_checks_on_gpu_transport(tmp_path, monkeypatch):
+ from harborrl.platform import preflight as platform_preflight
+
+ plan = training_plan(tmp_path)
+ plan["deployment"] = {"config": service_deployment(tmp_path),
+ "source": "deployment.yaml"}
+ run_ssh, _ = fake_remote({"nvidia-smi": completed(stdout="0\n" * 8)})
+ monkeypatch.setattr(platform_preflight, "_deployment",
+ lambda p: p.get("deployment", {}).get("config"))
+ monkeypatch.setattr(deployment_preflight.remote, "run_ssh", run_ssh)
+ checks = platform_preflight.doctor(plan)
+ # Local GPU dependency probes do not run for a remote GPU target.
+ assert not any(check.get("dependency") == "ray" for check in checks)
+ assert not any(check.get("resource") == "visible GPU budget" for check in checks)
+ assert not any(check.get("service") == "external Harbor worker" for check in checks)
+ assert any(check.get("role") == "gpu_target" for check in checks)
+
+ embedded = {"config": embedded_local(run_root=str(tmp_path / "runs")),
+ "source": "deployment.yaml"}
+ plan["deployment"] = embedded
+ checks = platform_preflight.doctor(plan)
+ assert any(check.get("dependency") == "ray" for check in checks)
+ assert any(check.get("resource") == "visible GPU budget" for check in checks)
+
+ # The auto-derived embedded-local topology keeps the legacy check set.
+ plan["deployment"] = {"config": embedded_local(), "source": "embedded-local"}
+ legacy = platform_preflight.doctor(plan)
+ assert not any(check.get("role") == "gpu_target" for check in legacy)
+ assert any(check.get("dependency") == "ray" for check in legacy)
diff --git a/tests/harborrl/test_deployment_targets.py b/tests/harborrl/test_deployment_targets.py
new file mode 100644
index 000000000..ef609d9ad
--- /dev/null
+++ b/tests/harborrl/test_deployment_targets.py
@@ -0,0 +1,235 @@
+"""TrainingTarget (P1): local child process and SSH launcher over shared storage."""
+from __future__ import annotations
+
+import json
+import subprocess
+import time
+
+import pytest
+
+from harborrl.deployment import training_target
+from harborrl.deployment.training_target import (
+ LocalTrainingTarget,
+ SshTrainingTarget,
+ TrainingTargetError,
+)
+
+
+def completed(returncode=0, stdout="", stderr=""):
+ return subprocess.CompletedProcess([], returncode, stdout, stderr)
+
+
+def test_local_target_runs_and_forwards_exit_code(tmp_path):
+ target = LocalTrainingTarget(["bash", "-c", "exit 3"], cwd=str(tmp_path), env={"PATH": "/bin"})
+ target.launch()
+ assert target.wait() == 3
+ assert target.query()["state"] == "exited"
+
+
+def test_local_target_cancel_interrupts(tmp_path):
+ target = LocalTrainingTarget(["bash", "-c", "trap '' INT; sleep 30"],
+ cwd=str(tmp_path), env={"PATH": "/bin"})
+ target.launch()
+ time.sleep(0.2)
+ assert target.query()["state"] == "running"
+ target.cancel()
+ with pytest.raises(subprocess.TimeoutExpired):
+ target.child.wait(timeout=2)
+ import os
+ import signal
+ os.killpg(os.getpgid(target.child.pid), signal.SIGKILL)
+ target.child.wait()
+
+
+def test_ssh_launcher_quotes_environment_and_captures_exit_code(tmp_path):
+ run_dir = tmp_path / "run"
+ run_dir.mkdir()
+ target = SshTrainingTarget({"host": "gpu"}, source_dir="/gpu/source", run_dir=run_dir)
+ launcher = target.write_launcher(
+ ["/manager/source/harborrl/platform/slime_train.sh", "--flag", "value with spaces"],
+ {"LR": "1e-6", "NOTE": "two spaces"},
+ path_rewrites=[("/manager/source", "/gpu/source")])
+ text = launcher.read_text()
+ assert "cd /gpu/source" in text
+ assert "/gpu/source/harborrl/platform/slime_train.sh" in text
+ assert "/manager/source" not in text
+ assert "export LR=1e-6" in text
+ assert "export NOTE='two spaces'" in text
+ assert "'value with spaces'" in text
+ assert "exit-code" in text
+ assert launcher.stat().st_mode & 0o111, "launcher must be executable"
+
+
+def test_ssh_target_environment_file_is_loaded_via_python(tmp_path):
+ run_dir = tmp_path / "run"
+ run_dir.mkdir()
+ environment_file = tmp_path / "gpu-env.json"
+ environment_file.write_text(json.dumps({"SLIME_DIR": "/opt/slime"}))
+ target = SshTrainingTarget({"host": "gpu"}, source_dir="/gpu/source", run_dir=run_dir,
+ environment_file=environment_file)
+ launcher = target.write_launcher(["true"], {})
+ text = launcher.read_text()
+ assert "HARBORRL_ENV_JSON" in text
+ assert "json.load" in text
+
+
+def test_ssh_target_lifecycle_with_fake_remote(tmp_path, monkeypatch):
+ run_dir = tmp_path / "run"
+ run_dir.mkdir()
+ target = SshTrainingTarget({"host": "gpu", "port": 2222}, source_dir="/gpu/source",
+ run_dir=run_dir, poll_interval=0.05)
+ calls = []
+ killed = {"done": False}
+
+ def fake_run_ssh(spec, command, *, timeout=30, connect_timeout=15):
+ calls.append((dict(spec), command))
+ if "setsid" in command:
+ (run_dir / "launcher.pid").write_text("4242\n")
+ return completed()
+ if "kill -0" in command:
+ return (completed(stdout="ok\n") if not killed["done"]
+ else completed(returncode=1))
+ if command.startswith("kill -INT"):
+ killed["done"] = True
+ (run_dir / "exit-code").write_text("130\n")
+ return completed()
+ return completed()
+
+ monkeypatch.setattr(training_target.remote, "run_ssh", fake_run_ssh)
+ target.write_launcher(["true"], {})
+ target.launch()
+ with pytest.raises(TrainingTargetError):
+ target.launch() # double launch is refused
+ assert target.query() == {"state": "running", "exit_code": None, "pid": 4242,
+ "run_dir": str(run_dir)}
+ assert target.cancel()
+ assert target.wait() == 130
+ assert target.query()["exit_code"] == 130
+ # The ssh destination honors user/port from the normalized spec.
+ assert any(spec.get("port") == 2222 for spec, _ in calls), (
+ "spec with port 2222 must reach run_ssh")
+ assert any("setsid" in command for _, command in calls)
+
+
+def test_ssh_target_wait_timeout(tmp_path, monkeypatch):
+ run_dir = tmp_path / "run"
+ run_dir.mkdir()
+ target = SshTrainingTarget({"host": "gpu"}, source_dir="/gpu/source", run_dir=run_dir,
+ poll_interval=0.05)
+
+ def fake_run_ssh(spec, command, *, timeout=30, connect_timeout=15):
+ if "setsid" in command:
+ (run_dir / "launcher.pid").write_text("99\n")
+ return completed()
+
+ monkeypatch.setattr(training_target.remote, "run_ssh", fake_run_ssh)
+ target.write_launcher(["true"], {})
+ target.launch()
+ with pytest.raises(TimeoutError):
+ target.wait(timeout=0.2)
+
+
+def test_ssh_command_assembles_noninteractive_ssh():
+ from harborrl.deployment.remote import ssh_command
+
+ argv = ssh_command({"host": "gpu", "user": "root", "port": 2222,
+ "identity": "/key"}, "true", connect_timeout=7)
+ assert argv[0] == "ssh"
+ assert argv[1:7] == ["-o", "BatchMode=yes", "-o", "ClearAllForwardings=yes",
+ "-o", "StrictHostKeyChecking=accept-new"]
+ assert argv[7:9] == ["-o", "ConnectTimeout=7"]
+ assert argv[9:13] == ["-p", "2222", "-i", "/key"]
+ assert argv[-2:] == ["root@gpu", "true"]
+
+
+def test_service_dispatch_launches_gpu_job_through_controller(tmp_path, monkeypatch, capsys):
+ """P1+P3 manager flow: materialize, register the run, launch, wait, report."""
+ import json as json_module
+ from pathlib import Path
+
+ from test_deployment_config import deployment_mapping
+ from test_native_training import native_config
+
+ from harborrl.config import launch_plan, load_config
+ from harborrl.data.harbor.native_inspector import inspect_native
+ from harborrl.deployment import controller as controller_module
+ from harborrl.deployment.schema import summary, validate
+
+ config_path, task = native_config(tmp_path)
+ catalog_path = Path(json_module.loads(
+ config_path.read_text().split("tasks: ", 1)[1].split("\n", 1)[0])["catalog"])
+ catalog = json_module.loads(catalog_path.read_text())
+ catalog[0]["task_digest"] = inspect_native(task)["task_digest"]
+ catalog_path.write_text(json_module.dumps(catalog))
+ config = load_config(config_path)
+ config["output"]["root"] = str(tmp_path / "shared-runs")
+ plan = launch_plan(config)
+
+ deployment = validate(deployment_mapping(tmp_path, storage={
+ "run_root": str(tmp_path / "shared-runs")}))
+ manager = deployment["manager"]
+ token_file = Path(manager["run_token_file"])
+ token_file.parent.mkdir(parents=True, exist_ok=True)
+ token_file.write_text("token\n")
+ token_file.chmod(0o600)
+ (tmp_path / "shared-runs").mkdir()
+ plan["deployment"] = summary(deployment, "deployment.yaml")
+
+ launched = {}
+
+ class FakeControl:
+ def __init__(self, service, host, port):
+ self.address = (host, port)
+
+ def serve_forever(self):
+ pass
+
+ def shutdown(self):
+ pass
+
+ def server_close(self):
+ pass
+
+ class FakeTarget:
+ def __init__(self, spec, *, source_dir, run_dir, **kwargs):
+ self.source_dir, self.run_dir = source_dir, Path(run_dir)
+
+ def write_launcher(self, command, environment, *, path_rewrites=()):
+ launched["command"] = list(command)
+ launched["environment"] = dict(environment)
+ launched["rewrites"] = list(path_rewrites)
+ return self.run_dir / "launcher.sh"
+
+ def launch(self):
+ launched["run_dir"] = str(self.run_dir)
+ (self.run_dir / "exit-code").write_text("0\n")
+
+ def wait(self):
+ return 0
+
+ def cancel(self):
+ return True
+
+ monkeypatch.setattr(controller_module, "make_control_server", FakeControl)
+ monkeypatch.setattr(controller_module, "SshTrainingTarget", FakeTarget)
+ monkeypatch.setattr(controller_module, 'check_gpu_control', lambda _: {'gpu_to_controller': True})
+
+ exit_code = controller_module.dispatch_service(plan, deployment, checks=[])
+ assert exit_code == 0
+ run_dir = Path(launched["run_dir"])
+ assert run_dir.is_dir()
+ environment = launched["environment"]
+ assert environment["HARBORRL_ROLLOUT_MODE"] == "service"
+ assert environment["HARBORRL_ROLLOUT_SERVICE_URL"] == manager["control_url"]
+ assert environment['HARBORRL_ROLLOUT_SERVICE_TOKEN_FILE'] == str(token_file)
+ assert 'HARBORRL_ROLLOUT_SERVICE_TOKEN' not in environment
+ assert environment["HARBORRL_NATIVE_GATEWAY_URL"] == manager["gateway_url"]
+ assert json_module.loads(environment["HARBORRL_NATIVE_RUNNER_WORKERS"]) == [
+ "worker-a", "worker-b"]
+ # The launcher runs inside the GPU checkout with a rewritten script path.
+ assert launched["rewrites"] == [(manager["source_dir"],
+ deployment["gpu_target"]["source_dir"])]
+ launch_record = json_module.loads((run_dir / "launch.json").read_text())
+ assert launch_record["control_url"] == manager["control_url"]
+ effective = json_module.loads((run_dir / "effective-config.json").read_text())
+ assert effective["components"]["execution_backend"] == "harbor_job"
diff --git a/tests/harborrl/test_math_interfaces.py b/tests/harborrl/test_math_interfaces.py
new file mode 100644
index 000000000..a17788fc1
--- /dev/null
+++ b/tests/harborrl/test_math_interfaces.py
@@ -0,0 +1,126 @@
+"""CPU contracts for the explicit MATH component interfaces."""
