From 637310bdec712f5bc47be7e9471d2411b58c26eb Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Fri, 16 Oct 2026 23:23:42 +0530 Subject: [PATCH 01/24] Add RFC 014: Environment Services --- rfcs/014-environment-services.md | 207 +++++++++++++++++++++++++++++++ rfcs/README.md | 3 + 2 files changed, 210 insertions(+) create mode 100644 rfcs/014-environment-services.md diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md new file mode 100644 index 0000000000..a1898fb728 --- /dev/null +++ b/rfcs/014-environment-services.md @@ -0,0 +1,207 @@ +# RFC: Environment Services + +**Status**: In Review +**Created**: 2026-10-16 +**Authors**: @rycerzes +**RFC ID**: 014 + +Discussion: [#1382](https://github.com/huggingface/OpenEnv/issues/1382). Prototype: [`envs/posttrain_env`](../envs/posttrain_env/). + +## Summary + +More environments give the agent real services instead of in-env tools: post-training tasks that train models on rented GPUs, hill-climbing tasks that call an evaluator on dedicated hardware, simulators and render farms. Today each environment decides alone who holds the credentials, who pays, what happens to a job that outlives the episode and whether the agent can reach the grader. + +This RFC defines **Environment Services**: a brokered layer between the agent and external services, with three rules. The agent never holds a real credential. The agent never reaches the grader. Nothing is spent without a reservation. v1 covers two service profiles, **batch job** and **evaluator**, and a **per-rollout budget**, which is enough for post-training and kernel optimisation. `envs/posttrain_env` implements v1 on HF Jobs without core changes. + +## Motivation + +### Problem Statement + +Environments answer five questions ad hoc: + +1. **Who holds the credential?** Raw keys reach the agent environment. Examples: Harbor `environment.env`, Prime sandbox `secrets`, and OpenHands exporting secrets into the agent's shell. +2. **Who pays?** Existing caps cover LLM tokens only and are checked after spending, so they overshoot (OpenHands `max_budget_per_run`, LiteLLM MCP cost, RSIGym). Nothing defines a budget per rollout, per group or per run. +3. **What happens to a job that outlives the episode?** There is no job handle, no cancel on reset and no orphan handling. `kernrl` runs its GPU evaluator inside `step`. +4. **Can the agent reach the grader?** [ROLL](https://github.com/alibaba/ROLL/blob/581046abce143e643221f8e292895c46a3b20112/roll/pipeline/agentic/env/rock/sanbox_manager.py#L1230-L1332) and [rLLM](https://github.com/rllm-org/rllm/blob/4e828c81680e5a85b638ceedeaf09d1e90109ca8/rllm/eval/script_evaluator.py#L73-L94) upload tests into the agent's sandbox after the episode; in AReaL a rollout's own key can set its reward. METR measured [reward hacking in 30.4% of RE-Bench runs vs 0.7% on HCAST](https://metr.org/blog/2025-06-05-recent-reward-hacking). #1232 and #1357 raise the same concern for OpenEnv. +5. **What was called, and what did it produce?** No framework links service calls to trajectory steps. + +### Goals + +1. A service contract an environment declares, instead of per-env plumbing. +2. Credentials that stay with a broker; the agent and its jobs hold none. +3. A per-rollout budget that reserves before spending and settles after, published in a stable form for cost-aware samplers (RFC 013). +4. Jobs with handles, cancellation at episode end and a defined orphan state. +5. Evaluation split into an agent-callable dev endpoint and a grader-only score. + +### Non-Goals + +- Hosting services. Brokers wrap existing ones (HF Jobs first). +- Profiles beyond batch job and evaluator in v1 (see [Future Work](#future-work)). +- Changing `/mcp` in this RFC. The move to MCP 2026-07-28 is a later RFC 003 amendment (decision 4). + +## Design + +### Architecture Overview + +``` +agent sandbox ──MCP tools──▶ environment / openenvd (per env) + (no credentials) ledger · artifacts · audit · token exchange + │ broker credential, audience-bound + ▼ + shared brokers: jobs (HF Jobs) · eval +grader ◀── artifact handles + score jobs ──┘ +``` + +Without openenvd (RFC 009), the environment server is the broker, as in `posttrain_env`. With openenvd, the daemon holds the broker credential as a backend credential and the environment only sees run tokens. + +### Core Abstractions + +**Service manifest.** An environment declares its services by profile: + +```yaml +services: + - {name: jobs, profile: batch_job, surface: agent, flavors: [a10g-small], max_timeout: 60m, meter: [usd, gpu_s]} + - {name: kernel_eval, profile: evaluator, surface: {agent: dev_evaluate, grader: score}, + hardware: {class: h100, exclusive: true}} +budget: {usd: 3.0, scope: rollout} +``` + +`posttrain_env` keeps this in its task definitions (`server/tasks.py`); the manifest becomes a core type in step 2 of the plan below. + +**Service profiles (v1).** + +| Profile | Examples | Adds to the contract | +|---|---|---| +| batch job | HF Jobs; later Modal, Slurm, render farms | job handle, worst-case reservation, cancel, orphans | +| evaluator | held-out model eval, kernel timing | `dev_evaluate` vs `score`, exclusive hardware, cache | + +**Budget ledger.** Money is integer micro-USD. Before a job starts the broker reserves its worst case, the flavor's per-minute price times the job's timeout, and refuses the job with a typed `budget_exhausted` error if it does not fit. When the job ends the reservation is replaced by the real cost, the per-minute price times the minutes the job ran, rounded up to whole minutes as HF Jobs bills. Budgets are per rollout and set by the orchestrator, so every rollout in a GRPO group faces the same limit. The agent sees its remaining budget as data, never as reward. The ledger is the broker's estimate; the provider's bill is authoritative, and a provider-side cap is the backstop (see Credentials). + +Every observation carries a stable spend entry. RFC 013's sampler never reads the environment: the trainer reads this entry from the final observation and forwards one value per rollout as `costs` to `update(task, rewards, costs)`. + +```python +obs.metadata["service_spend"] = { + "version": 1, + "settled": {"usd": 0.62, "gpu_s": 410.0, "jobs": 2, "calls": 1}, # cumulative for the episode + "reserved": {"usd": 0.0}, # open reservations; empty once every job has settled + "final": True, # false while a job is live or was orphaned +} +``` + +Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. `jobs` counts agent jobs and `calls` counts dev evaluations. Grader evaluations are not charged to the agent. + +**Jobs.** Tools return a handle. States are `queued → running → succeeded | failed | cancelled`, plus `cancelling` while a cancel is pending and `orphaned` when the provider does not confirm one in time. An orphaned job is settled at its full reservation, keeps `final: false` and blocks the next `reset` with an error, because it may still be running and billing. `reset`, `close`, `submit`, the step limit and the time limit cancel the episode's live jobs. Submissions take an idempotency key; a retry with the same key returns the same job and makes no second reservation. + +**Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; anything else, including a score outside [0, 1], counts as `invalid` with score 0. + +**Artifacts.** An artifact is the output folder of a succeeded job, exposed as `openenv-artifact:////outputs` with each file's path, size and content hash. Graders mount the folder read-only; nothing is copied through the agent. + +### Key Design Decisions + +#### D1. The broker is the only credential holder + +- **Chosen approach**: The broker starts jobs with `HfApi.run_uv_job`, with explicit flavor, namespace, timeout and labels and an empty `secrets` map. It never runs `hf jobs uv run`, whose script header can pull the caller's `HF_TOKEN` into the job and set the namespace; the Python API [ignores that header](https://github.com/huggingface/huggingface_hub/blob/bb2ef7579c3d2dc2eb49b16ccf15a070fe5b7023/src/huggingface_hub/hf_api.py#L12665-L12668). +- **Rationale**: A fine-grained token with only `job.write` cannot touch repos, but a job holding it can start further jobs that skip the broker's budget checks (verified on HF Jobs). So the token never enters a job. +- **Broker token**: "Start and manage Jobs" plus write access to the namespace's `jobs-artifacts` bucket. A `job.write`-only token is refused (HTTP 403) when the broker uploads the job script to that bucket. +- **Backstop**: HF tokens carry no spend cap. Billing broker jobs to a Resource Group with a Jobs spend limit (Enterprise) caps the total; over the limit, HF rejects new jobs and cancels running ones. + +#### D2. Artifacts move through the HF Jobs artifacts bucket + +- **Chosen approach**: Follow `run_uv_job`'s own convention. It uploads the script to the namespace's private `jobs-artifacts` bucket and mounts that job's folder read-write at `/data`. Jobs write outputs to `/data/outputs/`. Evaluator jobs mount `/outputs` read-only at `/model`. +- **Rationale**: No token inside the job, no extra repository, and HF's existing behaviour. Content hashes come from the bucket listing (Xet hashes). +- **Trade-offs**: Every agent job and evaluation creates a bucket folder; cleanup follows the bucket's retention. + +#### D3. Tool names use an underscore prefix + +- **Chosen approach**: `jobs_submit`, `jobs_status`, `jobs_wait`, `jobs_logs`, `jobs_cancel`, `dev_evaluate`, `submit` and `budget`. +- **Rationale**: OpenAI-compatible function names allow only `[a-zA-Z0-9_-]`, and most trainers convert MCP tools to that form, so `jobs.submit` breaks them. The reserved-name prefix rule in core (step 2) covers `jobs_`. + +#### D4. Long calls block with a declared timeout + +- **Chosen approach**: `submit` waits for the score job and returns the reward in the same step; `jobs_wait` waits up to a capped number of seconds. The environment raises its tool timeout for these calls and reports `score_timeout_s` at `reset` so clients can set their message deadline (the same need as #1026). +- **Rationale**: The reward stays inside the environment (RFC 004) and the episode ends with it, without a side channel. + +### Decisions from the discussion + +1. **Long-running services and GRPO.** A service runs live at train time only if it finishes within the trainer's staleness window; for example, TRL's experimental `AsyncGRPOTrainer` drops samples more than [`max_staleness`](https://github.com/huggingface/trl/blob/0dcd18c48bf7e63b49a23da0cb030b0a93a2f39f/trl/experimental/async_grpo/async_grpo_config.py#L126-L128) weight updates old (default 4). Slower services declare a stand-in and the real service runs only at eval. Post-training tasks are multi-hour episodes, so their near-term use is evaluation. +2. **HF Spaces.** No services on Spaces for now. A Space gives no isolation guarantee, so it must not hold a broker credential. +3. **Shared brokers are operator-run services outside the environment**, with the standing of RFC 009's backend gateways. The orchestrator's run config lists each broker's URL and expected TLS identity; that registration is the trust anchor. The daemon exchanges its broker credential for per-rollout tokens bound to one broker ([RFC 8693](https://www.rfc-editor.org/rfc/rfc8693), [RFC 8707](https://www.rfc-editor.org/rfc/rfc8707)); brokers are MCP resource servers ([RFC 9728](https://www.rfc-editor.org/rfc/rfc9728)) that enforce a hard cap per token. This follows RSIGym, LiteLLM virtual keys and OpenMLE Sandbox. +4. **Transport targets MCP 2026-07-28**, which [removes protocol sessions](https://github.com/modelcontextprotocol/modelcontextprotocol/blob/86f6e0c675afea9a8f233e09b6faf566b126086c/docs/specification/2026-07-28/changelog.mdx) in favour of server-minted handles passed as arguments ([SEP-2567](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/2567)). That matches `openenv/session/create` and the job handles above. The upgrade adds `server/discover`, per-request protocol versions, `resultType`, the `Mcp-Method`/`Mcp-Name` headers and cacheable `tools/list`, and enables the [Tasks extension](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/2663). It comes later as a backward-compatible RFC 003 amendment; v1 jobs do not need it. + +### Not allowed + +- Raw provider keys in the agent sandbox or in agent jobs. +- Grading in the agent's sandbox or jobs. +- Reward writes the agent can reach. +- Passing the broker's token into jobs. +- Judges reading unescaped trajectories. +- Building on MCP sampling or payment protocols; MCP has no official cost or payment extension. + +## Examples + +`posttrain_env` with task `t0_arithmetic_format`, driven by any agent loop: + +```python +from posttrain_env import PostTrainEnv + +with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync() as env: + result = env.reset(task_id="t0_arithmetic_format") + print(result.observation.metadata["prompt"]) + print(env.call_tool("budget")) + # {'remaining_usd': 3.0, 'flavors': {'t4-small': {'usd_per_hour': 0.4}, ...}, 'jobs_left': 6, ...} + + job = env.call_tool("jobs_submit", script=train_py, flavor="t4-small", timeout_minutes=30) + status = env.call_tool("jobs_wait", job_id=job["job_id"], timeout_s=300) + # {'state': 'succeeded', 'cost_usd': 0.04, + # 'artifact': {'uri': 'openenv-artifact:////outputs', 'files': [...]}} + + dev = env.call_tool("dev_evaluate", job_id=job["job_id"]) + env.call_tool("jobs_wait", job_id=dev["job_id"], timeout_s=300) # result: {'score': ...} + + final = env.call_tool("submit", job_id=job["job_id"]) # ends the episode; reward = hidden-split score +``` + +The final observation carries `reward`, `metadata["service_spend"]` and `metadata["episode_end"]`. + +## Prototype results + +`envs/posttrain_env` runs this design on HF Jobs. Its unit tests replace HF Jobs with an in-memory fake, with no network access. Two live runs on 2026-10-16: + +| Task | Agent job | Dev eval | Hidden-split reward | Rollout spend | +|---|---|---|---|---| +| `t0_artifact` | `cpu-basic`, 4 s | 1.0 | 1.0 | $0.000334 | +| `t0_arithmetic_format` | SFT of SmolLM2-135M-Instruct, `t4-small`, 333 s | 0.94 | 0.95 | $0.041 (`gpu_s` 333) | + +Both runs ended with no orphaned jobs and `final: true`. The score jobs were not charged to the agent. A run with a `job.write`-only broker token was refused with HTTP 403 when the script upload reached the `jobs-artifacts` bucket (D1). + +## Plan + +1. **This PR**: the RFC and `envs/posttrain_env`, with no core changes. Two T0 tasks: `t0_artifact` checks the plumbing for a fraction of a cent; `t0_arithmetic_format` is a real SFT task with a hidden test split. +2. **Core types**: manifest, ledger, job and artifact models outside `server/`; the reserved-name prefix rule for `jobs_`; cleanup that surfaces cancel failures (`_cleanup_session_resources` swallows errors today) and the idle reaper on when services are declared. +3. **openenvd integration** (RFC 009): credential custody, token exchange, the grader zone, and MCP Tasks after the RFC 003 amendment. + +## Future Work + +Profiles added when an environment needs one: + +| Profile | Examples | Adds | +|---|---|---| +| model call | inference, LLM/VLM judge | token metering | +| training session | Tinker-compatible APIs | session state, checkpoints as artifacts | +| interactive simulator | Blender, Isaac, browser, VM | lease, snapshot/fork, hardware capability | +| sub-sandbox | E2B, Daytona | nested isolation, per-service network policy | +| data access | search, paper/chem DBs | licensing, contamination audit, caching | +| licensed tool | FlexLM EDA, quantum queues | non-money leases (seats, daily quotas) | +| real-world actuation | cloud lab, robot, deploy | irreversible spend, orchestrator approval | +| human in the loop | expert review | hours-to-days latency, scarcity | +| artifact registry | HF Hub push | write-scoped credentials, provenance | +| peer agent | A2A | budget passed down to the peer (#779) | + +Also: audit records keyed to `(episode, step, tool_call_id)` as OpenTelemetry GenAI/MCP spans, signed checkpoints (OpenSSF model signing), and exclusive-hardware leases for timing evaluators. + +## Related + +- RFC 009 openenvd (#1053, #1354, #1355): the base for credential custody and the grader zone. +- RFC 013 task sampling (#1256): consumes `service_spend`. +- RFC 003: the MCP 2026-07-28 amendment. RFC 004: `score` feeds rubrics. RFC 005/006: LLM proxying is the future model-call profile. RFC 008: contract checks for declared services, plus #1357's adversarial panel. RFC 012: separate-verifier precedent. +- Issues: #898, #1026, #1232, #1299, #1357, #779. diff --git a/rfcs/README.md b/rfcs/README.md index 8c2824b2a5..836a05f962 100644 --- a/rfcs/README.md +++ b/rfcs/README.md @@ -101,6 +101,9 @@ Each RFC should include the following sections: ### World Modeling - [010-echo-env-token-world-model.md](./010-echo-env-token-world-model.md) - Env-token World Modeling (ECHO): trajectory token-role masks + an optimizer world-loss seam +### Environment Services +- [014-environment-services.md](./014-environment-services.md) - Environment Services: brokered, budgeted access to external services (HF Jobs batch jobs and evaluators in v1) + ### Discovery & Distribution - [011-ard-catalog-discovery.md](./011-ard-catalog-discovery.md) - Versioned environment metadata, ARD interchange, and a read-only producer-consumer path From 1f016454606affcc96a61ef7703c98aa446b0636 Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Sat, 17 Oct 2026 03:23:58 +0530 Subject: [PATCH 02/24] Add posttrain_env: post-train models through brokered, budgeted HF Jobs --- envs/posttrain_env/README.md | 136 +++++ envs/posttrain_env/__init__.py | 14 + envs/posttrain_env/client.py | 32 ++ envs/posttrain_env/openenv.yaml | 43 ++ envs/posttrain_env/pyproject.toml | 36 ++ envs/posttrain_env/server/Dockerfile | 65 +++ envs/posttrain_env/server/__init__.py | 7 + envs/posttrain_env/server/app.py | 32 ++ envs/posttrain_env/server/broker.py | 500 ++++++++++++++++++ .../eval_scripts/t0_arithmetic_format_eval.py | 71 +++ .../server/eval_scripts/t0_artifact_eval.py | 21 + .../server/posttrain_environment.py | 495 +++++++++++++++++ envs/posttrain_env/server/tasks.py | 118 +++++ 13 files changed, 1570 insertions(+) create mode 100644 envs/posttrain_env/README.md create mode 100644 envs/posttrain_env/__init__.py create mode 100644 envs/posttrain_env/client.py create mode 100644 envs/posttrain_env/openenv.yaml create mode 100644 envs/posttrain_env/pyproject.toml create mode 100644 envs/posttrain_env/server/Dockerfile create mode 100644 envs/posttrain_env/server/__init__.py create mode 100644 envs/posttrain_env/server/app.py create mode 100644 envs/posttrain_env/server/broker.py create mode 100644 envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py create mode 100644 envs/posttrain_env/server/eval_scripts/t0_artifact_eval.py create mode 100644 envs/posttrain_env/server/posttrain_environment.py create mode 100644 envs/posttrain_env/server/tasks.py diff --git a/envs/posttrain_env/README.md b/envs/posttrain_env/README.md new file mode 100644 index 0000000000..0cbead655a --- /dev/null +++ b/envs/posttrain_env/README.md @@ -0,0 +1,136 @@ +# Post-training Environment + +`posttrain_env` is an MCP environment where the agent post-trains a model on +[HF Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs). It is the +prototype for [RFC 014: Environment Services](../../rfcs/014-environment-services.md). + +The agent never holds a credential. It submits training scripts through MCP +tools, and the server's broker starts them on HF Jobs with its own token. Each +job's worst-case cost is reserved against a per-rollout budget before it starts +and settled at the real cost when it ends. `dev_evaluate` scores a checkpoint +on a public dev split. `submit` scores it on a hidden split in a grader-only +job, and that score is the reward. + +## Tools + +| Tool | What it does | +|---|---| +| `budget()` | Remaining budget, allowed hardware with its price, jobs and dev evaluations left | +| `jobs_submit(script, flavor, timeout_minutes, dependencies=None, idempotency_key=None)` | Run a Python script as an HF Job, like `hf jobs uv run`. Reserves `price/min × timeout` first | +| `jobs_status(job_id)` | State, cost once settled, artifact files of a succeeded job, result of a finished evaluation | +| `jobs_wait(job_id, timeout_s=60)` | Wait up to 300 s for a job to finish | +| `jobs_logs(job_id, tail=100)` | Last lines of one of your own jobs' logs | +| `jobs_cancel(job_id)` | Cancel a job; it settles for the minutes it ran | +| `dev_evaluate(job_id)` | Score a succeeded job's artifact on the dev split, as a new job charged to your budget | +| `submit(job_id)` | Final answer: cancels your other jobs, scores the artifact on the hidden split, ends the episode | + +Errors come back as data, with a type the agent can act on: `budget_exhausted`, +`flavor_not_allowed`, `timeout_not_allowed`, `limit_reached`, `not_ready`, +`unknown_job`, `not_allowed`, `episode_over` or `hf_error`. + +## How a job runs + +Agent scripts run with `HfApi.run_uv_job`, following its usual behaviour: the +script is uploaded to the namespace's private `jobs-artifacts` bucket, and the +job's folder there is mounted read-write at `/data`. Anything the job writes +under `/data/outputs/` is its artifact: + +``` +openenv-artifact:////outputs +``` + +Evaluations run the task's evaluator script as their own job with that folder +mounted read-only at `/model`. The evaluator prints one `POSTTRAIN_SCORE {...}` +line. A missing line, or a score outside [0, 1], scores 0 as `invalid`. + +Jobs are started with an explicit flavor, namespace, timeout and labels, and +with no secrets. The broker never passes its own token into a job: a job holding +a "Start and manage Jobs" token could start further jobs that skip the budget. +It also never shells out to `hf jobs uv run`, whose script header can pull the +caller's `HF_TOKEN` into the job. + +## Budget and spend + +Money is tracked in micro-USD from the prices `list_jobs_hardware` reports. +A job settles at its price per minute times its running minutes, rounded up to +whole minutes. Grader evaluations are not charged to the agent. + +Every observation carries the episode's spend, so a trainer can pass it to a +cost-aware sampler (RFC 013) as `costs`: + +```python +obs.metadata["service_spend"] +# {"version": 1, +# "settled": {"usd": 0.000334, "gpu_s": 0.0, "jobs": 1, "calls": 1}, +# "reserved": {"usd": 0.0}, +# "final": True} +``` + +`jobs` counts agent jobs and `calls` counts dev evaluations. `final` is false +while a job is still running or a cancellation was never confirmed. + +## Episode end + +An episode ends when the agent calls `submit`, after `max_steps` tool calls +(default 100) or after the task's time limit. The last two give reward 0. +Ending the episode cancels every live job. `reset` cancels the previous +episode's jobs first and raises if HF Jobs does not confirm a cancellation, +since that job may still be running and billing. + +`submit` waits for the score job, so it can take several minutes. `reset` +reports `score_timeout_s`; set the client's `message_timeout_s` above it. + +## Tasks + +| Task | What the agent does | Hardware | Budget | +|---|---|---|---| +| `t0_artifact` | Write the episode's nonce to `/data/outputs/answer.txt`. Checks the plumbing | `cpu-basic` | $0.05 | +| `t0_arithmetic_format` | Fine-tune `HuggingFaceTB/SmolLM2-135M-Instruct` to answer two-digit addition as `#### {a+b}`. Reward is exact-match accuracy on a hidden test split | `cpu-upgrade`, `t4-small`, `a10g-small` | $3.00 | + +## Running the server + +```bash +export HF_TOKEN=... # the broker's token, see below +cd envs/posttrain_env +uv run --project . server +``` + +Configuration, all read by the server only: + +| Variable | Meaning | +|---|---| +| `HF_TOKEN` | Broker token. A fine-grained token with "Start and manage Jobs" plus write access to `/jobs-artifacts`. A Jobs-only token is refused when the script is uploaded | +| `POSTTRAIN_NAMESPACE` | Namespace jobs run and bill in. Defaults to the token's owner | +| `POSTTRAIN_RESOURCE_GROUP_ID` | Resource Group to bill. Its Jobs spend limit caps total spend even if the ledger is wrong | +| `POSTTRAIN_TEST_SEED` | Seed of the hidden test split. Random per process if unset, so scores are only comparable within one server process | + +Run the server where its token is not shared with the agent, for example a +container you control. Do not deploy it as a public Space: a Space gives no +isolation guarantee for the credential it holds. + +## Client + +```python +from posttrain_env import PostTrainEnv + +with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync() as env: + result = env.reset(task_id="t0_artifact") + print(result.observation.metadata["prompt"]) + job = env.call_tool( + "jobs_submit", + script=open("train.py").read(), + flavor="cpu-basic", + timeout_minutes=5, + ) + env.call_tool("jobs_wait", job_id=job["job_id"], timeout_s=300) + print(env.call_tool("submit", job_id=job["job_id"])) +``` + +## Tests + +```bash +PYTHONPATH=src:envs uv run pytest tests/envs/test_posttrain_environment.py -v +``` + +The tests replace HF Jobs with an in-memory fake, so they make no network calls +and spend nothing. diff --git a/envs/posttrain_env/__init__.py b/envs/posttrain_env/__init__.py new file mode 100644 index 0000000000..c62dfe96d8 --- /dev/null +++ b/envs/posttrain_env/__init__.py @@ -0,0 +1,14 @@ +# SPDX-License-Identifier: BSD-3-Clause + +"""Post-training environment for OpenEnv. + +The agent post-trains a model by submitting scripts to HF Jobs through a broker +that holds the credentials and enforces a per-rollout budget. A grader-only +evaluation of the submitted checkpoint, on a hidden split, is the reward. +""" + +from openenv.core.env_server.mcp_types import CallToolAction, ListToolsAction + +from .client import PostTrainEnv + +__all__ = ["PostTrainEnv", "CallToolAction", "ListToolsAction"] diff --git a/envs/posttrain_env/client.py b/envs/posttrain_env/client.py new file mode 100644 index 0000000000..903366c11a --- /dev/null +++ b/envs/posttrain_env/client.py @@ -0,0 +1,32 @@ +# SPDX-License-Identifier: BSD-3-Clause + +"""Client for the post-training environment.""" + +from openenv.core.mcp_client import MCPToolClient + + +class PostTrainEnv(MCPToolClient): + """ + Client for the post-training environment. + + All interaction happens through MCP tools: `budget`, `jobs_submit`, + `jobs_status`, `jobs_wait`, `jobs_logs`, `jobs_cancel`, `dev_evaluate` and + `submit`. `submit` ends the episode and can take several minutes, so give + the client a message timeout of at least the `score_timeout_s` reported by + `reset`. + + Examples: + + ```python + from posttrain_env import PostTrainEnv + + with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync() as env: + result = env.reset(task_id="t0_artifact") + print(result.observation.metadata["prompt"]) + job = env.call_tool("jobs_submit", script=script, flavor="cpu-basic", timeout_minutes=5) + env.call_tool("jobs_wait", job_id=job["job_id"], timeout_s=300) + env.call_tool("submit", job_id=job["job_id"]) + ``` + """ + + pass diff --git a/envs/posttrain_env/openenv.yaml b/envs/posttrain_env/openenv.yaml new file mode 100644 index 0000000000..11011120c4 --- /dev/null +++ b/envs/posttrain_env/openenv.yaml @@ -0,0 +1,43 @@ +spec_version: 1 +name: posttrain_env +type: space +runtime: fastapi +app: server.app:app +port: 8000 + +description: > + The agent post-trains a model by submitting scripts to HF Jobs through a + broker that holds the credentials and enforces a per-rollout budget. A + grader-only evaluation of the submitted checkpoint, on a hidden split, is the + reward. Prototype for RFC 014 (Environment Services). +version: "0.1.0" + +# Run this server where its HF token is not shared with the agent: a container +# or host you control. Do not deploy it as a public Space; a Space gives no +# isolation guarantee for the credential it holds (RFC 014, decision 2). +# +# Server configuration, never stored in this file: +# HF_TOKEN broker token: "Start and manage Jobs" plus +# write access to /jobs-artifacts +# POSTTRAIN_NAMESPACE namespace jobs run in (default: token owner) +# POSTTRAIN_RESOURCE_GROUP_ID optional Resource Group with a Jobs spend limit +# POSTTRAIN_TEST_SEED hidden test split seed (default: random per process) + +validation: + reward: + range: [0.0, 1.0] + oracle_tolerance: 0.0 + floor_margin: 0.5 + # The server only brokers; training and evaluation run on HF Jobs. + resources: + cpu: 1.0 + memory_mb: 1024 + disk_mb: 512 + episode_timeout_s: 3600.0 # the policy maximum; t0_artifact fits, longer tasks set their own limit + capabilities: + verifier: + kind: reward_channel + declared_tools: + [budget, jobs_submit, jobs_status, jobs_wait, jobs_logs, jobs_cancel, dev_evaluate, submit] + types: + tags: [post-training, hf-jobs, environment-services] diff --git a/envs/posttrain_env/pyproject.toml b/envs/posttrain_env/pyproject.toml new file mode 100644 index 0000000000..bbc832a730 --- /dev/null +++ b/envs/posttrain_env/pyproject.toml @@ -0,0 +1,36 @@ +# SPDX-License-Identifier: BSD-3-Clause + +[build-system] +requires = ["setuptools>=45", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "openenv-posttrain-env" +version = "0.1.0" +description = "Post-training environment for OpenEnv: train models through brokered, budgeted HF Jobs" +requires-python = ">=3.10" +dependencies = [ + "openenv>=0.9.0", + "fastapi>=0.115.0", + "pydantic>=2.0.0", + "uvicorn>=0.24.0", + # Jobs, job artifact buckets and volume mounts. + "huggingface_hub>=1.29.0", +] + +[project.optional-dependencies] +dev = [ + "pytest>=9.0.3", + "pytest-cov>=4.0.0", +] + +[project.scripts] +server = "posttrain_env.server.app:main" + +[tool.setuptools] +include-package-data = true +packages = ["posttrain_env", "posttrain_env.server"] +package-dir = { "posttrain_env" = ".", "posttrain_env.server" = "server" } + +[tool.setuptools.package-data] +"posttrain_env.server" = ["eval_scripts/*.py"] diff --git a/envs/posttrain_env/server/Dockerfile b/envs/posttrain_env/server/Dockerfile new file mode 100644 index 0000000000..12c57b41a5 --- /dev/null +++ b/envs/posttrain_env/server/Dockerfile @@ -0,0 +1,65 @@ +# SPDX-License-Identifier: BSD-3-Clause + +# Multi-stage build using openenv-base +# This Dockerfile is flexible and works for both: +# - In-repo environments (with local src/core) +# - Standalone environments (with openenv from pip) +# The build script (openenv build) handles context detection and sets appropriate build args. + +ARG BASE_IMAGE=ghcr.io/huggingface/openenv-base:latest +FROM ${BASE_IMAGE} AS builder + +WORKDIR /app + +# Build argument to control whether we're building standalone or in-repo +ARG BUILD_MODE=in-repo + +# Copy environment code (always at root of build context) +COPY . /app/env + +# For in-repo builds, openenv is already in the pyproject.toml dependencies +# For standalone builds, openenv will be installed from pip via pyproject.toml +WORKDIR /app/env + +# Install dependencies using uv sync +# First pass: install dependencies without the project (for better caching) +# Second pass: install the project itself +RUN --mount=type=cache,target=/root/.cache/uv \ + if [ -f uv.lock ]; then \ + uv sync --frozen --no-install-project --no-editable; \ + else \ + uv sync --no-install-project --no-editable; \ + fi + +RUN --mount=type=cache,target=/root/.cache/uv \ + if [ -f uv.lock ]; then \ + uv sync --frozen --no-editable; \ + else \ + uv sync --no-editable; \ + fi + +# Final runtime stage +FROM ${BASE_IMAGE} + +WORKDIR /app + +# Copy the virtual environment from builder +COPY --from=builder /app/env/.venv /app/.venv + +# Copy the environment code +COPY --from=builder /app/env /app/env + +# Set PATH to use the virtual environment +ENV PATH="/app/.venv/bin:$PATH" + +# Set PYTHONPATH so imports work correctly +ENV PYTHONPATH="/app/env:$PYTHONPATH" +ENV ENABLE_WEB_INTERFACE=false + +# Health check using Python (more portable than curl/wget) +HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ + CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1 + +# Run the FastAPI server +# The module path is constructed to work with the /app/env structure +CMD ["sh", "-c", "cd /app/env && uvicorn server.app:app --host 0.0.0.0 --port 8000"] diff --git a/envs/posttrain_env/server/__init__.py b/envs/posttrain_env/server/__init__.py new file mode 100644 index 0000000000..141022a536 --- /dev/null +++ b/envs/posttrain_env/server/__init__.py @@ -0,0 +1,7 @@ +# SPDX-License-Identifier: BSD-3-Clause + +"""Post-training environment server components.""" + +from .posttrain_environment import PostTrainEnvironment + +__all__ = ["PostTrainEnvironment"] diff --git a/envs/posttrain_env/server/app.py b/envs/posttrain_env/server/app.py new file mode 100644 index 0000000000..d2ff813efe --- /dev/null +++ b/envs/posttrain_env/server/app.py @@ -0,0 +1,32 @@ +# SPDX-License-Identifier: BSD-3-Clause + +"""FastAPI application for the post-training environment.""" + +import os + +from openenv.core.env_server.http_server import create_app +from openenv.core.env_server.mcp_types import CallToolAction, CallToolObservation +from openenv.core.env_server.types import Observation + +from .posttrain_environment import PostTrainEnvironment + +# The class is passed rather than an instance so each WebSocket session gets +# its own environment, broker and budget. +app = create_app( + PostTrainEnvironment, + CallToolAction, + CallToolObservation, + reset_observation_cls=Observation, + env_name="posttrain_env", + max_concurrent_envs=int(os.getenv("MAX_CONCURRENT_ENVS", "8")), +) + + +def main(): + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) + + +if __name__ == "__main__": + main() diff --git a/envs/posttrain_env/server/broker.py b/envs/posttrain_env/server/broker.py new file mode 100644 index 0000000000..5cdddb6600 --- /dev/null +++ b/envs/posttrain_env/server/broker.py @@ -0,0 +1,500 @@ +# SPDX-License-Identifier: BSD-3-Clause + +"""HF Jobs broker with a reserve-then-settle budget ledger. + +The broker is the only holder of the HF token. Agents never see it: jobs are +started with `run_uv_job` and explicit flavor, namespace, timeout and labels, +and with no secrets. A `job.write` token passed into a job could start further +jobs that skip this ledger, so the broker never passes one in. + +Money is tracked in integer micro-USD. A job reserves its worst case (the +flavor's per-minute rate times its timeout) before it starts, and settles at +the per-minute rate for the minutes it actually ran. +""" + +import json +import math +import tempfile +import time +from contextlib import suppress +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from huggingface_hub import Volume +from huggingface_hub.errors import HfHubHTTPError + +# HF Jobs stage -> job state exposed to the agent. +STATE_FROM_STAGE = { + "SCHEDULING": "queued", + "RUNNING": "running", + "COMPLETED": "succeeded", + "ERROR": "failed", + "CANCELED": "cancelled", + "DELETED": "failed", +} +# States HF Jobs has confirmed; "orphaned" is terminal for the ledger but not confirmed. +CONFIRMED_STATES = {"succeeded", "failed", "cancelled"} +TERMINAL_STATES = CONFIRMED_STATES | {"orphaned"} +TERMINAL_STAGES = {"COMPLETED", "ERROR", "CANCELED", "DELETED"} +SCORE_MARKER = "POSTTRAIN_SCORE " +ARTIFACTS_MOUNT = "/data" +MAX_SCRIPT_BYTES = 200_000 +MAX_DEPENDENCIES = 20 +MAX_ARTIFACT_FILES = 50 +# Logs can lag a finished job; re-read before declaring the score missing. +MAX_RESULT_READS = 3 + + +class BrokerError(Exception): + """A request the broker refuses, with a typed reason for the agent.""" + + def __init__(self, kind: str, message: str): + super().