+import json
+from dataclasses import replace
+
+import pytest
+
+from harborrl.math_registry import (
+ BACKEND_CONTRACT,
+ list_components,
+ select_math,
+ validate_combination,
+)
+from harborrl.models.qwen import QwenModelAdapter
+from harborrl.tasks.jsonl_provider import JsonlTaskProvider
+from harborrl.tasks.provider import NativeTaskProvider
+
+
+def math_config():
+ return {
+ "execution": {"backend": "harbor_job"},
+ "harness": {"name": "claude_code"},
+ "harbor": {"version": "0.23.0"},
+ "gateway": {"audited_raw_logprobs": True},
+ "model": {"args_file": "qwen3-8B"},
+ "training": {"backend_contract": BACKEND_CONTRACT},
+ "sampling": {"group_size": 2},
+ }
+
+
+def test_registry_lists_versioned_default_components():
+ components = list_components()
+ assert [spec["id"] for spec in components["models"]] == ["qwen"]
+ assert [spec["id"] for spec in components["algorithms"]] == ["grpo"]
+ assert [spec["id"] for spec in components["tasks"]] == ["harbor_jsonl", "native_harbor"]
+ assert [spec["id"] for spec in components["harnesses"]] == ["claude_code"]
+ for dimension in components.values():
+ for spec in dimension:
+ assert spec["version"]
+ assert spec["evidence"]
+ assert spec["limitations"]
+
+def test_combination_checks_reject_incompatible_evidence_and_groups():
+ config = math_config()
+ selection = select_math(config)
+ assert validate_combination(selection, config) is selection
+
+ unaudited = {**config, "gateway": {"audited_raw_logprobs": False}}
+ with pytest.raises(ValueError, match="requires audited raw logprobs"):
+ validate_combination(selection, unaudited)
+
+ larger_group = replace(selection.algorithm, minimum_group_size=3)
+ with pytest.raises(ValueError, match="requires group size"):
+ validate_combination(replace(selection, algorithm=larger_group), config)
+
+ class Tokenizer:
+ def get(self, key):
+ assert key == "input_ids"
+ return [1, 2]
+
+ def apply_chat_template(self, *args, **kwargs):
+ assert kwargs["enable_thinking"] is False
+ return self
+
+ adapter = QwenModelAdapter()
+ converted = {"messages": [{"role": "user", "content": "run"}], "tools": []}
+ assert adapter.render_chat(Tokenizer(), converted) == [1, 2]
+ blocks = adapter.parse_output(
+ '{"name":"bash","arguments":{"command":"pwd"}} ',
+ response_id="message",
+ tools=[{"function": {"name": "bash"}}],
+ )
+ assert blocks[0]["type"] == "tool_use"
+
+
+def test_jsonl_task_provider_is_selected_by_catalog_format(tmp_path):
+ task = tmp_path / "task"
+ (task / "environment").mkdir(parents=True)
+ (task / "tests").mkdir()
+ (task / "task.toml").write_text('version = "1.0"\n')
+ (task / "instruction.md").write_text("jsonl instruction\n")
+ (task / "environment/Dockerfile").write_text("FROM debian:bookworm\n")
+ (task / "tests/test.sh").write_text("true\n")
+ provider = JsonlTaskProvider()
+ digest = provider.inspect(task)["task_digest"]
+ catalog = tmp_path / "catalog.jsonl"
+ catalog.write_text(json.dumps({
+ "id": "task", "revision": "1", "path": str(task),
+ "task_digest": digest, "reward_profile": {"key": "reward"},
+ }) + "\n")
+ config = {**math_config(), "tasks": {"catalog": str(catalog)}}
+ selection = select_math(config)
+ assert selection.task.id == "harbor_jsonl"
+ entries = provider.catalog(config)
+ rows = provider.prompt_rows(entries)
+ assert rows[0]["task"] == "jsonl instruction\n"
+ assert rows[0]["metadata"]["task_provider"] == "harbor_jsonl"
+
+ incompatible = {**config, "execution": {"backend": "process"}}
+ with pytest.raises(ValueError, match="does not support execution backend"):
+ validate_combination(select_math(incompatible), incompatible)
+
+ catalog.write_text("{broken\n")
+ with pytest.raises(ValueError, match="invalid JSONL catalog line"):
+ provider.catalog(config)
+
+
+def test_native_task_provider_locks_and_materializes_prompts(tmp_path):
+ task = tmp_path / "task"
+ (task / "environment").mkdir(parents=True)
+ (task / "tests").mkdir()
+ (task / "task.toml").write_text('version = "1.0"\n')
+ (task / "instruction.md").write_text("original instruction\n")
+ (task / "environment/Dockerfile").write_text("FROM debian:bookworm\n")
+ (task / "tests/test.sh").write_text("true\n")
+ provider = NativeTaskProvider()
+ digest = provider.inspect(task)["task_digest"]
+ catalog = tmp_path / "catalog.json"
+ catalog.write_text(json.dumps([{
+ "id": "task", "revision": "1", "path": str(task),
+ "task_digest": digest, "reward_profile": {"key": "reward"},
+ }]))
+ config = {**math_config(), "tasks": {"catalog": str(catalog)}}
+ entries = provider.catalog(config)
+ rows = provider.prompt_rows(entries)
+ assert rows[0]["task"] == "original instruction\n"
+ assert rows[0]["metadata"]["native_task"]["task_digest"] == digest
diff --git a/tests/harborrl/test_native_gateway.py b/tests/harborrl/test_native_gateway.py
index d3a6d5ffb..75ab7416e 100644
--- a/tests/harborrl/test_native_gateway.py
+++ b/tests/harborrl/test_native_gateway.py
@@ -65,9 +65,10 @@ def test_tool_roundtrip():
convert(p, model="policy")
-def test_claude_2_1_220_control_turns_are_accepted_without_hidden_sampling():
+@pytest.mark.parametrize("effort", ["high", "xhigh"])
+def test_claude_2_1_control_turns_are_accepted_without_hidden_sampling(effort):
converted = convert(payload(
- output_config={"effort": "high"},
+ output_config={"effort": effort},
thinking={"type": "adaptive"},
context_management={"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
system=[{"type": "text", "text": "locked system prompt"}],
@@ -239,6 +240,32 @@ def test_nonfinite_generated_tool_is_rejected():
response_id="m", tools=[{"function": {"name": "bash"}}])
+def test_generated_tool_errors_distinguish_name_and_arguments():
+ tools = [{"function": {"name": "bash"}}]
+ with pytest.raises(ProtocolError, match="unknown generated tool"):
+ parse_qwen_output('{"name":"sh","arguments":{}} ',
+ response_id="m", tools=tools)
+ with pytest.raises(ProtocolError, match="arguments are not an object"):
+ parse_qwen_output('{"name":"bash","arguments":[]} ',
+ response_id="m", tools=tools)
+
+
+def test_generated_tool_name_alias_is_canonicalized_only_when_unique():
+ tools = [{"function": {"name": "Bash"}}]
+ output = parse_qwen_output('{"name":"bash","arguments":{"command":"pwd"}} ',
+ response_id="m", tools=tools)
+ assert output[0]["name"] == "Bash"
+ with pytest.raises(ProtocolError, match="unknown generated tool"):
+ parse_qwen_output('{"name":"sh","arguments":{}} ',
+ response_id="m", tools=tools)
+
+
+def test_qwen_finish_marker_maps_to_empty_end_turn():
+ output = parse_qwen_output('{"name":"finish","arguments":{}} ',
+ response_id="m", tools=[{"function": {"name": "Bash"}}])
+ assert output == []
+
+
def test_sglang_generation_version_and_logprob_evidence():
from harborrl.gateway.sglang_backend import SGLangBackend
class Tokenizer:
@@ -246,7 +273,11 @@ def apply_chat_template(self, *args, **kwargs):
return [11, 12]
def decode(self, token_ids, skip_special_tokens):
return "done"
- backend = SGLangBackend("http://engine", Tokenizer(), tokenizer_digest="tok", template_digest="tpl")
+ from harborrl.models.qwen import QwenModelAdapter
+ backend = SGLangBackend(
+ "http://engine", Tokenizer(), QwenModelAdapter(),
+ tokenizer_digest="tok", template_digest="tpl",
+ )
converted = convert(payload(), model="policy")
serving = {"input_ids": backend.prepare(converted)}
output = {"text": "done", "meta_info": {"weight_version": "1", "output_token_logprobs": [[-.2, 13]],
@@ -293,7 +324,31 @@ def open(self, request, timeout):
assert timeout == 30
return Response()
- backend = SGLangBackend("http://engine", SimpleNamespace(), tokenizer_digest="tok",
- template_digest="tpl", timeout=30)
+ from harborrl.models.qwen import QwenModelAdapter
+ backend = SGLangBackend(
+ "http://engine", SimpleNamespace(), QwenModelAdapter(),
+ tokenizer_digest="tok", template_digest="tpl", timeout=30,
+ )
backend.opener = Opener()
assert backend.weight_version() == "7"
+
+
+def test_native_gateway_thread_does_not_own_asyncio_default_executor():
+ from harborrl.rollout.native_generate import NativeRolloutRuntime
+
+ started, stopped = threading.Event(), threading.Event()
+
+ class Server:
+ def serve_forever(self):
+ started.set()
+ stopped.wait(5)
+
+ runtime = object.__new__(NativeRolloutRuntime)
+ runtime.server = Server()
+ runtime._start_gateway()
+ try:
+ assert started.wait(1)
+ assert runtime.thread.daemon is True
+ finally:
+ stopped.set()
+ runtime.thread.join(1)
diff --git a/tests/harborrl/test_native_lifecycle.py b/tests/harborrl/test_native_lifecycle.py
index bdc76b9ea..a36ef0fd9 100644
--- a/tests/harborrl/test_native_lifecycle.py
+++ b/tests/harborrl/test_native_lifecycle.py
@@ -35,7 +35,12 @@ def test_restart_cannot_reopen_persisted_attempt(tmp_path):
def test_profile_controls_reach_agent_and_host():
config = claude_config(PROFILE, "http://gateway:8000", "token")
+ assert config["kwargs"]["disallowed_tools"] == "Agent,Task"
+ disabled = claude_config({**PROFILE, "disallowed_tools": "Agent,Task,WebSearch,WebFetch"},
+ "http://gateway:8000", "token")
+ assert disabled["kwargs"]["disallowed_tools"] == "Agent,Task,WebSearch,WebFetch"
assert config["env"]["DISABLE_AUTO_COMPACT"] == "1"
+ assert config["env"]["ANTHROPIC_MODEL"] == "policy"
assert config["env"]["NO_PROXY"] == "gateway"
assert config["env"]["no_proxy"] == "gateway"
assert config["kwargs"]["version"] == "2.1.0"
diff --git a/tests/harborrl/test_native_terminal.py b/tests/harborrl/test_native_terminal.py
new file mode 100644
index 000000000..eb94c8a98
--- /dev/null
+++ b/tests/harborrl/test_native_terminal.py
@@ -0,0 +1,111 @@
+"""Regression coverage for the full coordinator success path and empty finals."""
+import asyncio
+import json
+import sys
+
+import pytest
+from test_native_contracts import identity, make_ir
+from test_native_lifecycle import PROFILE
+
+from harborrl.gateway.trace import TraceRegistry
+from harborrl.harnesses.claude_code import ClaudeCodeHarnessAdapter
+from harborrl.rollout.harbor_job.coordinator import run_attempt
+from harborrl.trajectories.native import RewardProfile, digest
+
+
+def test_casefold_tool_alias_must_be_unique():
+ from harborrl.gateway.messages import ProtocolError, parse_qwen_output
+ with pytest.raises(ProtocolError, match="ambiguous"):
+ parse_qwen_output('{"name":"BASH","arguments":{}} ',
+ response_id="r", tools=[{"function": {"name": "Bash"}},
+ {"function": {"name": "bash"}}])
+
+
+@pytest.mark.parametrize("empty", [False, True])
+def test_coordinator_publishes_complete_success(tmp_path, monkeypatch, empty):
+ ident = identity()
+ trial_id = "trial-" + ident.attempt_id
+ root = tmp_path / "attempt"
+ trial = root / "runner/harbor/trial"
+ template = make_ir(tmp_path / "fixture", nturns=2)
+
+ class Registry(TraceRegistry):
+ def register(self, ident, trial_id, **kwargs):
+ token = super().register(ident, trial_id, **kwargs)
+ turns = template["turns"]
+ for index, original in enumerate(turns):
+ aid, request, _, _ = self.begin(token)
+ turn = {**original, "request_id": request, "delivery": "sent"}
+ if empty and index == 1:
+ turn["response"] = {"content": []}
+ turn["response_digest"] = digest(turn["response"])
+ self.finish(aid, request, turn)
+ self.delivery(aid, turn["response_id"], "sent")
+ session = trial / "agent/sessions/projects/session.jsonl"
+ session.parent.mkdir(parents=True)
+ session.write_text("\n".join(json.dumps({
+ "type": "assistant", "sessionId": "session", "message": {
+ "id": turn["response_id"], "role": "assistant",
+ "content": turn["response"]["content"]}})
+ for turn in turns[:1 if empty else 2]))
+ (trial / "agent/claude-code.txt").write_text(json.dumps({
+ "type": "result", "subtype": "success", "is_error": False,
+ "result": "", "stop_reason": "end_turn", "session_id": "session",
+ "terminal_reason": "completed"}))
+ (trial / "verifier").mkdir()
+ (trial / "verifier/reward.json").write_text('{"reward": 1}')
+ (trial / "result.json").write_text(json.dumps({
+ "id": trial_id, "finished_at": "2026-10-19T00:00:00Z",
+ "verifier_environment_mode": "shared",
+ "verifier_result": {"rewards": {"reward": 1}}}))
+ return token
+
+ worker = tmp_path / "worker.py"
+ terminal = {"type": "terminal", "trial_id": trial_id,
+ "trial_dir": str(trial), "result_path": str(trial / "result.json")}
+ worker.write_text("import json, sys\njson.loads(sys.stdin.readline())\n"
+ f"print({json.dumps({'type': 'prepared', 'trial_id': trial_id})!r}, flush=True)\n"
+ "sys.stdin.readline()\n"
+ f"print({json.dumps(terminal)!r}, flush=True)\n")
+ spawn = asyncio.create_subprocess_exec
+
+ async def fake_spawn(*args, **kwargs):
+ return await spawn(sys.executable, "-S", str(worker), **kwargs)
+
+ monkeypatch.setattr(asyncio, "create_subprocess_exec", fake_spawn)
+ ir = asyncio.run(run_attempt(
+ ident, task_path=tmp_path, profile=PROFILE, gateway_url="http://gateway",
+ runner_python=sys.executable, root=root, registry=Registry(tmp_path / "trace"),
+ reward_profile=RewardProfile()))
+ assert ir["readiness"] == "CONTRACT_READY"
+ assert json.loads((root / "trajectory.v2.json").read_text()) == ir
+ assert (root / "evaluation.json").is_file()
+ assert not (root / "cleanup-required.json").exists()
+ assert all(turn["delivery"] == "consumed_confirmed" for turn in ir["turns"])
+
+
+@pytest.mark.parametrize("change", [
+ {"content": [{"type": "text", "text": "missing"}]},
+ {"finish_reason": "length"}, {"is_final": False},
+])
+def test_empty_final_rejects_other_missing_responses(tmp_path, change):
+ class Registry:
+ def unconsumed_responses(self, aid):
+ return [{"response_id": "r", "content": [], "finish_reason": "end_turn",
+ "is_final": True, **change}]
+ with pytest.raises(ValueError, match="only one empty final"):
+ ClaudeCodeHarnessAdapter().audit_empty_final(tmp_path / "absent", Registry(), "a", {})
+
+
+@pytest.mark.parametrize("change", [{"session_id": "wrong"}, {"is_error": True},
+ {"subtype": "error_max_turns"}, {"result": "nonempty"}, {"stop_reason": "max_tokens"}])
+def test_empty_final_requires_cli_consumption_evidence(tmp_path, change):
+ class Registry:
+ def unconsumed_responses(self, aid):
+ return [{"response_id": "r", "content": [], "finish_reason": "end_turn", "is_final": True}]
+ path = tmp_path / "claude-code.txt"
+ path.write_text(json.dumps({"type": "result", "subtype": "success", "is_error": False,
+ "result": "", "stop_reason": "end_turn", "session_id": "session",
+ "terminal_reason": "completed", **change}))
+ with pytest.raises(ValueError, match="matching session result"):
+ ClaudeCodeHarnessAdapter().audit_empty_final(path, Registry(), "a", {"session_id": "session"})
diff --git a/tests/harborrl/test_native_training.py b/tests/harborrl/test_native_training.py
index 40ce2b565..ecfd6a24e 100644
--- a/tests/harborrl/test_native_training.py
+++ b/tests/harborrl/test_native_training.py
@@ -142,8 +142,19 @@ def test_cli_command_mode_selects_experiment_dimensions(tmp_path, capsys, monkey
assert positional_plan["config"]["schema_version"] == 2
-def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path):
- native_config(tmp_path)
+@pytest.mark.parametrize('overrides, expected_lr', [
+ ([], '0.0001'),
+ (['training.learning_rate=0.000025'], '2.5e-05'),
+])
+def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path, overrides, expected_lr):
+ config_path, task = native_config(tmp_path)
+ from harborrl.data.harbor.native_inspector import inspect_native
+ config = load_config(config_path, overrides)
+ catalog_path = Path(config['tasks']['catalog'])
+ entries = json.loads(catalog_path.read_text())
+ entries[0]['task_digest'] = inspect_native(task)['task_digest']
+ catalog_path.write_text(json.dumps(entries))
+ plan = launch_plan(config)
prompt = tmp_path / "native.jsonl"
prompt.write_text('{"task":"fixture","metadata":{"harborrl_native":true}}\n')
root = tmp_path / "run"
@@ -154,6 +165,7 @@ def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path):
(model_args / "qwen3-8B.sh").write_text("MODEL_ARGS=(--hidden-size 4096)\n")
env = {key: value for key, value in os.environ.items()
if key in {"PATH", "HOME", "USER", "LANG", "LC_ALL", "TMPDIR"}}
+ env.update(plan["environment"])
env.update({
"DRY_RUN": "1", "DATASET": "native", "ALGO": "grpo",
"HARNESS_OPTION": "native-claude-code", "HARBORRL_NATIVE_ROLLOUT": "1",
@@ -162,14 +174,16 @@ def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path):
"MODEL_TAG": "qwen3-8B", "NUM_GPUS": "8", "ACTOR_GPUS": "4",
"ROLLOUT_GPUS": "4", "TP_SIZE": "4", "ROLLOUT_NUM_GPUS_PER_ENGINE": "4",
"HARBORRL_GPU_LAYOUT": "split", "NUM_ROLLOUT": "1", "SAVE_INTERVAL": "1",
- "ROLLOUT_BATCH_SIZE": "1", "N_SAMPLES": "2", "RUN_DIR": str(root), "RUN_ID": "run",
+ "RUN_DIR": str(root), "RUN_ID": "run",
"RUNS_ROOT": str(tmp_path), "CKPT_ROOT": str(root / "checkpoints"),
"HARBORRL_NATIVE_MAX_ATTEMPTS": "2", "HARBORRL_NATIVE_DRAIN_TIMEOUT": "1",
"HARBORRL_NATIVE_RUNNER_WORKERS": '["worker-a", "worker-b"]',
"HARBORRL_NATIVE_GATEWAY_HOST": "0.0.0.0",
"HARBORRL_NATIVE_SLIME_CONTRACT": "slime-v032",
"SLIME_DIR": str(slime),
+ "TRAIN_PYTHON": sys.executable,
})
+ env['LR'] = plan['environment']['LR']
script = Path(__file__).resolve().parents[2] / "harborrl/platform/slime_train.sh"
output = tmp_path / "shell.log"
with output.open("w") as stream:
@@ -179,6 +193,24 @@ def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path):
assert result.returncode == 0, result.stdout
command = next(line for line in result.stdout.splitlines() if line.startswith("[dry-run] "))
assert "--loss-type custom_loss" in command
+ import shlex
+ arguments = shlex.split(command.removeprefix('[dry-run] '))
+ assert arguments[arguments.index('--lr') + 1] == expected_lr
+ expected_flags = {
+ '--num-rollout': '1', '--save-interval': '1',
+ '--rollout-batch-size': '1', '--n-samples-per-prompt': '2',
+ '--rollout-max-response-len': '128', '--rollout-max-context-len': '1024',
+ '--num-gpus-per-node': '8', '--actor-num-gpus-per-node': '4',
+ '--rollout-num-gpus': '4', '--tensor-model-parallel-size': '4',
+ '--rollout-num-gpus-per-engine': '4', '--hf-checkpoint': '/model',
+ '--ref-load': '/reference', '--prompt-data': str(prompt),
+ '--save': str(root / 'checkpoints' / 'run'),
+ }
+ for flag, value in expected_flags.items():
+ assert arguments[arguments.index(flag) + 1] == value
+ recorded = json.loads(next((root / 'dry-run-commands').glob('*.json')).read_text())
+ assert recorded['command'] == arguments
+ assert recorded['mode'] == 'dry_run'
for argument in (
"--rollout-function-path harborrl.backends.slime_v032.rollout.generate_rollout",
"--custom-convert-samples-to-train-data-path harborrl.backends.slime_v032.converter.convert_samples_to_train_data",
@@ -208,96 +240,57 @@ def test_native_shell_dry_run_uses_official_v032_hooks(tmp_path):
assert "--no-load-optim" in command
-def test_native_runtime_binds_gateway_with_registry_and_backend(tmp_path, monkeypatch):
- from harborrl.gateway import server as gateway_server
- from harborrl.gateway import sglang_backend, trace
- from harborrl.rollout import native_generate
+def test_slime_runtime_config_maps_args_and_environment(tmp_path, monkeypatch):
+ from harborrl.backends.slime_v032 import rollout as slime_rollout
+ from harborrl.rollout.runtime import RolloutRuntimeConfig
catalog = tmp_path / "catalog.json"
catalog.write_text("[]")
profile = tmp_path / "profile.json"
profile.write_text(json.dumps({"model": "policy"}))
- tokenizer = SimpleNamespace(
- apply_chat_template=lambda *args, **kwargs: [1, 2],
- get_vocab=lambda: {"token": 1},
- )
- tokenizer_digest, template_digest = native_generate.tokenizer_fingerprint(tokenizer)
environment = {
- "RUN_DIR": str(tmp_path), "HARBORRL_NATIVE_CATALOG": str(catalog),
+ "RUN_ID": "run-1", "RUN_DIR": str(tmp_path),
+ "HARBORRL_NATIVE_CATALOG": str(catalog),
"HARBORRL_NATIVE_PROFILE": str(profile),
- "HARBORRL_NATIVE_TOKENIZER_DIGEST": tokenizer_digest,
- "HARBORRL_NATIVE_TEMPLATE_DIGEST": template_digest,
"HARBORRL_NATIVE_GATEWAY_HOST": "127.0.0.1",
"HARBORRL_NATIVE_GATEWAY_PORT": "49183",
+ "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:49183",
+ "HARBORRL_NATIVE_RUNNER_PYTHON": "/runner/python",
+ "HARBORRL_NATIVE_RUNNER_WORKERS": '["worker-a", "worker-b"]',
+ "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64,
+ "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64,
+ "ROLLOUT_MAX_RESPONSE_LEN": "128", "ROLLOUT_MAX_CONTEXT_LEN": "1024",
}
for key, value in environment.items():
monkeypatch.setenv(key, value)
-
- calls = {}
-
- class FakeGateway:
- def __init__(self, registry, backend, *, model, max_output_tokens):
- calls["gateway"] = (registry, backend, model, max_output_tokens)
- calls["gateway_object"] = self
-
- class FakeServer:
- def serve_forever(self):
- pass
-
- def shutdown(self):
- pass
-
- def server_close(self):
- pass
-
- class FakeTraceRegistry:
- def __init__(self, path):
- calls["registry_path"] = path
-
- class FakeBackend:
- def __init__(self, *args, **kwargs):
- calls["backend_args"] = (args, kwargs)
-
- def weight_version(self):
- return "7"
-
- def fake_make_server(gateway, host, port):
- calls["server"] = (gateway, host, port)
- return FakeServer()
-
+ args = SimpleNamespace(rollout_num_gpus=8, rollout_num_gpus_per_engine=4,
+ sglang_server_concurrency=2, hf_checkpoint="/model",
+ sglang_router_ip="127.0.0.1", sglang_router_port=30000,
+ rollout_max_context_len=1024, rollout_max_response_len=128,
+ n_samples_per_prompt=4)
+ config = slime_rollout.runtime_config_from_args(args)
+ assert isinstance(config, RolloutRuntimeConfig)
+ assert config.endpoint == "http://127.0.0.1:30000"
+ assert config.concurrency_limit == 4 # 2 concurrency x 2 engines
+ assert config.group_size == 4
+ assert config.workers == ("worker-a", "worker-b")
+ assert config.max_output_tokens == 128
+
+ # The tokenizer loader keeps the single Slime dependency at the boundary.
slime = ModuleType("slime")
utils = ModuleType("slime.utils")
processing = ModuleType("slime.utils.processing_utils")
+ tokenizer = SimpleNamespace()
processing.load_tokenizer = lambda *args, **kwargs: tokenizer
slime.utils = utils
utils.processing_utils = processing
monkeypatch.setitem(sys.modules, "slime", slime)
monkeypatch.setitem(sys.modules, "slime.utils", utils)
monkeypatch.setitem(sys.modules, "slime.utils.processing_utils", processing)
- monkeypatch.setattr(gateway_server, "Gateway", FakeGateway)
- monkeypatch.setattr(gateway_server, "make_server", fake_make_server)
- monkeypatch.setattr(sglang_backend, "SGLangBackend", FakeBackend)
- monkeypatch.setattr(trace, "TraceRegistry", FakeTraceRegistry)
- monkeypatch.setattr(native_generate, "_RUNTIME", None)
-
- args = SimpleNamespace(rollout_num_gpus=4, rollout_num_gpus_per_engine=4,
- sglang_server_concurrency=1, hf_checkpoint="/model",
- sglang_router_ip="127.0.0.1", sglang_router_port=30000,
- rollout_max_context_len=1024, rollout_max_response_len=128)
- runtime = asyncio.run(native_generate.runtime(args))
- try:
- registry = calls["gateway"][0]
- backend = calls["gateway"][1]
- assert calls["gateway"] == (registry, backend, "policy", 128)
- assert calls["server"][0] is calls["gateway_object"]
- assert calls["server"][1:] == ("127.0.0.1", 49183)
- assert runtime.backend.weight_version() == "7"
- finally:
- runtime.close()
- native_generate._RUNTIME = None
+ assert slime_rollout.slime_tokenizer_loader(args)() is tokenizer
def test_native_policy_identity_uses_synchronized_pool_version(tmp_path, monkeypatch):
- from harborrl.rollout import native_generate
+ from harborrl.rollout import runtime as rollout_runtime
class Backend:
endpoint = "http://router:30000"
@@ -314,26 +307,37 @@ def publish_version(self, states, expected):
monkeypatch.setenv("RUN_DIR", str(tmp_path))
registry = Registry()
- runtime = SimpleNamespace(run_root=tmp_path, backend=Backend(), registry=registry)
- assert native_generate.current_policy_version(runtime) == "2"
+ runtime = SimpleNamespace(run_root=tmp_path, backend=Backend(), registry=registry,
+ config=SimpleNamespace(drain_timeout=30))
+ assert rollout_runtime.current_policy_version(runtime) == "2"
payload = json.loads((tmp_path / "policy-pool.json").read_text())
assert payload["weight_version"] == "2"
assert registry.published[1] == "2"
+ assert registry.timeout == 30
def test_native_slots_spread_workers_and_shift_retries(tmp_path, monkeypatch):
- from harborrl.rollout import native_generate
+ from harborrl.rollout import runtime as rollout_runtime
entry = {"id": "task", "revision": "1", "path": "/task", "task_digest": "d" * 64,
"reward_profile": {"key": "reward", "scale": 1.0, "offset": 0.0, "raw_range": [0.0, 1.0]}}
profile = tmp_path / "profile.json"
profile.write_text(json.dumps({"model": "policy"}))
- monkeypatch.setenv("RUN_ID", "run")
- monkeypatch.setenv("HARBORRL_NATIVE_PROFILE", str(profile))
- monkeypatch.setenv("HARBORRL_NATIVE_RUNNER_WORKERS", json.dumps(["worker-a", "worker-b"]))
- monkeypatch.setenv("HARBORRL_NATIVE_MAX_ATTEMPTS", "2")
- monkeypatch.setenv("HARBORRL_NATIVE_GATEWAY_URL", "http://gateway:32123")
- monkeypatch.setenv("HARBORRL_NATIVE_RUNNER_PYTHON", "/python")
+ environment = {
+ "RUN_ID": "run", "RUN_DIR": str(tmp_path), "HARBORRL_NATIVE_PROFILE": str(profile),
+ "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(["worker-a", "worker-b"]),
+ "HARBORRL_NATIVE_MAX_ATTEMPTS": "2",
+ "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:32123",
+ "HARBORRL_NATIVE_GATEWAY_PORT": "32123",
+ "HARBORRL_NATIVE_RUNNER_PYTHON": "/python",
+ "HARBORRL_NATIVE_CATALOG": str(tmp_path / "catalog.json"),
+ "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64,
+ "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64,
+ }
+ for key, value in environment.items():
+ monkeypatch.setenv(key, value)
+ config = rollout_runtime.RolloutRuntimeConfig.from_environment(
+ environment, endpoint="http://router:30000", concurrency_limit=8, group_size=4)
class FakeSlots:
max_attempts = 2
@@ -360,19 +364,15 @@ async def fake_run_attempt(identity, *, runner_host, **kwargs):
raise RuntimeError("simulated first-attempt failure")
return {"identity": identity.__dict__}
- monkeypatch.setattr(native_generate, "GroupSlots", fake_group_slots)
- monkeypatch.setattr(native_generate, "run_attempt", fake_run_attempt)
+ monkeypatch.setattr(rollout_runtime, "GroupSlots", fake_group_slots)
+ monkeypatch.setattr(rollout_runtime, "run_attempt", fake_run_attempt)
- args = SimpleNamespace(n_samples_per_prompt=4, rollout_max_response_len=64)
runtime_ = SimpleNamespace(rollout_root=tmp_path, semaphore=asyncio.Semaphore(8),
registry=SimpleNamespace(), profile={"model": "policy"})
- sample = SimpleNamespace(metadata={"native_task": entry}, group_index=0)
async def gather_slots():
return await asyncio.gather(*[
- native_generate._run_slot(
- args, runtime_, sample, slot, 0, entry, "sampling", "1"
- )
+ rollout_runtime.run_slot(config, runtime_, 0, slot, 0, entry, "sampling", "1")
for slot in range(4)])
results = asyncio.run(gather_slots())
@@ -413,13 +413,96 @@ def test_native_dispatch_passes_materialized_prompt_data(tmp_path, monkeypatch):
monkeypatch.setenv("SLIME_DIR", "/opt/slime-v0.3.2")
monkeypatch.setenv("MEGATRON_DIR", "/opt/megatron")
monkeypatch.setenv("SGLANG_IMAGE", "sglang:v0.5.15.post1-cu129")
+ monkeypatch.setenv("LR", "99")
+ monkeypatch.setenv("N_SAMPLES", "99")
+ monkeypatch.setenv("ANTHROPIC_API_KEY", "ambient-provider-credential")
+ monkeypatch.setenv("HTTPS_PROXY", "http://user:credential@proxy.example:8080")
monkeypatch.setattr(native_train, "doctor", lambda *_args, **_kwargs: [])
assert native_train.dispatch(plan) == 0
launch_path = next((tmp_path / "runs" / "training").glob("*/launch.json"))
launch = json.loads(launch_path.read_text())
assert launch["prompt_data"] == output.read_text()
+ effective_text = (launch_path.parent / "effective-config.json").read_text()
+ effective = json.loads(effective_text)
+ assert effective['environment']['LR'] == '0.0001'
+ assert effective['environment']['N_SAMPLES'] == '2'
+ assert effective['environment']['ROLLOUT_PROMPT_DATA'] == output.read_text()
+ assert effective['components']['algorithm'] == 'grpo'
+ assert effective['deployment_environment']['SLIME_DIR'] == '/opt/slime-v0.3.2'
+ assert 'ambient-provider-credential' not in effective_text
+ assert 'user:credential' not in effective_text
assert path_output.read_text().split(os.pathsep, 1)[0] == str(Path(plan["command"][0]).parent)
assert json.loads(workers_output.read_text()) == ["worker-a", "worker-b"]
assert gateway_output.read_text() == "gateway"
assert backend_output.read_text() == (
"/opt/slime-v0.3.2:/opt/megatron:sglang:v0.5.15.post1-cu129")
+
+
+def test_all_schema_fields_have_declared_consumers():
+ from harborrl.backends.slime_v032.config import field_usage
+ from harborrl.config.native import FIELDS
+ assert set(field_usage()) == {
+ f'{section}.{field}' for section, fields in FIELDS.items() for field in fields
+ }
+
+
+def test_schema_validation_does_not_read_harness_files(tmp_path):
+ from harborrl.config.native import validate, validate_files
+ config_path, _ = native_config(tmp_path)
+ config = load_config(config_path)
+ Path(config['harness']['profile']).unlink()
+ assert validate(config, config_path) is config
+ with pytest.raises(FileNotFoundError):
+ validate_files(config)
+
+
+@pytest.mark.parametrize('field, value, message', [
+ ('training', None, 'training'),
+ ('training.backend_contract', [], 'backend_contract'),
+ ('gateway.advertised_url', [], 'gateway.advertised_url'),
+ ('sampling.max_tokens', 'many', 'sampling.max_tokens'),
+])
+def test_invalid_config_types_are_cli_errors(tmp_path, capsys, field, value, message):
+ import yaml
+
+ from harborrl.cli import main
+ config_path, _ = native_config(tmp_path)
+ config = yaml.safe_load(config_path.read_text())
+ section, _, key = field.partition('.')
+ if key:
+ config[section][key] = value
+ else:
+ config[section] = value
+ config_path.write_text(yaml.safe_dump(config))
+ with pytest.raises(SystemExit) as exc:
+ main(['train', str(config_path), '--dry-run'])
+ assert exc.value.code == 2
+ assert message in capsys.readouterr().err
+
+
+def test_invalid_override_yaml_is_cli_error(tmp_path, capsys):
+ from harborrl.cli import main
+ config_path, _ = native_config(tmp_path)
+ with pytest.raises(SystemExit) as exc:
+ main(['train', str(config_path), '--set', 'training.learning_rate=[', '--dry-run'])
+ assert exc.value.code == 2
+ assert 'training.learning_rate: invalid YAML override' in capsys.readouterr().err
+
+
+def test_harness_file_override_is_checked_after_override(tmp_path):
+ config_path, _ = native_config(tmp_path)
+ config = load_config(config_path)
+ old_profile = Path(config['harness']['profile'])
+ new_profile = tmp_path / 'replacement.json'
+ old_profile.rename(new_profile)
+ config = load_config(config_path, [f'harness.profile={new_profile}'])
+ assert config['harness']['profile'] == str(new_profile)
+
+
+def test_catalog_rejects_nonmapping_entry(tmp_path):
+ from harborrl.tasks.catalog import catalog
+ config_path, _ = native_config(tmp_path)
+ config = load_config(config_path)
+ Path(config['tasks']['catalog']).write_text('[null]')
+ with pytest.raises(ValueError, match='catalog identity'):
+ catalog(config)
diff --git a/tests/harborrl/test_options.py b/tests/harborrl/test_options.py
new file mode 100644
index 000000000..aa3739c4b
--- /dev/null
+++ b/tests/harborrl/test_options.py
@@ -0,0 +1,308 @@
+"""Offline wizard contracts, including the existing training CLI boundary."""