__init__(message) + self.kind = kind + self.message = message + + def as_dict(self) -> dict: + return {"error": {"type": self.kind, "message": self.message}} + + +@dataclass +class Job: + """One HF Job started by the broker, as tracked in the ledger.""" + + job_id: str + role: str # "agent", "dev_eval" or "score" + flavor: str + timeout_minutes: int + reserved_micro_usd: int + bucket: str | None = None + path: str | None = None + source_job_id: str | None = None + state: str = "queued" + reason: str | None = None + running_secs: int | None = None + settled_micro_usd: int = 0 + settled: bool = False + result: dict | None = None + result_reads: int = 0 + + def view(self) -> dict: + view = { + "job_id": self.job_id, + "role": self.role, + "state": self.state, + "flavor": self.flavor, + "timeout_minutes": self.timeout_minutes, + "running_secs": self.running_secs or 0, + } + if self.reason: + view["reason"] = self.reason + if self.source_job_id: + view["evaluates"] = self.source_job_id + if self.settled: + view["cost_usd"] = self.settled_micro_usd / 1e6 + if self.result is not None: + view["result"] = self.result + return view + + +class Broker: + """ + Start, track and cancel HF Jobs for one episode, against a budget. + + Args: + api (`huggingface_hub.HfApi`): + Client holding the broker's token. + namespace (`str`): + Namespace jobs run and are billed in. + episode_id (`str`): + Episode the jobs belong to, added as a label. + budget_usd (`float`): + Budget for agent jobs and dev evaluations. + max_jobs (`int`): + Maximum number of agent jobs. + max_dev_evals (`int`): + Maximum number of dev evaluations. + resource_group_id (`str`, *optional*): + Resource Group to bill, so its Jobs spend limit is a hard backstop. + """ + + def __init__( + self, + api: Any, + *, + namespace: str, + episode_id: str, + budget_usd: float, + max_jobs: int, + max_dev_evals: int, + resource_group_id: str | None = None, + ): + self._api = api + self.namespace = namespace + self.episode_id = episode_id + self.budget_micro_usd = round(budget_usd * 1e6) + self.max_jobs = max_jobs + self.max_dev_evals = max_dev_evals + self.resource_group_id = resource_group_id + self.jobs: dict[str, Job] = {} + self._idempotency: dict[str, str] = {} + self._hardware: dict[str, Any] | None = None + + # -- ledger ------------------------------------------------------------- + + def count(self, role: str) -> int: + """Return how many jobs of `role` this episode has started.""" + return sum(1 for j in self.jobs.values() if j.role == role) + + def _charged(self) -> list[Job]: + return [j for j in self.jobs.values() if j.role in ("agent", "dev_eval")] + + def remaining_micro_usd(self) -> int: + used = sum( + j.settled_micro_usd if j.settled else j.reserved_micro_usd + for j in self._charged() + ) + return self.budget_micro_usd - used + + def rate_micro_usd_per_minute(self, flavor: str) -> int: + return self._hardware_info(flavor).unit_cost_micro_usd + + def _hardware_info(self, flavor: str) -> Any: + if self._hardware is None: + self._hardware = {h.name: h for h in self._api.list_jobs_hardware()} + if flavor not in self._hardware: + raise BrokerError("flavor_unknown", f"Unknown hardware flavor {flavor!r}") + return self._hardware[flavor] + + def _reserve(self, flavor: str, timeout_minutes: int, charged: bool) -> int: + reservation = self.rate_micro_usd_per_minute(flavor) * timeout_minutes + if charged and reservation > self.remaining_micro_usd(): + raise BrokerError( + "budget_exhausted", + f"Reserving ${reservation / 1e6:.4f} ({flavor} for {timeout_minutes} min) " + f"exceeds the remaining ${self.remaining_micro_usd() / 1e6:.4f}", + ) + return reservation + + def _settle(self, job: Job) -> None: + if job.running_secs is None: + # A job that ended without a reported duration may still have run; + # charge its full reservation unless it never started. + cost = 0 if job.state == "cancelled" else job.reserved_micro_usd + else: + minutes = math.ceil(job.running_secs / 60) + cost = minutes * self.rate_micro_usd_per_minute(job.flavor) + job.settled_micro_usd = min(cost, job.reserved_micro_usd) + job.settled = True + + def spend(self) -> dict: + """Return the `service_spend` observation entry for this episode.""" + charged = self._charged() + gpu_s = 0.0 + for job in charged: + accelerator = self._hardware_info(job.flavor).accelerator + if accelerator is not None: + gpu_s += (job.running_secs or 0) * int(accelerator.quantity) + return { + "version": 1, + "settled": { + "usd": sum(j.settled_micro_usd for j in charged if j.settled) / 1e6, + "gpu_s": gpu_s, + "jobs": self.count("agent"), + "calls": self.count("dev_eval"), + }, + "reserved": { + "usd": sum(j.reserved_micro_usd for j in charged if not j.settled) / 1e6 + }, + "final": all(j.state in CONFIRMED_STATES for j in self.jobs.values()), + } + + # -- jobs --------------------------------------------------------------- + + def submit( + self, + script: str, + *, + flavor: str, + timeout_minutes: int, + allowed_flavors: tuple[str, ...], + max_timeout_minutes: int, + dependencies: list[str] | None = None, + idempotency_key: str | None = None, + ) -> Job: + """Start an agent job after checking limits and reserving its worst case.""" + if idempotency_key and idempotency_key in self._idempotency: + return self.jobs[self._idempotency[idempotency_key]] + if flavor not in allowed_flavors: + raise BrokerError( + "flavor_not_allowed", + f"Flavor {flavor!r} is not one of {list(allowed_flavors)}", + ) + if not 1 <= timeout_minutes <= max_timeout_minutes: + raise BrokerError( + "timeout_not_allowed", + f"timeout_minutes must be in [1, {max_timeout_minutes}]", + ) + if not script.strip() or len(script.encode()) > MAX_SCRIPT_BYTES: + raise BrokerError( + "invalid_script", f"script must be 1 to {MAX_SCRIPT_BYTES} bytes" + ) + dependencies = dependencies or [] + if len(dependencies) > MAX_DEPENDENCIES: + raise BrokerError( + "invalid_script", f"At most {MAX_DEPENDENCIES} dependencies" + ) + if self.count("agent") >= self.max_jobs: + raise BrokerError( + "limit_reached", f"This episode allows {self.max_jobs} jobs" + ) + reservation = self._reserve(flavor, timeout_minutes, charged=True) + job = self._run( + script, + role="agent", + flavor=flavor, + timeout_minutes=timeout_minutes, + reservation=reservation, + dependencies=dependencies, + ) + if idempotency_key: + self._idempotency[idempotency_key] = job.job_id + return job + + def evaluate( + self, + source: Job, + *, + role: str, + script_path: Path, + flavor: str, + timeout_minutes: int, + dependencies: list[str], + env: dict[str, str], + ) -> Job: + """Run an evaluator job on `source`'s artifact, mounted read-only at `/model`.""" + self.require_artifact(source) + if role == "dev_eval" and self.count("dev_eval") >= self.max_dev_evals: + raise BrokerError( + "limit_reached", + f"This episode allows {self.max_dev_evals} dev evaluations", + ) + reservation = self._reserve(flavor, timeout_minutes, charged=role == "dev_eval") + model = Volume( + type="bucket", + source=source.bucket, + path=f"{source.path}/outputs", + mount_path="/model", + read_only=True, + ) + job = self._run( + script_path.read_text(), + role=role, + flavor=flavor, + timeout_minutes=timeout_minutes, + reservation=reservation, + dependencies=dependencies, + env=env, + volumes=[model], + ) + job.source_job_id = source.job_id + return job + + def _run( + self, + script: str, + *, + role: str, + flavor: str, + timeout_minutes: int, + reservation: int, + dependencies: list[str], + env: dict[str, str] | None = None, + volumes: list[Volume] | None = None, + ) -> Job: + # run_uv_job uploads a local script to the namespace's jobs-artifacts + # bucket and mounts that job's folder read-write at /data, so outputs + # come back without a token inside the job. + with tempfile.TemporaryDirectory() as tmp: + path = Path(tmp) / f"{role}.py" + path.write_text(script) + info = self._api.run_uv_job( + script=str(path), + dependencies=dependencies, + env=env or {}, + secrets={}, + flavor=flavor, + timeout=f"{timeout_minutes}m", + labels={ + "openenv-env": "posttrain_env", + "openenv-episode": self.episode_id, + "openenv-role": role, + }, + volumes=volumes, + namespace=self.namespace, + resource_group_id=self.resource_group_id, + ) + artifacts = [ + v + for v in info.volumes or [] + if v.type == "bucket" and v.mount_path == ARTIFACTS_MOUNT + ] + job = Job( + job_id=info.id, + role=role, + flavor=flavor, + timeout_minutes=timeout_minutes, + reserved_micro_usd=reservation, + bucket=artifacts[0].source if artifacts else None, + path=artifacts[0].path if artifacts else None, + ) + self.jobs[job.job_id] = job + return job + + def require_artifact(self, job: Job) -> None: + """Raise `not_ready` unless `job` is a succeeded agent job with an artifact.""" + if job.role != "agent" or job.state != "succeeded" or job.bucket is None: + raise BrokerError( + "not_ready", f"Job {job.job_id} has no successful artifact" + ) + + def get(self, job_id: str) -> Job: + if job_id not in self.jobs: + raise BrokerError("unknown_job", f"No job {job_id!r} in this episode") + return self.jobs[job_id] + + def finished(self, job: Job) -> bool: + """True once `job` is settled and, for evaluations, its result is read.""" + return job.settled and (job.role == "agent" or job.result is not None) + + def refresh(self, job: Job) -> Job: + """Update `job` from HF Jobs, settling it and reading its score once it ends.""" + if self.finished(job): + return job + if not job.settled: + info = self._api.inspect_job(job_id=job.job_id, namespace=self.namespace) + stage = info.status.stage + if job.state != "cancelling" or stage in TERMINAL_STAGES: + job.state = STATE_FROM_STAGE.get(stage, job.state) + if info.status.message and job.state in ("failed", "cancelled"): + job.reason = job.reason or info.status.message + if info.durations is not None and info.durations.running_secs is not None: + job.running_secs = info.durations.running_secs + if job.state in TERMINAL_STATES: + self._settle(job) + if job.settled and job.role != "agent": + job.result = self._read_result(job) + return job + + def _read_result(self, job: Job) -> dict | None: + if job.state != "succeeded": + return {"status": job.state, "score": 0.0} + job.result_reads += 1 + lines = self._api.fetch_job_logs( + job_id=job.job_id, namespace=self.namespace, tail=200 + ) + for line in reversed(list(lines)): + if not line.startswith(SCORE_MARKER): + continue + try: + result = json.loads(line[len(SCORE_MARKER) :]) + score = float(result["score"]) + except (ValueError, KeyError, TypeError): + score = -1.0 + if not 0.0 <= score <= 1.0: + return { + "status": "invalid", + "score": 0.0, + "feedback": "malformed score", + } + metrics = result.get("metrics") + return { + "status": str(result.get("status", "ok")), + "score": score, + "metrics": dict(list(metrics.items())[:5]) + if isinstance(metrics, dict) + else {}, + "feedback": str(result.get("feedback", ""))[:500], + } + if job.result_reads < MAX_RESULT_READS: + return None + return { + "status": "invalid", + "score": 0.0, + "feedback": "evaluator printed no score", + } + + def cancel(self, job: Job, reason: str) -> Job: + if self.refresh(job).state in TERMINAL_STATES: + return job + # The job may have just finished; the next refresh decides. + with suppress(HfHubHTTPError): + self._api.cancel_job(job_id=job.job_id, namespace=self.namespace) + job.state = "cancelling" + job.reason = reason + return self.refresh(job) + + def wait(self, job: Job, timeout_s: float, poll_s: float) -> Job: + """Poll `job` until it is finished or `timeout_s` passes.""" + deadline = time.monotonic() + timeout_s + while not self.finished(self.refresh(job)) and time.monotonic() < deadline: + time.sleep(poll_s) + return job + + def cancel_all(self, reason: str, timeout_s: float, poll_s: float) -> list[str]: + """ + Cancel every live job, re-check earlier orphans, and wait for HF Jobs to confirm. + + Jobs still live after `timeout_s` are marked `orphaned` and settled at + their full reservation, since their real cost is unknown. Orphans are + retried on every later call until HF Jobs confirms they ended. + + Returns: + `list[str]`: ids of the orphaned jobs. + """ + live = [j for j in self.jobs.values() if j.state not in CONFIRMED_STATES] + for job in live: + if job.state == "orphaned": + job.state, job.settled = "cancelling", False + with suppress(HfHubHTTPError): + self.cancel(job, reason) + deadline = time.monotonic() + timeout_s + while ( + any(j.state not in TERMINAL_STATES for j in live) + and time.monotonic() < deadline + ): + time.sleep(poll_s) + for job in live: + with suppress(HfHubHTTPError): + self.refresh(job) + orphaned = [] + for job in live: + if job.state not in TERMINAL_STATES: + job.state = "orphaned" + job.reason = f"{reason}: cancel not confirmed" + job.settled_micro_usd = job.reserved_micro_usd + job.settled = True + orphaned.append(job.job_id) + return orphaned + + def artifacts(self, job: Job) -> list[dict]: + """List the files under the job's `/data/outputs/`, with their content hashes.""" + if job.state != "succeeded" or job.bucket is None: + return [] + prefix = f"{job.path}/outputs/" + files = [] + for entry in self._api.list_bucket_tree( + job.bucket, prefix=prefix, recursive=True + ): + if entry.type == "file": + files.append( + { + "path": entry.path[len(prefix) :], + "size": entry.size, + "xet_hash": entry.xet_hash, + } + ) + if len(files) >= MAX_ARTIFACT_FILES: + break + return files diff --git a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py new file mode 100644 index 0000000000..9815b05f2a --- /dev/null +++ b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py @@ -0,0 +1,71 @@ +# /// script +# requires-python = ">=3.10" +# dependencies = ["torch", "transformers"] +# /// +# SPDX-License-Identifier: BSD-3-Clause + +"""Score `t0_arithmetic_format`: exact-match accuracy of `#### {a+b}` replies. + +The split is drawn from `POSTTRAIN_SEED`. The dev seed is public; the test seed +is held by the environment server and only reaches score jobs. +""" + +import json +import os +import random +import re + +N_PROMPTS = {"dev": 50, "test": 100} + + +def emit(result): + print("POSTTRAIN_SCORE " + json.dumps(result), flush=True) + + +split = os.environ["POSTTRAIN_SPLIT"] +rng = random.Random(int(os.environ["POSTTRAIN_SEED"])) +pairs = [(rng.randint(0, 99), rng.randint(0, 99)) for _ in range(N_PROMPTS[split])] + +try: + import torch + from transformers import AutoModelForCausalLM, AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained("/model") + # The checkpoint is agent output: never unpickle it or run its code. + model = AutoModelForCausalLM.from_pretrained( + "/model", torch_dtype=torch.float32, use_safetensors=True + ) + model.eval() +except Exception as e: # Report a bad checkpoint as a score, don't crash. + emit( + { + "status": "invalid", + "score": 0.0, + "feedback": f"cannot load /model: {type(e).__name__}", + } + ) + raise SystemExit(0) + +correct = formatted = 0 +for a, b in pairs: + messages = [{"role": "user", "content": f"What is {a} + {b}?"}] + inputs = tokenizer.apply_chat_template( + messages, add_generation_prompt=True, return_tensors="pt", return_dict=True + ) + with torch.no_grad(): + out = model.generate(**inputs, max_new_tokens=16, do_sample=False) + reply = tokenizer.decode( + out[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True + ).strip() + match = re.fullmatch(r"#### (\d+)", reply) + formatted += match is not None + correct += match is not None and int(match.group(1)) == a + b + +accuracy = correct / len(pairs) +emit( + { + "status": "ok", + "score": accuracy, + "metrics": {"accuracy": accuracy, "format_rate": formatted / len(pairs)}, + } +) diff --git a/envs/posttrain_env/server/eval_scripts/t0_artifact_eval.py b/envs/posttrain_env/server/eval_scripts/t0_artifact_eval.py new file mode 100644 index 0000000000..b7d4518bc1 --- /dev/null +++ b/envs/posttrain_env/server/eval_scripts/t0_artifact_eval.py @@ -0,0 +1,21 @@ +# /// script +# requires-python = ">=3.10" +# /// +# SPDX-License-Identifier: BSD-3-Clause + +"""Score `t0_artifact`: 1.0 if `/model/answer.txt` holds the expected text.""" + +import json +import os +from pathlib import Path + +expected = os.environ["POSTTRAIN_EXPECTED"] +answer = Path("/model/answer.txt") + +if answer.is_file(): + text = answer.read_text(encoding="utf-8", errors="replace").strip() + result = {"status": "ok", "score": float(text == expected)} +else: + result = {"status": "invalid", "score": 0.0, "feedback": "answer.txt not found"} + +print("POSTTRAIN_SCORE " + json.dumps(result), flush=True) diff --git a/envs/posttrain_env/server/posttrain_environment.py b/envs/posttrain_env/server/posttrain_environment.py new file mode 100644 index 0000000000..3810d59463 --- /dev/null +++ b/envs/posttrain_env/server/posttrain_environment.py @@ -0,0 +1,495 @@ +# SPDX-License-Identifier: BSD-3-Clause + +"""Post-training environment: the agent trains models through brokered HF Jobs. + +The agent never holds a credential. It submits training scripts through MCP +tools; the server's broker starts them on HF Jobs with its own token, reserves +each job's worst-case cost against the episode budget before it starts, and +settles the real cost when it ends. `dev_evaluate` scores a checkpoint on a +public dev split. `submit` runs the grader-only evaluation on a hidden split, +in its own job, and ends the episode with that score as the reward. + +Server configuration (environment variables): + +- `HF_TOKEN`: the broker's token. A fine-grained token with only "Start and + manage Jobs" plus write access to the namespace's `jobs-artifacts` bucket. +- `POSTTRAIN_NAMESPACE`: namespace jobs run in, defaults to the token's owner. +- `POSTTRAIN_RESOURCE_GROUP_ID`: optional Resource Group to bill, so its Jobs + spend limit caps what the server can spend in total. +- `POSTTRAIN_TEST_SEED`: seed of the hidden test split. Random per process if + unset, which keeps it unguessable but makes scores comparable only within + one server process. +""" + +import logging +import os +import secrets +import time +from typing import Any, Optional +from uuid import uuid4 + +from fastmcp import FastMCP +from huggingface_hub import HfApi +from huggingface_hub.errors import HfHubHTTPError +from openenv.core.env_server.mcp_environment import MCPEnvironment +from openenv.core.env_server.mcp_types import CallToolAction +from openenv.core.env_server.types import Action, Observation, State + +from .broker import Broker, BrokerError, TERMINAL_STATES +from .tasks import Task, TASKS + +logger = logging.getLogger(__name__) + +TEST_SEED = int(os.environ.get("POSTTRAIN_TEST_SEED") or secrets.randbelow(2**31)) +TOOL_TIMEOUT_S = 120.0 +MAX_LOG_CHARS = 20_000 + + +class PostTrainEnvironment(MCPEnvironment): + """ + MCP environment where the agent post-trains a model on HF Jobs. + + Args: + api (`huggingface_hub.HfApi`, *optional*): + HF client holding the broker's token. Defaults to `HfApi()`, which + reads `HF_TOKEN`. + task_id (`str`, *optional*, defaults to `"t0_arithmetic_format"`): + Task used when `reset` is called without `task_id`. + max_steps (`int`, *optional*, defaults to `100`): + Tool calls before the episode ends with reward 0. + max_wait_s (`int`, *optional*, defaults to `300`): + Longest single `jobs_wait` call. + poll_s (`float`, *optional*, defaults to `10.0`): + Interval between job status polls. + cancel_timeout_s (`float`, *optional*, defaults to `120.0`): + How long to wait for HF Jobs to confirm cancellations at episode end. + """ + + SUPPORTS_CONCURRENT_SESSIONS = True + + def __init__( + self, + api: Any | None = None, + task_id: str = "t0_arithmetic_format", + max_steps: int = 100, + max_wait_s: int = 300, + poll_s: float = 10.0, + cancel_timeout_s: float = 120.0, + ): + mcp = FastMCP("posttrain_env") + self._api = api if api is not None else HfApi() + self._default_task_id = task_id + self._max_steps = max_steps + self._max_wait_s = max_wait_s + self._poll_s = poll_s + self._cancel_timeout_s = cancel_timeout_s + self._namespace: str | None = None + self._task: Task | None = None + self._broker: Broker | None = None + self._nonce = "" + self._started_at = 0.0 + self._done = False + self._reward: float | None = None + self._score_job_id: str | None = None + self._end: dict = {} + + @mcp.tool + def budget() -> dict: + """ + Show the remaining budget, the hardware you may use with its price, + and how many jobs and dev evaluations are left. + """ + return self._call(self._budget) + + @mcp.tool + def jobs_submit( + script: str, + flavor: str, + timeout_minutes: int, + dependencies: list[str] | None = None, + idempotency_key: str | None = None, + ) -> dict: + """ + Run a Python script as an HF Job, like `hf jobs uv run`. + + The job's worst-case cost (price per minute times `timeout_minutes`) + is reserved before it starts and must fit the remaining budget. The + job has no credentials. Write outputs to `/data/outputs/`. + + Args: + script: Python source of the job. A PEP 723 header may list dependencies. + flavor: Hardware flavor, one of those listed by `budget`. + timeout_minutes: Job timeout; the job is stopped when it is reached. + dependencies: Extra packages, passed to `uv run --with`. + idempotency_key: Retrying with the same key returns the same job. + """ + return self._call( + lambda: self._job_view( + self._broker.submit( + script, + flavor=flavor, + timeout_minutes=timeout_minutes, + allowed_flavors=self._task.flavors, + max_timeout_minutes=self._task.max_timeout_minutes, + dependencies=dependencies, + idempotency_key=idempotency_key, + ) + ) + ) + + @mcp.tool + def jobs_status(job_id: str) -> dict: + """ + Get a job's state. Finished agent jobs list their artifact files; + finished evaluations include their result. + + Args: + job_id: Id returned by `jobs_submit` or `dev_evaluate`. + """ + return self._call( + lambda: self._job_view(self._broker.refresh(self._broker.get(job_id))) + ) + + @mcp.tool + def jobs_wait(job_id: str, timeout_s: int = 60) -> dict: + """ + Wait until a job finishes, or up to `timeout_s` seconds (at most 300). + + Args: + job_id: Id returned by `jobs_submit` or `dev_evaluate`. + timeout_s: Seconds to wait before returning the current state. + """ + wait_s = min(max(timeout_s, 0), self._max_wait_s) + return self._call( + lambda: self._job_view( + self._broker.wait(self._broker.get(job_id), wait_s, self._poll_s) + ) + ) + + @mcp.tool + def jobs_logs(job_id: str, tail: int = 100) -> dict: + """ + Read the last lines of one of your jobs' logs. + + Args: + job_id: Id returned by `jobs_submit`. + tail: Number of lines, at most 500. + """ + return self._call(lambda: self._logs(job_id, tail)) + + @mcp.tool + def jobs_cancel(job_id: str) -> dict: + """ + Cancel a job. Its cost settles for the minutes it ran. + + Args: + job_id: Id returned by `jobs_submit` or `dev_evaluate`. + """ + return self._call( + lambda: self._job_view( + self._broker.cancel(self._broker.get(job_id), "cancelled by agent") + ) + ) + + @mcp.tool + def dev_evaluate(job_id: str) -> dict: + """ + Score a finished job's artifact on the public dev split, in a new + evaluation job charged to your budget. Poll it with `jobs_status` + or `jobs_wait`; the result appears once it finishes. + + Args: + job_id: Id of a succeeded job from `jobs_submit`. + """ + return self._call( + lambda: self._job_view(self._evaluate(job_id, "dev_eval")) + ) + + @mcp.tool + def submit(job_id: str) -> dict: + """ + Submit a finished job's artifact as your final answer. This cancels + your other jobs, scores the artifact on the hidden test split and + ends the episode. It can take several minutes. + + Args: + job_id: Id of a succeeded job from `jobs_submit`. + """ + return self._call(lambda: self._submit(job_id)) + + super().__init__(mcp) + self._state = State(episode_id=str(uuid4()), step_count=0) + + # -- episode lifecycle -------------------------------------------------- + + def reset( + self, + seed: Optional[int] = None, + episode_id: Optional[str] = None, + task_id: Optional[str] = None, + **kwargs: Any, + ) -> Observation: + """ + Start an episode, after cancelling every job of the previous one. + + Args: + seed (`int`, *optional*): + Unused; tasks are deterministic apart from the artifact nonce. + episode_id (`str`, *optional*): + Episode id, also used as the jobs' `openenv-episode` label. + task_id (`str`, *optional*): + One of the task ids in `TASKS`. Defaults to the constructor's. + + Raises: + `RuntimeError`: If a job of the previous episode could not be + confirmed cancelled. It may still be running and billing. + """ + self._end_jobs("episode reset") + task_id = task_id or self._default_task_id + if task_id not in TASKS: + raise ValueError( + f"Unknown task_id {task_id!r}; choose from {sorted(TASKS)}" + ) + if self._namespace is None: + self._namespace = ( + os.environ.get("POSTTRAIN_NAMESPACE") or self._api.whoami()["name"] + ) + self._task = TASKS[task_id] + self._state = State( + episode_id=episode_id or str(uuid4()), step_count=0, task_id=task_id + ) + self._broker = Broker( + self._api, + namespace=self._namespace, + episode_id=self._state.episode_id, + budget_usd=self._task.budget_usd, + max_jobs=self._task.max_jobs, + max_dev_evals=self._task.max_dev_evals, + resource_group_id=os.environ.get("POSTTRAIN_RESOURCE_GROUP_ID"), + ) + self._nonce = secrets.token_hex(8) + self._started_at = time.monotonic() + self._done = False + self._reward = None + self._end = {} + self._score_job_id = None + return Observation( + done=False, + reward=0.0, + metadata={ + "task_id": task_id, + "prompt": self._task.prompt.format(nonce=self._nonce), + "budget_usd": self._task.budget_usd, + "max_steps": self._max_steps, + "max_episode_minutes": self._task.max_episode_minutes, + "score_timeout_s": self._score_timeout_s(), + "service_spend": self._broker.spend(), + }, + ) + + def _end_jobs(self, reason: str) -> None: + if self._broker is None: + return + orphaned = self._broker.cancel_all(reason, self._cancel_timeout_s, self._poll_s) + if orphaned: + raise RuntimeError( + f"HF Jobs did not confirm cancelling {orphaned} from episode " + f"{self._broker.episode_id}; they may still be running and billing" + ) + + def _score_timeout_s(self) -> float: + # Queueing plus install time come on top of the evaluator's own timeout. + return self._task.eval_timeout_minutes * 60 + 900.0 + + def _tool_timeout( + self, action: Action, timeout_s: Optional[float] + ) -> Optional[float]: + if not isinstance(action, CallToolAction) or self._task is None: + return timeout_s + if action.tool_name == "submit": + return self._score_timeout_s() + self._cancel_timeout_s + TOOL_TIMEOUT_S + if action.tool_name == "jobs_wait": + return self._max_wait_s + TOOL_TIMEOUT_S + return TOOL_TIMEOUT_S + + def _finish(self, obs: Observation) -> Observation: + if self._broker is None: + return obs + if not self._done: + elapsed_min = (time.monotonic() - self._started_at) / 60 + if self._state.step_count >= self._max_steps: + self._stop("max_steps", f"Reached {self._max_steps} tool calls") + elif elapsed_min >= self._task.max_episode_minutes: + self._stop( + "time_limit", f"Reached {self._task.max_episode_minutes} minutes" + ) + metadata = {**obs.metadata, "service_spend": self._broker.spend()} + if self._done: + obs.done = True + obs.reward = self._reward + metadata["episode_end"] = self._end + obs.metadata = metadata + return obs + + def _stop(self, kind: str, message: str) -> None: + orphaned = self._broker.cancel_all(kind, self._cancel_timeout_s, self._poll_s) + self._done = True + self._reward = 0.0 + self._end = {"reason": kind, "message": message, "orphaned_jobs": orphaned} + + def step( + self, + action: Action, + timeout_s: Optional[float] = None, + **kwargs: Any, + ) -> Observation: + self._state.step_count += 1 + obs = super().step( + action, timeout_s=self._tool_timeout(action, timeout_s), **kwargs + ) + return self._finish(obs) + + async def step_async( + self, + action: Action, + timeout_s: Optional[float] = None, + **kwargs: Any, + ) -> Observation: + self._state.step_count += 1 + obs = await super().step_async( + action, timeout_s=self._tool_timeout(action, timeout_s), **kwargs + ) + return self._finish(obs) + + def _step_impl( + self, + action: Action, + timeout_s: Optional[float] = None, + **kwargs: Any, + ) -> Observation: + return Observation( + done=False, + reward=0.0, + metadata={ + "error": f"Unknown action type: {type(action).