+import json
+import subprocess
+import sys
+from copy import deepcopy
+from pathlib import Path
+
+import pytest
+import yaml
+from test_native_training import native_config
+
+from harborrl.options.catalog import TRAINING_FIELDS, build_catalog, dataset_metadata
+from harborrl.options.cli import main
+from harborrl.options.presets import PRESETS, worker_defaults
+from harborrl.options.validation import validate_paths
+from harborrl.options.wizard import build_config_bundle, publish_bundle
+
+
+@pytest.fixture
+def answers(tmp_path):
+ _path, task = native_config(tmp_path)
+ from harborrl.data.harbor.native_inspector import inspect_native
+ catalog = tmp_path / "catalog.json"
+ entries = json.loads(catalog.read_text())
+ entries[0]["task_digest"] = inspect_native(task)["task_digest"]
+ catalog.write_text(json.dumps(entries))
+ model = tmp_path / "model"
+ model.mkdir()
+ return {"answers_version": 1, "preset": "embedded-local", "training": {
+ "tasks": {"catalog": str(catalog)}, "harness": {"profile": str(tmp_path / "claude.json")},
+ "harbor": {"python": sys.executable},
+ "model": {"checkpoint": str(model), "reference": str(model)},
+ "gateway": {"tokenizer_digest": "1" * 64, "template_digest": "2" * 64},
+ }}
+
+
+def service_answers(answers, preset):
+ answers = deepcopy(answers)
+ answers["preset"] = preset
+ answers["deployment"] = {
+ "manager": {"control_url": "http://controller.example:49200", "gateway_url": "http://controller.example:49184"},
+ "gpu_target": {"ssh": {"host": "gpu", "user": "trainer", "port": 22},
+ "source_dir": "/remote/source", "train_python": "/remote/python"},
+ }
+ if preset == "service-ssh":
+ worker = worker_defaults()
+ worker["ssh"]["host"] = "runner"
+ answers["deployment"]["rollout"] = {"workers": [worker]}
+ del answers["training"]["harbor"]["python"]
+ else:
+ answers["sandbox_config"] = "/site/sandbox.yaml"
+ return answers
+
+
+def test_catalog_contract(monkeypatch):
+ def forbidden(*args, **kwargs):
+ raise AssertionError("offline operation attempted subprocess")
+ monkeypatch.setattr(subprocess, "run", forbidden)
+ monkeypatch.setattr(subprocess, "Popen", forbidden)
+ from harborrl.config.native import FIELDS
+ assert set(TRAINING_FIELDS) == {f"{s}.{f}" for s, fields in FIELDS.items() for f in fields}
+ first = build_catalog()
+ assert json.dumps(first, sort_keys=True) == json.dumps(build_catalog(), sort_keys=True)
+ assert all(not row["available"] for row in first["datasets"] if row["status"] == "planned")
+ from harborrl.config import ROOT
+ assert dataset_metadata() == yaml.safe_load((ROOT / "configs/harbor_hub/manifests.yaml").read_text())
+
+
+@pytest.mark.parametrize("dimension", [None, "models", "algorithms", "tasks", "harnesses", "datasets", "deployments", "fields", "compat"])
+def test_catalog_cli(dimension, capsys):
+ assert main(["options", *([dimension] if dimension else []), "--json"]) == 0
+ captured = capsys.readouterr()
+ result = json.loads(captured.out)
+ assert not captured.err
+ assert result["catalog_version"] == 1
+ if dimension:
+ assert len(result) == 3
+
+
+@pytest.mark.parametrize("preset", [p["id"] for p in PRESETS])
+def test_presets_and_legacy_dry_run(preset, answers, tmp_path, capsys):
+ if preset != "embedded-local":
+ answers = service_answers(answers, preset)
+ original = deepcopy(answers)
+ output = tmp_path / "new site"
+ bundle = build_config_bundle(answers, output_directory=output)
+ assert answers == original
+ assert not output.exists()
+ publish_bundle(bundle, output)
+ result = validate_paths(output / "config.yaml", output / "deployment.yaml")
+ assert result["status"] == "valid_with_deferred_checks", result
+ if preset != "service-sandbox-local-runner":
+ from harborrl.cli import main as legacy_main
+ assert legacy_main(["train", str(output / "config.yaml"), "--deployment", str(output / "deployment.yaml"), "--dry-run"]) == 0
+ assert json.loads(capsys.readouterr().out)["config"]["schema_version"] == 2
+
+
+def test_local_files_and_remote_ownership(answers, tmp_path):
+ for preset in ("embedded-local", "service-ssh"):
+ current = answers if preset == "embedded-local" else service_answers(answers, preset)
+ out = tmp_path / preset
+ publish_bundle(build_config_bundle(current, output_directory=out), out)
+ result = validate_paths(out / "config.yaml", out / "deployment.yaml", check_files=True)
+ assert result["status"] == "valid_with_deferred_checks", result
+ if preset == "service-ssh":
+ assert any(c["code"] == "remote_paths" for c in result["checks"])
+ (tmp_path / "claude.json").unlink()
+ assert validate_paths(out / "config.yaml", out / "deployment.yaml")["status"] != "invalid"
+ assert validate_paths(out / "config.yaml", out / "deployment.yaml", check_files=True)["status"] == "invalid"
+
+
+@pytest.mark.parametrize("mutation", ["unknown", "missing", "zero_digest", "gpu", "gateway", "provider"])
+def test_reject_bad_answers(mutation, answers, tmp_path):
+ answers = service_answers(answers, "service-ssh")
+ if mutation == "unknown":
+ answers["training"]["sampling"] = {"typo": 1}
+ elif mutation == "missing":
+ del answers["training"]["tasks"]
+ elif mutation == "zero_digest":
+ answers["training"]["gateway"]["tokenizer_digest"] = "0" * 64
+ elif mutation == "gpu":
+ answers["deployment"]["gpu_target"]["expected_gpus"] = 0
+ elif mutation == "gateway":
+ answers["training"]["gateway"]["advertised_url"] = "http://other.example:49184"
+ else:
+ answers["deployment"]["rollout"]["provider"] = "sandbox"
+ with pytest.raises(ValueError):
+ build_config_bundle(answers, output_directory=tmp_path / "output")
+ assert not (tmp_path / "output").exists()
+
+
+def test_noninteractive_and_failure_json(answers, tmp_path, monkeypatch, capsys):
+ monkeypatch.setattr(sys.stdin, "isatty", lambda: False)
+ with pytest.raises(SystemExit) as exc:
+ main(["init", "--out-dir", str(tmp_path / "output")])
+ assert exc.value.code == 2
+ capsys.readouterr()
+ assert main(["validate", str(tmp_path / "missing"), "--json"]) == 1
+ assert json.loads(capsys.readouterr().out)["status"] == "invalid"
+ file = tmp_path / "answers.yaml"
+ file.write_text(yaml.safe_dump(answers))
+ assert main(["init", "--answers", str(file), "--out-dir", str(tmp_path / "output")]) == 0
+ assert "harborrl train" in capsys.readouterr().out
+ assert main(["init", "--answers", str(file), "--out-dir", str(tmp_path / "output")]) == 1
+
+
+def test_publication_failure_lock_and_traversal(answers, tmp_path, monkeypatch):
+ output = tmp_path / "output"
+ bundle = build_config_bundle(answers, output_directory=output)
+ lock = tmp_path / ".output.init.lock"
+ lock.write_text("other process")
+ with pytest.raises(FileExistsError):
+ publish_bundle(bundle, output)
+ assert lock.read_text() == "other process"
+ lock.unlink()
+ bad = deepcopy(bundle)
+ bad["files"]["../escape"] = "bad"
+ with pytest.raises(ValueError):
+ publish_bundle(bad, output)
+ def fail(*args, **kwargs):
+ raise OSError("injected rename failure")
+ with monkeypatch.context() as context:
+ context.setattr(Path, "rename", fail)
+ with pytest.raises(OSError, match="injected"):
+ publish_bundle(bundle, output)
+ assert not output.exists() and not list(tmp_path.glob(".output.init*"))
+ publish_bundle(bundle, output)
+ before = (output / "config.yaml").read_bytes()
+ with pytest.raises(FileExistsError):
+ publish_bundle(bundle, output)
+ assert (output / "config.yaml").read_bytes() == before
+
+
+@pytest.mark.parametrize("confirmation", ["yes", "no"])
+def test_interactive(answers, tmp_path, monkeypatch, confirmation):
+ t = answers["training"]
+ responses = iter(["1", sys.executable, sys.executable, t["tasks"]["catalog"], t["harness"]["profile"],
+ t["model"]["checkpoint"], t["model"]["reference"], "", "1" * 64, "2" * 64,
+ "", "", "1", "", "", "", "", confirmation])
+ monkeypatch.setattr(sys.stdin, "isatty", lambda: True)
+ monkeypatch.setattr("builtins.input", lambda _: next(responses))
+ output = tmp_path / "interactive"
+ assert main(["init", "--out-dir", str(output)]) == 0
+ assert output.exists() == (confirmation == "yes")
+
+
+def test_probe_failure_and_alias_parsing(tmp_path, monkeypatch):
+ from harborrl.options.probe import probe_local, ssh_aliases
+ config = tmp_path / "ssh-config"
+ config.write_text("Host train runner *.example !excluded\n HostName host\nMatch exec unsafe\n User user\n")
+ assert ssh_aliases(config) == ["runner", "train"]
+ def timeout(*args, **kwargs):
+ assert kwargs["timeout"] == 3
+ raise subprocess.TimeoutExpired(args[0], 3)
+ monkeypatch.setattr(subprocess, "run", timeout)
+ result = probe_local(tmp_path / "output")
+ assert result["gpu"]["status"] == result["python"]["status"] == "unknown"
+ assert not (tmp_path / "output").exists()
+
+
+def test_sandbox_files_and_remote_runner(answers, tmp_path, capsys):
+ from harborrl.cli import main as legacy_main
+ from harborrl.data.harbor.native_inspector import inspect_native
+ task = tmp_path / "task"
+ (task / "task.toml").write_text('version = "1.0"\n[environment]\ndocker_image = "task:locked"\n')
+ catalog = tmp_path / "catalog.json"
+ entries = json.loads(catalog.read_text())
+ entries[0]["task_digest"] = inspect_native(task)["task_digest"]
+ catalog.write_text(json.dumps(entries))
+ sandbox = tmp_path / "sandbox.yaml"
+ sandbox.write_text(yaml.safe_dump({"domain": "https://sandbox.example", "credential_file": "/missing/secret",
+ "bindings": [{"image": "task:locked", "max_live_instances": 2}]}))
+ answers = service_answers(answers, "service-sandbox-local-runner")
+ answers["sandbox_config"] = str(sandbox)
+ for remote in (False, True):
+ if remote:
+ worker = worker_defaults()
+ worker["ssh"]["host"] = "runner"
+ worker["docker"] = False
+ answers["deployment"]["rollout"] = {"workers": [worker]}
+ answers["training"]["harbor"] = {"runner_transport": "ssh"}
+ output = tmp_path / f"sandbox-{remote}"
+ publish_bundle(build_config_bundle(answers, output_directory=output), output)
+ report = validate_paths(output / "config.yaml", output / "deployment.yaml", check_files=True)
+ assert report["status"] == "valid_with_deferred_checks", report
+ assert legacy_main(["train", str(output / "config.yaml"), "--deployment", str(output / "deployment.yaml"), "--dry-run"]) == 0
+ capsys.readouterr()
+ sandbox.write_text(sandbox.read_text().replace("task:locked", "other:locked"))
+ assert validate_paths(output / "config.yaml", output / "deployment.yaml", check_files=True)["status"] == "invalid"
+
+
+def test_offline_import_boundary(answers, tmp_path):
+ from harborrl.config import ROOT
+ output = tmp_path / "offline"
+ publish_bundle(build_config_bundle(answers, output_directory=output), output)
+ script = '''
+import builtins, socket, subprocess, sys
+sys.path.insert(0, sys.argv[1])
+original = builtins.__import__
+def guarded(name, *args, **kwargs):
+ assert name.split('.')[0] not in {'torch', 'transformers', 'sglang', 'harbor', 'opensandbox', 'httpx'}
+ return original(name, *args, **kwargs)
+def forbidden(*args, **kwargs):
+ raise AssertionError('offline command attempted an external operation')
+builtins.__import__ = guarded
+socket.socket = forbidden
+subprocess.run = subprocess.Popen = forbidden
+from harborrl.cli import main
+assert main(['options', '--json']) == 0
+assert main(['validate', sys.argv[2], '--deployment', sys.argv[3], '--check-files', '--json']) == 0
+'''
+ result = subprocess.run([sys.executable, "-I", "-c", script, str(ROOT), str(output / "config.yaml"),
+ str(output / "deployment.yaml")], capture_output=True, text=True, timeout=20, check=False)
+ assert result.returncode == 0, result.stderr
+
+
+def test_concurrent_publication(answers, tmp_path):
+ from concurrent.futures import ThreadPoolExecutor
+ from threading import Barrier
+ output = tmp_path / "concurrent"
+ bundle = build_config_bundle(answers, output_directory=output)
+ barrier = Barrier(2)
+ def attempt():
+ barrier.wait(timeout=10)
+ try:
+ publish_bundle(bundle, output)
+ except FileExistsError:
+ return False
+ return True
+ with ThreadPoolExecutor(max_workers=2) as pool:
+ assert sorted(pool.map(lambda _: attempt(), range(2))) == [False, True]
+ assert {p.name for p in output.iterdir()} == set(bundle["files"])
+
+
+def test_relative_paths_and_invalid_yaml(answers, tmp_path, capsys):
+ answers["training"]["tasks"]["catalog"] = "../catalog.json"
+ output = tmp_path / "relative"
+ publish_bundle(build_config_bundle(answers, output_directory=output), output)
+ config = yaml.safe_load((output / "config.yaml").read_text())
+ assert config["tasks"]["catalog"] == str(tmp_path / "catalog.json")
+ bad = tmp_path / "bad.yaml"
+ bad.write_text("[invalid")
+ assert main(["validate", str(bad), "--json"]) == 1
+ assert json.loads(capsys.readouterr().out)["status"] == "invalid"
+
+
+def test_embedded_explicit_conflict(answers, tmp_path):
+ answers["training"]["gateway"]["advertised_url"] = "http://localhost:49184"
+ answers["deployment"] = {"manager": {"gateway_url": "http://127.0.0.1:49184"}}
+ with pytest.raises(ValueError, match="conflicts"):
+ build_config_bundle(answers, output_directory=tmp_path / "conflict")
+
+
+def test_options_text_is_concise(capsys):
+ assert main(["options"]) == 0
+ output = capsys.readouterr().out
+ assert len(output.splitlines()) < 50
+ assert "locally_available=False" in output
+
+
+def test_public_answer_templates(tmp_path):
+ from harborrl.config import ROOT
+ for file in sorted((ROOT / "examples/options").glob("*.yaml")):
+ answers = yaml.safe_load(file.read_text())
+ with pytest.raises(ValueError, match="SHA-256"):
+ build_config_bundle(answers, output_directory=tmp_path / file.stem)
+ answers["training"]["gateway"].update(tokenizer_digest="1" * 64, template_digest="2" * 64)
+ assert build_config_bundle(answers, output_directory=tmp_path / file.stem)["validation"]["status"] != "invalid"
diff --git a/tests/harborrl/test_rollout_runtime.py b/tests/harborrl/test_rollout_runtime.py
new file mode 100644
index 000000000..8f33323c5
--- /dev/null
+++ b/tests/harborrl/test_rollout_runtime.py
@@ -0,0 +1,321 @@
+"""Extracted rollout core (P2): config mapping, slot scheduling, boundaries."""
+from __future__ import annotations
+
+import asyncio
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+
+def test_runtime_config_maps_environment(tmp_path, monkeypatch):
+ from harborrl.rollout.runtime import RolloutRuntimeConfig
+
+ environment = {
+ "RUN_ID": "run-1", "RUN_DIR": str(tmp_path),
+ "HARBORRL_NATIVE_CATALOG": str(tmp_path / "catalog.json"),
+ "HARBORRL_NATIVE_PROFILE": str(tmp_path / "profile.json"),
+ "HARBORRL_NATIVE_GATEWAY_HOST": "127.0.0.1",
+ "HARBORRL_NATIVE_GATEWAY_PORT": "49183",
+ "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:49183",
+ "HARBORRL_NATIVE_RUNNER_PYTHON": "/runner/python",
+ "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(["worker-a", "worker-b"]),
+ "HARBORRL_NATIVE_TASK_WORKER_OVERRIDES": json.dumps({"task": "worker-b"}),
+ "HARBORRL_NATIVE_MAX_ATTEMPTS": "3",
+ "ROLLOUT_MAX_RESPONSE_LEN": "128",
+ "ROLLOUT_MAX_CONTEXT_LEN": "1024",
+ "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64,
+ "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64,
+ "HARBORRL_NATIVE_AUDITED_LOGPROBS": "1",
+ "HARBORRL_NATIVE_DRAIN_TIMEOUT": "5",
+ "MODEL_TAG": "qwen3-8B",
+ }
+ for key, value in environment.items():
+ monkeypatch.setenv(key, value)
+ config = RolloutRuntimeConfig.from_environment(
+ environment, endpoint="http://router:30000/", concurrency_limit=4, group_size=2)
+ assert config.workers == ("worker-a", "worker-b")
+ assert config.task_worker_overrides == {"task": "worker-b"}
+ assert config.max_attempts == 3 and config.max_output_tokens == 128
+ assert config.endpoint == "http://router:30000"
+ assert config.environment()["HARBORRL_NATIVE_RUNNER_WORKERS"] == \
+ json.dumps(["worker-a", "worker-b"])
+ with pytest.raises(ValueError, match="group size"):
+ RolloutRuntimeConfig.from_environment(environment, endpoint="x", concurrency_limit=1,
+ group_size=1)
+ stripped = dict(environment)
+ stripped.pop("HARBORRL_NATIVE_RUNNER_WORKERS")
+ with pytest.raises(ValueError, match="RUNNER_WORKERS"):
+ RolloutRuntimeConfig.from_environment(stripped, endpoint="x",
+ concurrency_limit=1, group_size=2)
+
+
+def test_runtime_binds_gateway_with_registry_and_backend(tmp_path, monkeypatch):
+ from harborrl.gateway import server as gateway_server
+ from harborrl.gateway import sglang_backend, trace
+ from harborrl.rollout import runtime as rollout_runtime
+
+ catalog = tmp_path / "catalog.json"
+ catalog.write_text("[]")
+ profile = tmp_path / "profile.json"
+ profile.write_text(json.dumps({"model": "policy"}))
+ tokenizer = SimpleNamespace(
+ apply_chat_template=lambda *args, **kwargs: [1, 2],
+ get_vocab=lambda: {"token": 1},
+ )
+ tokenizer_digest, template_digest = rollout_runtime.tokenizer_fingerprint(tokenizer)
+ environment = {
+ "RUN_DIR": str(tmp_path), "RUN_ID": "run-1",
+ "HARBORRL_NATIVE_CATALOG": str(catalog),
+ "HARBORRL_NATIVE_PROFILE": str(profile),
+ "HARBORRL_NATIVE_TOKENIZER_DIGEST": tokenizer_digest,
+ "HARBORRL_NATIVE_TEMPLATE_DIGEST": template_digest,
+ "HARBORRL_NATIVE_GATEWAY_HOST": "127.0.0.1",
+ "HARBORRL_NATIVE_GATEWAY_PORT": "49183",
+ "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:49183",
+ "HARBORRL_NATIVE_RUNNER_PYTHON": "/runner/python",
+ "HARBORRL_NATIVE_RUNNER_WORKERS": '["worker-a"]',
+ "ROLLOUT_MAX_RESPONSE_LEN": "128",
+ "ROLLOUT_MAX_CONTEXT_LEN": "1024",
+ }
+ for key, value in environment.items():
+ monkeypatch.setenv(key, value)
+
+ calls = {}
+
+ class FakeGateway:
+ def __init__(self, registry, backend, *, model, max_output_tokens):
+ calls["gateway"] = (registry, backend, model, max_output_tokens)
+ calls["gateway_object"] = self
+
+ class FakeServer:
+ def serve_forever(self):
+ pass
+
+ def shutdown(self):
+ pass
+
+ def server_close(self):
+ pass
+
+ class FakeTraceRegistry:
+ def __init__(self, path):
+ calls["registry_path"] = path
+
+ class FakeBackend:
+ def __init__(self, *args, **kwargs):
+ calls["backend_args"] = (args, kwargs)
+
+ def weight_version(self):
+ return "7"
+
+ def fake_make_server(gateway, host, port):
+ calls["server"] = (gateway, host, port)
+ return FakeServer()
+
+ monkeypatch.setattr(gateway_server, "Gateway", FakeGateway)
+ monkeypatch.setattr(gateway_server, "make_server", fake_make_server)
+ monkeypatch.setattr(sglang_backend, "SGLangBackend", FakeBackend)
+ monkeypatch.setattr(trace, "TraceRegistry", FakeTraceRegistry)
+
+ config = rollout_runtime.RolloutRuntimeConfig.from_environment(
+ environment, endpoint="http://127.0.0.1:30000", concurrency_limit=4, group_size=2)
+ runtime_ = rollout_runtime.RolloutRuntime(config, tokenizer)
+ try:
+ assert calls["gateway"][2:] == ("policy", 128)
+ assert calls["server"][0] is calls["gateway_object"]
+ assert calls["server"][1:] == ("127.0.0.1", 49183)
+ assert calls["backend_args"][0][0] == "http://127.0.0.1:30000"
+ assert runtime_.backend.weight_version() == "7"
+ finally:
+ runtime_.close()
+
+ # A tokenizer that disagrees with the native lock fails closed.
+ other = SimpleNamespace(apply_chat_template=lambda *a, **k: [9],
+ get_vocab=lambda: {"other": 2})
+ with pytest.raises(ValueError, match="digest"):
+ rollout_runtime.RolloutRuntime(config, other)
+
+
+def test_policy_identity_uses_synchronized_pool_version(tmp_path, monkeypatch):
+ from harborrl.rollout import runtime as rollout_runtime
+
+ class Backend:
+ endpoint = "http://router:30000"
+
+ def pool_states(self):
+ return [{"endpoint": self.endpoint, "healthy": True, "weight_version": "2"}]
+
+ class Registry:
+ def __init__(self):
+ self.published = None
+
+ def drain(self, timeout):
+ self.timeout = timeout
+
+ def publish_version(self, states, expected):
+ self.published = (states, expected)
+
+ monkeypatch.setenv("RUN_DIR", str(tmp_path))
+ registry = Registry()
+ runtime_ = SimpleNamespace(run_root=tmp_path, backend=Backend(), registry=registry,
+ config=SimpleNamespace(drain_timeout=30))
+ assert rollout_runtime.current_policy_version(runtime_) == "2"
+ payload = json.loads((tmp_path / "policy-pool.json").read_text())
+ assert payload["weight_version"] == "2"
+ assert registry.published[1] == "2"
+ assert registry.timeout == 30
+
+
+def test_group_shares_slot_state_and_shifts_retry_workers(tmp_path, monkeypatch):
+ from harborrl.rollout import runtime as rollout_runtime
+
+ entry = {"id": "task", "revision": "1", "path": "/task", "task_digest": "d" * 64,
+ "reward_profile": {"key": "reward", "scale": 1.0, "offset": 0.0,
+ "raw_range": [0.0, 1.0]}}
+ profile = tmp_path / "profile.json"
+ profile.write_text(json.dumps({"model": "policy"}))
+ environment = {
+ "RUN_ID": "run", "RUN_DIR": str(tmp_path), "HARBORRL_NATIVE_PROFILE": str(profile),
+ "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(["worker-a", "worker-b"]),
+ "HARBORRL_NATIVE_MAX_ATTEMPTS": "2",
+ "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:32123",
+ "HARBORRL_NATIVE_GATEWAY_PORT": "32123",
+ "HARBORRL_NATIVE_RUNNER_PYTHON": "/python",
+ "HARBORRL_NATIVE_CATALOG": str(tmp_path / "catalog.json"),
+ "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64,
+ "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64,
+ }
+ for key, value in environment.items():
+ monkeypatch.setenv(key, value)
+ config = rollout_runtime.RolloutRuntimeConfig.from_environment(
+ environment, endpoint="http://router:30000", concurrency_limit=8, group_size=4)
+
+ class FakeSlots:
+ max_attempts = 2
+
+ def __init__(self):
+ self.started = {}
+
+ def start(self, slot):
+ self.started[slot] = self.started.get(slot, 0) + 1
+ return SimpleNamespace(attempt_id=f"{slot}-{self.started[slot]}",
+ group_id="group-0", slot_id=slot,
+ attempt_no=self.started[slot])
+
+ def finish(self, slot, ir=None, error=None):
+ assert (ir is None) != (error is None)
+
+ created_groups = []
+
+ def fake_group_slots(identities, *, max_attempts=2):
+ created_groups.append(identities)
+ return FakeSlots()
+
+ calls = []
+
+ async def fake_run_attempt(identity, *, runner_host, **kwargs):
+ calls.append((identity.slot_id, runner_host))
+ if identity.slot_id == "1" and identity.attempt_no == 1:
+ raise RuntimeError("simulated first-attempt failure")
+ return {"identity": identity.__dict__}
+
+ monkeypatch.setattr(rollout_runtime, "GroupSlots", fake_group_slots)
+ monkeypatch.setattr(rollout_runtime, "run_attempt", fake_run_attempt)
+
+ runtime_ = SimpleNamespace(rollout_root=tmp_path, semaphore=asyncio.Semaphore(8),
+ registry=SimpleNamespace(), profile={"model": "policy"})
+
+ # The run owns its loaded profile; group setup must not reread the source file.
+ profile.unlink()
+ results = asyncio.run(rollout_runtime.run_group(
+ config, runtime_, entry=entry, group_index=0, rollout_id=0,
+ sampling_digest="sampling", policy_version="1"))
+ assert len(results) == 4
+ assert len(created_groups) == 1
+ assert len(created_groups[0]) == 4
+ assert {item.harness_digest for item in created_groups[0]} == {
+ rollout_runtime.digest(runtime_.profile)}
+ by_slot = {}
+ for slot, host in calls:
+ by_slot.setdefault(slot, []).append(host)
+ assert by_slot == {"0": ["worker-a"], "1": ["worker-b", "worker-a"],
+ "2": ["worker-a"], "3": ["worker-b"]}
+
+
+def test_task_worker_override_wins(tmp_path, monkeypatch):
+ from harborrl.rollout import runtime as rollout_runtime
+
+ entry = {"id": "task", "revision": "1", "path": "/task", "task_digest": "d" * 64,
+ "reward_profile": {"key": "reward"}}
+ profile = tmp_path / "profile.json"
+ profile.write_text(json.dumps({"model": "policy"}))
+ environment = {
+ "RUN_ID": "run", "RUN_DIR": str(tmp_path), "HARBORRL_NATIVE_PROFILE": str(profile),
+ "HARBORRL_NATIVE_RUNNER_WORKERS": json.dumps(["worker-a", "worker-b"]),
+ "HARBORRL_NATIVE_TASK_WORKER_OVERRIDES": json.dumps({"task": "worker-b"}),
+ "HARBORRL_NATIVE_GATEWAY_URL": "http://gateway:32123",
+ "HARBORRL_NATIVE_GATEWAY_PORT": "32123", "HARBORRL_NATIVE_RUNNER_PYTHON": "/python",
+ "HARBORRL_NATIVE_CATALOG": str(tmp_path / "catalog.json"),
+ "HARBORRL_NATIVE_TOKENIZER_DIGEST": "0" * 64,
+ "HARBORRL_NATIVE_TEMPLATE_DIGEST": "1" * 64,
+ }
+ for key, value in environment.items():
+ monkeypatch.setenv(key, value)
+ config = rollout_runtime.RolloutRuntimeConfig.from_environment(
+ environment, endpoint="http://router:30000", concurrency_limit=2, group_size=2)
+
+ seen = []
+
+ class PermissiveSlots:
+ max_attempts = 2
+
+ def __init__(self, identities, *, max_attempts=2):
+ self.identities = {identity.slot_id: identity for identity in identities}
+
+ def start(self, slot):
+ return self.identities[slot]
+
+ def finish(self, slot, ir=None, error=None):
+ assert (ir is None) != (error is None)
+
+ async def fake_run_attempt(identity, *, runner_host, **kwargs):
+ seen.append(runner_host)
+ return {"identity": identity.to_dict()}
+
+ monkeypatch.setattr(rollout_runtime, "GroupSlots", PermissiveSlots)
+ monkeypatch.setattr(rollout_runtime, "run_attempt", fake_run_attempt)
+ runtime_ = SimpleNamespace(rollout_root=tmp_path, semaphore=asyncio.Semaphore(2),
+ registry=SimpleNamespace(), profile={"model": "policy"})
+ irs = asyncio.run(rollout_runtime.run_group(
+ config, runtime_, entry=entry, group_index=0, rollout_id=0,
+ sampling_digest="sampling", policy_version="1"))
+ assert len(irs) == 2
+ assert seen == ["worker-b", "worker-b"]
+
+
+def test_rollout_core_does_not_import_training_frameworks():
+ import re
+
+ import harborrl.rollout
+
+ root = Path(harborrl.rollout.__file__).parent
+ offenders = []
+ for path in sorted(root.rglob("*.py")):
+ for number, line in enumerate(path.read_text().splitlines(), 1):
+ if re.match(r"\s*(from|import)\s+(slime|ray)(\.|\s|$)", line):
+ offenders.append(f"{path.relative_to(root)}:{number}: {line.strip()}")
+ assert not offenders, "rollout core must stay framework-free: " + "; ".join(offenders)
+
+
+def test_native_generate_shim_reexports_the_core():
+ from harborrl import rollout as package
+ from harborrl.rollout import native_generate, runtime
+
+ for name in ("NativeRolloutRuntime", "RolloutRuntime", "RolloutRuntimeConfig",
+ "close_runtime", "current_policy_version", "publish_native_version",
+ "run_attempt", "run_group", "run_slot", "runtime", "task_entry",
+ "tokenizer_fingerprint", "GroupSlots"):
+ assert getattr(native_generate, name) is getattr(runtime, name), name
+ assert package.native_generate.__doc__ is not None
diff --git a/tests/harborrl/test_rollout_service.py b/tests/harborrl/test_rollout_service.py
new file mode 100644
index 000000000..f12994feb
--- /dev/null
+++ b/tests/harborrl/test_rollout_service.py
@@ -0,0 +1,415 @@
+"""RolloutService + SlimeAdapter contracts (P3) over real local HTTP."""
+from __future__ import annotations
+
+import asyncio
+import json
+import threading
+import time
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+from test_native_contracts import SAMPLING, receipt
+
+from harborrl.backends.slime_v032.rollout_service_client import (
+ RolloutServiceClient,
+ ServiceRolloutError,
+ load_group_result,
+ service_group_request,
+)
+from harborrl.rollout.service import (
+ RolloutService,
+ make_control_server,
+ manifest_path_from,
+)
+from harborrl.trajectories.native import Identity, RewardProfile, digest, publish
+
+TASK = {"id": "task", "revision": "1", "path": "/task", "task_digest": "d" * 64,
+ "reward_profile": {"key": "reward", "scale": 1.0, "offset": 0.0,
+ "raw_range": [0.0, 1.0]}}
+
+
+def service_identity(slot, *, run_id="run-1", group_id="group-0", policy_version="1"):
+ return Identity(run_id=run_id, batch_id="rollout-0", group_id=group_id,
+ slot_id=slot, attempt_id=f"attempt-{policy_version}-{slot}",
+ trajectory_id=f"trajectory-{policy_version}-{slot}",
+ task_digest=TASK["task_digest"], policy_version=policy_version,
+ harness_digest="claude", reward_digest=RewardProfile().digest,
+ sampling_digest=digest(SAMPLING))
+
+
+def service_ir(tmp_path, slot, reward=1, **identity_overrides):
+ from harborrl.trajectories.native import assemble
+
+ ident = service_identity(slot, **identity_overrides)
+ evaluation = receipt(tmp_path / slot, ident, reward)
+ trial = evaluation["harbor_trial_id"]
+ turn = {"identity": ident.to_dict(), "harbor_trial_id": trial,
+ "request_id": f"r-{slot}", "response_id": f"m-{slot}", "engine_id": "engine",
+ "input_ids": [1, 2], "output_ids": [3, 4], "logprobs": [-0.2, -0.3],
+ "logprob_semantics": "raw_model", "policy_version": ident.policy_version,
+ "tokenizer_digest": "tok", "template_digest": "template",
+ "delivery": "consumed_confirmed", "consumption_ref": "session#digest",
+ "role": "policy", "sampling": SAMPLING, "client_request": {"model": "policy"},
+ "serving_input": {"input_ids": [1, 2]},
+ "response": {"content": [{"type": "text", "text": "done"}]},
+ "finish_reason": "end_turn", "evidence_kind": "synthetic"}
+ for key in ("client_request", "serving_input", "response"):
+ turn[key + "_digest"] = digest(turn[key])
+ seal = {"identity": ident.to_dict(), "harbor_trial_id": trial,
+ "pending_requests": 0, "status": "sealed",
+ "request_ids": [turn["request_id"]], "turns_digest": digest([turn]),
+ "errors": []}
+ return assemble(ident, trial, [turn], seal, evaluation)
+
+
+class FakeRegistry:
+ def __init__(self):
+ self.version = None
+ self.accepting = False
+
+ def drain(self, timeout):
+ self.accepting = False
+
+ def publish_version(self, states, expected):
+ self.version = str(expected)
+ self.accepting = True
+
+
+class FakeRuntime:
+ def __init__(self, root):
+ self.registry = FakeRegistry()
+ self.rollout_root = Path(root)
+
+ def manifest_path(self, identity):
+ return manifest_path_from(self.rollout_root, identity)
+
+ def close(self):
+ pass
+
+
+def write_group_irs(tmp_path, request, rewards=(1, 0)):
+ runtime = FakeRuntime(tmp_path)
+ irs = []
+ for descriptor, reward in zip(request["slots"], rewards):
+ ir = service_ir(tmp_path, descriptor["slot_id"], reward,
+ policy_version=request["policy_version"])
+ publish(runtime.manifest_path(ir["identity"]), ir)
+ irs.append(ir)
+ return irs
+
+
+def group_request(group_index=0, policy_version="1", slots=2, task=TASK):
+ return {
+ "group_id": f"group-{group_index}", "rollout_id": 0,
+ "group_index": group_index, "policy_version": policy_version,
+ "task": task, "sampling": dict(SAMPLING),
+ "slots": [{"slot_id": str(slot)} for slot in range(slots)],
+ }
+
+
+class ServiceHarness:
+ def __init__(self, tmp_path, executor, **kwargs):
+ self.tmp_path = tmp_path
+ self.runtime = FakeRuntime(tmp_path)
+ self.service = RolloutService(run_root=tmp_path, executor=executor, allow_synthetic=True, **kwargs)
+ self.control = make_control_server(self.service, host="127.0.0.1", port=0)
+ self.thread = threading.Thread(target=self.control.serve_forever, daemon=True)
+ self.thread.start()
+ self.url = f"http://127.0.0.1:{self.control.server_address[1]}"
+ self.run_id = "run-1"
+ registration = self.service.register_run(
+ self.run_id, {"run_id": self.run_id, "group_size": 2})
+ self.token = registration["run_token"]
+ self.service.attach_runtime(self.run_id, SimpleNamespace(drain_timeout=1),
+ self.runtime)
+
+ def client(self):
+ return RolloutServiceClient(self.url, self.run_id, self.token, timeout=10,
+ poll_interval=0.05)
+
+ def close(self):
+ self.control.shutdown()
+ self.control.server_close()
+ self.thread.join(5)
+ self.service.close()
+
+
+@pytest.fixture()
+def harness(tmp_path):
+ calls = []
+
+ async def executor(config, runtime_, request):
+ calls.append(json.loads(json.dumps(request)))
+ return write_group_irs(tmp_path, request)
+
+ instance = ServiceHarness(tmp_path, executor, group_wait_timeout=5)
+ instance.executor_calls = calls
+ try:
+ yield instance
+ finally:
+ instance.close()
+
+
+def activated_states(version="1"):
+ return [{"endpoint": f"http://engine-{index}", "healthy": True,
+ "weight_version": version} for index in range(2)]
+
+
+def test_register_auth_and_idempotency(harness):
+ client = harness.client()
+ assert client.health()["ok"]
+ # Wrong tokens are rejected on every run-scoped endpoint.
+ bad = RolloutServiceClient(harness.url, harness.run_id, "wrong-token", timeout=10)
+ with pytest.raises(ServiceRolloutError) as exc:
+ bad.group_status("group-0")
+ assert exc.value.status == 401
+ with pytest.raises(ServiceRolloutError) as exc:
+ bad.submit_group(group_request())
+ assert exc.value.status == 401
+
+
+def test_policy_gate_and_activation(harness, tmp_path):
+ client = harness.client()
+ with pytest.raises(ServiceRolloutError) as exc:
+ client.submit_group(group_request())
+ assert exc.value.status == 409 and exc.value.code == "stale_policy"
+ assert not (tmp_path / "policy-pool.json").exists()
+
+ client.activate_policy(activated_states(), "1")
+ assert harness.runtime.registry.version == "1"
+ assert harness.runtime.registry.accepting
+ pool = json.loads((tmp_path / "policy-pool.json").read_text())
+ assert pool["weight_version"] == "1"
+
+ # A partially unhealthy pool is never admitted.
+ from harborrl.rollout.service import ServiceError
+
+ broken = activated_states()
+ broken[1]["healthy"] = False
+ with pytest.raises(ServiceError, match="pool"):
+ harness.service.activate_policy(harness.run_id, broken, "2")
+
+
+def test_submit_is_idempotent_and_conflicts_rejected(harness):
+ client = harness.client()
+ client.activate_policy(activated_states(), "1")
+ request = group_request()
+ first = client.submit_group(request)
+ assert first["status"] == "complete"
+ assert len(first["result"]["slots"]) == 2
+ assert len(harness.executor_calls) == 1
+
+ again = client.submit_group(json.loads(json.dumps(request)))
+ assert again["status"] == "complete"
+ assert again["result"]["request_digest"] == first["result"]["request_digest"]
+ assert len(harness.executor_calls) == 1 # no second execution
+
+ conflicting = group_request()
+ conflicting["sampling"]["temperature"] = 0.5
+ with pytest.raises(ServiceRolloutError) as exc:
+ client.submit_group(conflicting)
+ assert exc.value.status == 409 and exc.value.code == "conflict"
+
+ other = client.submit_group(group_request(group_index=1))
+ assert other["status"] == "complete"
+ assert len(harness.executor_calls) == 2
+
+
+def test_client_validates_result_and_rejects_tampering(harness, tmp_path):
+ client = harness.client()
+ client.activate_policy(activated_states(), "1")
+ request = group_request()
+ result = client.submit_group(request)["result"]
+ trajectories = load_group_result(harness.run_id, request, result,
+ allow_synthetic=True)
+ assert [ir["identity"]["slot_id"] for ir in trajectories] == ["0", "1"]
+
+ slot = result["slots"][0]
+ manifest = Path(slot["manifest_path"])
+ forged = json.loads(json.dumps(result))
+ forged["slots"][0]["manifest_sha256"] = "0" * 64
+ with pytest.raises(ServiceRolloutError, match="digest mismatch"):
+ load_group_result(harness.run_id, request, forged, allow_synthetic=True)
+ tampered = json.loads(json.dumps(result))
+ tampered["slots"][1]["manifest_path"] = str(tmp_path / "missing.json")
+ with pytest.raises(ServiceRolloutError, match="cannot read slot manifest"):
+ load_group_result(harness.run_id, request, tampered, allow_synthetic=True)
+ assert manifest.is_file() # nothing above mutated shared evidence
+
+ # A stale policy version inside a result is rejected.