__name__}. " + "Use ListToolsAction or CallToolAction for MCP interactions." + }, + ) + + @property + def state(self) -> State: + return self._state + + def close(self) -> None: + try: + self._end_jobs("environment closed") + finally: + super().close() + + # -- tools -------------------------------------------------------------- + + def _call(self, fn) -> dict: + if self._broker is None: + return BrokerError("not_started", "Call reset before using tools").as_dict() + if self._done: + return BrokerError("episode_over", "The episode has ended").as_dict() + try: + return fn() + except BrokerError as e: + return e.as_dict() + except HfHubHTTPError as e: + # Hub errors carry request ids and URLs; the agent gets the status only. + logger.warning("HF Jobs request failed: %s", e) + return BrokerError( + "hf_error", f"HF Jobs request failed (HTTP {e.response.status_code})" + ).as_dict() + except Exception: + logger.exception("posttrain_env tool failed") + return BrokerError( + "internal_error", "The environment hit an internal error" + ).as_dict() + + def _budget(self) -> dict: + broker = self._broker + return { + "remaining_usd": broker.remaining_micro_usd() / 1e6, + "budget_usd": self._task.budget_usd, + "flavors": { + f: {"usd_per_hour": broker.rate_micro_usd_per_minute(f) * 60 / 1e6} + for f in self._task.flavors + }, + "max_timeout_minutes": self._task.max_timeout_minutes, + "jobs_left": self._task.max_jobs - broker.count("agent"), + "dev_evals_left": self._task.max_dev_evals - broker.count("dev_eval"), + "dev_evaluate_usd": broker.rate_micro_usd_per_minute(self._task.eval_flavor) + * self._task.eval_timeout_minutes + / 1e6, + } + + def _job_view(self, job) -> dict: + view = job.view() + if job.role == "agent" and job.state == "succeeded": + view["artifact"] = { + "uri": f"openenv-artifact://{self._state.episode_id}/{job.job_id}/outputs", + "files": self._broker.artifacts(job), + } + view["remaining_usd"] = self._broker.remaining_micro_usd() / 1e6 + return view + + def _logs(self, job_id: str, tail: int) -> dict: + job = self._broker.get(job_id) + if job.role != "agent": + raise BrokerError("not_allowed", "Only your own jobs' logs are readable") + lines = self._api.fetch_job_logs( + job_id=job.job_id, namespace=self._namespace, tail=min(max(tail, 1), 500) + ) + return {"job_id": job_id, "logs": "\n".join(lines)[-MAX_LOG_CHARS:]} + + def _evaluate(self, job_id: str, role: str): + task = self._task + source = self._broker.refresh(self._broker.get(job_id)) + split, seed = ( + ("dev", task.dev_seed) if role == "dev_eval" else ("test", TEST_SEED) + ) + return self._broker.evaluate( + source, + role=role, + script_path=task.eval_script_path, + flavor=task.eval_flavor, + timeout_minutes=task.eval_timeout_minutes, + dependencies=list(task.eval_dependencies), + env={ + "POSTTRAIN_SPLIT": split, + "POSTTRAIN_SEED": str(seed), + "POSTTRAIN_EXPECTED": self._nonce, + }, + ) + + def _submit(self, job_id: str) -> dict: + # A retry after a transient HF error reuses the score job already started. + if self._score_job_id is None: + self._broker.require_artifact( + self._broker.refresh(self._broker.get(job_id)) + ) + for job in self._broker.jobs.values(): + if job.state not in TERMINAL_STATES: + self._broker.cancel(job, "submitted") + self._score_job_id = self._evaluate(job_id, "score").job_id + score_job = self._broker.get(self._score_job_id) + self._broker.wait(score_job, self._score_timeout_s(), self._poll_s) + if score_job.state not in TERMINAL_STATES: + self._broker.cancel(score_job, "score timed out") + result = score_job.result or {"status": "timeout", "score": 0.0} + orphaned = self._broker.cancel_all( + "submitted", self._cancel_timeout_s, self._poll_s + ) + self._done = True + self._reward = result["score"] + self._end = { + "reason": "submitted", + "job_id": score_job.source_job_id, + "score_job_id": score_job.job_id, + "score": result, + "orphaned_jobs": orphaned, + } + return {"submitted": score_job.source_job_id, "result": result} diff --git a/envs/posttrain_env/server/tasks.py b/envs/posttrain_env/server/tasks.py new file mode 100644 index 0000000000..05c0ee1de2 --- /dev/null +++ b/envs/posttrain_env/server/tasks.py @@ -0,0 +1,118 @@ +# SPDX-License-Identifier: BSD-3-Clause + +"""T0 post-training tasks. + +Each task tells the agent what to train, which HF Jobs hardware it may use and +how much it may spend. The evaluator script runs as its own job: the agent's +checkpoint is mounted read-only at `/model`, and the job prints one +`POSTTRAIN_SCORE {...}` line that the broker parses. +""" + +from dataclasses import dataclass +from pathlib import Path + +EVAL_DIR = Path(__file__).parent / "eval_scripts" + + +@dataclass(frozen=True) +class Task: + """ + One post-training task. + + Attributes: + task_id (`str`): + Stable task identifier, passed to `reset(task_id=...)`. + prompt (`str`): + Instructions shown to the agent. `{nonce}` is filled in at reset. + flavors (`tuple[str, ...]`): + HF Jobs hardware flavors the agent may request. + max_timeout_minutes (`int`): + Longest timeout a single agent job may request. + budget_usd (`float`): + Per-rollout budget for agent jobs and dev evaluations. + max_jobs (`int`): + Maximum number of agent jobs per episode. + max_dev_evals (`int`): + Maximum number of `dev_evaluate` calls per episode. + eval_script (`str`): + File name of the evaluator under `server/eval_scripts/`. + eval_flavor (`str`): + Hardware flavor for dev and score evaluations. + eval_timeout_minutes (`int`): + Timeout for one evaluation job. + eval_dependencies (`tuple[str, ...]`): + Packages the evaluator needs, passed to `uv run --with`. + dev_seed (`int`): + Seed of the public dev split. + max_episode_minutes (`int`): + Wall-clock limit for the whole episode. + """ + + task_id: str + prompt: str + flavors: tuple[str, ...] + max_timeout_minutes: int + budget_usd: float + max_jobs: int + max_dev_evals: int + eval_script: str + eval_flavor: str + eval_timeout_minutes: int + eval_dependencies: tuple[str, ...] = () + dev_seed: int = 0 + max_episode_minutes: int = 240 + + @property + def eval_script_path(self) -> Path: + return EVAL_DIR / self.eval_script + + +OUTPUTS_NOTE = ( + "Your job's working storage is mounted at `/data`. Anything you write under " + "`/data/outputs/` is the job's artifact: `dev_evaluate` and `submit` mount " + "it read-only at `/model`." +) + +TASKS = { + "t0_artifact": Task( + task_id="t0_artifact", + prompt=( + "Plumbing check. Submit one HF Jobs script that writes the exact text " + "`{nonce}` to `/data/outputs/answer.txt`, then call `submit` with that " + "job's id. " + OUTPUTS_NOTE + ), + flavors=("cpu-basic",), + max_timeout_minutes=10, + budget_usd=0.05, + max_jobs=3, + max_dev_evals=2, + eval_script="t0_artifact_eval.py", + eval_flavor="cpu-basic", + eval_timeout_minutes=5, + max_episode_minutes=30, + ), + "t0_arithmetic_format": Task( + task_id="t0_arithmetic_format", + prompt=( + "Fine-tune `HuggingFaceTB/SmolLM2-135M-Instruct` so that it answers " + "two-digit addition in an exact format. The evaluator sends the chat " + "message `What is {{a}} + {{b}}?` (0 <= a, b <= 99) through the " + "tokenizer's chat template with `add_generation_prompt=True`, decodes " + "up to 16 new tokens greedily, and counts an answer as correct only if " + "the stripped reply is exactly `#### {{a+b}}`. Save the full model and " + "tokenizer with `save_pretrained` to `/data/outputs/`. The reward is " + "the accuracy on a hidden test split; `dev_evaluate` scores the public " + "dev split (seed 1234). " + OUTPUTS_NOTE + ), + flavors=("cpu-upgrade", "t4-small", "a10g-small"), + max_timeout_minutes=60, + budget_usd=3.0, + max_jobs=6, + max_dev_evals=3, + eval_script="t0_arithmetic_format_eval.py", + eval_flavor="cpu-upgrade", + eval_timeout_minutes=20, + eval_dependencies=("torch", "transformers"), + dev_seed=1234, + ), +} From e140f93a00aca17688c5da15e4b2ab6f3cc57d8d Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Sat, 17 Oct 2026 03:24:05 +0530 Subject: [PATCH 03/24] Add tests for posttrain_env --- tests/envs/test_posttrain_environment.py | 625 +++++++++++++++++++++++ 1 file changed, 625 insertions(+) create mode 100644 tests/envs/test_posttrain_environment.py diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py new file mode 100644 index 0000000000..e662b39fec --- /dev/null +++ b/tests/envs/test_posttrain_environment.py @@ -0,0 +1,625 @@ +# SPDX-License-Identifier: BSD-3-Clause + +"""Tests for posttrain_env: the HF Jobs broker, its budget ledger and the episode flow. + +HF Jobs is replaced by an in-memory fake, so these tests make no network calls +and spend nothing. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from types import SimpleNamespace + +import pytest +import yaml +from envs.posttrain_env.server import posttrain_environment +from envs.posttrain_env.server.broker import MAX_RESULT_READS, SCORE_MARKER +from envs.posttrain_env.server.posttrain_environment import PostTrainEnvironment +from huggingface_hub import Volume +from openenv.core.env_server.mcp_types import CallToolAction, ListToolsAction + +ENV_DIR = Path(__file__).parents[2] / "envs" / "posttrain_env" +CPU_BASIC = 167 # micro-USD per minute +A10G_SMALL = 16_667 + + +def hardware(name, micro_usd_per_minute, gpus=None): + accelerator = None if gpus is None else SimpleNamespace(quantity=str(gpus)) + return SimpleNamespace( + name=name, unit_cost_micro_usd=micro_usd_per_minute, accelerator=accelerator + ) + + +class FakeHfApi: + """The subset of `HfApi` the broker uses, backed by a dict of jobs.""" + + def __init__(self): + self.jobs = {} + self.runs = [] + self.cancel_confirms = True + # Applied the first time a job with this role label is inspected. + self.on_inspect = {} + + def whoami(self): + return {"name": "operator"} + + def list_jobs_hardware(self): + return [ + hardware("cpu-basic", CPU_BASIC), + hardware("cpu-upgrade", 500), + hardware("t4-small", 6_667, gpus=1), + hardware("a10g-small", A10G_SMALL, gpus=1), + ] + + def run_uv_job(self, *, script, **kwargs): + n = len(self.runs) + job_id = f"job{n}" + self.runs.append( + {"job_id": job_id, "script": Path(script).read_text(), **kwargs} + ) + self.jobs[job_id] = { + "stage": "SCHEDULING", + "running_secs": None, + "logs": [], + "role": kwargs["labels"]["openenv-role"], + } + artifacts = Volume( + type="bucket", + source="operator/jobs-artifacts", + path=f"run{n}", + mount_path="/data", + read_only=False, + ) + return SimpleNamespace( + id=job_id, volumes=(kwargs["volumes"] or []) + [artifacts] + ) + + def inspect_job(self, *, job_id, namespace): + job = self.jobs[job_id] + if job["stage"] == "SCHEDULING" and job["role"] in self.on_inspect: + job.update(self.on_inspect[job["role"]]) + return SimpleNamespace( + status=SimpleNamespace(stage=job["stage"], message=job.get("message")), + durations=SimpleNamespace(running_secs=job["running_secs"]), + ) + + def cancel_job(self, *, job_id, namespace): + job = self.jobs[job_id] + job["cancel_requested"] = True + if self.cancel_confirms: + job["stage"] = "CANCELED" + + def fetch_job_logs(self, *, job_id, namespace, tail): + return iter(self.jobs[job_id]["logs"][-tail:]) + + def list_bucket_tree(self, bucket_id, prefix, recursive): + return [ + SimpleNamespace( + type="file", path=f"{prefix}model.safetensors", size=10, xet_hash="h1" + ), + SimpleNamespace(type="directory", path=f"{prefix}sub"), + ] + + def finish(self, job_id, stage="COMPLETED", running_secs=90, logs=()): + self.jobs[job_id].update( + stage=stage, running_secs=running_secs, logs=list(logs) + ) + + +def score_line(score, **extra): + return SCORE_MARKER + json.dumps({"status": "ok", "score": score, **extra}) + + +@pytest.fixture +def api(): + return FakeHfApi() + + +@pytest.fixture +def env(api): + env = PostTrainEnvironment(api=api, poll_s=0.0, cancel_timeout_s=0.01) + yield env + api.cancel_confirms = True + env.close() + + +def call(env, tool, **arguments): + obs = env.step(CallToolAction(tool_name=tool, arguments=arguments)) + assert obs.error is None, obs.error + return obs.result.data, obs + + +def submit_job(env, flavor="cpu-basic", timeout_minutes=10, **extra): + data, _ = call( + env, + "jobs_submit", + script="print('train')", + flavor=flavor, + timeout_minutes=timeout_minutes, + **extra, + ) + return data + + +class TestReset: + def test_reset_reports_task_budget_and_zero_spend(self, env): + obs = env.reset(task_id="t0_artifact") + meta = obs.metadata + assert meta["task_id"] == "t0_artifact" + assert "/data/outputs/answer.txt" in meta["prompt"] + assert meta["budget_usd"] == 0.05 + assert meta["service_spend"] == { + "version": 1, + "settled": {"usd": 0.0, "gpu_s": 0.0, "jobs": 0, "calls": 0}, + "reserved": {"usd": 0.0}, + "final": True, + } + + def test_unknown_task_is_rejected(self, env): + with pytest.raises(ValueError, match="Unknown task_id"): + env.reset(task_id="nope") + + def test_tools_match_the_manifest(self, env): + env.reset(task_id="t0_artifact") + names = {t.name for t in env.step(ListToolsAction()).tools} + manifest = yaml.safe_load((ENV_DIR / "openenv.yaml").read_text()) + assert names == set(manifest["validation"]["capabilities"]["declared_tools"]) + + def test_tools_before_reset_are_refused(self, env): + data, _ = call(env, "budget") + assert data["error"]["type"] == "not_started" + + +class TestSubmitJobs: + def test_job_starts_with_explicit_settings_and_no_secrets(self, env, api): + env.reset(task_id="t0_artifact", episode_id="ep1") + job = submit_job(env, timeout_minutes=10, dependencies=["numpy"]) + run = api.runs[0] + assert run["secrets"] == {} + assert run["namespace"] == "operator" + assert run["flavor"] == "cpu-basic" + assert run["timeout"] == "10m" + assert run["dependencies"] == ["numpy"] + assert run["labels"] == { + "openenv-env": "posttrain_env", + "openenv-episode": "ep1", + "openenv-role": "agent", + } + assert run["script"] == "print('train')" + assert job["state"] == "queued" + assert job["remaining_usd"] == pytest.approx(0.05 - 10 * CPU_BASIC / 1e6) + + def test_resource_group_bills_the_backstop(self, env, api, monkeypatch): + monkeypatch.setenv("POSTTRAIN_RESOURCE_GROUP_ID", "rg-123") + env.reset(task_id="t0_artifact") + submit_job(env) + assert api.runs[0]["resource_group_id"] == "rg-123" + + def test_flavor_timeout_and_budget_are_enforced_before_starting(self, env, api): + env.reset(task_id="t0_arithmetic_format") + denied = submit_job(env, flavor="a100-large") + assert denied["error"]["type"] == "flavor_not_allowed" + denied = submit_job(env, flavor="a10g-small", timeout_minutes=61) + assert denied["error"]["type"] == "timeout_not_allowed" + # Each a10g-small hour reserves about $1.00 of the $3.00 budget. + for _ in range(2): + assert "error" not in submit_job( + env, flavor="a10g-small", timeout_minutes=60 + ) + denied = submit_job(env, flavor="a10g-small", timeout_minutes=60) + assert denied["error"]["type"] == "budget_exhausted" + assert len(api.runs) == 2 + + def test_job_count_limit(self, env): + env.reset(task_id="t0_artifact") + for _ in range(3): + assert "error" not in submit_job(env, timeout_minutes=1) + assert submit_job(env, timeout_minutes=1)["error"]["type"] == "limit_reached" + + def test_idempotency_key_reuses_the_job_and_its_reservation(self, env, api): + env.reset(task_id="t0_artifact") + first = submit_job(env, idempotency_key="k1") + again = submit_job(env, idempotency_key="k1") + assert again["job_id"] == first["job_id"] + assert again["remaining_usd"] == first["remaining_usd"] + assert len(api.runs) == 1 + + +class TestJobLifecycle: + def test_finished_job_settles_per_started_minute_and_lists_its_artifact( + self, env, api + ): + env.reset(task_id="t0_artifact", episode_id="ep1") + job = submit_job(env, timeout_minutes=10) + api.finish(job["job_id"], running_secs=90) + data, obs = call(env, "jobs_status", job_id=job["job_id"]) + assert data["state"] == "succeeded" + assert data["cost_usd"] == pytest.approx(2 * CPU_BASIC / 1e6) + assert data["remaining_usd"] == pytest.approx(0.05 - 2 * CPU_BASIC / 1e6) + assert data["artifact"] == { + "uri": f"openenv-artifact://ep1/{job['job_id']}/outputs", + "files": [{"path": "model.safetensors", "size": 10, "xet_hash": "h1"}], + } + spend = obs.metadata["service_spend"] + assert spend["settled"]["usd"] == pytest.approx(2 * CPU_BASIC / 1e6) + assert spend["reserved"]["usd"] == 0.0 + assert spend["final"] is True + + def test_gpu_seconds_count_accelerators(self, env, api): + env.reset(task_id="t0_arithmetic_format") + job = submit_job(env, flavor="t4-small", timeout_minutes=5) + api.finish(job["job_id"], running_secs=120) + _, obs = call(env, "jobs_status", job_id=job["job_id"]) + assert obs.metadata["service_spend"]["settled"]["gpu_s"] == 120 + + def test_failed_job_reports_its_reason(self, env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env) + api.jobs[job["job_id"]]["message"] = "OOM" + api.finish(job["job_id"], stage="ERROR", running_secs=30) + data, _ = call(env, "jobs_status", job_id=job["job_id"]) + assert data["state"] == "failed" + assert data["reason"] == "OOM" + assert "artifact" not in data + + def test_cancel_settles_the_minutes_that_ran(self, env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env, timeout_minutes=10) + api.jobs[job["job_id"]].update(stage="RUNNING", running_secs=61) + data, _ = call(env, "jobs_cancel", job_id=job["job_id"]) + assert data["state"] == "cancelled" + assert data["cost_usd"] == pytest.approx(2 * CPU_BASIC / 1e6) + + def test_unknown_job(self, env): + env.reset(task_id="t0_artifact") + data, _ = call(env, "jobs_status", job_id="other") + assert data["error"]["type"] == "unknown_job" + + def test_logs_are_readable_for_agent_jobs(self, env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env) + api.finish(job["job_id"], logs=["step 1", "step 2"]) + data, _ = call(env, "jobs_logs", job_id=job["job_id"], tail=1) + assert data["logs"] == "step 2" + + +class TestEvaluation: + def _finished_job(self, env, api): + job = submit_job(env) + api.finish(job["job_id"]) + return job + + def test_dev_evaluate_mounts_the_artifact_read_only_and_reads_the_score( + self, env, api + ): + env.reset(task_id="t0_arithmetic_format") + job = submit_job(env, flavor="cpu-upgrade", timeout_minutes=5) + api.finish(job["job_id"]) + evaluation, _ = call(env, "dev_evaluate", job_id=job["job_id"]) + run = api.runs[-1] + model = run["volumes"][0] + assert (model.source, model.path, model.mount_path, model.read_only) == ( + "operator/jobs-artifacts", + "run0/outputs", + "/model", + True, + ) + assert run["env"]["POSTTRAIN_SPLIT"] == "dev" + assert run["env"]["POSTTRAIN_SEED"] == "1234" + assert run["dependencies"] == ["torch", "transformers"] + assert run["labels"]["openenv-role"] == "dev_eval" + assert "POSTTRAIN_SCORE" in run["script"] + + api.finish( + evaluation["job_id"], + logs=["loading", score_line(0.42, metrics={"accuracy": 0.42})], + ) + data, obs = call(env, "jobs_status", job_id=evaluation["job_id"]) + assert data["result"]["score"] == 0.42 + assert data["result"]["metrics"] == {"accuracy": 0.42} + assert obs.metadata["service_spend"]["settled"]["calls"] == 1 + + def test_dev_evaluate_needs_a_succeeded_agent_job(self, env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env) + data, _ = call(env, "dev_evaluate", job_id=job["job_id"]) + assert data["error"]["type"] == "not_ready" + + def test_dev_evaluate_limit(self, env, api): + env.reset(task_id="t0_artifact") + job = self._finished_job(env, api) + for _ in range(2): + assert "error" not in call(env, "dev_evaluate", job_id=job["job_id"])[0] + data, _ = call(env, "dev_evaluate", job_id=job["job_id"]) + assert data["error"]["type"] == "limit_reached" + + def test_evaluation_logs_are_not_readable(self, env, api): + env.reset(task_id="t0_artifact") + job = self._finished_job(env, api) + evaluation, _ = call(env, "dev_evaluate", job_id=job["job_id"]) + data, _ = call(env, "jobs_logs", job_id=evaluation["job_id"]) + assert data["error"]["type"] == "not_allowed" + + @pytest.mark.parametrize( + "logs", + [ + [], + ["POSTTRAIN_SCORE not json"], + [SCORE_MARKER + json.dumps({"score": 1.5})], + [SCORE_MARKER + json.dumps([0.5])], + ], + ) + def test_missing_or_out_of_range_scores_count_as_invalid(self, env, api, logs): + env.reset(task_id="t0_artifact") + job = self._finished_job(env, api) + evaluation, _ = call(env, "dev_evaluate", job_id=job["job_id"]) + api.finish(evaluation["job_id"], logs=logs) + # A missing score line is re-read a few times in case the logs lag. + for _ in range(MAX_RESULT_READS): + data, _ = call(env, "jobs_status", job_id=evaluation["job_id"]) + assert data["result"]["status"] == "invalid" + assert data["result"]["score"] == 0.0 + + +class TestSubmitEpisode: + def test_submit_scores_the_hidden_split_and_ends_the_episode(self, env, api): + env.reset(task_id="t0_arithmetic_format") + best = submit_job(env, flavor="cpu-upgrade", timeout_minutes=5) + api.finish(best["job_id"]) + straggler = submit_job(env, flavor="cpu-upgrade", timeout_minutes=5) + api.jobs[straggler["job_id"]].update(stage="RUNNING", running_secs=30) + api.on_inspect["score"] = { + "stage": "COMPLETED", + "running_secs": 200, + "logs": [score_line(0.8)], + } + + data, obs = call(env, "submit", job_id=best["job_id"]) + + assert data["result"]["score"] == 0.8 + assert obs.done is True + assert obs.reward == 0.8 + score_run = api.runs[-1] + assert score_run["labels"]["openenv-role"] == "score" + assert score_run["env"]["POSTTRAIN_SPLIT"] == "test" + assert score_run["env"]["POSTTRAIN_SEED"] == str( + posttrain_environment.TEST_SEED + ) + assert api.jobs[straggler["job_id"]]["cancel_requested"] + end = obs.metadata["episode_end"] + assert end["reason"] == "submitted" + assert end["orphaned_jobs"] == [] + # The grader's evaluation is not charged to the agent's budget. + spend = obs.metadata["service_spend"] + assert spend["settled"]["usd"] == pytest.approx((2 + 1) * 500 / 1e6) + assert spend["final"] is True + + def test_artifact_task_expects_the_episode_nonce(self, env, api): + obs = env.reset(task_id="t0_artifact") + job = self._finished(env, api) + call(env, "dev_evaluate", job_id=job["job_id"]) + assert api.runs[-1]["env"]["POSTTRAIN_EXPECTED"] in obs.metadata["prompt"] + + def _finished(self, env, api): + job = submit_job(env) + api.finish(job["job_id"]) + return job + + def test_tools_after_the_episode_ends_are_refused(self, env, api): + env.reset(task_id="t0_artifact") + job = self._finished(env, api) + api.on_inspect["score"] = { + "stage": "COMPLETED", + "running_secs": 10, + "logs": [score_line(1.0)], + } + call(env, "submit", job_id=job["job_id"]) + data, obs = call(env, "budget") + assert data["error"]["type"] == "episode_over" + assert obs.done is True + + def test_failed_score_job_gives_zero_reward(self, env, api): + env.reset(task_id="t0_artifact") + job = self._finished(env, api) + api.on_inspect["score"] = {"stage": "ERROR", "running_secs": 10, "logs": []} + data, obs = call(env, "submit", job_id=job["job_id"]) + assert data["result"] == {"status": "failed", "score": 0.0} + assert obs.reward == 0.0 + + def test_max_steps_ends_the_episode_and_cancels_jobs(self, api): + env = PostTrainEnvironment( + api=api, max_steps=2, poll_s=0.0, cancel_timeout_s=0.01 + ) + env.reset(task_id="t0_artifact") + job = submit_job(env) + _, obs = call(env, "budget") + assert obs.done is True + assert obs.reward == 0.0 + assert obs.metadata["episode_end"]["reason"] == "max_steps" + assert api.jobs[job["job_id"]]["stage"] == "CANCELED" + + +class TestEpisodeBoundaries: + def test_reset_cancels_the_previous_episodes_jobs(self, env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env) + env.reset(task_id="t0_artifact") + assert api.jobs[job["job_id"]]["stage"] == "CANCELED" + + def test_reset_refuses_while_a_cancel_is_unconfirmed(self, env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env) + api.jobs[job["job_id"]]["stage"] = "RUNNING" + api.cancel_confirms = False + with pytest.raises(RuntimeError, match="did not confirm cancelling"): + env.reset(task_id="t0_artifact") + + +def test_websocket_path_ends_the_episode_with_the_score(api): + """`step_async` is what the WebSocket server calls; it must end episodes too.""" + import asyncio + + env = PostTrainEnvironment(api=api, poll_s=0.0, cancel_timeout_s=0.01) + env.reset(task_id="t0_artifact") + + async def run(): + job = await env.step_async( + CallToolAction( + tool_name="jobs_submit", + arguments={ + "script": "print(1)", + "flavor": "cpu-basic", + "timeout_minutes": 5, + }, + ) + ) + job_id = job.result.data["job_id"] + api.finish(job_id) + api.on_inspect["score"] = { + "stage": "COMPLETED", + "running_secs": 10, + "logs": [score_line(1.0)], + } + return await env.step_async( + CallToolAction(tool_name="submit", arguments={"job_id": job_id}) + ) + + obs = asyncio.run(run()) + assert obs.done is True + assert obs.reward == 1.0 + env.close() + + +def http_error(status=500): + import httpx + from huggingface_hub.errors import HfHubHTTPError + + request = httpx.Request("GET", "https://huggingface.co/api/jobs/x") + return HfHubHTTPError("boom", response=httpx.Response(status, request=request)) + + +class TestFailureHandling: + """Regressions for HF Jobs behaving badly: lagging logs, failed calls, lost cancels.""" + + def _finished(self, env, api): + job = submit_job(env) + api.finish(job["job_id"]) + return job + + def test_score_line_that_arrives_late_is_still_read(self, env, api): + env.reset(task_id="t0_artifact") + job = self._finished(env, api) + evaluation, _ = call(env, "dev_evaluate", job_id=job["job_id"]) + api.finish(evaluation["job_id"], logs=[]) + data, _ = call(env, "jobs_status", job_id=evaluation["job_id"]) + assert data["state"] == "succeeded" + assert "result" not in data + api.jobs[evaluation["job_id"]]["logs"] = [score_line(0.9)] + data, _ = call(env, "jobs_status", job_id=evaluation["job_id"]) + assert data["result"]["score"] == 0.9 + + def test_job_without_a_duration_is_charged_its_reservation(self, env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env, timeout_minutes=10) + api.finish(job["job_id"], running_secs=None) + data, _ = call(env, "jobs_status", job_id=job["job_id"]) + assert data["cost_usd"] == pytest.approx(10 * CPU_BASIC / 1e6) + + def test_job_cancelled_before_it_started_costs_nothing(self, env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env, timeout_minutes=10) + data, _ = call(env, "jobs_cancel", job_id=job["job_id"]) + assert data["state"] == "cancelled" + assert data["cost_usd"] == 0.0 + + def test_a_failing_cancel_call_does_not_stop_the_others( + self, env, api, monkeypatch + ): + env.reset(task_id="t0_artifact") + first, second = submit_job(env), submit_job(env) + cancel = api.cancel_job + + def flaky_cancel(*, job_id, namespace): + if job_id == first["job_id"]: + raise http_error(409) + cancel(job_id=job_id, namespace=namespace) + + monkeypatch.setattr(api, "cancel_job", flaky_cancel) + with pytest.raises(RuntimeError, match=first["job_id"]): + env.reset(task_id="t0_artifact") + assert api.jobs[second["job_id"]]["stage"] == "CANCELED" + + def test_orphans_are_retried_until_hf_confirms_them(self, env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env) + api.jobs[job["job_id"]]["stage"] = "RUNNING" + api.cancel_confirms = False + for _ in range(2): + with pytest.raises(RuntimeError, match="did not confirm cancelling"): + env.reset(task_id="t0_artifact") + api.cancel_confirms = True + env.reset(task_id="t0_artifact") + assert api.jobs[job["job_id"]]["stage"] == "CANCELED" + + def test_submit_retry_after_an_hf_error_reuses_the_score_job( + self, env, api, monkeypatch + ): + env.reset(task_id="t0_artifact") + job = self._finished(env, api) + inspect = api.inspect_job + failures = {"left": 1} + + def flaky_inspect(*, job_id, namespace): + if api.jobs[job_id]["role"] == "score" and failures["left"]: + failures["left"] -= 1 + raise http_error(503) + return inspect(job_id=job_id, namespace=namespace) + + monkeypatch.setattr(api, "inspect_job", flaky_inspect) + api.on_inspect["score"] = { + "stage": "COMPLETED", + "running_secs": 10, + "logs": [score_line(1.0)], + } + data, obs = call(env, "submit", job_id=job["job_id"]) + assert data == { + "error": { + "type": "hf_error", + "message": "HF Jobs request failed (HTTP 503)", + } + } + assert obs.done is False + data, obs = call(env, "submit", job_id=job["job_id"]) + assert data["result"]["score"] == 1.0 + assert obs.done is True + assert ( + sum(1 for run in api.runs if run["labels"]["openenv-role"] == "score") == 1 + ) + + def test_unexpected_errors_are_not_shown_to_the_agent(self, env, api, monkeypatch): + env.reset(task_id="t0_artifact") + + def broken(**kwargs): + raise ConnectionError("https://internal.example/secret-path") + + monkeypatch.setattr(api, "run_uv_job", broken) + data = submit_job(env) + assert data == { + "error": { + "type": "internal_error", + "message": "The environment hit an internal error", + } + } + + +def test_non_object_metrics_are_dropped(env, api): + env.reset(task_id="t0_artifact") + job = submit_job(env) + api.finish(job["job_id"]) + evaluation, _ = call(env, "dev_evaluate", job_id=job["job_id"]) + api.finish(evaluation["job_id"], logs=[score_line(0.5, metrics=[1, 2])]) + data, _ = call(env, "jobs_status", job_id=evaluation["job_id"]) + assert data["result"]["score"] == 0.5 + assert data["result"]["metrics"] == {} From f705f734af7936502a17f9762fa1c94f1ea953e9 Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Sat, 17 Oct 2026 03:24:10 +0530 Subject: [PATCH 04/24] Add posttrain_env to the docs --- docs/source/_toctree.yml | 2 + docs/source/environments.md | 7 ++ docs/source/environments/posttrain.md | 137 ++++++++++++++++++++++++++ 3 files changed, 146 insertions(+) create mode 100644 docs/source/environments/posttrain.md diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index fafe6faca5..1950f75ad5 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -153,6 +153,8 @@ title: Agent World Model - local: environments/pelican_svg title: Pelican SVG + - local: environments/posttrain + title: Post-training - local: environments/sophistry_bench_sprint title: Sophistry Bench Sprint title: Environments diff --git a/docs/source/environments.md b/docs/source/environments.md index efee769cd3..5c3118080b 100644 --- a/docs/source/environments.md +++ b/docs/source/environments.md @@ -287,6 +287,13 @@ The OpenEnv community has built a catalog of ready-to-run environments that cove 🤗 HF +
+
Post-training
+

posttrain_env lets an agent post-train a model on HF Jobs through a broker that holds the credentials and enforces a per-rollout budget. A grader-only evaluation on a hidden split is the reward. Prototype for RFC 014.

+
+ 📄 Docs +
+
Sophistry Bench Sprint

sophistry_bench_sprint_env is a single-turn advocacy reward-hacking environment on QuALITY passages: the policy defends an assigned answer and the reward proxy peaks at 8 <claim> tags, with four weight-0 canaries that detect format hacking.