+ fresh = write_group_irs(tmp_path, group_request(policy_version="2"), rewards=(1, 0))
+ stale_result = json.loads(json.dumps(result))
+ stale_result["slots"][0]["manifest_path"] = str(
+ harness.runtime.manifest_path(fresh[0]["identity"]))
+ stale_result["slots"][0]["manifest_sha256"] = digest(fresh[0])
+ with pytest.raises(ServiceRolloutError, match="identity differs"):
+ load_group_result(harness.run_id, request, stale_result, allow_synthetic=True)
+
+
+def test_drain_closes_admission(harness):
+ client = harness.client()
+ client.activate_policy(activated_states(), "1")
+ client.drain()
+ with pytest.raises(ServiceRolloutError) as exc:
+ client.submit_group(group_request())
+ assert exc.value.status == 409 and exc.value.code == "admission_closed"
+ assert not harness.runtime.registry.accepting
+
+
+@pytest.mark.parametrize("cancel_whole_run", [False, True])
+def test_cancel_and_query_after_timeout(tmp_path, cancel_whole_run):
+ release = threading.Event()
+
+ async def slow_executor(config, runtime_, request):
+ if request["group_index"] == 0:
+ while not release.is_set():
+ await asyncio.sleep(0.05) # cancellable wait
+ return write_group_irs(tmp_path, request)
+ while True: # the second group blocks until it is cancelled
+ await asyncio.sleep(0.05)
+
+ harness = ServiceHarness(tmp_path, slow_executor, group_wait_timeout=0.2)
+ try:
+ client = harness.client()
+ client.activate_policy(activated_states(), "1")
+ request = group_request()
+ # Short server wait: the response must say running and hint a query.
+ pending = harness.service.submit_group(harness.run_id, request)
+ assert pending["status"] in ("queued", "running")
+ assert "hint" in pending
+ status = client.group_status("group-0")
+ assert status["status"] in ("queued", "running")
+ release.set()
+ deadline = time.time() + 5
+ while time.time() < deadline and status["status"] in ("queued", "running"):
+ time.sleep(0.05)
+ status = client.group_status("group-0")
+ assert status["status"] == "complete"
+
+ request2 = group_request(group_index=1)
+ submitted = harness.service.submit_group(harness.run_id, request2)
+ assert submitted["status"] in ("queued", "running")
+ if cancel_whole_run:
+ result = harness.service.cancel_run(harness.run_id)
+ assert result["groups"] == {"group-0": "complete", "group-1": "cancelled"}
+ cancelled = client.group_status("group-1")
+ else:
+ cancelled = client.cancel_group("group-1")
+ assert cancelled["status"] == "cancelled"
+ with pytest.raises(ServiceRolloutError, match="cancelled|not complete"):
+ load_group_result(harness.run_id, request2, cancelled)
+ finally:
+ release.set()
+ harness.close()
+
+
+def test_register_run_conflict_on_different_manifest(tmp_path):
+ async def executor(config, runtime_, request):
+ return write_group_irs(tmp_path, request)
+
+ harness = ServiceHarness(tmp_path, executor)
+ try:
+ from harborrl.rollout.service import ServiceError
+
+ same_manifest = {"run_id": "run-1", "group_size": 2}
+ assert harness.service.register_run("run-1", same_manifest)["existed"]
+ with pytest.raises(ServiceError) as exc:
+ harness.service.register_run("run-1", {"run_id": "run-1", "x": 1})
+ assert exc.value.status == 409
+ finally:
+ harness.close()
+
+
+def test_service_group_request_and_run_service_groups(tmp_path, monkeypatch):
+ """Full SlimeAdapter path: fake Slime samples -> HTTP -> IRs bound to samples."""
+ from harborrl.backends.slime_v032 import rollout as slime_rollout
+
+ calls = []
+
+ async def executor(config, runtime_, request):
+ calls.append(request["group_id"])
+ return write_group_irs(tmp_path, request)
+
+ harness = ServiceHarness(tmp_path, executor)
+ try:
+ monkeypatch.setenv("HARBORRL_ROLLOUT_SERVICE_URL", harness.url)
+ monkeypatch.setenv("HARBORRL_ROLLOUT_SERVICE_TOKEN", harness.token)
+ monkeypatch.setenv("RUN_ID", harness.run_id)
+ monkeypatch.setenv("RUN_DIR", str(tmp_path))
+
+ class Sample:
+ class Status:
+ COMPLETED = "completed"
+
+ def __init__(self, group_index):
+ self.metadata = {"native_task": dict(TASK)}
+ self.group_index = group_index
+
+ args = SimpleNamespace(rollout_max_response_len=SAMPLING["max_new_tokens"],
+ rollout_temperature=1.0, rollout_top_p=1.0,
+ rollout_top_k=-1, rollout_stop=[],
+ sglang_router_ip="127.0.0.1", sglang_router_port=30000)
+ group = [Sample(0), Sample(0)]
+ monkeypatch.setenv("HARBORRL_ROLLOUT_SERVICE_TOKEN", harness.token)
+ # The adapter announces serving and derives the admitted version from
+ # the live pool itself before requesting groups.
+ from harborrl.backends.slime_v032 import rollout_service_client
+
+ monkeypatch.setattr(rollout_service_client, "fetch_pool_states",
+ lambda endpoint, **kwargs: activated_states())
+
+ with pytest.raises(ServiceRolloutError, match='synthetic_evidence'):
+ asyncio.run(slime_rollout.run_service_groups(args, [group], 0))
+ assert all('native_ir' not in sample.metadata for sample in group)
+ assert calls == ["group-0"]
+ assert harness.service.runs[harness.run_id].serving == (
+ "http://127.0.0.1:30000")
+
+ request = service_group_request(
+ group, slime_rollout.sampling_params(args), 0, "1")
+ assert request["group_id"] == "group-0"
+ assert request["group_index"] == 0
+ assert [slot["slot_id"] for slot in request["slots"]] == ["0", "1"]
+ finally:
+ harness.close()
+
+
+def test_announce_factory_attaches_config_and_runtime(tmp_path):
+ """A GPU announce through the factory makes the run executable alone."""
+ async def executor(config, runtime_, request):
+ assert config is runtime_.config
+ return write_group_irs(tmp_path, request)
+
+ service = RolloutService(run_root=tmp_path, executor=executor, allow_synthetic=True,
+ group_wait_timeout=5,
+ runtime_factory=lambda run_id, router: SimpleNamespace(
+ registry=FakeRegistry(), rollout_root=tmp_path,
+ manifest_path=lambda ident: manifest_path_from(tmp_path, ident),
+ config=SimpleNamespace(drain_timeout=1)))
+ try:
+ registration = service.register_run("run-9", {"run_id": "run-9"})
+ result = service.announce_serving("run-9", "http://gpu:30000/")
+ assert result["runtime"] == "attached"
+ assert service.runs["run-9"].serving == "http://gpu:30000"
+ assert service.runs["run-9"].config is not None
+ service.activate_policy("run-9", activated_states(), "1")
+ submitted = service.submit_group("run-9", group_request())
+ assert submitted["status"] == "complete"
+ assert registration["run_token"]
+ finally:
+ service.close()
+
+
+def test_service_group_request_rejects_mixed_groups():
+ class Sample:
+ def __init__(self, group_index, task):
+ self.metadata = {"native_task": dict(task)}
+ self.group_index = group_index
+
+ other = dict(TASK)
+ other["revision"] = "2"
+ with pytest.raises(ValueError, match="mixes task locks"):
+ service_group_request([Sample(0, TASK), Sample(0, other)], dict(SAMPLING), 0, "1")
+ with pytest.raises(ValueError, match="group identity"):
+ service_group_request([Sample(0, TASK), Sample(1, TASK)], dict(SAMPLING), 0, "1")
diff --git a/tests/harborrl/test_sandbox.py b/tests/harborrl/test_sandbox.py
new file mode 100644
index 000000000..b98d27197
--- /dev/null
+++ b/tests/harborrl/test_sandbox.py
@@ -0,0 +1,97 @@
+"""Sandbox routing and fail-closed capacity contracts, without live resources."""
+import asyncio
+import json
+from pathlib import Path
+
+import pytest
+import yaml
+from test_native_training import native_config
+
+from harborrl.config.native import load_config
+from harborrl.rollout.harbor_job.sandbox_config import (
+ SandboxAdmission,
+ load_sandbox,
+ task_binding,
+)
+
+
+def settings(tmp_path):
+ return {'domain': 'https://sandbox.example', 'credential_file': 'key',
+ 'bindings': [{'image': 'registry.example/task@sha256:' + 'a' * 64,
+ 'max_live_instances': 1}],
+ 'installation': {'mode': 'offline_binary', 'version': '2.1.0', 'binary': 'claude', 'sha256': 'b' * 64}}
+
+
+def test_local_runner_opt_in_and_unknown_fields(tmp_path):
+ path, _ = native_config(tmp_path)
+ old = load_config(path)
+ assert old['harbor']['workers'] == ['worker-a', 'worker-b']
+ local = load_config(path, ['harbor.runner_transport=local', 'harbor.workers=[]',
+ 'harbor.environment={runtime: sandbox, config: sandbox.yaml}'])
+ assert local['harbor']['environment']['config'] == str(tmp_path / 'sandbox.yaml')
+ with pytest.raises(ValueError, match='local runner'):
+ load_config(path, ['harbor.runner_transport=local'])
+ with pytest.raises(ValueError, match='fields'):
+ load_config(path, ['harbor.environment={runtime: sandbox, config: x, api_key: secret}'])
+
+
+def test_sandbox_binding_and_secret_rejection(tmp_path):
+ cfg = settings(tmp_path)
+ path = tmp_path / 'sandbox.yaml'
+ path.write_text(yaml.safe_dump(cfg))
+ result = load_sandbox(path)
+ assert result['credential_file'] == str(tmp_path / 'key')
+ (tmp_path / 'task.toml').write_text('[environment]\ndocker_image = "' + cfg['bindings'][0]['image'] + '"\n')
+ assert task_binding(result, tmp_path)['image'] == cfg['bindings'][0]['image']
+ (tmp_path / 'task.toml').write_text('[environment]\ndocker_image = "wrong"\n')
+ with pytest.raises(ValueError, match='exact binding'):
+ task_binding(result, tmp_path)
+ cfg['api_key'] = 'must-not-be-serialized'
+ path.write_text(yaml.safe_dump(cfg))
+ with pytest.raises(ValueError, match='fields'):
+ load_sandbox(path)
+
+
+def test_admission_across_groups_and_cleanup_debt(tmp_path):
+ async def run():
+ cfg = {'max_live_instances': 2, 'bindings': [{'image': 'image', 'max_live_instances': 1}]}
+ admission = SandboxAdmission(cfg)
+ active, peak = 0, 0
+
+ async def trial(index):
+ nonlocal active, peak
+ async with admission.lease(cfg['bindings'][0], tmp_path / str(index)):
+ active += 1
+ peak = max(peak, active)
+ await asyncio.sleep(0.01)
+ active -= 1
+ await asyncio.gather(*(trial(i) for i in range(6)))
+ assert peak == 1
+ root = tmp_path / 'failed'
+ root.mkdir()
+ async with admission.lease(cfg['bindings'][0], root):
+ (root / 'cleanup-required.json').write_text('{}')
+ with pytest.raises(RuntimeError, match='blocked'):
+ async with admission.lease(cfg['bindings'][0], tmp_path):
+ pytest.fail('must not allocate')
+ asyncio.run(run())
+
+
+def test_sandbox_launch_freezes_nonsecret_runtime(tmp_path):
+ from harborrl.backends.slime_v032.config import launch_plan
+ from harborrl.data.harbor.native_inspector import inspect_native
+ path, task = native_config(tmp_path)
+ cfg = settings(tmp_path)
+ (tmp_path / 'sandbox.yaml').write_text(yaml.safe_dump(cfg))
+ (task / 'task.toml').write_text('[environment]\ndocker_image = "' + cfg['bindings'][0]['image'] + '"\n')
+ catalog = tmp_path / 'catalog.json'
+ entries = json.loads(catalog.read_text())
+ entries[0]['task_digest'] = inspect_native(task)['task_digest']
+ catalog.write_text(json.dumps(entries))
+ config = load_config(path, ['harbor.runner_transport=local', 'harbor.workers=[]',
+ 'harbor.environment={runtime: sandbox, config: sandbox.yaml}'])
+ plan = launch_plan(config)
+ runtime = json.loads(plan['environment']['HARBORRL_NATIVE_SANDBOX'])
+ assert runtime['installation']['version'] == '2.1.0'
+ assert Path(runtime['installation']['binary']).is_absolute()
+ assert plan['environment']['HARBORRL_NATIVE_RUNNER_WORKERS'] == '[]'
diff --git a/tests/harborrl/test_sandbox_allocation_transport.py b/tests/harborrl/test_sandbox_allocation_transport.py
new file mode 100644
index 000000000..db34cf098
--- /dev/null
+++ b/tests/harborrl/test_sandbox_allocation_transport.py
@@ -0,0 +1,24 @@
+import asyncio
+
+import httpx
+
+from harborrl.rollout.harbor_job.sandbox_transport import SandboxStartupTransport
+
+
+def test_generic_transport_observes_allocation_without_retry():
+ async def run():
+ calls, allocations = [], []
+
+ def handler(request):
+ calls.append(request.method)
+ return httpx.Response(200, json={'id': 'sbx-one'}) if request.method == 'POST' else httpx.Response(503)
+
+ transport = SandboxStartupTransport('https://sandbox.example/v1', allocations.append,
+ transport=httpx.MockTransport(handler))
+ async with httpx.AsyncClient(transport=transport) as client:
+ await client.post('https://sandbox.example/v1/sandboxes')
+ result = await client.get('https://sandbox.example/v1/sandboxes/sbx-one/endpoints/44772')
+ assert result.status_code == 503
+ assert calls == ['POST', 'GET'] and allocations == ['sbx-one']
+
+ asyncio.run(run())
diff --git a/tests/harborrl/test_sandbox_cleanup.py b/tests/harborrl/test_sandbox_cleanup.py
new file mode 100644
index 000000000..7364ac483
--- /dev/null
+++ b/tests/harborrl/test_sandbox_cleanup.py
@@ -0,0 +1,36 @@
+import asyncio
+
+import httpx
+import pytest
+
+pytest.importorskip('harbor.environments.opensandbox')
+
+from harborrl.rollout.harbor_job.environments.opensandbox import (
+ ManagedOpenSandboxEnvironment,
+)
+
+
+@pytest.mark.parametrize('delete_status,expected', [(204, 'deleted'), (500, 'delete_failed')])
+def test_allocated_id_cleanup_requires_confirmed_absence(tmp_path, monkeypatch, delete_status, expected):
+ calls = []
+
+ async def handler(request):
+ calls.append((request.method, request.url.path))
+ assert request.url.path == '/v1/sandboxes/sbx-owned'
+ if request.method == 'DELETE':
+ return httpx.Response(delete_status)
+ return httpx.Response(200 if len(calls) == 1 else 404)
+
+ client_class = httpx.AsyncClient
+ monkeypatch.setattr(httpx, 'AsyncClient', lambda **kwargs: client_class(
+ **kwargs, transport=httpx.MockTransport(handler)))
+ env = object.__new__(ManagedOpenSandboxEnvironment)
+ env.cfg = {'domain': 'https://platform.test', 'cleanup_timeout_s': 1}
+ env.binding = {'image': 'owned'}
+ env._api_key = 'test-only'
+ env.evidence = tmp_path
+ env.record = {'sandbox_id': 'sbx-owned', 'runtime_digest': 'test', 'events': []}
+ asyncio.run(env._cleanup_allocated())
+ assert env.record['state'] == expected
+ assert (tmp_path / 'cleanup-required.json').exists() == (expected == 'delete_failed')
+ assert calls[:2] == [('GET', '/v1/sandboxes/sbx-owned'), ('DELETE', '/v1/sandboxes/sbx-owned')]
diff --git a/tests/harborrl/test_sandbox_portability.py b/tests/harborrl/test_sandbox_portability.py
new file mode 100644
index 000000000..1a4c5bb6a
--- /dev/null
+++ b/tests/harborrl/test_sandbox_portability.py
@@ -0,0 +1,57 @@
+"""Public core must remain usable without the optional SDK or cluster package."""
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+import yaml
+
+from harborrl.rollout.harbor_job.sandbox_config import load_sandbox, read_credential
+
+
+def test_config_loading_does_not_import_optional_packages(tmp_path):
+ config = tmp_path / 'sandbox.yaml'
+ config.write_text(yaml.safe_dump({'domain': 'https://sandbox.example', 'credential_file': 'key',
+ 'bindings': [{'image': 'image', 'max_live_instances': 1}]}))
+ code = """
+import sys
+from harborrl.rollout.harbor_job.sandbox_config import load_sandbox
+cfg = load_sandbox(sys.argv[1])
+assert cfg['installation'] is None and cfg['options'] == {}
+assert not any(k == 'harbor' or k.startswith(('harbor.', 'opensandbox', 'harborrl_h_cluster', 'torch', 'ray')) for k in sys.modules)
+"""
+ subprocess.run([sys.executable, '-c', code, str(config)], check=True)
+
+
+def test_core_has_no_cluster_imports_or_platform_keys():
+ root = Path(__file__).resolve().parents[2] / 'harborrl'
+ for path in root.rglob('*.py'):
+ source = path.read_text()
+ assert 'harborrl_h_cluster' not in source, str(path)
+ assert 'brainbox' not in source.lower(), str(path)
+ assert 'environment_id' not in source, str(path)
+
+
+@pytest.mark.parametrize('adapter', ['invalid', '../module:Class', 'module:Class:Other'])
+def test_adapter_path_rejected_without_import(tmp_path, adapter):
+ path = tmp_path / 'sandbox.yaml'
+ path.write_text(yaml.safe_dump({'domain': 'https://sandbox.example', 'credential_file': 'key',
+ 'bindings': [{'image': 'image', 'max_live_instances': 1}], 'adapter': adapter}))
+ with pytest.raises(ValueError, match='module:Class'):
+ load_sandbox(path)
+
+
+def test_generic_token_does_not_require_platform_key_format(tmp_path):
+ path = tmp_path / 'credential'
+ path.write_text('test-only\n')
+ path.chmod(0o600)
+ assert read_credential({'credential_file': str(path)}) == 'test-only'
+
+
+def test_nested_provider_secrets_never_enter_runtime_manifest(tmp_path):
+ path = tmp_path / 'sandbox.yaml'
+ path.write_text(yaml.safe_dump({'domain': 'https://sandbox.example', 'credential_file': 'key',
+ 'bindings': [{'image': 'image', 'max_live_instances': 1}],
+ 'options': {'nested': {'api_key': 'test-only'}}}))
+ with pytest.raises(ValueError, match='secrets'):
+ load_sandbox(path)
diff --git a/tests/harborrl/test_sandbox_runtime.py b/tests/harborrl/test_sandbox_runtime.py
new file mode 100644
index 000000000..9f896818f
--- /dev/null
+++ b/tests/harborrl/test_sandbox_runtime.py
@@ -0,0 +1,92 @@
+"""Shared Sandbox scheduling and service lifecycle contracts."""
+import asyncio
+import json
+from dataclasses import replace
+from types import SimpleNamespace
+
+import pytest
+from test_deployment_config import deployment_mapping
+
+from harborrl.backends.slime_v032.rollout_service_client import (
+ RolloutServiceClient,
+ ServiceRolloutError,
+)
+from harborrl.deployment.schema import validate
+from harborrl.rollout.harbor_job.sandbox_config import SandboxAdmission
+from harborrl.rollout.runtime import RolloutRuntimeConfig, run_slot
+
+
+def test_sandbox_deployment_allows_local_or_remote_runners(tmp_path):
+ cfg = deployment_mapping(tmp_path)
+ cfg['rollout'] = {'provider': 'sandbox', 'workers': []}
+ assert validate(cfg)['rollout']['workers'] == []
+ cfg['rollout']['workers'] = deployment_mapping(tmp_path)['rollout']['workers']
+ assert validate(cfg)['rollout']['workers']
+
+
+def test_slow_group_is_posted_once_even_after_transport_timeout(monkeypatch):
+ client = RolloutServiceClient('http://manager', 'run', 'token', poll_interval=0)
+ calls = []
+ def request(method, path, body=None):
+ calls.append(method)
+ if method == 'POST':
+ raise ServiceRolloutError('timeout', code='transport')
+ return {'status': 'running' if len(calls) == 2 else 'complete', 'result': {}}
+ monkeypatch.setattr(client, '_request', request)
+ assert client.submit_group({'group_id': 'group-0'})['status'] == 'complete'
+ assert calls == ['POST', 'GET', 'GET']
+
+
+def test_sandbox_cleanup_debt_prevents_retry(tmp_path, monkeypatch):
+ from harborrl.rollout import runtime
+ from harborrl.rollout.harbor_job import sandbox_config
+ profile = tmp_path / 'profile.json'
+ profile.write_text('{}')
+ binding = {'image': 'image', 'max_live_instances': 1}
+ sandbox = {'max_live_instances': 1, 'bindings': [binding]}
+ config = RolloutRuntimeConfig('run', str(tmp_path), '', str(profile), '127.0.0.1', 1,
+ 'http://gateway', '/python', (), {}, 2, 128, 1024, 2, 2, 'tok', 'tpl', True, 1,
+ 'qwen3-8B', 'http://router', sandbox=sandbox, runner_transport='local')
+ current = SimpleNamespace(rollout_root=tmp_path, semaphore=asyncio.Semaphore(2),
+ registry=None, profile={}, sandbox_admission=SandboxAdmission(sandbox))
+ calls = []
+ async def attempt(identity, **kwargs):
+ calls.append(kwargs)
+ assert kwargs['runner_host'] is None
+ assert kwargs['sandbox'] == sandbox
+ kwargs['root'].mkdir(parents=True)
+ (kwargs['root'] / 'cleanup-required.json').write_text('{}')
+ raise RuntimeError('deletion unconfirmed')
+ monkeypatch.setattr(runtime, 'run_attempt', attempt)
+ monkeypatch.setattr(sandbox_config, 'task_binding', lambda *args: binding)
+ entry = {'id': 'task', 'path': '/task', 'task_digest': 'digest', 'reward_profile': {}}
+ with pytest.raises(RuntimeError, match='cleanup unresolved'):
+ asyncio.run(run_slot(config, current, 0, 0, 0, entry, 'sampling', '1'))
+ assert len(calls) == 1 and current.sandbox_admission.blocked
+ restored = replace(config, workers=('worker',), runner_transport='ssh')
+ assert json.loads(restored.environment()['HARBORRL_NATIVE_SANDBOX']) == sandbox
+
+
+def test_group_failure_waits_for_sibling_cleanup(tmp_path, monkeypatch):
+ from harborrl.rollout import runtime
+ events = []
+ async def attempt(identity, **kwargs):
+ if identity.slot_id == '0':
+ await asyncio.sleep(.01)
+ raise RuntimeError('failed slot')
+ try:
+ await asyncio.sleep(60)
+ finally:
+ await asyncio.sleep(.01)
+ events.append('cleanup complete')
+ monkeypatch.setattr(runtime, 'run_attempt', attempt)
+ config = SimpleNamespace(run_id='run', group_size=2, max_attempts=1, workers=(),
+ task_worker_overrides={}, runner_transport='local', sandbox=None,
+ gateway_url='http://gateway', runner_python='/python', max_output_tokens=128)
+ current = SimpleNamespace(profile={}, rollout_root=tmp_path,
+ semaphore=asyncio.Semaphore(2), registry=None)
+ entry = {'id': 'task', 'path': '/task', 'task_digest': 'digest', 'reward_profile': {}}
+ with pytest.raises(RuntimeError, match='exhausted its retry budget'):
+ asyncio.run(runtime.run_group(config, current, entry=entry,
+ group_index=0, rollout_id=0, sampling_digest='x', policy_version='1'))
+ assert events == ['cleanup complete']
diff --git a/tests/harborrl/test_sandbox_scale.py b/tests/harborrl/test_sandbox_scale.py
new file mode 100644
index 000000000..2e6f39e2a
--- /dev/null
+++ b/tests/harborrl/test_sandbox_scale.py
@@ -0,0 +1,36 @@
+"""A saturated image pool must not reserve unrelated global capacity."""
+import asyncio
+from contextlib import suppress
+
+from harborrl.rollout.harbor_job.sandbox_config import SandboxAdmission
+
+
+def test_waiting_binding_does_not_starve_other_environments(tmp_path):
+ async def run():
+ first = {'image': 'first', 'max_live_instances': 1}
+ second = {'image': 'second', 'max_live_instances': 1}
+ admission = SandboxAdmission({'max_live_instances': 2, 'bindings': [first, second]})
+ entered, release = asyncio.Event(), asyncio.Event()
+
+ async def waiting():
+ entered.set()
+ async with admission.lease(first, tmp_path / 'waiting'):
+ await release.wait()
+
+ async with admission.lease(first, tmp_path / 'active'):
+ task = asyncio.create_task(waiting())
+ try:
+ await entered.wait()
+ async def independent():
+ async with admission.lease(second, tmp_path / 'independent'):
+ pass
+ await asyncio.wait_for(independent(), 1)
+ finally:
+ task.cancel()
+ with suppress(asyncio.CancelledError):
+ await task
+ # Cancellation of a queued request must not leak either admission permit.
+ async with admission.lease(first, tmp_path / 'after'), admission.lease(second, tmp_path / 'also-after'):
+ pass
+
+ asyncio.run(run())
diff --git a/tests/harborrl/test_service_group_concurrency.py b/tests/harborrl/test_service_group_concurrency.py
new file mode 100644
index 000000000..3cbf4c9be
--- /dev/null
+++ b/tests/harborrl/test_service_group_concurrency.py
@@ -0,0 +1,63 @@
+import asyncio
+import threading
+from types import SimpleNamespace
+
+import pytest
+
+from harborrl.backends.slime_v032 import rollout, rollout_service_client
+
+
+@pytest.mark.parametrize('fail', [False, True])
+def test_service_groups_overlap_preserve_order_and_await_failure_cleanup(monkeypatch, fail):
+ barrier = threading.Barrier(2, timeout=3)
+ cancelled, completed = threading.Event(), threading.Event()
+ activated, failed = threading.Event(), threading.Event()
+
+ class Client:
+ def __init__(self, *args):
+ self.run_id = 'run'
+
+ def announce_serving(self, _):
+ pass
+
+ def activate_policy(self, *_):
+ activated.set()
+
+ def submit_group(self, request):
+ assert activated.is_set()
+ barrier.wait()
+ if fail and request['group_id'] == 'first':
+ failed.set()
+ raise ValueError('group failed')
+ if fail:
+ assert failed.wait(3)
+ assert not cancelled.is_set()
+ completed.set()
+ return {'result': {'id': request['group_id']}}
+
+ def cancel_run(self):
+ cancelled.set()
+
+ def load_result(run_id, request, result, *, allow_synthetic):
+ assert allow_synthetic is False
+ return [result['id']]
+
+ monkeypatch.setenv('HARBORRL_ROLLOUT_SERVICE_URL', 'http://localhost')
+ monkeypatch.setenv('RUN_ID', 'run')
+ monkeypatch.delenv('HARBORRL_ROLLOUT_SERVICE_TOKEN_FILE', raising=False)
+ monkeypatch.setattr(rollout_service_client, 'RolloutServiceClient', Client)
+ monkeypatch.setattr(rollout_service_client, 'fetch_pool_states', lambda _: [{'weight_version': '1'}])
+ monkeypatch.setattr(rollout_service_client, 'service_group_request', lambda group, *_: {'group_id': group})
+ monkeypatch.setattr(rollout_service_client, 'load_group_result', load_result)
+ monkeypatch.setattr(rollout, 'lock_policy_version', lambda _: '1')
+ monkeypatch.setattr(rollout, 'sampling_params', lambda _: {})
+ monkeypatch.setattr(rollout, 'bind_group_samples', lambda *_: None)
+ args = SimpleNamespace(sglang_router_ip='localhost', sglang_router_port=8000)
+ if fail:
+ with pytest.raises(ValueError, match='group failed'):
+ asyncio.run(rollout.run_service_groups(args, ['first', 'second'], 0))
+ assert cancelled.is_set()
+ else:
+ assert asyncio.run(rollout.run_service_groups(args, ['first', 'second'], 0)) == [['first'], ['second']]
+ assert not cancelled.is_set()
+ assert completed.is_set()
diff --git a/tests/harborrl/test_slime_v032_exporter.py b/tests/harborrl/test_slime_v032_exporter.py
index d93e84746..41ce72ef9 100644
--- a/tests/harborrl/test_slime_v032_exporter.py
+++ b/tests/harborrl/test_slime_v032_exporter.py
@@ -221,6 +221,46 @@ def test_pure_clipped_loss_numerics():
assert LOSS_SCALE_CORRECTION == 1.0
+def test_postprocess_accepts_multiple_groups_and_partial_actor_shards(tmp_path):
+ args = SimpleNamespace(n_samples_per_prompt=2)
+ groups = native_groups(tmp_path)
+ batch = export_training_batch(groups, rollout_ids=[10, 11])
+ data = convert_samples_to_train_data(args, fake_samples(batch, groups))
+ rollout_data_postprocess(args, 0, data)
+ shard = {key: values[::2] for key, values in data.items()}
+ rollout_data_postprocess(args, 0, shard)
+
+
+def test_postprocess_rejects_individual_group_overweight():
+ args = SimpleNamespace(n_samples_per_prompt=2)
+ data = {
+ "tokens": [[1, 2]] * 4,
+ "response_lengths": [1] * 4,
+ "rewards": [0.0] * 4,
+ "raw_reward": [0.0] * 4,
+ "truncated": [0] * 4,
+ "sample_indices": list(range(4)),
+ "rollout_ids": [10, 10, 10, 11],
+ "loss_masks": [[1]] * 4,
+ "rollout_log_probs": [[-0.2]] * 4,
+ "rollout_mask_sums": [1, 1, 1, 2],
+ }
+ # Total weight is below two, but group 10 exceeds its own budget.
+ with pytest.raises(ValueError, match="budget.*10"):
+ rollout_data_postprocess(args, 0, data)
+
+
+@pytest.mark.parametrize("group_id", [-1, 0.5, float("nan"), "invalid"])
+def test_postprocess_rejects_invalid_group_id(tmp_path, group_id):
+ args = SimpleNamespace(n_samples_per_prompt=2)
+ groups = native_groups(tmp_path)
+ batch = export_training_batch(groups, rollout_ids=[10, 11])
+ data = convert_samples_to_train_data(args, fake_samples(batch, groups))
+ data["rollout_ids"][0] = group_id
+ with pytest.raises(ValueError, match="rollout id"):
+ rollout_data_postprocess(args, 0, data)
+
+
def test_postprocess_rejects_drift():
args = SimpleNamespace(n_samples_per_prompt=2)
base = {
@@ -265,7 +305,7 @@ def __float__(self):
"raw_reward": [Scalar()],
"truncated": [IntegerScalar()],
"sample_indices": [0],
- "rollout_ids": [0],
+ "rollout_ids": [IntegerScalar()],
"loss_masks": [[1]],
"rollout_log_probs": [[Scalar()]],
"rollout_mask_sums": [IntegerScalar()],
@@ -402,3 +442,45 @@ def test_export_package_has_no_backend_imports():
else:
continue
assert not (names & forbidden), f"{path} imports backend dependencies: {names & forbidden}"
+
+
+@pytest.mark.parametrize('group_index, expected_advantages, expected_counts', [
+ (0, [-0.5 / 0.500001, 0.5 / 0.500001], [2, 6]),
+ (1, [0.0, 0.0], [4, 2]),
+])
+def test_legacy_export_preserves_grpo_record_semantics(
+ tmp_path, group_index, expected_advantages, expected_counts,
+):
+ from harborrl.rollout.exporters.native import export_group
+ group = native_groups(tmp_path)[group_index]
+ exported = export_group(group, 2)
+ assert exported['export_version'] == 'native-grpo-v2'
+ assert exported['advantages'] == expected_advantages
+ assert exported['reduction'] == 'sum_token_weighted_loss; no secondary whitening'
+ assert exported['synthetic'] is False
+ for ir, advantage, count in zip(group, expected_advantages, expected_counts):
+ records = [record for record in exported['records']
+ if record['identity']['trajectory_id'] == ir['identity']['trajectory_id']]
+ assert len(records) == len(ir['turns'])
+ for index, (turn, record) in enumerate(zip(ir['turns'], records)):
+ n = len(turn['output_ids'])
+ assert record == {
+ 'identity': ir['identity'], 'turn_index': index,
+ 'tokens': turn['input_ids'] + turn['output_ids'],
+ 'prompt_length': len(turn['input_ids']), 'response_length': n,
+ 'loss_mask': [0] * len(turn['input_ids']) + [1] * n,
+ 'old_logprobs': turn['logprobs'], 'advantages': [advantage] * n,
+ 'token_weights': [1.0 / (2 * count)] * n,
+ 'training_reward': ir['evaluation']['training_reward'], 'padding': False,
+ }
+
+
+def test_native_task_inspection_avoids_legacy_conversion_imports():
+ import subprocess
+ import sys
+ code = (
+ 'import sys; import harborrl.data.harbor.native_inspector; '
+ 'assert "harborrl.data.harbor.inspector" not in sys.modules; '
+ 'assert "harborrl.data.harbor.materializer" not in sys.modules'
+ )
+ subprocess.run([sys.executable, '-c', code], check=True)
diff --git a/tests/harborrl/test_webui.py b/tests/harborrl/test_webui.py
new file mode 100644
index 000000000..ddb397ffd
--- /dev/null
+++ b/tests/harborrl/test_webui.py
@@ -0,0 +1,244 @@
+"""HTTP and process lifecycle contracts without allocating training resources."""