diff --git a/docs/source/environments/posttrain.md b/docs/source/environments/posttrain.md new file mode 100644 index 0000000000..7e77dfc4ab --- /dev/null +++ b/docs/source/environments/posttrain.md @@ -0,0 +1,137 @@ + +# Post-training Environment + +`posttrain_env` is an MCP environment where the agent post-trains a model on +[HF Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs). It is the +prototype for [RFC 014: Environment Services](https://github.com/huggingface/OpenEnv/blob/main/rfcs/014-environment-services.md). + +The agent never holds a credential. It submits training scripts through MCP +tools, and the server's broker starts them on HF Jobs with its own token. Each +job's worst-case cost is reserved against a per-rollout budget before it starts +and settled at the real cost when it ends. `dev_evaluate` scores a checkpoint +on a public dev split. `submit` scores it on a hidden split in a grader-only +job, and that score is the reward. + +## Tools + +| Tool | What it does | +|---|---| +| `budget()` | Remaining budget, allowed hardware with its price, jobs and dev evaluations left | +| `jobs_submit(script, flavor, timeout_minutes, dependencies=None, idempotency_key=None)` | Run a Python script as an HF Job, like `hf jobs uv run`. Reserves `price/min × timeout` first | +| `jobs_status(job_id)` | State, cost once settled, artifact files of a succeeded job, result of a finished evaluation | +| `jobs_wait(job_id, timeout_s=60)` | Wait up to 300 s for a job to finish | +| `jobs_logs(job_id, tail=100)` | Last lines of one of your own jobs' logs | +| `jobs_cancel(job_id)` | Cancel a job; it settles for the minutes it ran | +| `dev_evaluate(job_id)` | Score a succeeded job's artifact on the dev split, as a new job charged to your budget | +| `submit(job_id)` | Final answer: cancels your other jobs, scores the artifact on the hidden split, ends the episode | + +Errors come back as data, with a type the agent can act on: `budget_exhausted`, +`flavor_not_allowed`, `timeout_not_allowed`, `limit_reached`, `not_ready`, +`unknown_job`, `not_allowed`, `episode_over` or `hf_error`. + +## How a job runs + +Agent scripts run with `HfApi.run_uv_job`, following its usual behaviour: the +script is uploaded to the namespace's private `jobs-artifacts` bucket, and the +job's folder there is mounted read-write at `/data`. Anything the job writes +under `/data/outputs/` is its artifact: + +``` +openenv-artifact:////outputs +``` + +Evaluations run the task's evaluator script as their own job with that folder +mounted read-only at `/model`. The evaluator prints one `POSTTRAIN_SCORE {...}` +line. A missing line, or a score outside [0, 1], scores 0 as `invalid`. + +Jobs are started with an explicit flavor, namespace, timeout and labels, and +with no secrets. The broker never passes its own token into a job: a job holding +a "Start and manage Jobs" token could start further jobs that skip the budget. +It also never shells out to `hf jobs uv run`, whose script header can pull the +caller's `HF_TOKEN` into the job. + +## Budget and spend + +Money is tracked in micro-USD from the prices `list_jobs_hardware` reports. +A job settles at its price per minute times its running minutes, rounded up to +whole minutes. Grader evaluations are not charged to the agent. + +Every observation carries the episode's spend, so a trainer can pass it to a +cost-aware sampler (RFC 013) as `costs`: + +```python +obs.metadata["service_spend"] +# {"version": 1, +# "settled": {"usd": 0.000334, "gpu_s": 0.0, "jobs": 1, "calls": 1}, +# "reserved": {"usd": 0.0}, +# "final": True} +``` + +`jobs` counts agent jobs and `calls` counts dev evaluations. `final` is false +while a job is still running or a cancellation was never confirmed. + +## Episode end + +An episode ends when the agent calls `submit`, after `max_steps` tool calls +(default 100) or after the task's time limit. The last two give reward 0. +Ending the episode cancels every live job. `reset` cancels the previous +episode's jobs first and raises if HF Jobs does not confirm a cancellation, +since that job may still be running and billing. + +`submit` waits for the score job, so it can take several minutes. `reset` +reports `score_timeout_s`; set the client's `message_timeout_s` above it. + +## Tasks + +| Task | What the agent does | Hardware | Budget | +|---|---|---|---| +| `t0_artifact` | Write the episode's nonce to `/data/outputs/answer.txt`. Checks the plumbing | `cpu-basic` | $0.05 | +| `t0_arithmetic_format` | Fine-tune `HuggingFaceTB/SmolLM2-135M-Instruct` to answer two-digit addition as `#### {a+b}`. Reward is exact-match accuracy on a hidden test split | `cpu-upgrade`, `t4-small`, `a10g-small` | $3.00 | + +## Running the server + +```bash +export HF_TOKEN=... # the broker's token, see below +cd envs/posttrain_env +uv run --project . server +``` + +Configuration, all read by the server only: + +| Variable | Meaning | +|---|---| +| `HF_TOKEN` | Broker token. A fine-grained token with "Start and manage Jobs" plus write access to `/jobs-artifacts`. A Jobs-only token is refused when the script is uploaded | +| `POSTTRAIN_NAMESPACE` | Namespace jobs run and bill in. Defaults to the token's owner | +| `POSTTRAIN_RESOURCE_GROUP_ID` | Resource Group to bill. Its Jobs spend limit caps total spend even if the ledger is wrong | +| `POSTTRAIN_TEST_SEED` | Seed of the hidden test split. Random per process if unset, so scores are only comparable within one server process | + +Run the server where its token is not shared with the agent, for example a +container you control. Do not deploy it as a public Space: a Space gives no +isolation guarantee for the credential it holds. + +## Client + +```python +from posttrain_env import PostTrainEnv + +with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync() as env: + result = env.reset(task_id="t0_artifact") + print(result.observation.metadata["prompt"]) + job = env.call_tool( + "jobs_submit", + script=open("train.py").read(), + flavor="cpu-basic", + timeout_minutes=5, + ) + env.call_tool("jobs_wait", job_id=job["job_id"], timeout_s=300) + print(env.call_tool("submit", job_id=job["job_id"])) +``` + +## Tests + +```bash +PYTHONPATH=src:envs uv run pytest tests/envs/test_posttrain_environment.py -v +``` + +The tests replace HF Jobs with an in-memory fake, so they make no network calls +and spend nothing. From a77ae5eee743907c5619c2f5f05422d886e2ba21 Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Sat, 17 Oct 2026 14:35:46 +0530 Subject: [PATCH 05/24] Score only the checkpoint's weights in the posttrain_env evaluator --- docs/source/environments/posttrain.md | 7 ++- envs/posttrain_env/README.md | 7 ++- envs/posttrain_env/server/broker.py | 3 +- .../eval_scripts/t0_arithmetic_format_eval.py | 60 +++++++++++++------ .../server/posttrain_environment.py | 10 +++- envs/posttrain_env/server/tasks.py | 19 +++--- rfcs/014-environment-services.md | 2 +- tests/envs/test_posttrain_environment.py | 2 +- 8 files changed, 74 insertions(+), 36 deletions(-) diff --git a/docs/source/environments/posttrain.md b/docs/source/environments/posttrain.md index 7e77dfc4ab..ae606d1d58 100644 --- a/docs/source/environments/posttrain.md +++ b/docs/source/environments/posttrain.md @@ -43,6 +43,11 @@ openenv-artifact:////outputs Evaluations run the task's evaluator script as their own job with that folder mounted read-only at `/model`. The evaluator prints one `POSTTRAIN_SCORE {...}` line. A missing line, or a score outside [0, 1], scores 0 as `invalid`. +Evaluators take only weights from the artifact, loaded as safetensors. The +tokenizer, chat template, config and generation settings come from the base +model at a pinned revision, because a chat template is a program that sees the +question and could write the answer into the prompt. Each evaluator pins its +dependencies in its PEP 723 header, with an `exclude-newer` date for the rest. Jobs are started with an explicit flavor, namespace, timeout and labels, and with no secrets. The broker never passes its own token into a job: a job holding @@ -103,7 +108,7 @@ Configuration, all read by the server only: | `HF_TOKEN` | Broker token. A fine-grained token with "Start and manage Jobs" plus write access to `/jobs-artifacts`. A Jobs-only token is refused when the script is uploaded | | `POSTTRAIN_NAMESPACE` | Namespace jobs run and bill in. Defaults to the token's owner | | `POSTTRAIN_RESOURCE_GROUP_ID` | Resource Group to bill. Its Jobs spend limit caps total spend even if the ledger is wrong | -| `POSTTRAIN_TEST_SEED` | Seed of the hidden test split. Random per process if unset, so scores are only comparable within one server process | +| `POSTTRAIN_TEST_SEED` | Seed of the hidden test split. Set it for any run whose scores you compare: if unset it is random per process (the server logs a warning), so scores are only comparable within one server process. Keep it secret from the agent | Run the server where its token is not shared with the agent, for example a container you control. Do not deploy it as a public Space: a Space gives no diff --git a/envs/posttrain_env/README.md b/envs/posttrain_env/README.md index 0cbead655a..80a5dd53f6 100644 --- a/envs/posttrain_env/README.md +++ b/envs/posttrain_env/README.md @@ -42,6 +42,11 @@ openenv-artifact:////outputs Evaluations run the task's evaluator script as their own job with that folder mounted read-only at `/model`. The evaluator prints one `POSTTRAIN_SCORE {...}` line. A missing line, or a score outside [0, 1], scores 0 as `invalid`. +Evaluators take only weights from the artifact, loaded as safetensors. The +tokenizer, chat template, config and generation settings come from the base +model at a pinned revision, because a chat template is a program that sees the +question and could write the answer into the prompt. Each evaluator pins its +dependencies in its PEP 723 header, with an `exclude-newer` date for the rest. Jobs are started with an explicit flavor, namespace, timeout and labels, and with no secrets. The broker never passes its own token into a job: a job holding @@ -102,7 +107,7 @@ Configuration, all read by the server only: | `HF_TOKEN` | Broker token. A fine-grained token with "Start and manage Jobs" plus write access to `/jobs-artifacts`. A Jobs-only token is refused when the script is uploaded | | `POSTTRAIN_NAMESPACE` | Namespace jobs run and bill in. Defaults to the token's owner | | `POSTTRAIN_RESOURCE_GROUP_ID` | Resource Group to bill. Its Jobs spend limit caps total spend even if the ledger is wrong | -| `POSTTRAIN_TEST_SEED` | Seed of the hidden test split. Random per process if unset, so scores are only comparable within one server process | +| `POSTTRAIN_TEST_SEED` | Seed of the hidden test split. Set it for any run whose scores you compare: if unset it is random per process (the server logs a warning), so scores are only comparable within one server process. Keep it secret from the agent | Run the server where its token is not shared with the agent, for example a container you control. Do not deploy it as a public Space: a Space gives no diff --git a/envs/posttrain_env/server/broker.py b/envs/posttrain_env/server/broker.py index 5cdddb6600..79fa512b0a 100644 --- a/envs/posttrain_env/server/broker.py +++ b/envs/posttrain_env/server/broker.py @@ -270,7 +270,6 @@ def evaluate( script_path: Path, flavor: str, timeout_minutes: int, - dependencies: list[str], env: dict[str, str], ) -> Job: """Run an evaluator job on `source`'s artifact, mounted read-only at `/model`.""" @@ -294,7 +293,7 @@ def evaluate( flavor=flavor, timeout_minutes=timeout_minutes, reservation=reservation, - dependencies=dependencies, + dependencies=[], env=env, volumes=[model], ) diff --git a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py index 9815b05f2a..a8d9cc8c88 100644 --- a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py +++ b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py @@ -1,6 +1,9 @@ # /// script # requires-python = ">=3.10" -# dependencies = ["torch", "transformers"] +# dependencies = ["torch==2.14.1", "transformers==5.19.0"] +# +# [tool.uv] +# exclude-newer = "2026-10-10T00:00:00Z" # /// # SPDX-License-Identifier: BSD-3-Clause @@ -8,6 +11,11 @@ The split is drawn from `POSTTRAIN_SEED`. The dev seed is public; the test seed is held by the environment server and only reaches score jobs. + +Only the weights come from the checkpoint. The tokenizer, chat template, model +config and generation settings are the base model's, at a pinned revision: a +chat template is a program that sees the question, so the agent's own template +could write the answer into the prompt. """ import json @@ -15,6 +23,16 @@ import random import re +import torch +from transformers import ( + AutoConfig, + AutoModelForCausalLM, + AutoTokenizer, + GenerationConfig, +) + +BASE_MODEL = "HuggingFaceTB/SmolLM2-135M-Instruct" +BASE_REVISION = "12fd25f77366fa6b3b4b768ec3050bf629380bac" N_PROMPTS = {"dev": 50, "test": 100} @@ -22,29 +40,35 @@ def emit(result): print("POSTTRAIN_SCORE " + json.dumps(result), flush=True) +def invalid(feedback): + emit({"status": "invalid", "score": 0.0, "feedback": feedback}) + raise SystemExit(0) + + split = os.environ["POSTTRAIN_SPLIT"] rng = random.Random(int(os.environ["POSTTRAIN_SEED"])) pairs = [(rng.randint(0, 99), rng.randint(0, 99)) for _ in range(N_PROMPTS[split])] -try: - import torch - from transformers import AutoModelForCausalLM, AutoTokenizer +tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, revision=BASE_REVISION) +config = AutoConfig.from_pretrained(BASE_MODEL, revision=BASE_REVISION) +generation_config = GenerationConfig.from_pretrained(BASE_MODEL, revision=BASE_REVISION) +generation_config.update(max_new_tokens=16, do_sample=False) - tokenizer = AutoTokenizer.from_pretrained("/model") - # The checkpoint is agent output: never unpickle it or run its code. - model = AutoModelForCausalLM.from_pretrained( - "/model", torch_dtype=torch.float32, use_safetensors=True +try: + # The checkpoint is agent output: never unpickle it or run its code, and + # ignore its config so the weights must fit the base architecture. + model, info = AutoModelForCausalLM.from_pretrained( + "/model", + config=config, + dtype=torch.float32, + use_safetensors=True, + output_loading_info=True, ) - model.eval() except Exception as e: # Report a bad checkpoint as a score, don't crash. - emit( - { - "status": "invalid", - "score": 0.0, - "feedback": f"cannot load /model: {type(e).__name__}", - } - ) - raise SystemExit(0) + invalid(f"cannot load /model: {type(e).__name__}") +if info["missing_keys"] or info["mismatched_keys"]: + invalid(f"/model weights do not match {BASE_MODEL}") +model.eval() correct = formatted = 0 for a, b in pairs: @@ -53,7 +77,7 @@ def emit(result): messages, add_generation_prompt=True, return_tensors="pt", return_dict=True ) with torch.no_grad(): - out = model.generate(**inputs, max_new_tokens=16, do_sample=False) + out = model.generate(**inputs, generation_config=generation_config) reply = tokenizer.decode( out[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ).strip() diff --git a/envs/posttrain_env/server/posttrain_environment.py b/envs/posttrain_env/server/posttrain_environment.py index 3810d59463..d20490a2f6 100644 --- a/envs/posttrain_env/server/posttrain_environment.py +++ b/envs/posttrain_env/server/posttrain_environment.py @@ -40,7 +40,14 @@ logger = logging.getLogger(__name__) -TEST_SEED = int(os.environ.get("POSTTRAIN_TEST_SEED") or secrets.randbelow(2**31)) +if os.environ.get("POSTTRAIN_TEST_SEED"): + TEST_SEED = int(os.environ["POSTTRAIN_TEST_SEED"]) +else: + TEST_SEED = secrets.randbelow(2**31) + logger.warning( + "POSTTRAIN_TEST_SEED is not set: the hidden test split is random for this " + "process, so scores are not comparable with other server processes" + ) TOOL_TIMEOUT_S = 120.0 MAX_LOG_CHARS = 20_000 @@ -457,7 +464,6 @@ def _evaluate(self, job_id: str, role: str): script_path=task.eval_script_path, flavor=task.eval_flavor, timeout_minutes=task.eval_timeout_minutes, - dependencies=list(task.eval_dependencies), env={ "POSTTRAIN_SPLIT": split, "POSTTRAIN_SEED": str(seed), diff --git a/envs/posttrain_env/server/tasks.py b/envs/posttrain_env/server/tasks.py index 05c0ee1de2..431bb925a3 100644 --- a/envs/posttrain_env/server/tasks.py +++ b/envs/posttrain_env/server/tasks.py @@ -5,7 +5,8 @@ Each task tells the agent what to train, which HF Jobs hardware it may use and how much it may spend. The evaluator script runs as its own job: the agent's checkpoint is mounted read-only at `/model`, and the job prints one -`POSTTRAIN_SCORE {...}` line that the broker parses. +`POSTTRAIN_SCORE {...}` line that the broker parses. Evaluators pin their +dependencies in their own PEP 723 header, so scores don't drift with releases. """ from dataclasses import dataclass @@ -40,8 +41,6 @@ class Task: Hardware flavor for dev and score evaluations. eval_timeout_minutes (`int`): Timeout for one evaluation job. - eval_dependencies (`tuple[str, ...]`): - Packages the evaluator needs, passed to `uv run --with`. dev_seed (`int`): Seed of the public dev split. max_episode_minutes (`int`): @@ -58,7 +57,6 @@ class Task: eval_script: str eval_flavor: str eval_timeout_minutes: int - eval_dependencies: tuple[str, ...] = () dev_seed: int = 0 max_episode_minutes: int = 240 @@ -96,11 +94,13 @@ def eval_script_path(self) -> Path: prompt=( "Fine-tune `HuggingFaceTB/SmolLM2-135M-Instruct` so that it answers " "two-digit addition in an exact format. The evaluator sends the chat " - "message `What is {{a}} + {{b}}?` (0 <= a, b <= 99) through the " - "tokenizer's chat template with `add_generation_prompt=True`, decodes " - "up to 16 new tokens greedily, and counts an answer as correct only if " - "the stripped reply is exactly `#### {{a+b}}`. Save the full model and " - "tokenizer with `save_pretrained` to `/data/outputs/`. The reward is " + "message `What is {{a}} + {{b}}?` (0 <= a, b <= 99) through the base " + "model's chat template with `add_generation_prompt=True`, decodes up to " + "16 new tokens greedily, and counts an answer as correct only if the " + "stripped reply is exactly `#### {{a+b}}`. Only your weights are used: " + "the tokenizer, chat template, config and generation settings are the " + "base model's, so keep its architecture and vocabulary. Save the model " + "with `save_pretrained` (safetensors) to `/data/outputs/`. The reward is " "the accuracy on a hidden test split; `dev_evaluate` scores the public " "dev split (seed 1234). " + OUTPUTS_NOTE ), @@ -112,7 +112,6 @@ def eval_script_path(self) -> Path: eval_script="t0_arithmetic_format_eval.py", eval_flavor="cpu-upgrade", eval_timeout_minutes=20, - eval_dependencies=("torch", "transformers"), dev_seed=1234, ), } diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md index a1898fb728..e891025c6c 100644 --- a/rfcs/014-environment-services.md +++ b/rfcs/014-environment-services.md @@ -92,7 +92,7 @@ Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. **Jobs.** Tools return a handle. States are `queued → running → succeeded | failed | cancelled`, plus `cancelling` while a cancel is pending and `orphaned` when the provider does not confirm one in time. An orphaned job is settled at its full reservation, keeps `final: false` and blocks the next `reset` with an error, because it may still be running and billing. `reset`, `close`, `submit`, the step limit and the time limit cancel the episode's live jobs. Submissions take an idempotency key; a retry with the same key returns the same job and makes no second reservation. -**Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; anything else, including a score outside [0, 1], counts as `invalid` with score 0. +**Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; anything else, including a score outside [0, 1], counts as `invalid` with score 0. The evaluator takes only data from the artifact, never code or anything that shapes the evaluator's own input: `t0_arithmetic_format` loads safetensors weights into the base model's config, with the base tokenizer and chat template at a pinned revision, since a chat template could compute the answer into the prompt. Its dependencies are pinned too, so scores don't change with a new release. **Artifacts.** An artifact is the output folder of a succeeded job, exposed as `openenv-artifact:////outputs` with each file's path, size and content hash. Graders mount the folder read-only; nothing is copied through the agent. diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py index e662b39fec..877ae623ad 100644 --- a/tests/envs/test_posttrain_environment.py +++ b/tests/envs/test_posttrain_environment.py @@ -308,7 +308,7 @@ def test_dev_evaluate_mounts_the_artifact_read_only_and_reads_the_score( ) assert run["env"]["POSTTRAIN_SPLIT"] == "dev" assert run["env"]["POSTTRAIN_SEED"] == "1234" - assert run["dependencies"] == ["torch", "transformers"] + assert run["dependencies"] == [] assert run["labels"]["openenv-role"] == "dev_eval" assert "POSTTRAIN_SCORE" in run["script"] From 0bd799196c9b46916eee9ce1dee3ef99c8eeb99b Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Mon, 19 Oct 2026 00:19:03 +0530 Subject: [PATCH 06/24] Keep spend out of sampling weights in RFC 014 --- docs/source/environments/posttrain.md | 4 ++-- envs/posttrain_env/README.md | 4 ++-- rfcs/014-environment-services.md | 6 +++--- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/environments/posttrain.md b/docs/source/environments/posttrain.md index ae606d1d58..d8203b5810 100644 --- a/docs/source/environments/posttrain.md +++ b/docs/source/environments/posttrain.md @@ -61,8 +61,8 @@ Money is tracked in micro-USD from the prices `list_jobs_hardware` reports. A job settles at its price per minute times its running minutes, rounded up to whole minutes. Grader evaluations are not charged to the agent. -Every observation carries the episode's spend, so a trainer can pass it to a -cost-aware sampler (RFC 013) as `costs`: +Every observation carries the episode's spend, for accounting and for planning +budgets and concurrency. It is not a sampling weight (RFC 013): ```python obs.metadata["service_spend"] diff --git a/envs/posttrain_env/README.md b/envs/posttrain_env/README.md index 80a5dd53f6..b8942ad473 100644 --- a/envs/posttrain_env/README.md +++ b/envs/posttrain_env/README.md @@ -60,8 +60,8 @@ Money is tracked in micro-USD from the prices `list_jobs_hardware` reports. A job settles at its price per minute times its running minutes, rounded up to whole minutes. Grader evaluations are not charged to the agent. -Every observation carries the episode's spend, so a trainer can pass it to a -cost-aware sampler (RFC 013) as `costs`: +Every observation carries the episode's spend, for accounting and for planning +budgets and concurrency. It is not a sampling weight (RFC 013): ```python obs.metadata["service_spend"] diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md index e891025c6c..2e6cf8a270 100644 --- a/rfcs/014-environment-services.md +++ b/rfcs/014-environment-services.md @@ -29,7 +29,7 @@ Environments answer five questions ad hoc: 1. A service contract an environment declares, instead of per-env plumbing. 2. Credentials that stay with a broker; the agent and its jobs hold none. -3. A per-rollout budget that reserves before spending and settles after, published in a stable form for cost-aware samplers (RFC 013). +3. A per-rollout budget that reserves before spending and settles after, published in a stable form so trainers can account for spend and plan budgets and concurrency. 4. Jobs with handles, cancellation at episode end and a defined orphan state. 5. Evaluation split into an agent-callable dev endpoint and a grader-only score. @@ -77,7 +77,7 @@ budget: {usd: 3.0, scope: rollout} **Budget ledger.** Money is integer micro-USD. Before a job starts the broker reserves its worst case, the flavor's per-minute price times the job's timeout, and refuses the job with a typed `budget_exhausted` error if it does not fit. When the job ends the reservation is replaced by the real cost, the per-minute price times the minutes the job ran, rounded up to whole minutes as HF Jobs bills. Budgets are per rollout and set by the orchestrator, so every rollout in a GRPO group faces the same limit. The agent sees its remaining budget as data, never as reward. The ledger is the broker's estimate; the provider's bill is authoritative, and a provider-side cap is the backstop (see Credentials). -Every observation carries a stable spend entry. RFC 013's sampler never reads the environment: the trainer reads this entry from the final observation and forwards one value per rollout as `costs` to `update(task, rewards, costs)`. +Every observation carries a stable spend entry. It is for accounting and for planning budgets and concurrency, never a sampling weight: dividing a task's sampling weight by its cost would starve expensive, hard tasks. RFC 013's sampler never reads the environment; the trainer reads this entry from the final observation and can report one value per rollout as `costs` to `update(task, rewards, costs)`. ```python obs.metadata["service_spend"] = { @@ -202,6 +202,6 @@ Also: audit records keyed to `(episode, step, tool_call_id)` as OpenTelemetry Ge ## Related - RFC 009 openenvd (#1053, #1354, #1355): the base for credential custody and the grader zone. -- RFC 013 task sampling (#1256): consumes `service_spend`. +- RFC 013 task sampling (#1256): reports `service_spend` as cost, not as a sampling weight. - RFC 003: the MCP 2026-07-28 amendment. RFC 004: `score` feeds rubrics. RFC 005/006: LLM proxying is the future model-call profile. RFC 008: contract checks for declared services, plus #1357's adversarial panel. RFC 012: separate-verifier precedent. - Issues: #898, #1026, #1232, #1299, #1357, #779. 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[PATCH 08/24] End posttrain_env episodes unscored when the score job fails, and install CPU-only torch in its evaluator --- docs/source/environments/posttrain.md | 12 ++++++++++-- envs/posttrain_env/README.md | 12 ++++++++++-- .../eval_scripts/t0_arithmetic_format_eval.py | 12 ++++++++++++ envs/posttrain_env/server/posttrain_environment.py | 7 +++++-- envs/posttrain_env/server/tasks.py | 2 +- rfcs/014-environment-services.md | 2 +- tests/envs/test_posttrain_environment.py | 13 ++++++++++--- 7 files changed, 49 insertions(+), 11 deletions(-) diff --git a/docs/source/environments/posttrain.md b/docs/source/environments/posttrain.md index d8203b5810..83b6b48a7a 100644 --- a/docs/source/environments/posttrain.md +++ b/docs/source/environments/posttrain.md @@ -78,8 +78,11 @@ while a job is still running or a cancellation was never confirmed. ## Episode end An episode ends when the agent calls `submit`, after `max_steps` tool calls -(default 100) or after the task's time limit. The last two give reward 0. -Ending the episode cancels every live job. `reset` cancels the previous +(default 100) or after the task's time limit. The last two give reward 0. If +the score job itself fails, times out or is cancelled, the episode ends +unscored: `reward` is `None` and `episode_end.reason` is `score_failed`, since +the fault is the grader's. Trainers should drop such a rollout, not count it as +zero. Ending the episode cancels every live job. `reset` cancels the previous episode's jobs first and raises if HF Jobs does not confirm a cancellation, since that job may still be running and billing. @@ -132,6 +135,11 @@ with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync print(env.call_tool("submit", job_id=job["job_id"])) ``` +[`examples/posttrain_env_agent.py`](https://github.com/huggingface/OpenEnv/blob/main/examples/posttrain_env_agent.py) +plays a whole episode with an LLM through any OpenAI-compatible endpoint, +Hugging Face Inference Providers by default. The agent's API key is its own; +it never needs the broker's token. + ## Tests ```bash diff --git a/envs/posttrain_env/README.md b/envs/posttrain_env/README.md index b8942ad473..e809f415ba 100644 --- a/envs/posttrain_env/README.md +++ b/envs/posttrain_env/README.md @@ -77,8 +77,11 @@ while a job is still running or a cancellation was never confirmed. ## Episode end An episode ends when the agent calls `submit`, after `max_steps` tool calls -(default 100) or after the task's time limit. The last two give reward 0. -Ending the episode cancels every live job. `reset` cancels the previous +(default 100) or after the task's time limit. The last two give reward 0. If +the score job itself fails, times out or is cancelled, the episode ends +unscored: `reward` is `None` and `episode_end.reason` is `score_failed`, since +the fault is the grader's. Trainers should drop such a rollout, not count it as +zero. Ending the episode cancels every live job. `reset` cancels the previous episode's jobs first and raises if HF Jobs does not confirm a cancellation, since that job may still be running and billing. @@ -131,6 +134,11 @@ with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync print(env.call_tool("submit", job_id=job["job_id"])) ``` +[`examples/posttrain_env_agent.py`](../../examples/posttrain_env_agent.py) +plays a whole episode with an LLM through any OpenAI-compatible endpoint, +Hugging Face Inference Providers by default. The agent's API key is its own; +it never needs the broker's token. + ## Tests ```bash diff --git a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py index a8d9cc8c88..7b5067a607 100644 --- a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py +++ b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py @@ -4,6 +4,14 @@ # # [tool.uv] # exclude-newer = "2026-10-10T00:00:00Z" +# +# [tool.uv.sources] +# torch = { index = "pytorch-cpu" } +# +# [[tool.uv.index]] +# name = "pytorch-cpu" +# url = "https://download.pytorch.org/whl/cpu" +# explicit = true # /// # SPDX-License-Identifier: BSD-3-Clause @@ -16,6 +24,10 @@ config and generation settings are the base model's, at a pinned revision: a chat template is a program that sees the question, so the agent's own template could write the answer into the prompt. + +The job runs on CPU, so torch comes from the CPU-only index: the default wheel +pulls several GB of CUDA libraries, and that download alone can exceed the +job's timeout. """ import json diff --git a/envs/posttrain_env/server/posttrain_environment.py b/envs/posttrain_env/server/posttrain_environment.py index d20490a2f6..a2e15a0440 100644 --- a/envs/posttrain_env/server/posttrain_environment.py +++ b/envs/posttrain_env/server/posttrain_environment.py @@ -490,9 +490,12 @@ def _submit(self, job_id: str) -> dict: "submitted", self._cancel_timeout_s, self._poll_s ) self._done = True - self._reward = result["score"] + # A score job that did not run to the end is the grader's fault, not the + # agent's: the episode ends unscored, and a missing reward is not zero. + scored = score_job.state == "succeeded" + self._reward = result["score"] if scored else None self._end = { - "reason": "submitted", + "reason": "submitted" if scored else "score_failed", "job_id": score_job.source_job_id, "score_job_id": score_job.job_id, "score": result, diff --git a/envs/posttrain_env/server/tasks.py b/envs/posttrain_env/server/tasks.py index 431bb925a3..dd77f9bfeb 100644 --- a/envs/posttrain_env/server/tasks.py +++ b/envs/posttrain_env/server/tasks.py @@ -111,7 +111,7 @@ def eval_script_path(self) -> Path: max_dev_evals=3, eval_script="t0_arithmetic_format_eval.py", eval_flavor="cpu-upgrade", - eval_timeout_minutes=20, + eval_timeout_minutes=30, dev_seed=1234, ), } diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md index 2e6cf8a270..d58b7615a2 100644 --- a/rfcs/014-environment-services.md +++ b/rfcs/014-environment-services.md @@ -92,7 +92,7 @@ Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. **Jobs.** Tools return a handle. States are `queued → running → succeeded | failed | cancelled`, plus `cancelling` while a cancel is pending and `orphaned` when the provider does not confirm one in time. An orphaned job is settled at its full reservation, keeps `final: false` and blocks the next `reset` with an error, because it may still be running and billing. `reset`, `close`, `submit`, the step limit and the time limit cancel the episode's live jobs. Submissions take an idempotency key; a retry with the same key returns the same job and makes no second reservation. -**Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; anything else, including a score outside [0, 1], counts as `invalid` with score 0. The evaluator takes only data from the artifact, never code or anything that shapes the evaluator's own input: `t0_arithmetic_format` loads safetensors weights into the base model's config, with the base tokenizer and chat template at a pinned revision, since a chat template could compute the answer into the prompt. Its dependencies are pinned too, so scores don't change with a new release. +**Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; anything else, including a score outside [0, 1], counts as `invalid` with score 0. The evaluator takes only data from the artifact, never code or anything that shapes the evaluator's own input: `t0_arithmetic_format` loads safetensors weights into the base model's config, with the base tokenizer and chat template at a pinned revision, since a chat template could compute the answer into the prompt. Its dependencies are pinned too, so scores don't change with a new release, and as a CPU job it installs CPU-only torch. A score job that fails, times out or is cancelled ends the episode unscored (`reward` is `None`, `episode_end.reason` is `score_failed`): the fault is the grader's, and RFC 013 treats a missing reward as missing, not zero. **Artifacts.** An artifact is the output folder of a succeeded job, exposed as `openenv-artifact:////outputs` with each file's path, size and content hash. Graders mount the folder read-only; nothing is copied through the agent. diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py index 877ae623ad..9cdf1fa263 100644 --- a/tests/envs/test_posttrain_environment.py +++ b/tests/envs/test_posttrain_environment.py @@ -420,13 +420,20 @@ def test_tools_after_the_episode_ends_are_refused(self, env, api): assert data["error"]["type"] == "episode_over" assert obs.done is True - def test_failed_score_job_gives_zero_reward(self, env, api): + def test_failed_score_job_ends_the_episode_unscored(self, env, api): env.reset(task_id="t0_artifact") job = self._finished(env, api) - api.on_inspect["score"] = {"stage": "ERROR", "running_secs": 10, "logs": []} + # The evaluator printed a score, then the job hit its timeout. + api.on_inspect["score"] = { + "stage": "ERROR", + "running_secs": 10, + "logs": ['POSTTRAIN_SCORE {"score": 1.0}'], + } data, obs = call(env, "submit", job_id=job["job_id"]) assert data["result"] == {"status": "failed", "score": 0.0} - assert obs.reward == 0.0 + assert obs.done is True + assert obs.reward is None + assert obs.metadata["episode_end"]["reason"] == "score_failed" def test_max_steps_ends_the_episode_and_cancels_jobs(self, api): env = PostTrainEnvironment( From d3390659c1418b39a7abf3442ce8fdb672b0e31b Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 01:36:41 +0530 Subject: [PATCH 09/24] Add an LLM agent example for posttrain_env --- examples/posttrain_env_agent.py | 149 ++++++++++++++++++++++++++++++++ 1 file changed, 149 insertions(+) create mode 100644 examples/posttrain_env_agent.py diff --git a/examples/posttrain_env_agent.py b/examples/posttrain_env_agent.py new file mode 100644 index 0000000000..0dbd26c18b --- /dev/null +++ b/examples/posttrain_env_agent.py @@ -0,0 +1,149 @@ +# SPDX-License-Identifier: BSD-3-Clause + +""" +An LLM agent that plays one `posttrain_env` episode through its MCP tools. + +The agent reads the task prompt, writes and submits training scripts to HF +Jobs through the environment's broker, checks them with `dev_evaluate`, and +calls `submit`, which ends the episode with the hidden-split score as reward. +The agent never holds the broker's token: only the server does. + +Usage: + + # Terminal 1: the server, with the broker token (see envs/posttrain_env/README.md) + docker build -t posttrain-env -f envs/posttrain_env/server/Dockerfile envs/posttrain_env + docker run --rm -p 8000:8000 -e HF_TOKEN -e POSTTRAIN_TEST_SEED posttrain-env + + # Terminal 2: the agent, with any OpenAI-compatible endpoint. + # The default is Hugging Face Inference Providers, with an HF token + # allowed to call them; keep it separate from the broker's token. + export OPENAI_API_KEY=... + python examples/posttrain_env_agent.py --task t0_arithmetic_format \ + --model Qwen/Qwen3.8-2.4T-A95B --transcript episode.json +""" + +import argparse +import json +import os + +from openai import OpenAI +from openenv.core.env_server.mcp_types import CallToolAction +from posttrain_env import PostTrainEnv + +SYSTEM_PROMPT = ( + "You are an ML engineer working through tools. Each tool call is one step " + "of a limited episode, and jobs spend a limited budget, so plan before you " + "submit a job and read its logs when it fails. Calling `submit` ends the " + "episode." +) +MAX_RESULT_CHARS = 20_000 + + +def tool_specs(tools): + return [ + { + "type": "function", + "function": { + "name": t.name, + "description": t.description, + "parameters": t.input_schema, + }, + } + for t in tools + ] + + +def call(env, name, arguments): + """Run one tool call; return the text for the model and the step result.""" + result = env.step(CallToolAction(tool_name=name, arguments=arguments)) + obs = result.observation + if obs.error is not None: + return f"Error: {obs.error.message}", result + data = obs.result + if isinstance(data, dict) and "data" in data: + data = data["data"] + return json.dumps(data)[:MAX_RESULT_CHARS], result + + +def main(): + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--task", default="t0_artifact") + parser.add_argument("--model", default="Qwen/Qwen3.8-2.4T-A95B") + parser.add_argument( + "--base-url", default=os.environ.get("POSTTRAIN_URL", "http://localhost:8000") + ) + parser.add_argument( + "--endpoint", + default=os.environ.get("ENDPOINT_URL", "https://router.huggingface.co/v1"), + ) + parser.add_argument("--max-turns", type=int, default=60) + parser.add_argument("--transcript", help="write the messages and outcome as JSON") + args = parser.parse_args() + + llm = OpenAI(base_url=args.endpoint, api_key=os.environ["OPENAI_API_KEY"]) + usage = {"prompt_tokens": 0, "completion_tokens": 0} + + # `submit` blocks while the score job runs, so the client waits for the + # server's longest tool call instead of the default message timeout. + with PostTrainEnv(base_url=args.base_url, message_timeout_s=3600).sync() as env: + reset = env.reset(task_id=args.task) + tools = tool_specs(env.list_tools()) + messages = [ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": reset.observation.metadata["prompt"]}, + ] + result = reset + for turn in range(1, args.max_turns + 1): + response = llm.chat.completions.create( + model=args.model, messages=messages, tools=tools, max_tokens=8192 + ) + usage["prompt_tokens"] += response.usage.prompt_tokens + usage["completion_tokens"] += response.usage.completion_tokens + message = response.choices[0].message + messages.append(message.model_dump(exclude_none=True)) + if not message.tool_calls: + print(f"[{turn}] text: {(message.content or '')[:300]}") + messages.append( + { + "role": "user", + "content": "Use a tool, or call `submit` to finish.", + } + ) + continue + for tool_call in message.tool_calls: + name = tool_call.function.name + try: + arguments = json.loads(tool_call.function.arguments or "{}") + except json.JSONDecodeError as e: + text = f"Error: arguments are not valid JSON ({e})" + else: + print(f"[{turn}] {name} {json.dumps(arguments)[:300]}") + text, result = call(env, name, arguments) + print(f" -> {text[:300]}") + messages.append( + {"role": "tool", "tool_call_id": tool_call.id, "content": text} + ) + if result.done: + break + if result.done: + break + + metadata = result.observation.metadata + outcome = { + "task": args.task, + "model": args.model, + "turns": turn, + "done": result.done, + "reward": result.reward, + "episode_end": metadata.get("episode_end"), + "service_spend": metadata.get("service_spend"), + "llm_usage": usage, + } + print(json.dumps(outcome, indent=2)) + if args.transcript: + with open(args.transcript, "w") as f: + json.dump({"outcome": outcome, "messages": messages}, f, indent=2) + + +if __name__ == "__main__": + main() From 7c4ba3c33d056fae452b59e96285b665c692080b Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 02:47:23 +0530 Subject: [PATCH 10/24] Retry rate-limited LLM calls in the posttrain_env agent example --- examples/posttrain_env_agent.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/posttrain_env_agent.py b/examples/posttrain_env_agent.py index 0dbd26c18b..5f892262f3 100644 --- a/examples/posttrain_env_agent.py +++ b/examples/posttrain_env_agent.py @@ -80,7 +80,10 @@ def main(): parser.add_argument("--transcript", help="write the messages and outcome as JSON") args = parser.parse_args() - llm = OpenAI(base_url=args.endpoint, api_key=os.environ["OPENAI_API_KEY"]) + # Episodes run for an hour or more, so ride out provider rate limits. + llm = OpenAI( + base_url=args.endpoint, api_key=os.environ["OPENAI_API_KEY"], max_retries=10 + ) usage = {"prompt_tokens": 0, "completion_tokens": 0} # `submit` blocks while the score job runs, so the client waits for the From ebf0d15c0e547c6967c133458c54bf19f538bace Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 10:01:05 +0530 Subject: [PATCH 11/24] Run posttrain_env agent jobs without network access --- docs/source/environments/posttrain.md | 52 +- envs/posttrain_env/README.md | 52 +- envs/posttrain_env/pyproject.toml | 4 +- envs/posttrain_env/server/broker.py | 145 +- .../eval_scripts/t0_arithmetic_format_eval.py | 17 +- envs/posttrain_env/server/job_launcher.py | 138 ++ .../server/posttrain_environment.py | 12 +- envs/posttrain_env/server/tasks.py | 60 +- envs/posttrain_env/uv.lock | 1212 +---------------- tests/envs/test_posttrain_environment.py | 168 ++- 10 files changed, 625 insertions(+), 1235 deletions(-) create mode 100644 envs/posttrain_env/server/job_launcher.py diff --git a/docs/source/environments/posttrain.md b/docs/source/environments/posttrain.md index 83b6b48a7a..fa66342d38 100644 --- a/docs/source/environments/posttrain.md +++ b/docs/source/environments/posttrain.md @@ -17,7 +17,7 @@ job, and that score is the reward. | Tool | What it does | |---|---| | `budget()` | Remaining budget, allowed hardware with its price, jobs and dev evaluations left | -| `jobs_submit(script, flavor, timeout_minutes, dependencies=None, idempotency_key=None)` | Run a Python script as an HF Job, like `hf jobs uv run`. Reserves `price/min × timeout` first | +| `jobs_submit(script, flavor, timeout_minutes, dependencies=None, datasets=None, idempotency_key=None)` | Run a Python script as an HF Job, like `hf jobs uv run`, without network access. Reserves `price/min × timeout` first | | `jobs_status(job_id)` | State, cost once settled, artifact files of a succeeded job, result of a finished evaluation | | `jobs_wait(job_id, timeout_s=60)` | Wait up to 300 s for a job to finish | | `jobs_logs(job_id, tail=100)` | Last lines of one of your own jobs' logs | @@ -26,7 +26,8 @@ job, and that score is the reward. | `submit(job_id)` | Final answer: cancels your other jobs, scores the artifact on the hidden split, ends the episode | Errors come back as data, with a type the agent can act on: `budget_exhausted`, -`flavor_not_allowed`, `timeout_not_allowed`, `limit_reached`, `not_ready`, +`flavor_not_allowed`, `timeout_not_allowed`, `invalid_script`, +`invalid_dependency`, `dataset_not_allowed`, `limit_reached`, `not_ready`, `unknown_job`, `not_allowed`, `episode_over` or `hf_error`. ## How a job runs @@ -40,21 +41,50 @@ under `/data/outputs/` is its artifact: openenv-artifact:////outputs ``` -Evaluations run the task's evaluator script as their own job with that folder -mounted read-only at `/model`. The evaluator prints one `POSTTRAIN_SCORE {...}` -line. A missing line, or a score outside [0, 1], scores 0 as `invalid`. -Evaluators take only weights from the artifact, loaded as safetensors. The -tokenizer, chat template, config and generation settings come from the base -model at a pinned revision, because a chat template is a program that sees the -question and could write the answer into the prompt. Each evaluator pins its -dependencies in its PEP 723 header, with an `exclude-newer` date for the rest. - Jobs are started with an explicit flavor, namespace, timeout and labels, and with no secrets. The broker never passes its own token into a job: a job holding a "Start and manage Jobs" token could start further jobs that skip the budget. It also never shells out to `hf jobs uv run`, whose script header can pull the caller's `HF_TOKEN` into the job. +### No network in agent jobs + +A job that can reach the internet can download a model already trained for the +task, or the evaluation's own test data. So agent jobs get their inputs as +read-only mounts and run without network access: + +- The task's base model is mounted at `/base_model`, at a pinned revision. +- Each dataset in `datasets` is mounted at `/datasets/`, at its current + revision. It must be public and ungated, and not on the task's denylist. The + broker looks it up without its token, so the token can't open private repos. +- Packages from `dependencies` and the script's PEP 723 header are installed + before the script runs, from PyPI wheels only (`no-build = true`), so no + package code runs while the job still has network access. URLs and git + dependencies are refused. Other settings in the script's header are ignored. + +HF Jobs has no egress control, and its containers can't create network +namespaces or firewall rules. So the broker sends +[`server/job_launcher.py`](https://github.com/huggingface/OpenEnv/blob/main/envs/posttrain_env/server/job_launcher.py) as the job's script, with the +agent's script embedded. Once uv has installed the packages, the launcher +installs a seccomp filter that refuses every socket except `AF_UNIX`, and +io_uring and cross-process memory access; systemd's `RestrictAddressFamilies=` +works the same way. It then switches to uid 65534 and runs the agent's script +with `HF_HUB_OFFLINE=1`. The filter can't be removed and covers every child +process. If any step fails, the script doesn't run. Training on a single GPU, +DataLoader workers and writing `/data/outputs/` work under the filter; +`torch.distributed` over localhost TCP does not. + +### Evaluations + +Evaluations run the task's evaluator script as their own job with that folder +mounted read-only at `/model`, and the task's base model at `/base_model`. The +evaluator prints one `POSTTRAIN_SCORE {...}` line. A missing line, or a score +outside [0, 1], scores 0 as `invalid`. Evaluators take only weights from the +artifact, loaded as safetensors. The tokenizer, chat template, config and +generation settings come from the base model mount, because a chat template is +a program that sees the question and could write the answer into the prompt. Each evaluator pins its +dependencies in its PEP 723 header, with an `exclude-newer` date for the rest. + ## Budget and spend Money is tracked in micro-USD from the prices `list_jobs_hardware` reports. diff --git a/envs/posttrain_env/README.md b/envs/posttrain_env/README.md index e809f415ba..29e0c3fd8c 100644 --- a/envs/posttrain_env/README.md +++ b/envs/posttrain_env/README.md @@ -16,7 +16,7 @@ job, and that score is the reward. | Tool | What it does | |---|---| | `budget()` | Remaining budget, allowed hardware with its price, jobs and dev evaluations left | -| `jobs_submit(script, flavor, timeout_minutes, dependencies=None, idempotency_key=None)` | Run a Python script as an HF Job, like `hf jobs uv run`. Reserves `price/min × timeout` first | +| `jobs_submit(script, flavor, timeout_minutes, dependencies=None, datasets=None, idempotency_key=None)` | Run a Python script as an HF Job, like `hf jobs uv run`, without network access. Reserves `price/min × timeout` first | | `jobs_status(job_id)` | State, cost once settled, artifact files of a succeeded job, result of a finished evaluation | | `jobs_wait(job_id, timeout_s=60)` | Wait up to 300 s for a job to finish | | `jobs_logs(job_id, tail=100)` | Last lines of one of your own jobs' logs | @@ -25,7 +25,8 @@ job, and that score is the reward. | `submit(job_id)` | Final answer: cancels your other jobs, scores the artifact on the hidden split, ends the episode | Errors come back as data, with a type the agent can act on: `budget_exhausted`, -`flavor_not_allowed`, `timeout_not_allowed`, `limit_reached`, `not_ready`, +`flavor_not_allowed`, `timeout_not_allowed`, `invalid_script`, +`invalid_dependency`, `dataset_not_allowed`, `limit_reached`, `not_ready`, `unknown_job`, `not_allowed`, `episode_over` or `hf_error`. ## How a job runs @@ -39,21 +40,50 @@ under `/data/outputs/` is its artifact: openenv-artifact:////outputs ``` -Evaluations run the task's evaluator script as their own job with that folder -mounted read-only at `/model`. The evaluator prints one `POSTTRAIN_SCORE {...}` -line. A missing line, or a score outside [0, 1], scores 0 as `invalid`. -Evaluators take only weights from the artifact, loaded as safetensors. The -tokenizer, chat template, config and generation settings come from the base -model at a pinned revision, because a chat template is a program that sees the -question and could write the answer into the prompt. Each evaluator pins its -dependencies in its PEP 723 header, with an `exclude-newer` date for the rest. - Jobs are started with an explicit flavor, namespace, timeout and labels, and with no secrets. The broker never passes its own token into a job: a job holding a "Start and manage Jobs" token could start further jobs that skip the budget. It also never shells out to `hf jobs uv run`, whose script header can pull the caller's `HF_TOKEN` into the job. +### No network in agent jobs + +A job that can reach the internet can download a model already trained for the +task, or the evaluation's own test data. So agent jobs get their inputs as +read-only mounts and run without network access: + +- The task's base model is mounted at `/base_model`, at a pinned revision. +- Each dataset in `datasets` is mounted at `/datasets/`, at its current + revision. It must be public and ungated, and not on the task's denylist. The + broker looks it up without its token, so the token can't open private repos. +- Packages from `dependencies` and the script's PEP 723 header are installed + before the script runs, from PyPI wheels only (`no-build = true`), so no + package code runs while the job still has network access. URLs and git + dependencies are refused. Other settings in the script's header are ignored. + +HF Jobs has no egress control, and its containers can't create network +namespaces or firewall rules. So the broker sends +[`server/job_launcher.py`](server/job_launcher.py) as the job's script, with the +agent's script embedded. Once uv has installed the packages, the launcher +installs a seccomp filter that refuses every socket except `AF_UNIX`, and +io_uring and cross-process memory access; systemd's `RestrictAddressFamilies=` +works the same way. It then switches to uid 65534 and runs the agent's script +with `HF_HUB_OFFLINE=1`. The filter can't be removed and covers every child +process. If any step fails, the script doesn't run. Training on a single GPU, +DataLoader workers and writing `/data/outputs/` work under the filter; +`torch.distributed` over localhost TCP does not. + +### Evaluations + +Evaluations run the task's evaluator script as their own job with that folder +mounted read-only at `/model`, and the task's base model at `/base_model`. The +evaluator prints one `POSTTRAIN_SCORE {...}` line. A missing line, or a score +outside [0, 1], scores 0 as `invalid`. Evaluators take only weights from the +artifact, loaded as safetensors. The tokenizer, chat template, config and +generation settings come from the base model mount, because a chat template is +a program that sees the question and could write the answer into the prompt. Each evaluator pins its +dependencies in its PEP 723 header, with an `exclude-newer` date for the rest. + ## Budget and spend Money is tracked in micro-USD from the prices `list_jobs_hardware` reports. diff --git a/envs/posttrain_env/pyproject.toml b/envs/posttrain_env/pyproject.toml index bbc832a730..b95ae12830 100644 --- a/envs/posttrain_env/pyproject.toml +++ b/envs/posttrain_env/pyproject.toml @@ -8,7 +8,7 @@ build-backend = "setuptools.build_meta" name = "openenv-posttrain-env" version = "0.1.0" description = "Post-training environment for OpenEnv: train models through brokered, budgeted HF Jobs" -requires-python = ">=3.10" +requires-python = ">=3.11" dependencies = [ "openenv>=0.9.0", "fastapi>=0.115.0", @@ -16,6 +16,8 @@ dependencies = [ "uvicorn>=0.24.0", # Jobs, job artifact buckets and volume mounts. "huggingface_hub>=1.29.0", + # Checks the requirements agents pass to their jobs. + "packaging>=23.0", ] [project.optional-dependencies] diff --git a/envs/posttrain_env/server/broker.py b/envs/posttrain_env/server/broker.py index 79fa512b0a..479c674754 100644 --- a/envs/posttrain_env/server/broker.py +++ b/envs/posttrain_env/server/broker.py @@ -7,22 +7,32 @@ and with no secrets. A `job.write` token passed into a job could start further jobs that skip this ledger, so the broker never passes one in. +Agent scripts run inside `job_launcher.py`, which cuts the job's network +access before the script starts. Dependencies are installed first, from wheels +only, and inputs come as read-only mounts: the task's base model and the public +datasets the agent asks for. + Money is tracked in integer micro-USD. A job reserves its worst case (the flavor's per-minute rate times its timeout) before it starts, and settles at the per-minute rate for the minutes it actually ran. """ +import base64 import json import math +import re import tempfile import time +import tomllib from contextlib import suppress from dataclasses import dataclass from pathlib import Path from typing import Any from huggingface_hub import Volume -from huggingface_hub.errors import HfHubHTTPError +from huggingface_hub.errors import HfHubHTTPError, HFValidationError +from huggingface_hub.utils import validate_repo_id +from packaging.requirements import InvalidRequirement, Requirement # HF Jobs stage -> job state exposed to the agent. STATE_FROM_STAGE = { @@ -41,6 +51,11 @@ ARTIFACTS_MOUNT = "/data" MAX_SCRIPT_BYTES = 200_000 MAX_DEPENDENCIES = 20 +MAX_DATASETS = 5 +DATASETS_MOUNT = "/datasets" +LAUNCHER = Path(__file__).parent / "job_launcher.py" +# A PEP 723 `script` block, as in the specification's reference regex. +SCRIPT_BLOCK = re.compile(r"(?m)^# /// script$\s(?P(^#(| .*)$\s)+)^# ///$") MAX_ARTIFACT_FILES = 50 # Logs can lag a finished job; re-read before declaring the score missing. MAX_RESULT_READS = 3 @@ -221,9 +236,19 @@ def submit( allowed_flavors: tuple[str, ...], max_timeout_minutes: int, dependencies: list[str] | None = None, + datasets: list[str] | None = None, + denied_datasets: tuple[str, ...] = (), + volumes: list[Volume] | None = None, idempotency_key: str | None = None, ) -> Job: - """Start an agent job after checking limits and reserving its worst case.""" + """ + Start an agent job after checking limits and reserving its worst case. + + The script runs without network access, inside `job_launcher.py`. Its + dependencies, from `dependencies` and its PEP 723 header, are installed + from wheels first. Each of `datasets` is mounted read-only at + `/datasets/`, next to `volumes`. + """ if idempotency_key and idempotency_key in self._idempotency: return self.jobs[self._idempotency[idempotency_key]] if flavor not in allowed_flavors: @@ -240,28 +265,125 @@ def submit( raise BrokerError( "invalid_script", f"script must be 1 to {MAX_SCRIPT_BYTES} bytes" ) - dependencies = dependencies or [] - if len(dependencies) > MAX_DEPENDENCIES: - raise BrokerError( - "invalid_script", f"At most {MAX_DEPENDENCIES} dependencies" - ) + requirements = self._requirements(script, dependencies or []) + mounts = self._dataset_volumes(datasets or [], denied_datasets) if self.count("agent") >= self.max_jobs: raise BrokerError( "limit_reached", f"This episode allows {self.max_jobs} jobs" ) reservation = self._reserve(flavor, timeout_minutes, charged=True) job = self._run( - script, + self._launcher_script(script, requirements), role="agent", flavor=flavor, timeout_minutes=timeout_minutes, reservation=reservation, - dependencies=dependencies, + dependencies=[], + volumes=[*(volumes or []), *mounts], ) if idempotency_key: self._idempotency[idempotency_key] = job.job_id return job + @staticmethod + def _requirements(script: str, dependencies: list[str]) -> list[str]: + """Merge the script's PEP 723 dependencies with `dependencies`; packages only, no URLs.""" + match = SCRIPT_BLOCK.search(script) + if match: + content = "".join( + line[2:] if line.startswith("# ") else line[1:] + for line in match.group("content").splitlines(keepends=True) + ) + try: + header = tomllib.loads(content).get("dependencies", []) + except tomllib.TOMLDecodeError as e: + raise BrokerError("invalid_script", f"PEP 723 header: {e}") + if not isinstance(header, list): + raise BrokerError( + "invalid_script", "PEP 723 dependencies must be a list" + ) + dependencies = [*header, *dependencies] + if len(dependencies) > MAX_DEPENDENCIES: + raise BrokerError( + "invalid_dependency", f"At most {MAX_DEPENDENCIES} dependencies" + ) + requirements = [] + for dependency in dependencies: + try: + requirement = Requirement(str(dependency)) + except InvalidRequirement: + raise BrokerError( + "invalid_dependency", f"{dependency!r} is not a valid requirement" + ) + if requirement.url: + raise BrokerError( + "invalid_dependency", + f"{dependency!r}: only packages from PyPI, not URLs", + ) + requirements.append(str(requirement)) + return requirements + + def _dataset_volumes( + self, datasets: list[str], denied: tuple[str, ...] + ) -> list[Volume]: + """Mount each public, ungated dataset read-only at its current revision.""" + if len(datasets) > MAX_DATASETS: + raise BrokerError("dataset_not_allowed", f"At most {MAX_DATASETS} datasets") + denied_ids = {d.lower() for d in denied} + volumes = [] + for repo_id in dict.fromkeys(datasets): + try: + validate_repo_id(repo_id) + valid = "/" in repo_id + except HFValidationError: + valid = False + if not valid: + raise BrokerError( + "dataset_not_allowed", "Datasets are Hub repo ids like owner/name" + ) + if repo_id.lower() in denied_ids: + raise BrokerError( + "dataset_not_allowed", f"{repo_id} is not allowed in this task" + ) + # Looked up anonymously: the broker's token must not open private repos. + try: + info = self._api.dataset_info(repo_id, token=False) + except HfHubHTTPError: + info = None + if info is None or info.gated: + raise BrokerError( + "dataset_not_allowed", + f"{repo_id} is not a public, ungated dataset on the Hub", + ) + volumes.append( + Volume( + type="dataset", + source=repo_id, + revision=info.sha, + mount_path=f"{DATASETS_MOUNT}/{repo_id}", + read_only=True, + ) + ) + return volumes + + @staticmethod + def _launcher_script(script: str, requirements: list[str]) -> str: + """Embed `script` in the launcher, with `requirements` installed from wheels only.""" + header = [ + "# /// script", + '# requires-python = ">=3.10"', + f"# dependencies = {json.dumps(requirements)}", + "#", + "# [tool.uv]", + "# no-build = true", + "# ///", + ] + encoded = base64.b64encode(script.encode()).decode() + launcher = LAUNCHER.read_text().replace( + 'AGENT_SCRIPT = ""', f'AGENT_SCRIPT = "{encoded}"', 1 + ) + return "\n".join(header) + "\n" + launcher + def evaluate( self, source: Job, @@ -271,8 +393,9 @@ def evaluate( flavor: str, timeout_minutes: int, env: dict[str, str], + volumes: list[Volume] | None = None, ) -> Job: - """Run an evaluator job on `source`'s artifact, mounted read-only at `/model`.""" + """Run an evaluator job on `source`'s artifact, mounted read-only at `/model`, next to `volumes`.""" self.require_artifact(source) if role == "dev_eval" and self.count("dev_eval") >= self.max_dev_evals: raise BrokerError( @@ -295,7 +418,7 @@ def evaluate( reservation=reservation, dependencies=[], env=env, - volumes=[model], + volumes=[model, *(volumes or [])], ) job.source_job_id = source.job_id return job diff --git a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py index 7b5067a607..3e8f285976 100644 --- a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py +++ b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py @@ -21,9 +21,9 @@ is held by the environment server and only reaches score jobs. Only the weights come from the checkpoint. The tokenizer, chat template, model -config and generation settings are the base model's, at a pinned revision: a -chat template is a program that sees the question, so the agent's own template -could write the answer into the prompt. +config and generation settings are the base model's, which the broker mounts at +`/base_model` at a pinned revision: a chat template is a program that sees the +question, so the agent's own template could write the answer into the prompt. The job runs on CPU, so torch comes from the CPU-only index: the default wheel pulls several GB of CUDA libraries, and that download alone can exceed the @@ -43,8 +43,7 @@ GenerationConfig, ) -BASE_MODEL = "HuggingFaceTB/SmolLM2-135M-Instruct" -BASE_REVISION = "12fd25f77366fa6b3b4b768ec3050bf629380bac" +BASE_MODEL = "/base_model" N_PROMPTS = {"dev": 50, "test": 100} @@ -61,9 +60,9 @@ def invalid(feedback): rng = random.Random(int(os.environ["POSTTRAIN_SEED"])) pairs = [(rng.randint(0, 99), rng.randint(0, 99)) for _ in range(N_PROMPTS[split])] -tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, revision=BASE_REVISION) -config = AutoConfig.from_pretrained(BASE_MODEL, revision=BASE_REVISION) -generation_config = GenerationConfig.from_pretrained(BASE_MODEL, revision=BASE_REVISION) +tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) +config = AutoConfig.from_pretrained(BASE_MODEL) +generation_config = GenerationConfig.from_pretrained(BASE_MODEL) generation_config.update(max_new_tokens=16, do_sample=False) try: @@ -79,7 +78,7 @@ def invalid(feedback): except Exception as e: # Report a bad checkpoint as a score, don't crash. invalid(f"cannot load /model: {type(e).__name__}") if info["missing_keys"] or info["mismatched_keys"]: - invalid(f"/model weights do not match {BASE_MODEL}") + invalid("/model weights do not match the base model") model.eval() correct = formatted = 0 diff --git a/envs/posttrain_env/server/job_launcher.py b/envs/posttrain_env/server/job_launcher.py new file mode 100644 index 0000000000..a7b667de87 --- /dev/null +++ b/envs/posttrain_env/server/job_launcher.py @@ -0,0 +1,138 @@ +# SPDX-License-Identifier: BSD-3-Clause + +"""Run the agent's script in its HF Job without network access. + +The broker sends this file as the job's script, with the agent's script +embedded below and the agent's dependencies in its PEP 723 header. uv installs +them, wheels only, before any of the agent's code exists on disk. Then, still +as root, the launcher: + +1. lets an unprivileged user read the installed environment and gives it + `/data/outputs`; +2. installs a seccomp filter that refuses every socket except `AF_UNIX`, and + io_uring, which can open sockets without calling `socket()`, and the calls + that read or write another process's memory; +3. drops to uid 65534 and runs the agent's script. As root the script could + still write the unfiltered uv parent's `/proc//mem`. + +HF Jobs containers have no `CAP_NET_ADMIN` and cannot create namespaces, but +any process may filter its own system calls; systemd's +`RestrictAddressFamilies=` works the same way. The filter is inherited by every +child process and cannot be removed. If a step fails, the agent's script does +not run. +""" + +import base64 +import ctypes +import errno +import os +import socket +import subprocess +import sys +from pathlib import Path + +AGENT_SCRIPT = "" # base64, filled in by the broker +NOBODY = 65534 +WORKDIR = Path("/tmp/posttrain") +OUTPUTS = Path("/data/outputs") +SCMP_ACT_ALLOW = 0x7FFF0000 +SCMP_ACT_EACCES = 0x00050000 | errno.EACCES +SCMP_CMP_NE = 1 +DENIED_SYSCALLS = ( + "io_uring_setup", + "io_uring_enter", + "io_uring_register", + "ptrace", + "process_vm_readv", + "process_vm_writev", + "pidfd_getfd", +) + + +class ArgCmp(ctypes.Structure): + """libseccomp's `struct scmp_arg_cmp`.""" + + _fields_ = [ + ("arg", ctypes.c_uint), + ("op", ctypes.c_int), + ("datum_a", ctypes.c_uint64), + ("datum_b", ctypes.c_uint64), + ] + + +def open_environment() -> None: + """Let uid 65534 read the interpreter and packages uv installed under the home directory.""" + home = Path.home() + paths = {Path(p).resolve() for p in (*sys.path, sys.prefix) if p} + paths.add(Path(sys.executable).resolve().parent) + for path in paths: + if not path.is_dir() or not path.is_relative_to(home): + continue + subprocess.run(["chmod", "-R", "o+rX", str(path)], check=True) + for parent in path.parents: + if parent.is_relative_to(home): + parent.chmod(parent.stat().st_mode | 0o001) + + +def install_filter() -> None: + """Refuse every socket family but `AF_UNIX`, io_uring and cross-process memory access.""" + lib = ctypes.CDLL("libseccomp.so.2") + lib.seccomp_init.restype = ctypes.c_void_p + lib.seccomp_init.argtypes = [ctypes.c_uint32] + lib.seccomp_syscall_resolve_name.argtypes = [ctypes.c_char_p] + lib.seccomp_rule_add_array.argtypes = [ + ctypes.c_void_p, + ctypes.c_uint32, + ctypes.c_int, + ctypes.c_uint, + ctypes.POINTER(ArgCmp), + ] + lib.seccomp_load.argtypes = [ctypes.c_void_p] + ctx = lib.seccomp_init(SCMP_ACT_ALLOW) + if not ctx: + raise OSError("seccomp_init failed") + not_unix = (ArgCmp * 1)(ArgCmp(0, SCMP_CMP_NE, socket.AF_UNIX, 0)) + rules = [("socket", not_unix), ("socketpair", not_unix)] + rules += [(name, None) for name in DENIED_SYSCALLS] + for name, args in rules: + number = lib.seccomp_syscall_resolve_name(name.encode()) + if lib.seccomp_rule_add_array( + ctx, SCMP_ACT_EACCES, number, len(args or ()), args + ): + raise OSError(f"cannot filter {name}") + # Sets no_new_privs first, so loading needs no privileges. + if lib.seccomp_load(ctx): + raise OSError("seccomp_load failed") + try: + socket.socket(socket.AF_INET, socket.SOCK_STREAM).close() + except PermissionError: + return + raise OSError("the filter did not block an AF_INET socket") + + +def main() -> None: + try: + WORKDIR.mkdir(mode=0o755, exist_ok=True) + script = WORKDIR / "job.py" + script.write_bytes(base64.b64decode(AGENT_SCRIPT)) + home = WORKDIR / "home" + home.mkdir(exist_ok=True) + OUTPUTS.mkdir(parents=True, exist_ok=True) + for path in (home, OUTPUTS): + os.chown(path, NOBODY, NOBODY) + open_environment() + install_filter() + os.setgroups([]) + os.setgid(NOBODY) + os.setuid(NOBODY) + except Exception as e: + sys.exit( + f"posttrain launcher: {type(e).__name__}: {e}. The script did not run." + ) + os.chdir(home) + env = {**os.environ, "HOME": str(home), "HF_HUB_OFFLINE": "1"} + os.execve(sys.executable, [sys.executable, str(script)], env) + + +if __name__ == "__main__": + main() diff --git a/envs/posttrain_env/server/posttrain_environment.py b/envs/posttrain_env/server/posttrain_environment.py index a2e15a0440..d708a33130 100644 --- a/envs/posttrain_env/server/posttrain_environment.py +++ b/envs/posttrain_env/server/posttrain_environment.py @@ -114,6 +114,7 @@ def jobs_submit( flavor: str, timeout_minutes: int, dependencies: list[str] | None = None, + datasets: list[str] | None = None, idempotency_key: str | None = None, ) -> dict: """ @@ -121,13 +122,15 @@ def jobs_submit( The job's worst-case cost (price per minute times `timeout_minutes`) is reserved before it starts and must fit the remaining budget. The - job has no credentials. Write outputs to `/data/outputs/`. + job has no credentials and no network access, and runs as an + unprivileged user. Write outputs to `/data/outputs/`. Args: script: Python source of the job. A PEP 723 header may list dependencies. flavor: Hardware flavor, one of those listed by `budget`. timeout_minutes: Job timeout; the job is stopped when it is reached. - dependencies: Extra packages, passed to `uv run --with`. + dependencies: Extra PyPI packages, installed from wheels before the job starts. + datasets: Public Hub dataset repo ids, mounted read-only at `/datasets/`. idempotency_key: Retrying with the same key returns the same job. """ return self._call( @@ -139,6 +142,9 @@ def jobs_submit( allowed_flavors=self._task.flavors, max_timeout_minutes=self._task.max_timeout_minutes, dependencies=dependencies, + datasets=datasets, + denied_datasets=self._task.denied_datasets, + volumes=self._task.volumes(), idempotency_key=idempotency_key, ) ) @@ -468,7 +474,9 @@ def _evaluate(self, job_id: str, role: str): "POSTTRAIN_SPLIT": split, "POSTTRAIN_SEED": str(seed), "POSTTRAIN_EXPECTED": self._nonce, + "HF_HUB_OFFLINE": "1", }, + volumes=task.volumes(), ) def _submit(self, job_id: str) -> dict: diff --git a/envs/posttrain_env/server/tasks.py b/envs/posttrain_env/server/tasks.py index dd77f9bfeb..2265e8db6e 100644 --- a/envs/posttrain_env/server/tasks.py +++ b/envs/posttrain_env/server/tasks.py @@ -3,16 +3,21 @@ """T0 post-training tasks. Each task tells the agent what to train, which HF Jobs hardware it may use and -how much it may spend. The evaluator script runs as its own job: the agent's -checkpoint is mounted read-only at `/model`, and the job prints one -`POSTTRAIN_SCORE {...