+import http.client
+import json
+import subprocess
+import sys
+import threading
+import time
+from pathlib import Path
+
+import pytest
+import yaml
+from test_options import answers as options_answers
+
+from harborrl.options.wizard import build_config_bundle
+from harborrl.webui.cli import workspace_lock
+from harborrl.webui.jobs import Jobs
+from harborrl.webui.server import Console, make_server
+
+answers = options_answers
+
+
+@pytest.fixture
+def console(tmp_path):
+ instance = Console(tmp_path / "web")
+ yield instance
+ instance.jobs.close()
+
+
+@pytest.fixture
+def server(console):
+ server = make_server(console)
+ thread = threading.Thread(target=server.serve_forever)
+ thread.start()
+ yield server
+ server.shutdown()
+ thread.join(5)
+ server.server_close()
+
+
+def request(server, console, method, path, body=None, headers=None):
+ connection = http.client.HTTPConnection("127.0.0.1", server.server_port, timeout=5)
+ defaults = {"Authorization": "Bearer " + console.token, "Content-Type": "application/json",
+ "Origin": f"http://127.0.0.1:{server.server_port}"}
+ connection.request(method, path, body=json.dumps(body) if body is not None else None,
+ headers={**defaults, **(headers or {})})
+ response = connection.getresponse()
+ payload = response.read()
+ result = (response.status, dict(response.getheaders()), payload)
+ connection.close()
+ return result
+
+
+def test_static_and_bootstrap(server, console):
+ for path in ("/", "/app.js", "/style.css", "/lucide.min.js", "/brand.jpg"):
+ status, headers, payload = request(server, console, "GET", path)
+ assert status == 200 and payload
+ assert "frame-ancestors 'none'" in headers["Content-Security-Policy"]
+ assert console.token.encode() not in payload
+ status, _, payload = request(server, console, "GET", "/api/bootstrap")
+ assert status == 200
+ assert json.loads(payload)["catalog"]["catalog_version"] == 1
+
+
+@pytest.mark.parametrize("headers", [{"Authorization": "Bearer invalid"}, {"Origin": "https://external.example"},
+ {"Host": "external.example"}, {"Content-Type": "text/plain"}])
+def test_request_boundary(server, console, headers):
+ status, _, _ = request(server, console, "POST", "/api/configurations", {}, headers=headers)
+ assert status in {403, 415}
+ assert not console.list_configurations()
+
+
+@pytest.mark.parametrize("name", ["../outside", "/absolute", "", "a/b", "a\\b", "a b", ".hidden"])
+def test_output_paths(console, answers, name):
+ with pytest.raises(ValueError):
+ console.save({"name": name, "answers": answers})
+
+
+def test_preview_save_and_snapshot(server, console, answers):
+ body = {"name": "experiment", "answers": answers, "check_files": True}
+ status, _, payload = request(server, console, "POST", "/api/preview", body)
+ assert status == 200, payload
+ assert json.loads(payload)["validation"]["status"] == "valid_with_deferred_checks"
+ assert not console.directory("experiment").exists()
+ status, _, payload = request(server, console, "POST", "/api/configurations", body)
+ assert status == 201, payload
+ assert request(server, console, "POST", "/api/configurations", body)[0] == 400
+ status, _, payload = request(server, console, "POST", "/api/jobs", {"configuration": "experiment", "mode": "dry-run"})
+ assert status == 201, payload
+ job = json.loads(payload)
+ for _ in range(600):
+ record = console.jobs.list()[0]
+ if record["state"] != "running":
+ break
+ time.sleep(.05)
+ assert record["state"] == "succeeded", console.jobs.log(job["id"])
+ snapshot = Path(record["snapshot"])
+ training = yaml.safe_load((snapshot / "config.yaml").read_text())
+ deployment = yaml.safe_load((snapshot / "deployment.yaml").read_text())
+ assert training["output"]["root"] == deployment["storage"]["run_root"] == record["output_root"]
+ saved = console.directory("experiment") / "config.yaml"
+ assert yaml.safe_load(saved.read_text())["output"]["root"] != record["output_root"]
+ assert json.loads(console.jobs.log(job["id"])["text"])["config"]["schema_version"] == 2
+
+
+@pytest.mark.parametrize("mode", ["train", "doctor", "shell"])
+def test_live_requires_confirmation(console, answers, mode, monkeypatch):
+ console.save({"name": "test", "answers": answers})
+ def forbidden(*args, **kwargs):
+ raise AssertionError("Unconfirmed live job must not execute")
+ monkeypatch.setattr(subprocess, "Popen", forbidden)
+ with pytest.raises(ValueError):
+ console.start({"configuration": "test", "mode": mode})
+
+
+def test_process_lifecycle(console, answers, tmp_path, monkeypatch):
+ console.save({"name": "test", "answers": answers})
+ real_popen = subprocess.Popen
+ calls = []
+ def stand_in(command, **kwargs):
+ calls.append(command)
+ return real_popen([sys.executable, "-u", "-c", "import time; print('training stand-in', flush=True); time.sleep(30)"], **kwargs)
+ monkeypatch.setattr(subprocess, "Popen", stand_in)
+ job = console.start({"configuration": "test", "mode": "train", "confirmed": True})
+ assert calls[0][4] == "train" and "--dry-run" not in calls[0]
+ with pytest.raises(ValueError, match="active"):
+ console.start({"configuration": "test", "mode": "dry-run"})
+ for _ in range(600):
+ if console.jobs.log(job["id"])["text"]:
+ break
+ time.sleep(.05)
+ child = console.jobs.child
+ stopped = console.jobs.stop(job["id"])
+ assert stopped["state"] == "stopping"
+ child.wait(timeout=5)
+ assert console.jobs.list()[0]["state"] == "stopped"
+ assert console.jobs.log(job["id"])["text"]
+ recovered = Jobs(console.jobs.root)
+ assert recovered.list()[0]["state"] == "stopped"
+
+
+def test_restart_blocks_unknown_job(console, answers, tmp_path):
+ root = console.jobs.root / "abc"
+ root.mkdir()
+ (root / "job.json").write_text(json.dumps({"id": "abc", "state": "running", "created_at": "2026-10-22"}))
+ recovered = Jobs(console.jobs.root)
+ assert recovered.list()[0]["state"] == "unknown"
+ with pytest.raises(ValueError, match="interrupted"):
+ recovered.start("test", build_config_bundle(answers, output_directory=tmp_path / "out"), "dry-run")
+ recovered.reconcile("abc")
+ assert recovered.list()[0]["state"] == "interrupted"
+
+
+def test_workspace_lock(tmp_path):
+ with workspace_lock(tmp_path), pytest.raises(ValueError, match="Another console"), workspace_lock(tmp_path):
+ pass
+ with workspace_lock(tmp_path):
+ pass
+
+
+def test_symlink_cannot_escape(console, tmp_path, answers):
+ outside = tmp_path / "outside"
+ outside.mkdir()
+ (console.configurations / "escape").symlink_to(outside, target_is_directory=True)
+ with pytest.raises(ValueError):
+ console.save({"name": "escape", "answers": answers})
+ assert not list(outside.iterdir())
+
+
+def test_failed_spawn_and_log_limit(console, answers, tmp_path, monkeypatch):
+ def failed(*args, **kwargs):
+ raise OSError("injected launch failure")
+ monkeypatch.setattr(subprocess, "Popen", failed)
+ bundle = build_config_bundle(answers, output_directory=tmp_path / "out")
+ job = console.jobs.start("test", bundle, "dry-run")
+ assert job["state"] == "failed"
+ Path(job["log_path"]).write_bytes(b"x" * 100000)
+ result = console.jobs.log(job["id"])
+ assert len(result["text"]) == 65536 and result["truncated"]
+ training_log = Path(job["output_root"]) / "training/one/train-stdout.log"
+ training_log.parent.mkdir(parents=True)
+ training_log.write_text("step=1 reward=0.5")
+ assert console.jobs.log(job["id"])["training_text"] == "step=1 reward=0.5"
+
+
+def test_import_and_bad_body(server, console):
+ status, _, payload = request(server, console, "POST", "/api/import", {"text": "answers_version: 1\npreset: embedded-local"})
+ assert status == 200 and json.loads(payload)["answers"]["preset"] == "embedded-local"
+ assert request(server, console, "POST", "/api/import", {"text": "[]"})[0] == 400
+ assert request(server, console, "GET", "/../../pyproject.toml")[0] == 404
+
+
+@pytest.mark.parametrize("text", ["answers_version: true\npreset: embedded-local", "answers_version: 1\npreset: embedded-local\ntraining: []", "answers_version: 1\npreset: embedded-local\nunknown: x"])
+def test_import_rejects_invalid_answers(server, console, text):
+ assert request(server, console, "POST", "/api/import", {"text": text})[0] == 400
+
+
+def test_preview_never_starts_process(server, console, answers, monkeypatch):
+ def forbidden(*args, **kwargs):
+ raise AssertionError("Preview must not start a process")
+ monkeypatch.setattr(subprocess, "Popen", forbidden)
+ assert request(server, console, "POST", "/api/preview", {"name": "offline", "answers": answers, "check_files": True})[0] == 200
+
+
+def test_local_stop_reaches_descendants(tmp_path):
+ import os
+ import signal
+
+ from harborrl.config import ROOT
+ script = '''
+import os, signal, subprocess, sys, time
+from pathlib import Path
+from harborrl.deployment.training_target import LocalTrainingTarget
+root = Path(sys.argv[1])
+worker = "import signal,sys,time; from pathlib import Path; signal.signal(signal.SIGINT, lambda *a: (Path(sys.argv[1]).write_text('stopped'), sys.exit(0))); Path(sys.argv[1]+'.ready').touch(); time.sleep(30)"
+parent = "import subprocess,sys,time; from pathlib import Path; child=subprocess.Popen([sys.executable,'-c',sys.argv[1],sys.argv[2]]); Path(sys.argv[2]+'.pid').write_text(str(child.pid)); time.sleep(30)"
+target = LocalTrainingTarget([sys.executable, '-c', parent, worker, str(root / 'worker')], cwd=root, env=os.environ.copy())
+target.launch()
+(root / 'group').write_text(str(target.child.pid))
+sys.exit(target.wait())
+'''
+ env = {**os.environ, "PYTHONPATH": str(ROOT)}
+ proc = subprocess.Popen([sys.executable, "-c", script, str(tmp_path)], env=env, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+ try:
+ for _ in range(600):
+ if (tmp_path / "worker.ready").exists():
+ break
+ time.sleep(.05)
+ assert (tmp_path / "worker.ready").exists()
+ proc.send_signal(signal.SIGINT)
+ proc.wait(timeout=10)
+ for _ in range(100):
+ if (tmp_path / "worker").exists():
+ break
+ time.sleep(.05)
+ assert (tmp_path / "worker").read_text() == "stopped"
+ finally:
+ if (tmp_path / "group").exists():
+ try:
+ os.killpg(int((tmp_path / "group").read_text()), signal.SIGKILL)
+ except ProcessLookupError:
+ pass
+ if proc.poll() is None:
+ proc.kill()
+ proc.wait(timeout=10)
diff --git a/tests/test_consistency.py b/tests/test_consistency.py
new file mode 100644
index 000000000..04146beb5
--- /dev/null
+++ b/tests/test_consistency.py
@@ -0,0 +1,81 @@
+"""Dependency-free tests of consistency evidence and admission decisions."""
+import struct
+
+import pytest
+
+from harborrl.consistency.config import require_raw_sampling, validate
+from harborrl.consistency.metrics import merge, statistics
+from harborrl.consistency.report import assess, decide
+from harborrl.consistency.sglang import check_pool, score
+from harborrl.consistency.slime import evidence_key
+
+
+def test_weighted_statistics_ignore_padding_and_tp_duplicates():
+ settings = validate({'mode': 'enforce', 'max_abs_delta': 1})
+ a = assess([([-1], [-2], [1])], settings)
+ b = assess([([-2, -2, -100], [-2, -2, -1], [1, 1, 0])], settings)
+ duplicate = {**a, 'contribute': False}
+ report = decide([a, b, duplicate], settings, phase='test')
+ assert report['status'] == 'passed'
+ assert report['metrics']['token_count'] == 3
+ assert report['metrics']['mean_abs_delta'] == pytest.approx(1 / 3)
+ assert report['metrics']['p99_abs_delta_upper'] == 1
+
+
+@pytest.mark.parametrize('rows', [[], [([-1], [-1], [0])], [([float('nan')], [-1], [1])],
+ [([-1], [-1, -2], [1])], [([-1], [-1], [2])]])
+def test_structural_errors_fail_audit(rows):
+ settings = validate({'mode': 'audit'})
+ assert decide([assess(rows, settings)], settings, phase='test')['status'] == 'failed'
+
+
+def test_thresholds_are_explicit_and_sampling_is_raw():
+ with pytest.raises(ValueError):
+ validate({'mode': 'enforce'})
+ with pytest.raises(ValueError):
+ require_raw_sampling({'temperature': .7, 'top_p': 1})
+ settings = validate({'mode': 'audit', 'max_abs_delta': .01})
+ assert decide([assess([([-1], [-2], [1])], settings)], settings, phase='test')['status'] == 'warning'
+
+
+def test_serving_evidence_survives_float32_conversion():
+ q = [-1.123456789]
+ converted = [struct.unpack('!f', struct.pack('!f', q[0]))[0]]
+ assert evidence_key([1, 2], q, [1]) == evidence_key([1, 2], converted, [1])
+ assert evidence_key([1, 3], q, [1]) != evidence_key([1, 2], converted, [1])
+
+
+def transport(url, payload=None):
+ if payload is None:
+ return {'weight_version': '1'}
+ assert payload['sampling_params']['max_new_tokens'] == 0
+ assert payload['input_ids'] == [10, 11, 12]
+ return {'meta_info': {'weight_version': '1', 'input_token_logprobs': [[None, 10], [-1., 11], [-2., 12]]}}
+
+
+def test_teacher_forcing_exact_response_boundary():
+ assert score('http://engine', [10, 11], [12], '1', transport=transport) == [-2.]
+ with pytest.raises(ValueError, match='version'):
+ score('http://engine', [10, 11], [12], '2', transport=transport)
+
+
+def test_engine_canary_does_not_dilute_bad_engine():
+ settings = validate({'mode': 'enforce', 'max_abs_delta': 2, 'max_mean_abs_delta': .6})
+ reference = {'weight_version': '1', 'cases': [{'prompt_ids': [10, 11], 'response_ids': [12], 'logprobs': [-1.]}]}
+ states = [{'endpoint': 'http://bad', 'healthy': True, 'weight_version': '1'},
+ {'endpoint': 'http://good', 'healthy': True, 'weight_version': '1'}]
+ def serving(url, payload=None):
+ result = transport(url, payload)
+ if payload and 'good' in url:
+ result['meta_info']['input_token_logprobs'][-1][0] = -1.
+ return result
+ report = check_pool(states, reference, settings, transport=serving)
+ assert report['metrics']['mean_abs_delta'] == .5
+ assert report['status'] == 'failed'
+
+
+def test_histogram_merge_is_token_weighted():
+ parts = [statistics([([-1.] * n, [-1. - delta] * n, [1] * n)]) for n, delta in [(1, .1), (9, .01)]]
+ result = merge(parts)
+ assert result['mean_abs_delta'] == pytest.approx(.019)
+ assert result['p95_abs_delta_upper'] >= .1 - 1e-12
diff --git a/tests/test_consistency_torch.py b/tests/test_consistency_torch.py
new file mode 100644
index 000000000..0f81e6660
--- /dev/null
+++ b/tests/test_consistency_torch.py
@@ -0,0 +1,31 @@
+"""Distributed rejection tests; optional Torch dependency."""
+from pathlib import Path
+
+import pytest
+
+from harborrl.consistency.config import validate
+from harborrl.consistency.report import assess, distributed_gate
+
+torch = pytest.importorskip('torch')
+
+
+def rank_failure(rank, rendezvous, root):
+ from datetime import timedelta
+
+ import torch.distributed as dist
+ dist.init_process_group('gloo', init_method=f'file://{rendezvous}', rank=rank, world_size=2, timeout=timedelta(seconds=30))
+ try:
+ settings = validate({'mode': 'enforce', 'max_abs_delta': .01})
+ part = assess([([-1.], [-1. if rank == 0 else float('nan')], [1])], settings)
+ with pytest.raises(ValueError, match='rejected'):
+ distributed_gate(part, settings, Path(root) / 'failure.json', phase='injected-rank-failure')
+ (Path(root) / f'rank{rank}.ok').write_text('rejected')
+ finally:
+ dist.destroy_process_group()
+
+
+def test_one_rank_error_rejects_all_ranks(tmp_path):
+ import torch.multiprocessing as mp
+ mp.spawn(rank_failure, args=(str(tmp_path / 'rendezvous'), str(tmp_path)), nprocs=2, join=True)
+ assert (tmp_path / 'rank0.ok').exists()
+ assert (tmp_path / 'rank1.ok').exists()
diff --git a/uv.lock b/uv.lock
index d5e9dd73d..4efd07ea6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,34 +1,976 @@
version = 1
-revision = 3
+revision = 5
requires-python = ">=3.10"
+resolution-markers = [
+ "python_full_version >= '3.14'",
+ "python_full_version >= '3.12' and python_full_version < '3.14'",
+ "python_full_version < '3.12'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.7.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ce/f4/eec0465c2f67b2664688d0240b3212d5196fd89e741df67ddb81f8d35658/aiohappyeyeballs-2.7.1.tar.gz", hash = "sha256:065665c041c42a5938ed220bdcd7230f22527fbec085e1853d2402c8a3615d9d", size = 24757, upload-time = "2026-07-01T17:11:55.501Z" }
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+ { url = "https://files.pythonhosted.org/packages/71/43/1947f06babed6b3f1d7f38b0c767f52df66bfb2bc10b468c4a7de9eceff2/aiohappyeyeballs-2.7.1-py3-none-any.whl", hash = "sha256:9243213661e29250eb41368e5daa826fc017156c3b8a11440826b2e3ed376472", size = 15038, upload-time = "2026-07-01T17:11:54.055Z" },
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.14.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "aiohappyeyeballs" },
+ { name = "aiosignal" },
+ { name = "attrs" },
+ { name = "frozenlist" },
+ { name = "multidict" },
+ { name = "propcache" },
+ { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+ { name = "yarl" },
+]
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