}` line that the broker parses. Evaluators pin their -dependencies in their own PEP 723 header, so scores don't drift with releases. +how much it may spend. The task's base model, if any, is mounted read-only at +`/base_model` at a pinned revision, in agent jobs and evaluations alike. The +evaluator script runs as its own job: the agent's checkpoint is mounted +read-only at `/model`, and the job prints one `POSTTRAIN_SCORE {...}` line that +the broker parses. Evaluators pin their dependencies in their own PEP 723 +header, so scores don't drift with releases. """ from dataclasses import dataclass from pathlib import Path +from huggingface_hub import Volume + EVAL_DIR = Path(__file__).parent / "eval_scripts" +BASE_MODEL_MOUNT = "/base_model" @dataclass(frozen=True) @@ -45,6 +50,12 @@ class Task: Seed of the public dev split. max_episode_minutes (`int`): Wall-clock limit for the whole episode. + base_model (`str`, *optional*): + Hub model mounted read-only at `/base_model`. + base_revision (`str`, *optional*): + Commit of `base_model` to mount. + denied_datasets (`tuple[str, ...]`, *optional*, defaults to `()`): + Dataset repo ids agent jobs may not mount, such as the evaluation's own. """ task_id: str @@ -59,16 +70,37 @@ class Task: eval_timeout_minutes: int dev_seed: int = 0 max_episode_minutes: int = 240 + base_model: str | None = None + base_revision: str | None = None + denied_datasets: tuple[str, ...] = () @property def eval_script_path(self) -> Path: return EVAL_DIR / self.eval_script + def volumes(self) -> list[Volume]: + """Read-only mounts shared by agent jobs and evaluations.""" + if self.base_model is None: + return [] + return [ + Volume( + type="model", + source=self.base_model, + revision=self.base_revision, + mount_path=BASE_MODEL_MOUNT, + read_only=True, + ) + ] + -OUTPUTS_NOTE = ( - "Your job's working storage is mounted at `/data`. Anything you write under " - "`/data/outputs/` is the job's artifact: `dev_evaluate` and `submit` mount " - "it read-only at `/model`." +JOB_NOTE = ( + "Jobs have no network access and run as an unprivileged user. List the " + "packages a job needs in `dependencies` or its PEP 723 header: they are " + "installed from PyPI wheels before it starts. Datasets listed in `datasets` " + "(public Hub repo ids) are mounted read-only at `/datasets/`. " + "Anything a job writes under `/data/outputs/` is its artifact: `dev_evaluate` " + "and `submit` mount it read-only at `/model`. Only jobs that exit with code 0 " + "count, so keep checks after saving short." ) TASKS = { @@ -77,7 +109,7 @@ def eval_script_path(self) -> Path: prompt=( "Plumbing check. Submit one HF Jobs script that writes the exact text " "`{nonce}` to `/data/outputs/answer.txt`, then call `submit` with that " - "job's id. " + OUTPUTS_NOTE + "job's id. " + JOB_NOTE ), flavors=("cpu-basic",), max_timeout_minutes=10, @@ -92,8 +124,10 @@ def eval_script_path(self) -> Path: "t0_arithmetic_format": Task( task_id="t0_arithmetic_format", prompt=( - "Fine-tune `HuggingFaceTB/SmolLM2-135M-Instruct` so that it answers " - "two-digit addition in an exact format. The evaluator sends the chat " + "Fine-tune `HuggingFaceTB/SmolLM2-135M-Instruct`, mounted read-only at " + "`/base_model`, so that it answers two-digit addition in an exact " + "format. Load it with `from_pretrained('/base_model')`: jobs cannot " + "download it. The evaluator sends the chat " "message `What is {{a}} + {{b}}?` (0 <= a, b <= 99) through the base " "model's chat template with `add_generation_prompt=True`, decodes up to " "16 new tokens greedily, and counts an answer as correct only if the " @@ -102,7 +136,7 @@ def eval_script_path(self) -> Path: "base model's, so keep its architecture and vocabulary. Save the model " "with `save_pretrained` (safetensors) to `/data/outputs/`. The reward is " "the accuracy on a hidden test split; `dev_evaluate` scores the public " - "dev split (seed 1234). " + OUTPUTS_NOTE + "dev split (seed 1234). " + JOB_NOTE ), flavors=("cpu-upgrade", "t4-small", "a10g-small"), max_timeout_minutes=60, @@ -113,5 +147,7 @@ def eval_script_path(self) -> Path: eval_flavor="cpu-upgrade", eval_timeout_minutes=30, dev_seed=1234, + base_model="HuggingFaceTB/SmolLM2-135M-Instruct", + base_revision="12fd25f77366fa6b3b4b768ec3050bf629380bac", ), } diff --git a/envs/posttrain_env/uv.lock b/envs/posttrain_env/uv.lock index 6b798776c6..e343f8a968 100644 --- a/envs/posttrain_env/uv.lock +++ b/envs/posttrain_env/uv.lock @@ -1,6 +1,6 @@ version = 1 revision = 5 -requires-python = ">=3.10" +requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", @@ -10,46 +10,17 @@ resolution-markers = [ "python_full_version == '3.13.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version == '3.12.*' and sys_platform != 'emscripten'", - "python_full_version == '3.11.*' and sys_platform == 'win32'", - "python_full_version == '3.11.*' and sys_platform == 'emscripten'", - "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.11'", -] - -[[package]] -name = "aiofile" -version = "3.8.8" -source = { registry = "https://pypi.org/simple" } -resolution-markers = [ - "python_full_version < '3.11'", -] -dependencies = [ - { name = "caio", version = "0.9.25", source = { registry = "https://pypi.org/simple" } }, -] -sdist = { url = "https://files.pythonhosted.org/packages/41/6d/e03ef9694346a55da965538f3e28461bd9fc71e21f997269d03b3cfc8755/aiofile-3.8.8.tar.gz", hash = "sha256:41f3dc40bd730459d58610476e82e5efb2f84ae6e9fa088a9545385d838b8a43", size = 19456, upload-time = "2023-08-22T12:48:38.88Z" } -wheels = [ - { url = 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import Path from types import SimpleNamespace import pytest import yaml from envs.posttrain_env.server import posttrain_environment -from envs.posttrain_env.server.broker import MAX_RESULT_READS, SCORE_MARKER +from envs.posttrain_env.server.broker import LAUNCHER, MAX_RESULT_READS, SCORE_MARKER from envs.posttrain_env.server.posttrain_environment import PostTrainEnvironment +from envs.posttrain_env.server.tasks import TASKS from huggingface_hub import Volume +from huggingface_hub.errors import HfHubHTTPError from openenv.core.env_server.mcp_types import CallToolAction, ListToolsAction ENV_DIR = Path(__file__).parents[2] / "envs" / "posttrain_env" @@ -32,6 +40,13 @@ def hardware(name, micro_usd_per_minute, gpus=None): ) +class NotFound(HfHubHTTPError): + """A Hub 404, without the HTTP response the real error carries.""" + + def __init__(self, repo_id): + Exception.__init__(self, f"{repo_id} not found") + + class FakeHfApi: """The subset of `HfApi` the broker uses, backed by a dict of jobs.""" @@ -41,6 +56,11 @@ def __init__(self): self.cancel_confirms = True # Applied the first time a job with this role label is inspected. self.on_inspect = {} + self.datasets = { + "open/data": SimpleNamespace(sha="abc123", gated=False), + "gated/data": SimpleNamespace(sha="def456", gated="auto"), + } + self.dataset_lookups = [] def whoami(self): return {"name": "operator"} @@ -94,6 +114,12 @@ def cancel_job(self, *, job_id, namespace): def fetch_job_logs(self, *, job_id, namespace, tail): return iter(self.jobs[job_id]["logs"][-tail:]) + def dataset_info(self, repo_id, *, token): + self.dataset_lookups.append((repo_id, token)) + if repo_id not in self.datasets: + raise NotFound(repo_id) + return self.datasets[repo_id] + def list_bucket_tree(self, bucket_id, prefix, recursive): return [ SimpleNamespace( @@ -112,6 +138,13 @@ def score_line(score, **extra): return SCORE_MARKER + json.dumps({"status": "ok", "score": score, **extra}) +def launched(run): + """Return the agent script embedded in a launcher job, and the job's PEP 723 header.""" + encoded = re.search(r'^AGENT_SCRIPT = "([^"]*)"', run["script"], re.M).group(1) + header = run["script"].split("# ///\n", 1)[0] + return base64.b64decode(encoded).decode(), header + + @pytest.fixture def api(): return FakeHfApi() @@ -131,11 +164,13 @@ def call(env, tool, **arguments): return obs.result.data, obs -def submit_job(env, flavor="cpu-basic", timeout_minutes=10, **extra): +def submit_job( + env, flavor="cpu-basic", timeout_minutes=10, script="print('train')", **extra +): data, _ = call( env, "jobs_submit", - script="print('train')", + script=script, flavor=flavor, timeout_minutes=timeout_minutes, **extra, @@ -181,16 +216,109 @@ def test_job_starts_with_explicit_settings_and_no_secrets(self, env, api): assert run["namespace"] == "operator" assert run["flavor"] == "cpu-basic" assert run["timeout"] == "10m" - assert run["dependencies"] == ["numpy"] assert run["labels"] == { "openenv-env": "posttrain_env", "openenv-episode": "ep1", "openenv-role": "agent", } - assert run["script"] == "print('train')" + assert run["volumes"] == [] assert job["state"] == "queued" assert job["remaining_usd"] == pytest.approx(0.05 - 10 * CPU_BASIC / 1e6) + def test_script_runs_in_the_launcher_with_wheel_only_dependencies(self, env, api): + env.reset(task_id="t0_artifact") + script = ( + '# /// script\n# dependencies = ["torch==2.14.1"]\n# ///\nprint("hi")\n' + ) + submit_job(env, script=script, dependencies=["numpy>=2"]) + run = api.runs[0] + agent_script, header = launched(run) + assert agent_script == script + assert '# dependencies = ["torch==2.14.1", "numpy>=2"]' in header + assert "# no-build = true" in header + assert run["dependencies"] == [] + assert "install_filter()" in run["script"] + compile(run["script"], "launcher.py", "exec") + + @pytest.mark.parametrize( + "dependencies, script", + [ + (["pkg @ https://example.com/pkg.whl"], "print(1)"), + (["git+https://github.com/owner/repo"], "print(1)"), + (["numpy; nonsense"], "print(1)"), + (["numpy"] * 21, "print(1)"), + ([], "# /// script\n# dependencies = [\n# ///\nprint(1)\n"), + ([], '# /// script\n# dependencies = ["x @ file:///etc/passwd"]\n# ///\n'), + ], + ) + def test_dependencies_must_be_pypi_packages(self, env, api, dependencies, script): + env.reset(task_id="t0_artifact") + data, _ = call( + env, + "jobs_submit", + script=script, + flavor="cpu-basic", + timeout_minutes=5, + dependencies=dependencies, + ) + assert data["error"]["type"] in ("invalid_dependency", "invalid_script") + assert api.runs == [] + + def test_datasets_are_mounted_read_only_at_their_revision(self, env, api): + env.reset(task_id="t0_artifact") + submit_job(env, datasets=["open/data", "open/data"]) + assert api.runs[0]["volumes"] == [ + Volume( + type="dataset", + source="open/data", + revision="abc123", + mount_path="/datasets/open/data", + read_only=True, + ) + ] + # Looked up anonymously, so the broker's token cannot open private repos. + assert api.dataset_lookups == [("open/data", False)] + + @pytest.mark.parametrize( + "dataset", ["gated/data", "private/data", "denied/data", "../data", "data"] + ) + def test_gated_missing_denied_and_malformed_datasets_are_refused( + self, env, api, monkeypatch, dataset + ): + task = dataclasses.replace( + TASKS["t0_artifact"], denied_datasets=("Denied/Data",) + ) + monkeypatch.setitem(TASKS, "t0_artifact", task) + api.datasets["denied/data"] = SimpleNamespace(sha="x", gated=False) + env.reset(task_id="t0_artifact") + data, _ = call( + env, + "jobs_submit", + script="print(1)", + flavor="cpu-basic", + timeout_minutes=5, + datasets=[dataset], + ) + assert data["error"]["type"] == "dataset_not_allowed" + assert api.runs == [] + + def test_base_model_is_mounted_for_agent_jobs_and_evaluations(self, env, api): + env.reset(task_id="t0_arithmetic_format") + job = submit_job(env, flavor="cpu-upgrade", timeout_minutes=5) + api.finish(job["job_id"]) + call(env, "dev_evaluate", job_id=job["job_id"]) + base = Volume( + type="model", + source="HuggingFaceTB/SmolLM2-135M-Instruct", + revision="12fd25f77366fa6b3b4b768ec3050bf629380bac", + mount_path="/base_model", + read_only=True, + ) + agent_run, eval_run = api.runs + assert agent_run["volumes"] == [base] + assert eval_run["volumes"][1:] == [base] + assert eval_run["env"]["HF_HUB_OFFLINE"] == "1" + def test_resource_group_bills_the_backstop(self, env, api, monkeypatch): monkeypatch.setenv("POSTTRAIN_RESOURCE_GROUP_ID", "rg-123") env.reset(task_id="t0_artifact") @@ -630,3 +758,33 @@ def test_non_object_metrics_are_dropped(env, api): data, _ = call(env, "jobs_status", job_id=evaluation["job_id"]) assert data["result"]["score"] == 0.5 assert data["result"]["metrics"] == {} + + +@pytest.mark.skipif( + sys.platform != "linux" or ctypes.util.find_library("seccomp") is None, + reason="the launcher's seccomp filter needs Linux and libseccomp", +) +def test_launcher_filter_blocks_network_sockets_but_not_unix_sockets(): + code = f""" +import importlib.util, socket +spec = importlib.util.spec_from_file_location("launcher", {str(LAUNCHER)!r}) +launcher = importlib.util.module_from_spec(spec) +spec.loader.exec_module(launcher) +launcher.install_filter() +for family, kind in [ + (socket.AF_INET, socket.SOCK_STREAM), + (socket.AF_INET, socket.SOCK_DGRAM), + (socket.AF_INET6, socket.SOCK_STREAM), +]: + try: + socket.socket(family, kind) + print("open") + except PermissionError: + print("blocked") +socket.socket(socket.AF_UNIX).close() +print("unix") +""" + out = subprocess.run( + [sys.executable, "-c", code], capture_output=True, text=True, check=True + ) + assert out.stdout.split() == ["blocked", "blocked", "blocked", "unix"] From c32dbb29f50f69c732f97b46feb1395f5bf5d1a6 Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 10:01:06 +0530 Subject: [PATCH 12/24] Add the network policy and new runs to RFC 014 --- rfcs/014-environment-services.md | 24 +++++++++++++++++------- 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md index d58b7615a2..2b05f56ec1 100644 --- a/rfcs/014-environment-services.md +++ b/rfcs/014-environment-services.md @@ -92,6 +92,8 @@ Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. **Jobs.** Tools return a handle. States are `queued → running → succeeded | failed | cancelled`, plus `cancelling` while a cancel is pending and `orphaned` when the provider does not confirm one in time. An orphaned job is settled at its full reservation, keeps `final: false` and blocks the next `reset` with an error, because it may still be running and billing. `reset`, `close`, `submit`, the step limit and the time limit cancel the episode's live jobs. Submissions take an idempotency key; a retry with the same key returns the same job and makes no second reservation. +**Network.** Agent jobs run without network access. A job that can reach the internet can download a model already trained for the task, or the evaluation's own test data; [PostTrainBench](https://arxiv.org/abs/2603.08640) reports both. Inputs come as read-only mounts instead: the task's base model at a pinned revision, and the public, ungated Hub datasets the agent names, at their current revision, minus a per-task denylist. Packages are installed before the agent's code runs, from wheels only, so no package code runs while the job still has network access. HF Jobs has no egress control, and its containers can't create network namespaces or firewall rules (we checked on `cpu-basic` and `a10g-small`). So `posttrain_env` runs the agent's script inside a launcher that installs a seccomp filter refusing every socket except `AF_UNIX`, and io_uring and cross-process memory access, then drops to an unprivileged uid; systemd's `RestrictAddressFamilies=` works the same way. On hosts the operator controls, openenvd's `openshell` backend (RFC 009) gives the same `egress_control` guarantee with a network namespace. A denylist only catches the known copies of a test set, so tasks whose test data is public also need a contamination check after the run. + **Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; anything else, including a score outside [0, 1], counts as `invalid` with score 0. The evaluator takes only data from the artifact, never code or anything that shapes the evaluator's own input: `t0_arithmetic_format` loads safetensors weights into the base model's config, with the base tokenizer and chat template at a pinned revision, since a chat template could compute the answer into the prompt. Its dependencies are pinned too, so scores don't change with a new release, and as a CPU job it installs CPU-only torch. A score job that fails, times out or is cancelled ends the episode unscored (`reward` is `None`, `episode_end.reason` is `score_failed`): the fault is the grader's, and RFC 013 treats a missing reward as missing, not zero. **Artifacts.** An artifact is the output folder of a succeeded job, exposed as `openenv-artifact:////outputs` with each file's path, size and content hash. Graders mount the folder read-only; nothing is copied through the agent. @@ -131,6 +133,7 @@ Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. ### Not allowed - Raw provider keys in the agent sandbox or in agent jobs. +- Network access from agent jobs. - Grading in the agent's sandbox or jobs. - Reward writes the agent can reach. - Passing the broker's token into jobs. @@ -165,18 +168,25 @@ The final observation carries `reward`, `metadata["service_spend"]` and `metadat ## Prototype results -`envs/posttrain_env` runs this design on HF Jobs. Its unit tests replace HF Jobs with an in-memory fake, with no network access. Two live runs on 2026-10-16: +`envs/posttrain_env` runs this design on HF Jobs. Its unit tests replace HF Jobs with an in-memory fake, with no network access. Live runs: + +| Date | Task | Driver | Agent jobs' network | Dev eval | Reward | Rollout spend | +|---|---|---|---|---|---|---| +| 2026-10-16 | `t0_artifact` | script | open | 1.0 | 1.0 | $0.000334 | +| 2026-10-16 | `t0_arithmetic_format` | script: SFT on `t4-small`, 333 s | open | 0.94 | 0.95 | $0.041 (`gpu_s` 333) | +| 2026-10-26 | `t0_arithmetic_format` | Qwen3.8 through HF Inference Providers, 25 and 29 turns | open | 1.0 | 1.0, 1.0 | $0.21, $0.34 | +| 2026-10-27 | `t0_artifact` | script | none | | 1.0 | $0.000167 | +| 2026-10-27 | `t0_arithmetic_format` | script: SFT on `t4-small`, 333 s | none | 0.94 | 0.93 | $0.041 (`gpu_s` 333) | -| Task | Agent job | Dev eval | Hidden-split reward | Rollout spend | -|---|---|---|---|---| -| `t0_artifact` | `cpu-basic`, 4 s | 1.0 | 1.0 | $0.000334 | -| `t0_arithmetic_format` | SFT of SmolLM2-135M-Instruct, `t4-small`, 333 s | 0.94 | 0.95 | $0.041 (`gpu_s` 333) | +Spend is the agent's HF Jobs spend; LLM tokens are not included. Every run ended with no orphaned jobs and `final: true`, and score jobs were not charged to the agent. A run with a `job.write`-only broker token was refused with HTTP 403 when the script upload reached the `jobs-artifacts` bucket (D1). -Both runs ended with no orphaned jobs and `final: true`. The score jobs were not charged to the agent. A run with a `job.write`-only broker token was refused with HTTP 403 when the script upload reached the `jobs-artifacts` bucket (D1). +- **No network.** In the 27 Oct runs the agent's script could not open a socket and ran as uid 65534. CUDA training, DataLoader workers and writing `/data/outputs/` worked as before. Loading the base model from its mount instead of the Hub also cut the score job from about 20 minutes to 140 s. +- **T0 measures plumbing, not training skill.** Qwen3.8 trained on all 10,000 `(a, b)` pairs, so a hidden split of an input space that small hides nothing. Real tasks need inputs that cannot be enumerated, or test data the agent cannot reach. +- **A failed score job is not a zero.** One Qwen3.8 run scored 1.0 on dev, and its score job printed 1.0 and then hit its timeout; the episode got reward 0. Such episodes now end unscored (`score_failed`), and the evaluator installs CPU-only torch. ## Plan -1. **This PR**: the RFC and `envs/posttrain_env`, with no core changes. Two T0 tasks: `t0_artifact` checks the plumbing for a fraction of a cent; `t0_arithmetic_format` is a real SFT task with a hidden test split. +1. **This PR**: the RFC and `envs/posttrain_env`, with no core changes and no network in agent jobs. Two T0 tasks: `t0_artifact` checks the plumbing for a fraction of a cent; `t0_arithmetic_format` is a real SFT task with a hidden test split. 2. **Core types**: manifest, ledger, job and artifact models outside `server/`; the reserved-name prefix rule for `jobs_`; cleanup that surfaces cancel failures (`_cleanup_session_resources` swallows errors today) and the idle reaper on when services are declared. 3. **openenvd integration** (RFC 009): credential custody, token exchange, the grader zone, and MCP Tasks after the RFC 003 amendment. From 101d5429b073fd02a1f8eaf03b21b25f334e7fcd Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 10:57:49 +0530 Subject: [PATCH 13/24] Install posttrain_env job dependencies inside the launcher and fix review findings --- docs/source/environments/posttrain.md | 36 ++++-- envs/posttrain_env/README.md | 36 ++++-- envs/posttrain_env/pyproject.toml | 2 +- envs/posttrain_env/server/broker.py | 106 ++++++++++-------- .../eval_scripts/t0_arithmetic_format_eval.py | 3 + envs/posttrain_env/server/job_launcher.py | 62 +++++----- .../server/posttrain_environment.py | 29 +++-- envs/posttrain_env/uv.lock | 10 +- tests/envs/test_posttrain_environment.py | 89 +++++++++++---- 9 files changed, 239 insertions(+), 134 deletions(-) diff --git a/docs/source/environments/posttrain.md b/docs/source/environments/posttrain.md index fa66342d38..c93cf8332a 100644 --- a/docs/source/environments/posttrain.md +++ b/docs/source/environments/posttrain.md @@ -51,25 +51,32 @@ caller's `HF_TOKEN` into the job. A job that can reach the internet can download a model already trained for the task, or the evaluation's own test data. So agent jobs get their inputs as -read-only mounts and run without network access: +read-only mounts, which the broker checks and the episode records, and run +without network access: - The task's base model is mounted at `/base_model`, at a pinned revision. - Each dataset in `datasets` is mounted at `/datasets/`, at its current revision. It must be public and ungated, and not on the task's denylist. The broker looks it up without its token, so the token can't open private repos. - Packages from `dependencies` and the script's PEP 723 header are installed - before the script runs, from PyPI wheels only (`no-build = true`), so no - package code runs while the job still has network access. URLs and git - dependencies are refused. Other settings in the script's header are ignored. + into a fresh venv before the script runs, from PyPI wheels only. Installing a + wheel runs none of its code, so no package code runs while the job still has + network access. URLs and git dependencies are refused. Other settings in the + script's header are ignored. + +Mounts and wheels narrow what a job can fetch, but a public dataset or a PyPI +wheel can still hold weights or test data. Tasks whose test data is public also +need a contamination check after the run. HF Jobs has no egress control, and its containers can't create network namespaces or firewall rules. So the broker sends [`server/job_launcher.py`](https://github.com/huggingface/OpenEnv/blob/main/envs/posttrain_env/server/job_launcher.py) as the job's script, with the -agent's script embedded. Once uv has installed the packages, the launcher -installs a seccomp filter that refuses every socket except `AF_UNIX`, and -io_uring and cross-process memory access; systemd's `RestrictAddressFamilies=` -works the same way. It then switches to uid 65534 and runs the agent's script -with `HF_HUB_OFFLINE=1`. The filter can't be removed and covers every child +agent's script and packages embedded. Once it has installed the packages, the +launcher installs a seccomp filter that refuses every socket except `AF_UNIX`, +and io_uring and cross-process memory access; systemd's +`RestrictAddressFamilies=` works the same way. It then closes inherited file +descriptors, switches to a uid no other process in the job uses, and runs the +agent's script with `HF_HUB_OFFLINE=1`. The filter can't be removed and covers every child process. If any step fails, the script doesn't run. Training on a single GPU, DataLoader workers and writing `/data/outputs/` work under the filter; `torch.distributed` over localhost TCP does not. @@ -170,6 +177,17 @@ plays a whole episode with an LLM through any OpenAI-compatible endpoint, Hugging Face Inference Providers by default. The agent's API key is its own; it never needs the broker's token. +## Known gaps + +- The server has no authentication of its own: anyone who can reach it can + start episodes and spend its budget. Keep it on localhost or a private network. +- Job folders stay in the `jobs-artifacts` bucket, and the size of what a job + writes there is not capped or charged to the budget. +- `max_steps` and the time limit are checked in `step`; a client calling the + MCP endpoint directly bypasses them, though not the budget. +- `torch.distributed` over localhost TCP is blocked in agent jobs, so tasks use + single-GPU flavors. + ## Tests ```bash diff --git a/envs/posttrain_env/README.md b/envs/posttrain_env/README.md index 29e0c3fd8c..273b336929 100644 --- a/envs/posttrain_env/README.md +++ b/envs/posttrain_env/README.md @@ -50,25 +50,32 @@ caller's `HF_TOKEN` into the job. A job that can reach the internet can download a model already trained for the task, or the evaluation's own test data. So agent jobs get their inputs as -read-only mounts and run without network access: +read-only mounts, which the broker checks and the episode records, and run +without network access: - The task's base model is mounted at `/base_model`, at a pinned revision. - Each dataset in `datasets` is mounted at `/datasets/`, at its current revision. It must be public and ungated, and not on the task's denylist. The broker looks it up without its token, so the token can't open private repos. - Packages from `dependencies` and the script's PEP 723 header are installed - before the script runs, from PyPI wheels only (`no-build = true`), so no - package code runs while the job still has network access. URLs and git - dependencies are refused. Other settings in the script's header are ignored. + into a fresh venv before the script runs, from PyPI wheels only. Installing a + wheel runs none of its code, so no package code runs while the job still has + network access. URLs and git dependencies are refused. Other settings in the + script's header are ignored. + +Mounts and wheels narrow what a job can fetch, but a public dataset or a PyPI +wheel can still hold weights or test data. Tasks whose test data is public also +need a contamination check after the run. HF Jobs has no egress control, and its containers can't create network namespaces or firewall rules. So the broker sends [`server/job_launcher.py`](server/job_launcher.py) as the job's script, with the -agent's script embedded. Once uv has installed the packages, the launcher -installs a seccomp filter that refuses every socket except `AF_UNIX`, and -io_uring and cross-process memory access; systemd's `RestrictAddressFamilies=` -works the same way. It then switches to uid 65534 and runs the agent's script -with `HF_HUB_OFFLINE=1`. The filter can't be removed and covers every child +agent's script and packages embedded. Once it has installed the packages, the +launcher installs a seccomp filter that refuses every socket except `AF_UNIX`, +and io_uring and cross-process memory access; systemd's +`RestrictAddressFamilies=` works the same way. It then closes inherited file +descriptors, switches to a uid no other process in the job uses, and runs the +agent's script with `HF_HUB_OFFLINE=1`. The filter can't be removed and covers every child process. If any step fails, the script doesn't run. Training on a single GPU, DataLoader workers and writing `/data/outputs/` work under the filter; `torch.distributed` over localhost TCP does not. @@ -169,6 +176,17 @@ plays a whole episode with an LLM through any OpenAI-compatible endpoint, Hugging Face Inference Providers by default. The agent's API key is its own; it never needs the broker's token. +## Known gaps + +- The server has no authentication of its own: anyone who can reach it can + start episodes and spend its budget. Keep it on localhost or a private network. +- Job folders stay in the `jobs-artifacts` bucket, and the size of what a job + writes there is not capped or charged to the budget. +- `max_steps` and the time limit are checked in `step`; a client calling the + MCP endpoint directly bypasses them, though not the budget. +- `torch.distributed` over localhost TCP is blocked in agent jobs, so tasks use + single-GPU flavors. + ## Tests ```bash diff --git a/envs/posttrain_env/pyproject.toml b/envs/posttrain_env/pyproject.toml index b95ae12830..0aff8c897b 100644 --- a/envs/posttrain_env/pyproject.toml +++ b/envs/posttrain_env/pyproject.toml @@ -15,7 +15,7 @@ dependencies = [ "pydantic>=2.0.0", "uvicorn>=0.24.0", # Jobs, job artifact buckets and volume mounts. - "huggingface_hub>=1.29.0", + "huggingface_hub>=2.3.0", # Checks the requirements agents pass to their jobs. "packaging>=23.0", ] diff --git a/envs/posttrain_env/server/broker.py b/envs/posttrain_env/server/broker.py index 479c674754..f5832cafbc 100644 --- a/envs/posttrain_env/server/broker.py +++ b/envs/posttrain_env/server/broker.py @@ -7,10 +7,10 @@ and with no secrets. A `job.write` token passed into a job could start further jobs that skip this ledger, so the broker never passes one in. -Agent scripts run inside `job_launcher.py`, which cuts the job's network -access before the script starts. Dependencies are installed first, from wheels -only, and inputs come as read-only mounts: the task's base model and the public -datasets the agent asks for. +Agent scripts run inside `job_launcher.py`, which installs their dependencies +from wheels and cuts the job's network access before the script starts. Inputs +come as read-only mounts: the task's base model and the public datasets the +agent asks for. Money is tracked in integer micro-USD. A job reserves its worst case (the flavor's per-minute rate times its timeout) before it starts, and settles at @@ -28,6 +28,7 @@ from dataclasses import dataclass from pathlib import Path from typing import Any +from uuid import uuid4 from huggingface_hub import Volume from huggingface_hub.errors import HfHubHTTPError, HFValidationError @@ -246,7 +247,7 @@ def submit( The script runs without network access, inside `job_launcher.py`. Its dependencies, from `dependencies` and its PEP 723 header, are installed - from wheels first. Each of `datasets` is mounted read-only at + from wheels before that. Each of `datasets` is mounted read-only at `/datasets/`, next to `volumes`. """ if idempotency_key and idempotency_key in self._idempotency: @@ -288,7 +289,7 @@ def submit( @staticmethod def _requirements(script: str, dependencies: list[str]) -> list[str]: """Merge the script's PEP 723 dependencies with `dependencies`; packages only, no URLs.""" - match = SCRIPT_BLOCK.search(script) + match = SCRIPT_BLOCK.search(script.replace("\r\n", "\n")) if match: content = "".join( line[2:] if line.startswith("# ") else line[1:] @@ -311,7 +312,8 @@ def _requirements(script: str, dependencies: list[str]) -> list[str]: for dependency in dependencies: try: requirement = Requirement(str(dependency)) - except InvalidRequirement: + # packaging raises SyntaxError for some malformed markers. + except (InvalidRequirement, SyntaxError): raise BrokerError( "invalid_dependency", f"{dependency!r} is not a valid requirement" ) @@ -355,10 +357,15 @@ def _dataset_volumes( "dataset_not_allowed", f"{repo_id} is not a public, ungated dataset on the Hub", ) + # A renamed repo's old id redirects to the new one. + if info.id.lower() in denied_ids: + raise BrokerError( + "dataset_not_allowed", f"{repo_id} is not allowed in this task" + ) volumes.append( Volume( type="dataset", - source=repo_id, + source=info.id, revision=info.sha, mount_path=f"{DATASETS_MOUNT}/{repo_id}", read_only=True, @@ -368,21 +375,18 @@ def _dataset_volumes( @staticmethod def _launcher_script(script: str, requirements: list[str]) -> str: - """Embed `script` in the launcher, with `requirements` installed from wheels only.""" - header = [ - "# /// script", - '# requires-python = ">=3.10"', - f"# dependencies = {json.dumps(requirements)}", - "#", - "# [tool.uv]", - "# no-build = true", - "# ///", - ] + """Embed `script` and `requirements` in the launcher.""" encoded = base64.b64encode(script.encode()).decode() - launcher = LAUNCHER.read_text().replace( - 'AGENT_SCRIPT = ""', f'AGENT_SCRIPT = "{encoded}"', 1 + launcher = LAUNCHER.read_text() + for placeholder, value in ( + ('AGENT_SCRIPT = ""', f'AGENT_SCRIPT = "{encoded}"'), + ("REQUIREMENTS: list[str] = []", f"REQUIREMENTS = {requirements!r}"), + ): + launcher = launcher.replace(placeholder, value, 1) + header = ( + '# /// script\n# requires-python = ">=3.10"\n# dependencies = []\n# ///\n' ) - return "\n".join(header) + "\n" + launcher + return header + launcher def evaluate( self, @@ -393,6 +397,7 @@ def evaluate( flavor: str, timeout_minutes: int, env: dict[str, str], + secrets: dict[str, str] | None = None, volumes: list[Volume] | None = None, ) -> Job: """Run an evaluator job on `source`'s artifact, mounted read-only at `/model`, next to `volumes`.""" @@ -418,6 +423,7 @@ def evaluate( reservation=reservation, dependencies=[], env=env, + secrets=secrets, volumes=[model, *(volumes or [])], ) job.source_job_id = source.job_id @@ -433,6 +439,7 @@ def _run( reservation: int, dependencies: list[str], env: dict[str, str] | None = None, + secrets: dict[str, str] | None = None, volumes: list[Volume] | None = None, ) -> Job: # run_uv_job uploads a local script to the namespace's jobs-artifacts @@ -441,22 +448,34 @@ def _run( with tempfile.TemporaryDirectory() as tmp: path = Path(tmp) / f"{role}.py" path.write_text(script) - info = self._api.run_uv_job( - script=str(path), - dependencies=dependencies, - env=env or {}, - secrets={}, - flavor=flavor, - timeout=f"{timeout_minutes}m", - labels={ - "openenv-env": "posttrain_env", - "openenv-episode": self.episode_id, - "openenv-role": role, - }, - volumes=volumes, - namespace=self.namespace, - resource_group_id=self.resource_group_id, - ) + request = uuid4().hex + try: + info = self._api.run_uv_job( + script=str(path), + dependencies=dependencies, + env=env or {}, + secrets=secrets or {}, + flavor=flavor, + timeout=f"{timeout_minutes}m", + labels={ + "openenv-env": "posttrain_env", + "openenv-episode": self.episode_id, + "openenv-role": role, + "openenv-request": request, + }, + volumes=volumes, + namespace=self.namespace, + resource_group_id=self.resource_group_id, + ) + except Exception: + # The job can exist although the call failed, e.g. on a read + # timeout. Track it rather than leave it running unbudgeted. + found = self._api.list_jobs( + labels={"openenv-request": request}, namespace=self.namespace + ) + info = next(iter(found), None) + if info is None: + raise artifacts = [ v for v in info.volumes or [] @@ -525,11 +544,7 @@ def _read_result(self, job: Job) -> dict | None: except (ValueError, KeyError, TypeError): score = -1.0 if not 0.0 <= score <= 1.0: - return { - "status": "invalid", - "score": 0.0, - "feedback": "malformed score", - } + return {"status": "error", "score": 0.0, "feedback": "malformed score"} metrics = result.get("metrics") return { "status": str(result.get("status", "ok")), @@ -542,7 +557,7 @@ def _read_result(self, job: Job) -> dict | None: if job.result_reads < MAX_RESULT_READS: return None return { - "status": "invalid", + "status": "error", "score": 0.0, "feedback": "evaluator printed no score", } @@ -579,7 +594,8 @@ def cancel_all(self, reason: str, timeout_s: float, poll_s: float) -> list[str]: for job in live: if job.state == "orphaned": job.state, job.settled = "cancelling", False - with suppress(HfHubHTTPError): + # Best effort: the Hub client's network errors vary by version. + with suppress(Exception): self.cancel(job, reason) deadline = time.monotonic() + timeout_s while ( @@ -588,7 +604,7 @@ def cancel_all(self, reason: str, timeout_s: float, poll_s: float) -> list[str]: ): time.sleep(poll_s) for job in live: - with suppress(HfHubHTTPError): + with suppress(Exception): self.refresh(job) orphaned = [] for job in live: diff --git a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py index 3e8f285976..aeef0187de 100644 --- a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py +++ b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py @@ -79,6 +79,9 @@ def invalid(feedback): invalid(f"cannot load /model: {type(e).__name__}") if info["missing_keys"] or info["mismatched_keys"]: invalid("/model weights do not match the base model") +# from_pretrained also reads /model/generation_config.json; generate() would fill +# unset fields from it, so the checkpoint could change or break decoding. +model.generation_config = generation_config model.eval() correct = formatted = 0 diff --git a/envs/posttrain_env/server/job_launcher.py b/envs/posttrain_env/server/job_launcher.py index a7b667de87..c6ef20a591 100644 --- a/envs/posttrain_env/server/job_launcher.py +++ b/envs/posttrain_env/server/job_launcher.py @@ -2,18 +2,19 @@ """Run the agent's script in its HF Job without network access. -The broker sends this file as the job's script, with the agent's script -embedded below and the agent's dependencies in its PEP 723 header. uv installs -them, wheels only, before any of the agent's code exists on disk. Then, still -as root, the launcher: - -1. lets an unprivileged user read the installed environment and gives it - `/data/outputs`; -2. installs a seccomp filter that refuses every socket except `AF_UNIX`, and +The broker sends this file as the job's script, with the agent's script and +requirements embedded below. As root, the launcher: + +1. installs the requirements into a fresh venv, from wheels only. Installing a + wheel runs none of its code; its `.pth` files and modules first run when the + agent's script starts, under the filter; +2. gives an unprivileged uid `/data/outputs`; +3. installs a seccomp filter that refuses every socket except `AF_UNIX`, and io_uring, which can open sockets without calling `socket()`, and the calls that read or write another process's memory; -3. drops to uid 65534 and runs the agent's script. As root the script could - still write the unfiltered uv parent's `/proc//mem`. +4. closes inherited file descriptors, drops to that uid and runs the agent's + script. As root the script could still write the unfiltered uv parent's + `/proc//mem`. HF Jobs containers have no `CAP_NET_ADMIN` and cannot create namespaces, but any process may filter its own system calls; systemd's @@ -32,8 +33,11 @@ from pathlib import Path AGENT_SCRIPT = "" # base64, filled in by the broker -NOBODY = 65534 +REQUIREMENTS: list[str] = [] # filled in by the broker +# Used by no other process in the job, so the script can't reach an unfiltered one. +AGENT_UID = 10000 WORKDIR = Path("/tmp/posttrain") +VENV = WORKDIR / "venv" OUTPUTS = Path("/data/outputs") SCMP_ACT_ALLOW = 0x7FFF0000 SCMP_ACT_EACCES = 0x00050000 | errno.EACCES @@ -60,18 +64,14 @@ class ArgCmp(ctypes.Structure): ] -def open_environment() -> None: - """Let uid 65534 read the interpreter and packages uv installed under the home directory.""" - home = Path.home() - paths = {Path(p).resolve() for p in (*sys.path, sys.prefix) if p} - paths.add(Path(sys.executable).resolve().parent) - for path in paths: - if not path.is_dir() or not path.is_relative_to(home): - continue - subprocess.run(["chmod", "-R", "o+rX", str(path)], check=True) - for parent in path.parents: - if parent.is_relative_to(home): - parent.chmod(parent.stat().st_mode | 0o001) +def install_requirements() -> None: + """Install `REQUIREMENTS` from PyPI wheels into `VENV`, without running their code.""" + uv = ["uv", "--no-config", "--quiet"] + subprocess.run([*uv, "venv", "--python", sys.executable, str(VENV)], check=True) + if REQUIREMENTS: + python = str(VENV / "bin" / "python") + install = [*uv, "pip", "install", "--python", python, "--no-build"] + subprocess.run([*install, *REQUIREMENTS], check=True) def install_filter() -> None: @@ -115,23 +115,23 @@ def main() -> None: WORKDIR.mkdir(mode=0o755, exist_ok=True) script = WORKDIR / "job.py" script.write_bytes(base64.b64decode(AGENT_SCRIPT)) + install_requirements() home = WORKDIR / "home" home.mkdir(exist_ok=True) OUTPUTS.mkdir(parents=True, exist_ok=True) for path in (home, OUTPUTS): - os.chown(path, NOBODY, NOBODY) - open_environment() + os.chown(path, AGENT_UID, AGENT_UID) install_filter() + os.closerange(3, os.sysconf("SC_OPEN_MAX")) os.setgroups([]) - os.setgid(NOBODY) - os.setuid(NOBODY) + os.setgid(AGENT_UID) + os.setuid(AGENT_UID) except Exception as e: - sys.exit( - f"posttrain launcher: {type(e).__name__}: {e}. The script did not run." - ) + sys.exit(f"posttrain launcher failed, the script did not run: {e!r}") os.chdir(home) env = {**os.environ, "HOME": str(home), "HF_HUB_OFFLINE": "1"} - os.execve(sys.executable, [sys.executable, str(script)], env) + python = str(VENV / "bin" / "python") + os.execve(python, [python, str(script)], env) if __name__ == "__main__": diff --git a/envs/posttrain_env/server/posttrain_environment.py b/envs/posttrain_env/server/posttrain_environment.py index d708a33130..efc1fa7c86 100644 --- a/envs/posttrain_env/server/posttrain_environment.py +++ b/envs/posttrain_env/server/posttrain_environment.py @@ -461,21 +461,22 @@ def _logs(self, job_id: str, tail: int) -> dict: def _evaluate(self, job_id: str, role: str): task = self._task source = self._broker.refresh(self._broker.get(job_id)) - split, seed = ( - ("dev", task.dev_seed) if role == "dev_eval" else ("test", TEST_SEED) - ) + env = {"POSTTRAIN_EXPECTED": self._nonce, "HF_HUB_OFFLINE": "1"} + # The hidden seed goes as a secret, so it doesn't show in the job's spec. + if role == "dev_eval": + env |= {"POSTTRAIN_SPLIT": "dev", "POSTTRAIN_SEED": str(task.dev_seed)} + secrets = {} + else: + env |= {"POSTTRAIN_SPLIT": "test"} + secrets = {"POSTTRAIN_SEED": str(TEST_SEED)} return self._broker.evaluate( source, role=role, script_path=task.eval_script_path, flavor=task.eval_flavor, timeout_minutes=task.eval_timeout_minutes, - env={ - "POSTTRAIN_SPLIT": split, - "POSTTRAIN_SEED": str(seed), - "POSTTRAIN_EXPECTED": self._nonce, - "HF_HUB_OFFLINE": "1", - }, + env=env, + secrets=secrets, volumes=task.volumes(), ) @@ -498,9 +499,13 @@ def _submit(self, job_id: str) -> dict: "submitted", self._cancel_timeout_s, self._poll_s ) self._done = True - # A score job that did not run to the end is the grader's fault, not the - # agent's: the episode ends unscored, and a missing reward is not zero. - scored = score_job.state == "succeeded" + # A score job that did not run to the end, or printed no valid score, is + # the grader's fault, not the agent's: the episode ends unscored, and a + # missing reward is not zero. + scored = score_job.state == "succeeded" and result["status"] not in ( + "error", + "timeout", + ) self._reward = result["score"] if scored else None self._end = { "reason": "submitted" if scored else "score_failed", diff --git a/envs/posttrain_env/uv.lock b/envs/posttrain_env/uv.lock index e343f8a968..7e9f207113 100644 --- a/envs/posttrain_env/uv.lock +++ b/envs/posttrain_env/uv.lock @@ -3,12 +3,12 @@ revision = 5 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and sys_platform != 'emscripten'", "python_full_version < '3.12' and sys_platform == 'win32'", "python_full_version < '3.12' and sys_platform == 'emscripten'", @@ -1686,12 +1686,12 @@ version = "2.5.4" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and sys_platform != 'emscripten'", ] sdist = { url = "https://files.pythonhosted.org/packages/95/b0/c7453d0b6e2073c3264468b106ee1563750cecc910965e67357e3698c83e/numpy-2.5.4.tar.gz", hash = "sha256:9a94cf751c9ad8ebaa835bcd3d40dacf8534ad086b88c38029b65123c7999d2a", size = 20866315, upload-time = "2026-10-10T20:05:31.422Z" } @@ -1843,7 +1843,7 @@ dev = [ [package.metadata] requires-dist = [ { name = "fastapi", specifier = ">=0.115.0" }, - { name = "huggingface-hub", specifier = ">=1.29.0" }, + { name = "huggingface-hub", specifier = ">=2.3.0" }, { name = "openenv", specifier = ">=0.9.0" }, { name = "packaging", specifier = ">=23.0" }, { name = "pydantic", specifier = ">=2.0.0" }, diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py index a7b74dd1e0..ff57bb99fa 100644 --- a/tests/envs/test_posttrain_environment.py +++ b/tests/envs/test_posttrain_environment.py @@ -8,6 +8,7 @@ from __future__ import annotations +import ast import base64 import ctypes.util import dataclasses @@ -57,10 +58,12 @@ def __init__(self): # Applied the first time a job with this role label is inspected. self.on_inspect = {} self.datasets = { - "open/data": SimpleNamespace(sha="abc123", gated=False), - "gated/data": SimpleNamespace(sha="def456", gated="auto"), + "open/data": SimpleNamespace(id="open/data", sha="abc123", gated=False), + "gated/data": SimpleNamespace(id="gated/data", sha="def456", gated="auto"), } self.dataset_lookups = [] + # Raise from the next run_uv_job call after the job was created. + self.fail_after_create = False def whoami(self): return {"name": "operator"} @@ -92,9 +95,21 @@ def run_uv_job(self, *, script, **kwargs): mount_path="/data", read_only=False, ) - return SimpleNamespace( + info = SimpleNamespace( id=job_id, volumes=(kwargs["volumes"] or []) + [artifacts] ) + self.jobs[job_id].update(labels=kwargs["labels"], info=info) + if self.fail_after_create: + self.fail_after_create = False + raise ConnectionError("read timed out") + return info + + def list_jobs(self, *, labels, namespace): + return [ + job["info"] + for job in self.jobs.values() + if labels.items() <= job["labels"].items() + ] def inspect_job(self, *, job_id, namespace): job = self.jobs[job_id] @@ -139,10 +154,11 @@ def score_line(score, **extra): def launched(run): - """Return the agent script embedded in a launcher job, and the job's PEP 723 header.""" - encoded = re.search(r'^AGENT_SCRIPT = "([^"]*)"', run["script"], re.M).group(1) - header = run["script"].split("# ///\n", 1)[0] - return base64.b64decode(encoded).decode(), header + """Return the agent script and requirements embedded in a launcher job.""" + script = run["script"] + encoded = re.search(r'^AGENT_SCRIPT = "([^"]*)"', script, re.M).group(1) + requirements = re.search(r"^REQUIREMENTS = (.*)$", script, re.M).group(1) + return base64.b64decode(encoded).decode(), ast.literal_eval(requirements) @pytest.fixture @@ -216,6 +232,8 @@ def test_job_starts_with_explicit_settings_and_no_secrets(self, env, api): assert run["namespace"] == "operator" assert run["flavor"] == "cpu-basic" assert run["timeout"] == "10m" + request = run["labels"].pop("openenv-request") + assert len(request) == 32 assert run["labels"] == { "openenv-env": "posttrain_env", "openenv-episode": "ep1", @@ -227,15 +245,16 @@ def test_job_starts_with_explicit_settings_and_no_secrets(self, env, api): def test_script_runs_in_the_launcher_with_wheel_only_dependencies(self, env, api): env.reset(task_id="t0_artifact") - script = ( - '# /// script\n# dependencies = ["torch==2.14.1"]\n# ///\nprint("hi")\n' - ) + script = '# /// script\r\n# dependencies = ["torch==2.14.1"]\r\n# ///\r\n' submit_job(env, script=script, dependencies=["numpy>=2"]) run = api.runs[0] - agent_script, header = launched(run) + agent_script, requirements = launched(run) assert agent_script == script - assert '# dependencies = ["torch==2.14.1", "numpy>=2"]' in header - assert "# no-build = true" in header + assert requirements == ["torch==2.14.1", "numpy>=2"] + # The launcher's own header installs nothing: it installs the + # requirements itself, from wheels, before the filter. + assert "# dependencies = []" in run["script"] + assert '"--no-build"' in run["script"] assert run["dependencies"] == [] assert "install_filter()" in run["script"] compile(run["script"], "launcher.py", "exec") @@ -246,6 +265,7 @@ def test_script_runs_in_the_launcher_with_wheel_only_dependencies(self, env, api (["pkg @ https://example.com/pkg.whl"], "print(1)"), (["git+https://github.com/owner/repo"], "print(1)"), (["numpy; nonsense"], "print(1)"), + (['foo; python_version == "3.1\n0"'], "print(1)"), (["numpy"] * 21, "print(1)"), ([], "# /// script\n# dependencies = [\n# ///\nprint(1)\n"), ([], '# /// script\n# dependencies = ["x @ file:///etc/passwd"]\n# ///\n'), @@ -280,7 +300,8 @@ def test_datasets_are_mounted_read_only_at_their_revision(self, env, api): assert api.dataset_lookups == [("open/data", False)] @pytest.mark.parametrize( - "dataset", ["gated/data", "private/data", "denied/data", "../data", "data"] + "dataset", + ["gated/data", "private/data", "denied/data", "old/name", "../data", "data"], ) def test_gated_missing_denied_and_malformed_datasets_are_refused( self, env, api, monkeypatch, dataset @@ -289,7 +310,9 @@ def test_gated_missing_denied_and_malformed_datasets_are_refused( TASKS["t0_artifact"], denied_datasets=("Denied/Data",) ) monkeypatch.setitem(TASKS, "t0_artifact", task) - api.datasets["denied/data"] = SimpleNamespace(sha="x", gated=False) + denied = SimpleNamespace(id="denied/data", sha="x", gated=False) + # "old/name" is the denied repo's former id, which redirects to it. + api.datasets.update({"denied/data": denied, "old/name": denied}) env.reset(task_id="t0_artifact") data, _ = call( env, @@ -479,7 +502,7 @@ def test_evaluation_logs_are_not_readable(self, env, api): [SCORE_MARKER + json.dumps([0.5])], ], ) - def test_missing_or_out_of_range_scores_count_as_invalid(self, env, api, logs): + def test_missing_or_malformed_scores_are_evaluator_errors(self, env, api, logs): env.reset(task_id="t0_artifact") job = self._finished_job(env, api) evaluation, _ = call(env, "dev_evaluate", job_id=job["job_id"]) @@ -487,7 +510,7 @@ def test_missing_or_out_of_range_scores_count_as_invalid(self, env, api, logs): # A missing score line is re-read a few times in case the logs lag. for _ in range(MAX_RESULT_READS): data, _ = call(env, "jobs_status", job_id=evaluation["job_id"]) - assert data["result"]["status"] == "invalid" + assert data["result"]["status"] == "error" assert data["result"]["score"] == 0.0 @@ -512,9 +535,11 @@ def test_submit_scores_the_hidden_split_and_ends_the_episode(self, env, api): score_run = api.runs[-1] assert score_run["labels"]["openenv-role"] == "score" assert score_run["env"]["POSTTRAIN_SPLIT"] == "test" - assert score_run["env"]["POSTTRAIN_SEED"] == str( - posttrain_environment.TEST_SEED - ) + # The hidden seed is a secret, not part of the job's visible spec. + assert "POSTTRAIN_SEED" not in score_run["env"] + assert score_run["secrets"] == { + "POSTTRAIN_SEED": str(posttrain_environment.TEST_SEED) + } assert api.jobs[straggler["job_id"]]["cancel_requested"] end = obs.metadata["episode_end"] assert end["reason"] == "submitted" @@ -563,6 +588,15 @@ def test_failed_score_job_ends_the_episode_unscored(self, env, api): assert obs.reward is None assert obs.metadata["episode_end"]["reason"] == "score_failed" + def test_score_job_without_a_score_line_ends_the_episode_unscored(self, env, api): + env.reset(task_id="t0_artifact") + job = self._finished(env, api) + api.on_inspect["score"] = {"stage": "COMPLETED", "running_secs": 10} + data, obs = call(env, "submit", job_id=job["job_id"]) + assert data["result"]["status"] == "error" + assert obs.reward is None + assert obs.metadata["episode_end"]["reason"] == "score_failed" + def test_max_steps_ends_the_episode_and_cancels_jobs(self, api): env = PostTrainEnvironment( api=api, max_steps=2, poll_s=0.0, cancel_timeout_s=0.01 @@ -669,8 +703,9 @@ def test_job_cancelled_before_it_started_costs_nothing(self, env, api): assert data["state"] == "cancelled" assert data["cost_usd"] == 0.0 + @pytest.mark.parametrize("error", [http_error(409), ConnectionError("reset")]) def test_a_failing_cancel_call_does_not_stop_the_others( - self, env, api, monkeypatch + self, env, api, monkeypatch, error ): env.reset(task_id="t0_artifact") first, second = submit_job(env), submit_job(env) @@ -678,7 +713,7 @@ def test_a_failing_cancel_call_does_not_stop_the_others( def flaky_cancel(*, job_id, namespace): if job_id == first["job_id"]: - raise http_error(409) + raise error cancel(job_id=job_id, namespace=namespace) monkeypatch.setattr(api, "cancel_job", flaky_cancel) @@ -686,6 +721,16 @@ def flaky_cancel(*, job_id, namespace): env.reset(task_id="t0_artifact") assert api.jobs[second["job_id"]]["stage"] == "CANCELED" + def test_job_created_by_a_failed_call_is_still_tracked(self, env, api): + env.reset(task_id="t0_artifact") + api.fail_after_create = True + job = submit_job(env, timeout_minutes=10) + assert job["job_id"] == "job0" + assert len(api.runs) == 1 + assert job["remaining_usd"] == pytest.approx(0.05 - 10 * CPU_BASIC / 1e6) + env.reset(task_id="t0_artifact") + assert api.jobs["job0"]["stage"] == "CANCELED" + def test_orphans_are_retried_until_hf_confirms_them(self, env, api): env.reset(task_id="t0_artifact") job = submit_job(env) From 60ca41128b9009d187eb5f99c28850373b9da8f9 Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 10:57:52 +0530 Subject: [PATCH 14/24] Describe what the network policy does and doesn't stop in RFC 014 --- rfcs/014-environment-services.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md index 2b05f56ec1..afe36c5281 100644 --- a/rfcs/014-environment-services.md +++ b/rfcs/014-environment-services.md @@ -92,7 +92,7 @@ Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. **Jobs.** Tools return a handle. States are `queued → running → succeeded | failed | cancelled`, plus `cancelling` while a cancel is pending and `orphaned` when the provider does not confirm one in time. An orphaned job is settled at its full reservation, keeps `final: false` and blocks the next `reset` with an error, because it may still be running and billing. `reset`, `close`, `submit`, the step limit and the time limit cancel the episode's live jobs. Submissions take an idempotency key; a retry with the same key returns the same job and makes no second reservation. -**Network.** Agent jobs run without network access. A job that can reach the internet can download a model already trained for the task, or the evaluation's own test data; [PostTrainBench](https://arxiv.org/abs/2603.08640) reports both. Inputs come as read-only mounts instead: the task's base model at a pinned revision, and the public, ungated Hub datasets the agent names, at their current revision, minus a per-task denylist. Packages are installed before the agent's code runs, from wheels only, so no package code runs while the job still has network access. HF Jobs has no egress control, and its containers can't create network namespaces or firewall rules (we checked on `cpu-basic` and `a10g-small`). So `posttrain_env` runs the agent's script inside a launcher that installs a seccomp filter refusing every socket except `AF_UNIX`, and io_uring and cross-process memory access, then drops to an unprivileged uid; systemd's `RestrictAddressFamilies=` works the same way. On hosts the operator controls, openenvd's `openshell` backend (RFC 009) gives the same `egress_control` guarantee with a network namespace. A denylist only catches the known copies of a test set, so tasks whose test data is public also need a contamination check after the run. +**Network.** Agent jobs run without network access. A job that can reach the internet can download a model already trained for the task, or the evaluation's own test data; [PostTrainBench](https://arxiv.org/abs/2603.08640) reports both. Inputs come as read-only mounts instead: the task's base model at a pinned revision, and the public, ungated Hub datasets the agent names, at their current revision, minus a per-task denylist. Packages are installed into a fresh venv before the agent's code runs, from wheels only; installing a wheel runs none of its code, so no package code runs while the job still has network access. HF Jobs has no egress control, and its containers can't create network namespaces or firewall rules (we checked on `cpu-basic` and `a10g-small`). So `posttrain_env` runs the agent's script inside a launcher that installs a seccomp filter refusing every socket except `AF_UNIX`, and io_uring and cross-process memory access, then closes inherited file descriptors and drops to a uid no other process in the job uses; systemd's `RestrictAddressFamilies=` works the same way. On hosts the operator controls, openenvd's `openshell` backend (RFC 009) gives the same `egress_control` guarantee with a network namespace. This narrows what a job can fetch to declared, recorded inputs, but it doesn't make them clean: a public dataset or a PyPI wheel can still hold weights or test data, and a denylist only catches known copies. Tasks whose test data is public also need a contamination check after the run. **Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; anything else, including a score outside [0, 1], counts as `invalid` with score 0. The evaluator takes only data from the artifact, never code or anything that shapes the evaluator's own input: `t0_arithmetic_format` loads safetensors weights into the base model's config, with the base tokenizer and chat template at a pinned revision, since a chat template could compute the answer into the prompt. Its dependencies are pinned too, so scores don't change with a new release, and as a CPU job it installs CPU-only torch. A score job that fails, times out or is cancelled ends the episode unscored (`reward` is `None`, `episode_end.reason` is `score_failed`): the fault is the grader's, and RFC 013 treats a missing reward as missing, not zero. @@ -180,7 +180,7 @@ The final observation carries `reward`, `metadata["service_spend"]` and `metadat Spend is the agent's HF Jobs spend; LLM tokens are not included. Every run ended with no orphaned jobs and `final: true`, and score jobs were not charged to the agent. A run with a `job.write`-only broker token was refused with HTTP 403 when the script upload reached the `jobs-artifacts` bucket (D1). -- **No network.** In the 27 Oct runs the agent's script could not open a socket and ran as uid 65534. CUDA training, DataLoader workers and writing `/data/outputs/` worked as before. Loading the base model from its mount instead of the Hub also cut the score job from about 20 minutes to 140 s. +- **No network.** In the 27 Oct runs the agent's script could not open a socket and ran as an unprivileged uid. CUDA training, DataLoader workers and writing `/data/outputs/` worked as before. Loading the base model from its mount instead of the Hub also cut the score job from about 20 minutes to 140 s. - **T0 measures plumbing, not training skill.** Qwen3.8 trained on all 10,000 `(a, b)` pairs, so a hidden split of an input space that small hides nothing. Real tasks need inputs that cannot be enumerated, or test data the agent cannot reach. - **A failed score job is not a zero.** One Qwen3.8 run scored 1.0 on dev, and its score job printed 1.0 and then hit its timeout; the episode got reward 0. Such episodes now end unscored (`score_failed`), and the evaluator installs CPU-only torch. From 0dda477d69988177270e190c82b0445b0963f018 Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 11:22:46 +0530 Subject: [PATCH 15/24] Add the no-network Qwen3.8 run to RFC 014 --- rfcs/014-environment-services.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md index afe36c5281..6ce63f14b7 100644 --- a/rfcs/014-environment-services.md +++ b/rfcs/014-environment-services.md @@ -177,10 +177,11 @@ The final observation carries `reward`, `metadata["service_spend"]` and `metadat | 2026-10-26 | `t0_arithmetic_format` | Qwen3.8 through HF Inference Providers, 25 and 29 turns | open | 1.0 | 1.0, 1.0 | $0.21, $0.34 | | 2026-10-27 | `t0_artifact` | script | none | | 1.0 | $0.000167 | | 2026-10-27 | `t0_arithmetic_format` | script: SFT on `t4-small`, 333 s | none | 0.94 | 0.93 | $0.041 (`gpu_s` 333) | +| 2026-10-27 | `t0_arithmetic_format` | Qwen3.8 through HF Inference Providers, 23 turns | none | 1.0 | 1.0 | $0.12 | Spend is the agent's HF Jobs spend; LLM tokens are not included. Every run ended with no orphaned jobs and `final: true`, and score jobs were not charged to the agent. A run with a `job.write`-only broker token was refused with HTTP 403 when the script upload reached the `jobs-artifacts` bucket (D1). -- **No network.** In the 27 Oct runs the agent's script could not open a socket and ran as an unprivileged uid. CUDA training, DataLoader workers and writing `/data/outputs/` worked as before. Loading the base model from its mount instead of the Hub also cut the score job from about 20 minutes to 140 s. +- **No network.** In the 27 Oct runs the agent's script could not open a socket and ran as an unprivileged uid. CUDA training, DataLoader workers and writing `/data/outputs/` worked as before. Qwen3.8 loaded the base model from `/base_model` from its first script on; its three failed jobs were its own bugs and one CUDA allocation error, none from the network policy. Loading the base model from its mount instead of the Hub also cut the score job from about 20 minutes to 140 s. - **T0 measures plumbing, not training skill.** Qwen3.8 trained on all 10,000 `(a, b)` pairs, so a hidden split of an input space that small hides nothing. Real tasks need inputs that cannot be enumerated, or test data the agent cannot reach. - **A failed score job is not a zero.** One Qwen3.8 run scored 1.0 on dev, and its score job printed 1.0 and then hit its timeout; the episode got reward 0. Such episodes now end unscored (`score_failed`), and the evaluator installs CPU-only torch. From 073ebcdd61a3043896f5c4c28a102378ebe3536f Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 11:56:06 +0530 Subject: [PATCH 16/24] Scope RFC 014 to the agreed first slice and align it with RFC 013 --- docs/source/environments/posttrain.md | 5 +-- envs/posttrain_env/README.md | 5 +-- rfcs/014-environment-services.md | 48 +++++++++++++-------------- 3 files changed, 29 insertions(+), 29 deletions(-) diff --git a/docs/source/environments/posttrain.md b/docs/source/environments/posttrain.md index c93cf8332a..04f63e355f 100644 --- a/docs/source/environments/posttrain.md +++ b/docs/source/environments/posttrain.md @@ -98,8 +98,9 @@ Money is tracked in micro-USD from the prices `list_jobs_hardware` reports. A job settles at its price per minute times its running minutes, rounded up to whole minutes. Grader evaluations are not charged to the agent. -Every observation carries the episode's spend, for accounting and for planning -budgets and concurrency. It is not a sampling weight (RFC 013): +Every observation carries the episode's spend, for accounting and for budgets, +concurrency and rollout allocation. It is not a sampling weight (RFC 013), and +an episode that ends unscored still reports it: ```python obs.metadata["service_spend"] diff --git a/envs/posttrain_env/README.md b/envs/posttrain_env/README.md index 273b336929..e23000f079 100644 --- a/envs/posttrain_env/README.md +++ b/envs/posttrain_env/README.md @@ -97,8 +97,9 @@ Money is tracked in micro-USD from the prices `list_jobs_hardware` reports. A job settles at its price per minute times its running minutes, rounded up to whole minutes. Grader evaluations are not charged to the agent. -Every observation carries the episode's spend, for accounting and for planning -budgets and concurrency. It is not a sampling weight (RFC 013): +Every observation carries the episode's spend, for accounting and for budgets, +concurrency and rollout allocation. It is not a sampling weight (RFC 013), and +an episode that ends unscored still reports it: ```python obs.metadata["service_spend"] diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md index 6ce63f14b7..7c3071f3a3 100644 --- a/rfcs/014-environment-services.md +++ b/rfcs/014-environment-services.md @@ -37,22 +37,23 @@ Environments answer five questions ad hoc: - Hosting services. Brokers wrap existing ones (HF Jobs first). - Profiles beyond batch job and evaluator in v1 (see [Future Work](#future-work)). -- Changing `/mcp` in this RFC. The move to MCP 2026-07-28 is a later RFC 003 amendment (decision 4). +- Changing `/mcp` or the core types in this RFC. ## Design ### Architecture Overview ``` -agent sandbox ──MCP tools──▶ environment / openenvd (per env) - (no credentials) ledger · artifacts · audit · token exchange - │ broker credential, audience-bound - ▼ - shared brokers: jobs (HF Jobs) · eval -grader ◀── artifact handles + score jobs ──┘ +agent ──MCP tools──▶ environment server = broker (holds the HF token) + (no credentials) ledger · job handles · artifacts + │ run_uv_job, no secrets + ▼ + HF Jobs: agent jobs (no network) · dev_evaluate · score + │ + score job ◀────┘ artifact mounted read-only; its score is the reward ``` -Without openenvd (RFC 009), the environment server is the broker, as in `posttrain_env`. With openenvd, the daemon holds the broker credential as a backend credential and the environment only sees run tokens. +In v1 the environment server is the broker, as in `posttrain_env`. Moving the credential to openenvd (RFC 009) is later work. ### Core Abstractions @@ -73,11 +74,11 @@ budget: {usd: 3.0, scope: rollout} | Profile | Examples | Adds to the contract | |---|---|---| | batch job | HF Jobs; later Modal, Slurm, render farms | job handle, worst-case reservation, cancel, orphans | -| evaluator | held-out model eval, kernel timing | `dev_evaluate` vs `score`, exclusive hardware, cache | +| evaluator | held-out model eval, kernel timing | `dev_evaluate` vs `score`; exclusive hardware and caching for timing (not in the prototype) | -**Budget ledger.** Money is integer micro-USD. Before a job starts the broker reserves its worst case, the flavor's per-minute price times the job's timeout, and refuses the job with a typed `budget_exhausted` error if it does not fit. When the job ends the reservation is replaced by the real cost, the per-minute price times the minutes the job ran, rounded up to whole minutes as HF Jobs bills. Budgets are per rollout and set by the orchestrator, so every rollout in a GRPO group faces the same limit. The agent sees its remaining budget as data, never as reward. The ledger is the broker's estimate; the provider's bill is authoritative, and a provider-side cap is the backstop (see Credentials). +**Budget ledger.** Money is integer micro-USD. Before a job starts the broker reserves its worst case, the flavor's per-minute price times the job's timeout, and refuses the job with a typed `budget_exhausted` error if it does not fit. When the job ends the reservation is replaced by the real cost, the per-minute price times the minutes the job ran, rounded up to whole minutes as HF Jobs bills. Budgets are per rollout and set by the orchestrator, so every rollout in a GRPO group faces the same limit. The agent sees its remaining budget as data, never as reward. The ledger is the broker's estimate; the provider's bill is authoritative, and a provider-side cap is the backstop (D1). -Every observation carries a stable spend entry. It is for accounting and for planning budgets and concurrency, never a sampling weight: dividing a task's sampling weight by its cost would starve expensive, hard tasks. RFC 013's sampler never reads the environment; the trainer reads this entry from the final observation and can report one value per rollout as `costs` to `update(task, rewards, costs)`. +Every observation carries a stable spend entry. It is for accounting, and for budgets, concurrency and rollout allocation, never a sampling weight: dividing a task's sampling weight by its cost would starve expensive, hard tasks. RFC 013's sampler never reads the environment; the trainer reads this entry from the final observation and can report one value per rollout as `costs` to `update(task, rewards, costs)`. RFC 013 takes costs for every group or for none, so an episode that ends unscored still reports its spend. ```python obs.metadata["service_spend"] = { @@ -88,13 +89,13 @@ obs.metadata["service_spend"] = { } ``` -Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. `jobs` counts agent jobs and `calls` counts dev evaluations. Grader evaluations are not charged to the agent. +v1 reports `usd`, `gpu_s`, `jobs` and `calls`. `jobs` counts agent jobs and `calls` counts dev evaluations. Grader evaluations are not charged to the agent. **Jobs.** Tools return a handle. States are `queued → running → succeeded | failed | cancelled`, plus `cancelling` while a cancel is pending and `orphaned` when the provider does not confirm one in time. An orphaned job is settled at its full reservation, keeps `final: false` and blocks the next `reset` with an error, because it may still be running and billing. `reset`, `close`, `submit`, the step limit and the time limit cancel the episode's live jobs. Submissions take an idempotency key; a retry with the same key returns the same job and makes no second reservation. -**Network.** Agent jobs run without network access. A job that can reach the internet can download a model already trained for the task, or the evaluation's own test data; [PostTrainBench](https://arxiv.org/abs/2603.08640) reports both. Inputs come as read-only mounts instead: the task's base model at a pinned revision, and the public, ungated Hub datasets the agent names, at their current revision, minus a per-task denylist. Packages are installed into a fresh venv before the agent's code runs, from wheels only; installing a wheel runs none of its code, so no package code runs while the job still has network access. HF Jobs has no egress control, and its containers can't create network namespaces or firewall rules (we checked on `cpu-basic` and `a10g-small`). So `posttrain_env` runs the agent's script inside a launcher that installs a seccomp filter refusing every socket except `AF_UNIX`, and io_uring and cross-process memory access, then closes inherited file descriptors and drops to a uid no other process in the job uses; systemd's `RestrictAddressFamilies=` works the same way. On hosts the operator controls, openenvd's `openshell` backend (RFC 009) gives the same `egress_control` guarantee with a network namespace. This narrows what a job can fetch to declared, recorded inputs, but it doesn't make them clean: a public dataset or a PyPI wheel can still hold weights or test data, and a denylist only catches known copies. Tasks whose test data is public also need a contamination check after the run. +**Network.** Agent jobs run without network access. A job that can reach the internet can download a model already trained for the task, or the evaluation's own test data; [PostTrainBench](https://arxiv.org/abs/2603.08640) reports both. Inputs come as read-only mounts instead: the task's base model at a pinned revision, and the public, ungated Hub datasets the agent names, at their current revision, minus a per-task denylist. Packages are installed into a fresh venv from wheels only, which runs none of their code, before the agent's code runs. HF Jobs has no egress control, and its containers can't create network namespaces or firewall rules (checked on `cpu-basic` and `a10g-small`). So `posttrain_env` runs the agent's script inside a launcher that installs a seccomp filter refusing every socket except `AF_UNIX`, and io_uring and cross-process memory access, then closes inherited file descriptors and drops to a uid no other process in the job uses; systemd's `RestrictAddressFamilies=` works the same way. This limits a job to declared, recorded inputs, but doesn't make them clean: a public dataset or a PyPI wheel can still hold weights or test data. Tasks whose test data is public also need a contamination check after the run. -**Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; anything else, including a score outside [0, 1], counts as `invalid` with score 0. The evaluator takes only data from the artifact, never code or anything that shapes the evaluator's own input: `t0_arithmetic_format` loads safetensors weights into the base model's config, with the base tokenizer and chat template at a pinned revision, since a chat template could compute the answer into the prompt. Its dependencies are pinned too, so scores don't change with a new release, and as a CPU job it installs CPU-only torch. A score job that fails, times out or is cancelled ends the episode unscored (`reward` is `None`, `episode_end.reason` is `score_failed`): the fault is the grader's, and RFC 013 treats a missing reward as missing, not zero. +**Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward; the hidden split's seed reaches it as a secret. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; a checkpoint it can't use scores 0 as `invalid`. The evaluator takes only data from the artifact, never code or anything that shapes the evaluator's own input: `t0_arithmetic_format` loads safetensors weights into the base model's config, with the base tokenizer, chat template and generation settings from the base model mount, since a chat template could compute the answer into the prompt. Its dependencies are pinned too, so scores don't change with a new release. A score job that fails, times out, is cancelled or prints no valid score ends the episode unscored (`reward` is `None`, `episode_end.reason` is `score_failed`): the fault is the grader's, and RFC 013's `update` takes `None` for a rollout the environment did not score, which samplers leave out rather than count as zero. **Artifacts.** An artifact is the output folder of a succeeded job, exposed as `openenv-artifact:////outputs` with each file's path, size and content hash. Graders mount the folder read-only; nothing is copied through the agent. @@ -102,16 +103,16 @@ Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. #### D1. The broker is the only credential holder -- **Chosen approach**: The broker starts jobs with `HfApi.run_uv_job`, with explicit flavor, namespace, timeout and labels and an empty `secrets` map. It never runs `hf jobs uv run`, whose script header can pull the caller's `HF_TOKEN` into the job and set the namespace; the Python API [ignores that header](https://github.com/huggingface/huggingface_hub/blob/bb2ef7579c3d2dc2eb49b16ccf15a070fe5b7023/src/huggingface_hub/hf_api.py#L12665-L12668). +- **Chosen approach**: The broker starts jobs with `HfApi.run_uv_job`, with explicit flavor, namespace, timeout and labels. Agent jobs get an empty `secrets` map. It never runs `hf jobs uv run`, whose script header can [request secrets that resolve from the caller's environment](https://github.com/huggingface/huggingface_hub/blob/841c22b46cdc25b55a2bfb1c2ad97f418162c282/src/huggingface_hub/cli/jobs.py#L311-L323), `HF_TOKEN` included, and set the namespace; the Python API [ignores that header](https://github.com/huggingface/huggingface_hub/blob/841c22b46cdc25b55a2bfb1c2ad97f418162c282/src/huggingface_hub/hf_api.py#L12901-L12902). - **Rationale**: A fine-grained token with only `job.write` cannot touch repos, but a job holding it can start further jobs that skip the broker's budget checks (verified on HF Jobs). So the token never enters a job. - **Broker token**: "Start and manage Jobs" plus write access to the namespace's `jobs-artifacts` bucket. A `job.write`-only token is refused (HTTP 403) when the broker uploads the job script to that bucket. -- **Backstop**: HF tokens carry no spend cap. Billing broker jobs to a Resource Group with a Jobs spend limit (Enterprise) caps the total; over the limit, HF rejects new jobs and cancels running ones. +- **Backstop**: A token has no spend limit of its own; spend limits are set on a [Resource Group](https://huggingface.co/docs/hub/en/enterprise-resource-groups) (Enterprise). Billing broker jobs to a group with a Jobs spend limit caps the total; over the limit, HF refuses new jobs and cancels running ones. #### D2. Artifacts move through the HF Jobs artifacts bucket - **Chosen approach**: Follow `run_uv_job`'s own convention. It uploads the script to the namespace's private `jobs-artifacts` bucket and mounts that job's folder read-write at `/data`. Jobs write outputs to `/data/outputs/`. Evaluator jobs mount `/outputs` read-only at `/model`. - **Rationale**: No token inside the job, no extra repository, and HF's existing behaviour. Content hashes come from the bucket listing (Xet hashes). -- **Trade-offs**: Every agent job and evaluation creates a bucket folder; cleanup follows the bucket's retention. +- **Trade-offs**: Every agent job and evaluation creates a bucket folder; cleanup follows the bucket's retention, and what a job writes there is neither capped nor charged to the budget. #### D3. Tool names use an underscore prefix @@ -127,8 +128,6 @@ Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. 1. **Long-running services and GRPO.** A service runs live at train time only if it finishes within the trainer's staleness window; for example, TRL's experimental `AsyncGRPOTrainer` drops samples more than [`max_staleness`](https://github.com/huggingface/trl/blob/0dcd18c48bf7e63b49a23da0cb030b0a93a2f39f/trl/experimental/async_grpo/async_grpo_config.py#L126-L128) weight updates old (default 4). Slower services declare a stand-in and the real service runs only at eval. Post-training tasks are multi-hour episodes, so their near-term use is evaluation. 2. **HF Spaces.** No services on Spaces for now. A Space gives no isolation guarantee, so it must not hold a broker credential. -3. **Shared brokers are operator-run services outside the environment**, with the standing of RFC 009's backend gateways. The orchestrator's run config lists each broker's URL and expected TLS identity; that registration is the trust anchor. The daemon exchanges its broker credential for per-rollout tokens bound to one broker ([RFC 8693](https://www.rfc-editor.org/rfc/rfc8693), [RFC 8707](https://www.rfc-editor.org/rfc/rfc8707)); brokers are MCP resource servers ([RFC 9728](https://www.rfc-editor.org/rfc/rfc9728)) that enforce a hard cap per token. This follows RSIGym, LiteLLM virtual keys and OpenMLE Sandbox. -4. **Transport targets MCP 2026-07-28**, which [removes protocol sessions](https://github.com/modelcontextprotocol/modelcontextprotocol/blob/86f6e0c675afea9a8f233e09b6faf566b126086c/docs/specification/2026-07-28/changelog.mdx) in favour of server-minted handles passed as arguments ([SEP-2567](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/2567)). That matches `openenv/session/create` and the job handles above. The upgrade adds `server/discover`, per-request protocol versions, `resultType`, the `Mcp-Method`/`Mcp-Name` headers and cacheable `tools/list`, and enables the [Tasks extension](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/2663). It comes later as a backward-compatible RFC 003 amendment; v1 jobs do not need it. ### Not allowed @@ -137,7 +136,6 @@ Units are `usd`, `gpu_s`, `cpu_s`, `tokens`, `calls` and `jobs`, in base units. - Grading in the agent's sandbox or jobs. - Reward writes the agent can reach. - Passing the broker's token into jobs. -- Judges reading unescaped trajectories. - Building on MCP sampling or payment protocols; MCP has no official cost or payment extension. ## Examples @@ -187,9 +185,9 @@ Spend is the agent's HF Jobs spend; LLM tokens are not included. Every run ended ## Plan -1. **This PR**: the RFC and `envs/posttrain_env`, with no core changes and no network in agent jobs. Two T0 tasks: `t0_artifact` checks the plumbing for a fraction of a cent; `t0_arithmetic_format` is a real SFT task with a hidden test split. +1. **This PR**: the RFC and `envs/posttrain_env`, with no core changes and no network in agent jobs. Two T0 tasks that check the plumbing: `t0_artifact` for a fraction of a cent, and `t0_arithmetic_format`, a small SFT task with a hidden test split. 2. **Core types**: manifest, ledger, job and artifact models outside `server/`; the reserved-name prefix rule for `jobs_`; cleanup that surfaces cancel failures (`_cleanup_session_resources` swallows errors today) and the idle reaper on when services are declared. -3. **openenvd integration** (RFC 009): credential custody, token exchange, the grader zone, and MCP Tasks after the RFC 003 amendment. +3. **openenvd integration** (RFC 009): credential custody and the grader zone. ## Future Work @@ -208,11 +206,11 @@ Profiles added when an environment needs one: | artifact registry | HF Hub push | write-scoped credentials, provenance | | peer agent | A2A | budget passed down to the peer (#779) | -Also: audit records keyed to `(episode, step, tool_call_id)` as OpenTelemetry GenAI/MCP spans, signed checkpoints (OpenSSF model signing), and exclusive-hardware leases for timing evaluators. +Also: shared brokers outside the environment with per-rollout tokens ([RFC 8693](https://www.rfc-editor.org/rfc/rfc8693) token exchange), the MCP 2026-07-28 transport through an RFC 003 amendment, audit records keyed to `(episode, step, tool_call_id)` as OpenTelemetry GenAI/MCP spans, signed checkpoints (OpenSSF model signing), and exclusive-hardware leases for timing evaluators. ## Related - RFC 009 openenvd (#1053, #1354, #1355): the base for credential custody and the grader zone. -- RFC 013 task sampling (#1256): reports `service_spend` as cost, not as a sampling weight. -- RFC 003: the MCP 2026-07-28 amendment. RFC 004: `score` feeds rubrics. RFC 005/006: LLM proxying is the future model-call profile. RFC 008: contract checks for declared services, plus #1357's adversarial panel. RFC 012: separate-verifier precedent. +- RFC 013 task sampling (#1256): `service_spend` is one source of the trainer's `costs`, never a sampling weight; an unscored episode's `None` reward is left out of the sampler's estimates. +- RFC 004: `score` feeds rubrics. RFC 005/006: LLM proxying is the future model-call profile. RFC 008: contract checks for declared services, plus #1357's adversarial panel. RFC 012: separate-verifier precedent. - Issues: #898, #1026, #1232, #1299, #1357, #779. From 8429a25a5248fdc96427af5f56410f45740bce07 Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 13:50:52 +0530 Subject: [PATCH 17/24] List posttrain_env in the repo walkthrough and format its README example --- .claude/docs/REPO_WALKTHROUGH.md | 1 + docs/source/environments/posttrain.md | 4 +++- envs/posttrain_env/README.md | 4 +++- 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/.claude/docs/REPO_WALKTHROUGH.md b/.claude/docs/REPO_WALKTHROUGH.md index cc87eb77c7..63548b51b1 100644 --- a/.claude/docs/REPO_WALKTHROUGH.md +++ b/.claude/docs/REPO_WALKTHROUGH.md @@ -119,6 +119,7 @@ envs/ │ ├── harbor_env/ # Thin Harbor package and trainer session factory ├── thinkingbox_env/ # Stateful MCP business-workflow benchmark adapter +├── posttrain_env/ # Post-training through brokered, budgeted HF Jobs (RFC 014) ├── coding_env/ # Python code execution environment ├── chat_env/ # Conversational environment ├── textarena_env/ # Text-based games (TextArena) diff --git a/docs/source/environments/posttrain.md b/docs/source/environments/posttrain.md index 04f63e355f..cc71964fef 100644 --- a/docs/source/environments/posttrain.md +++ b/docs/source/environments/posttrain.md @@ -160,7 +160,9 @@ isolation guarantee for the credential it holds. ```python from posttrain_env import PostTrainEnv -with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync() as env: +with PostTrainEnv( + base_url="http://localhost:8000", message_timeout_s=3600 +).sync() as env: result = env.reset(task_id="t0_artifact") print(result.observation.metadata["prompt"]) job = env.call_tool( diff --git a/envs/posttrain_env/README.md b/envs/posttrain_env/README.md index e23000f079..54d0f4a2d7 100644 --- a/envs/posttrain_env/README.md +++ b/envs/posttrain_env/README.md @@ -159,7 +159,9 @@ isolation guarantee for the credential it holds. ```python from posttrain_env import PostTrainEnv -with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync() as env: +with PostTrainEnv( + base_url="http://localhost:8000", message_timeout_s=3600 +).sync() as env: result = env.reset(task_id="t0_artifact") print(result.observation.metadata["prompt"]) job = env.call_tool( From eb21148dbaac4947a471d5e7f30f8326017afbd7 Mon Sep 17 00:00:00 2001 From: swappy <59965507+rycerzes@users.noreply.github.com> Date: Tue, 27 Oct 2026 17:29:51 +0530 Subject: [PATCH 18/24] Keep posttrain_env episode teardown off the event loop and address review notes Ending an episode on the WebSocket path waited for HF Jobs to confirm cancels on the shared event loop. Run it in a thread instead. The evaluator now rejects checkpoints with unexpected keys, and RFC 014 links to openenv/OpenEnv and describes trainer staleness without naming one framework. --- .../eval_scripts/t0_arithmetic_format_eval.py | 2 +- .../server/posttrain_environment.py | 5 +++- rfcs/014-environment-services.md | 4 +-- tests/envs/test_posttrain_environment.py | 30 +++++++++++++++++++ 4 files changed, 37 insertions(+), 4 deletions(-) diff --git a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py index aeef0187de..2996317c88 100644 --- a/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py +++ b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py @@ -77,7 +77,7 @@ def invalid(feedback): ) except Exception as e: # Report a bad checkpoint as a score, don't crash. invalid(f"cannot load /model: {type(e).__name__}") -if info["missing_keys"] or info["mismatched_keys"]: +if info["missing_keys"] or info["unexpected_keys"] or info["mismatched_keys"]: invalid("/model weights do not match the base model") # from_pretrained also reads /model/generation_config.json; generate() would fill # unset fields from it, so the checkpoint could change or break decoding. diff --git a/envs/posttrain_env/server/posttrain_environment.py b/envs/posttrain_env/server/posttrain_environment.py index efc1fa7c86..fa0a0186ea 100644 --- a/envs/posttrain_env/server/posttrain_environment.py +++ b/envs/posttrain_env/server/posttrain_environment.py @@ -21,6 +21,7 @@ one server process. """ +import asyncio import logging import os import secrets @@ -372,7 +373,9 @@ async def step_async( obs = await super().step_async( action, timeout_s=self._tool_timeout(action, timeout_s), **kwargs ) - return self._finish(obs) + # Ending an episode waits on HF Jobs to confirm cancels; keep that off + # the event loop shared by every session. + return await asyncio.to_thread(self._finish, obs) def _step_impl( self, diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md index 7c3071f3a3..fcd1e649e3 100644 --- a/rfcs/014-environment-services.md +++ b/rfcs/014-environment-services.md @@ -5,7 +5,7 @@ **Authors**: @rycerzes **RFC ID**: 014 -Discussion: [#1382](https://github.com/huggingface/OpenEnv/issues/1382). Prototype: [`envs/posttrain_env`](../envs/posttrain_env/). +Discussion: [#1382](https://github.com/openenv/OpenEnv/issues/1382). Prototype: [`envs/posttrain_env`](../envs/posttrain_env/). ## Summary @@ -126,7 +126,7 @@ v1 reports `usd`, `gpu_s`, `jobs` and `calls`. `jobs` counts agent jobs and `cal ### Decisions from the discussion -1. **Long-running services and GRPO.** A service runs live at train time only if it finishes within the trainer's staleness window; for example, TRL's experimental `AsyncGRPOTrainer` drops samples more than [`max_staleness`](https://github.com/huggingface/trl/blob/0dcd18c48bf7e63b49a23da0cb030b0a93a2f39f/trl/experimental/async_grpo/async_grpo_config.py#L126-L128) weight updates old (default 4). Slower services declare a stand-in and the real service runs only at eval. Post-training tasks are multi-hour episodes, so their near-term use is evaluation. +1. **Long-running services and GRPO.** A service runs live at train time only if it finishes within the trainer's staleness window. Asynchronous GRPO trainers drop samples generated more than a few weight updates ago, so an episode has minutes, not hours. Slower services declare a stand-in and the real service runs only at eval. Post-training tasks are multi-hour episodes, so their near-term use is evaluation. 2. **HF Spaces.** No services on Spaces for now. A Space gives no isolation guarantee, so it must not hold a broker credential. ### Not allowed diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py index ff57bb99fa..fcdfb02c98 100644 --- a/tests/envs/test_posttrain_environment.py +++ b/tests/envs/test_posttrain_environment.py @@ -661,6 +661,36 @@ async def run(): env.close() +def test_websocket_path_waits_for_cancels_off_the_event_loop(api): + """Other sessions share the event loop, so ending an episode must not block it.""" + import asyncio + + env = PostTrainEnvironment(api=api, max_steps=2, poll_s=0.05, cancel_timeout_s=0.5) + env.reset(task_id="t0_artifact") + job = submit_job(env) + api.jobs[job["job_id"]]["stage"] = "RUNNING" + api.cancel_confirms = False + ticks = 0 + + async def tick(): + nonlocal ticks + while True: + await asyncio.sleep(0.01) + ticks += 1 + + async def run(): + ticker = asyncio.create_task(tick()) + obs = await env.step_async(CallToolAction(tool_name="budget", arguments={})) + ticker.cancel() + return obs + + obs = asyncio.run(run()) + assert obs.metadata["episode_end"]["orphaned_jobs"] == [job["job_id"]] + assert ticks >= 10 + api.cancel_confirms = True + env.close() + + def http_error(status=500): import httpx from huggingface_hub.errors import HfHubHTTPError From 5d450deec192d9dbe6054b468b7a9b95ea5ecfe6 Mon Sep 17 00:00:00 2001 From: burtenshaw Date: Tue, 27 Oct 2026 14:02:33 +0100 Subject: [PATCH 19/24] Fix posttrain admission races and episode cleanup ownership Reserve budget and job slots under a thread-safe admission condition before HF submission, and reuse pending idempotent requests. Publish successful jobs atomically and release only the failed request reservation. Keep direct close retries off the event loop with retained broker ownership. Serialize reset with completion and reject stale completion workers so cancelled steps cannot end later episodes. Cover the reproduced races with fake-API concurrency tests. --- envs/posttrain_env/server/broker.py | 179 ++++++--- .../server/posttrain_environment.py | 164 ++++---- tests/envs/test_posttrain_environment.py | 368 ++++++++++++++++++ 3 files changed, 589 insertions(+), 122 deletions(-) diff --git a/envs/posttrain_env/server/broker.py b/envs/posttrain_env/server/broker.py index f5832cafbc..ef85656090 100644 --- a/envs/posttrain_env/server/broker.py +++ b/envs/posttrain_env/server/broker.py @@ -22,6 +22,7 @@ import math import re import tempfile +import threading import time import tomllib from contextlib import suppress @@ -156,22 +157,34 @@ def __init__( self.jobs: dict[str, Job] = {} self._idempotency: dict[str, str] = {} self._hardware: dict[str, Any] | None = None + self._admission = threading.Condition(threading.RLock()) + self._pending: dict[str, tuple[str, int]] = {} + self._pending_keys: set[str] = set() + self._closing = False # -- ledger ------------------------------------------------------------- def count(self, role: str) -> int: """Return how many jobs of `role` this episode has started.""" - return sum(1 for j in self.jobs.values() if j.role == role) + with self._admission: + return sum(1 for j in self.jobs.values() if j.role == role) + sum( + 1 for pending_role, _ in self._pending.values() if pending_role == role + ) def _charged(self) -> list[Job]: return [j for j in self.jobs.values() if j.role in ("agent", "dev_eval")] def remaining_micro_usd(self) -> int: - used = sum( - j.settled_micro_usd if j.settled else j.reserved_micro_usd - for j in self._charged() - ) - return self.budget_micro_usd - used + with self._admission: + used = sum( + j.settled_micro_usd if j.settled else j.reserved_micro_usd + for j in self._charged() + ) + sum( + cost + for role, cost in self._pending.values() + if role in ("agent", "dev_eval") + ) + return self.budget_micro_usd - used def rate_micro_usd_per_minute(self, flavor: str) -> int: return self._hardware_info(flavor).unit_cost_micro_usd @@ -194,37 +207,48 @@ def _reserve(self, flavor: str, timeout_minutes: int, charged: bool) -> int: return reservation def _settle(self, job: Job) -> None: - if job.running_secs is None: - # A job that ended without a reported duration may still have run; - # charge its full reservation unless it never started. - cost = 0 if job.state == "cancelled" else job.reserved_micro_usd - else: - minutes = math.ceil(job.running_secs / 60) - cost = minutes * self.rate_micro_usd_per_minute(job.flavor) - job.settled_micro_usd = min(cost, job.reserved_micro_usd) - job.settled = True + with self._admission: + if job.running_secs is None: + # A job that ended without a reported duration may still have run; + # charge its full reservation unless it never started. + cost = 0 if job.state == "cancelled" else job.reserved_micro_usd + else: + minutes = math.ceil(job.running_secs / 60) + cost = minutes * self.rate_micro_usd_per_minute(job.flavor) + job.settled_micro_usd = min(cost, job.reserved_micro_usd) + job.settled = True def spend(self) -> dict: """Return the `service_spend` observation entry for this episode.""" - charged = self._charged() - gpu_s = 0.0 - for job in charged: - accelerator = self._hardware_info(job.flavor).accelerator - if accelerator is not None: - gpu_s += (job.running_secs or 0) * int(accelerator.quantity) - return { - "version": 1, - "settled": { - "usd": sum(j.settled_micro_usd for j in charged if j.settled) / 1e6, - "gpu_s": gpu_s, - "jobs": self.count("agent"), - "calls": self.count("dev_eval"), - }, - "reserved": { - "usd": sum(j.reserved_micro_usd for j in charged if not j.settled) / 1e6 - }, - "final": all(j.state in CONFIRMED_STATES for j in self.jobs.values()), - } + with self._admission: + charged = self._charged() + gpu_s = 0.0 + for job in charged: + accelerator = self._hardware_info(job.flavor).accelerator + if accelerator is not None: + gpu_s += (job.running_secs or 0) * int(accelerator.quantity) + return { + "version": 1, + "settled": { + "usd": sum(j.settled_micro_usd for j in charged if j.settled) / 1e6, + "gpu_s": gpu_s, + "jobs": self.count("agent"), + "calls": self.count("dev_eval"), + }, + "reserved": { + "usd": ( + sum(j.reserved_micro_usd for j in charged if not j.settled) + + sum( + cost + for role, cost in self._pending.values() + if role in ("agent", "dev_eval") + ) + ) + / 1e6 + }, + "final": not self._pending + and all(j.state in CONFIRMED_STATES for j in self.jobs.values()), + } # -- jobs --------------------------------------------------------------- @@ -250,8 +274,9 @@ def submit( from wheels before that. Each of `datasets` is mounted read-only at `/datasets/`, next to `volumes`. """ - if idempotency_key and idempotency_key in self._idempotency: - return self.jobs[self._idempotency[idempotency_key]] + with self._admission: + if idempotency_key and idempotency_key in self._idempotency: + return self.jobs[self._idempotency[idempotency_key]] if flavor not in allowed_flavors: raise BrokerError( "flavor_not_allowed", @@ -268,23 +293,70 @@ def submit( ) requirements = self._requirements(script, dependencies or []) mounts = self._dataset_volumes(datasets or [], denied_datasets) - if self.count("agent") >= self.max_jobs: - raise BrokerError( - "limit_reached", f"This episode allows {self.max_jobs} jobs" - ) - reservation = self._reserve(flavor, timeout_minutes, charged=True) - job = self._run( + return self._start( self._launcher_script(script, requirements), role="agent", flavor=flavor, timeout_minutes=timeout_minutes, - reservation=reservation, dependencies=[], volumes=[*(volumes or []), *mounts], + idempotency_key=idempotency_key, ) - if idempotency_key: - self._idempotency[idempotency_key] = job.job_id - return job + + def _start( + self, + script: str, + *, + role: str, + flavor: str, + timeout_minutes: int, + idempotency_key: str | None = None, + **kwargs: Any, + ) -> Job: + # Only admission and ledger publication hold the lock. Provider calls + # may block while other tools inspect or cancel existing jobs. + with self._admission: + if idempotency_key: + self._admission.wait_for( + lambda: idempotency_key not in self._pending_keys + ) + if idempotency_key in self._idempotency: + return self.jobs[self._idempotency[idempotency_key]] + if self._closing: + raise BrokerError("episode_over", "The episode is ending") + limit = self.max_jobs if role == "agent" else self.max_dev_evals + if role != "score" and self.count(role) >= limit: + raise BrokerError( + "limit_reached", f"This episode allows {limit} {role} jobs" + ) + reservation = self._reserve( + flavor, timeout_minutes, charged=role != "score" + ) + request = uuid4().hex + self._pending[request] = (role, reservation) + if idempotency_key: + self._pending_keys.add(idempotency_key) + job = None + try: + job = self._run( + script, + role=role, + flavor=flavor, + timeout_minutes=timeout_minutes, + reservation=reservation, + **kwargs, + ) + return job + finally: + with self._admission: + if job is not None: + self.jobs[job.job_id] = job + if idempotency_key: + self._idempotency[idempotency_key] = job.job_id + del self._pending[request] + if idempotency_key: + self._pending_keys.remove(idempotency_key) + self._admission.notify_all() @staticmethod def _requirements(script: str, dependencies: list[str]) -> list[str]: @@ -402,12 +474,6 @@ def evaluate( ) -> Job: """Run an evaluator job on `source`'s artifact, mounted read-only at `/model`, next to `volumes`.""" self.require_artifact(source) - if role == "dev_eval" and self.count("dev_eval") >= self.max_dev_evals: - raise BrokerError( - "limit_reached", - f"This episode allows {self.max_dev_evals} dev evaluations", - ) - reservation = self._reserve(flavor, timeout_minutes, charged=role == "dev_eval") model = Volume( type="bucket", source=source.bucket, @@ -415,12 +481,11 @@ def evaluate( mount_path="/model", read_only=True, ) - job = self._run( + job = self._start( script_path.read_text(), role=role, flavor=flavor, timeout_minutes=timeout_minutes, - reservation=reservation, dependencies=[], env=env, secrets=secrets, @@ -490,7 +555,6 @@ def _run( bucket=artifacts[0].source if artifacts else None, path=artifacts[0].path if artifacts else None, ) - self.jobs[job.job_id] = job return job def require_artifact(self, job: Job) -> None: @@ -590,7 +654,10 @@ def cancel_all(self, reason: str, timeout_s: float, poll_s: float) -> list[str]: Returns: `list[str]`: ids of the orphaned jobs. """ - live = [j for j in self.jobs.values() if j.state not in CONFIRMED_STATES] + with self._admission: + self._closing = True + self._admission.wait_for(lambda: not self._pending) + live = [j for j in self.jobs.values() if j.state not in CONFIRMED_STATES] for job in live: if job.state == "orphaned": job.state, job.settled = "cancelling", False diff --git a/envs/posttrain_env/server/posttrain_environment.py b/envs/posttrain_env/server/posttrain_environment.py index fa0a0186ea..8a739363eb 100644 --- a/envs/posttrain_env/server/posttrain_environment.py +++ b/envs/posttrain_env/server/posttrain_environment.py @@ -25,6 +25,7 @@ import logging import os import secrets +import threading import time from typing import Any, Optional from uuid import uuid4 @@ -51,6 +52,8 @@ ) TOOL_TIMEOUT_S = 120.0 MAX_LOG_CHARS = 20_000 +# Keep asynchronous close fallbacks alive after core releases the session. +_CLOSE_TASKS: set[asyncio.Task] = set() class PostTrainEnvironment(MCPEnvironment): @@ -100,6 +103,8 @@ def __init__( self._reward: float | None = None self._score_job_id: str | None = None self._end: dict = {} + self._lifecycle_lock = threading.RLock() + self._close_task: asyncio.Task | None = None @mcp.tool def budget() -> dict: @@ -258,57 +263,58 @@ def reset( `RuntimeError`: If a job of the previous episode could not be confirmed cancelled. It may still be running and billing. """ - self._end_jobs("episode reset") - task_id = task_id or self._default_task_id - if task_id not in TASKS: - raise ValueError( - f"Unknown task_id {task_id!r}; choose from {sorted(TASKS)}" + with self._lifecycle_lock: + self._end_jobs("episode reset", self._broker) + task_id = task_id or self._default_task_id + if task_id not in TASKS: + raise ValueError( + f"Unknown task_id {task_id!r}; choose from {sorted(TASKS)}" + ) + if self._namespace is None: + self._namespace = ( + os.environ.get("POSTTRAIN_NAMESPACE") or self._api.whoami()["name"] + ) + self._task = TASKS[task_id] + self._state = State( + episode_id=episode_id or str(uuid4()), step_count=0, task_id=task_id ) - if self._namespace is None: - self._namespace = ( - os.environ.get("POSTTRAIN_NAMESPACE") or self._api.whoami()["name"] + self._broker = Broker( + self._api, + namespace=self._namespace, + episode_id=self._state.episode_id, + budget_usd=self._task.budget_usd, + max_jobs=self._task.max_jobs, + max_dev_evals=self._task.max_dev_evals, + resource_group_id=os.environ.get("POSTTRAIN_RESOURCE_GROUP_ID"), + ) + self._nonce = secrets.token_hex(8) + self._started_at = time.monotonic() + self._done = False + self._reward = None + self._end = {} + self._score_job_id = None + return Observation( + done=False, + reward=0.0, + metadata={ + "task_id": task_id, + "prompt": self._task.prompt.format(nonce=self._nonce), + "budget_usd": self._task.budget_usd, + "max_steps": self._max_steps, + "max_episode_minutes": self._task.max_episode_minutes, + "score_timeout_s": self._score_timeout_s(), + "service_spend": self._broker.spend(), + }, ) - self._task = TASKS[task_id] - self._state = State( - episode_id=episode_id or str(uuid4()), step_count=0, task_id=task_id - ) - self._broker = Broker( - self._api, - namespace=self._namespace, - episode_id=self._state.episode_id, - budget_usd=self._task.budget_usd, - max_jobs=self._task.max_jobs, - max_dev_evals=self._task.max_dev_evals, - resource_group_id=os.environ.get("POSTTRAIN_RESOURCE_GROUP_ID"), - ) - self._nonce = secrets.token_hex(8) - self._started_at = time.monotonic() - self._done = False - self._reward = None - self._end = {} - self._score_job_id = None - return Observation( - done=False, - reward=0.0, - metadata={ - "task_id": task_id, - "prompt": self._task.prompt.format(nonce=self._nonce), - "budget_usd": self._task.budget_usd, - "max_steps": self._max_steps, - "max_episode_minutes": self._task.max_episode_minutes, - "score_timeout_s": self._score_timeout_s(), - "service_spend": self._broker.spend(), - }, - ) - def _end_jobs(self, reason: str) -> None: - if self._broker is None: + def _end_jobs(self, reason: str, broker: Broker | None) -> None: + if broker is None: return - orphaned = self._broker.cancel_all(reason, self._cancel_timeout_s, self._poll_s) + orphaned = broker.cancel_all(reason, self._cancel_timeout_s, self._poll_s) if orphaned: raise RuntimeError( f"HF Jobs did not confirm cancelling {orphaned} from episode " - f"{self._broker.episode_id}; they may still be running and billing" + f"{broker.episode_id}; they may still be running and billing" ) def _score_timeout_s(self) -> float: @@ -326,24 +332,26 @@ def _tool_timeout( return self._max_wait_s + TOOL_TIMEOUT_S return TOOL_TIMEOUT_S - def _finish(self, obs: Observation) -> Observation: - if self._broker is None: + def _finish(self, obs: Observation, broker: Broker | None) -> Observation: + with self._lifecycle_lock: + if broker is None or broker is not self._broker: + return obs + if not self._done: + elapsed_min = (time.monotonic() - self._started_at) / 60 + if self._state.step_count >= self._max_steps: + self._stop("max_steps", f"Reached {self._max_steps} tool calls") + elif elapsed_min >= self._task.max_episode_minutes: + self._stop( + "time_limit", + f"Reached {self._task.max_episode_minutes} minutes", + ) + metadata = {**obs.metadata, "service_spend": self._broker.spend()} + if self._done: + obs.done = True + obs.reward = self._reward + metadata["episode_end"] = self._end + obs.metadata = metadata return obs - if not self._done: - elapsed_min = (time.monotonic() - self._started_at) / 60 - if self._state.step_count >= self._max_steps: - self._stop("max_steps", f"Reached {self._max_steps} tool calls") - elif elapsed_min >= self._task.max_episode_minutes: - self._stop( - "time_limit", f"Reached {self._task.max_episode_minutes} minutes" - ) - metadata = {**obs.metadata, "service_spend": self._broker.spend()} - if self._done: - obs.done = True - obs.reward = self._reward - metadata["episode_end"] = self._end - obs.metadata = metadata - return obs def _stop(self, kind: str, message: str) -> None: orphaned = self._broker.cancel_all(kind, self._cancel_timeout_s, self._poll_s) @@ -357,11 +365,12 @@ def step( timeout_s: Optional[float] = None, **kwargs: Any, ) -> Observation: + broker = self._broker self._state.step_count += 1 obs = super().step( action, timeout_s=self._tool_timeout(action, timeout_s), **kwargs ) - return self._finish(obs) + return self._finish(obs, broker) async def step_async( self, @@ -369,13 +378,14 @@ async def step_async( timeout_s: Optional[float] = None, **kwargs: Any, ) -> Observation: + broker = self._broker self._state.step_count += 1 obs = await super().step_async( action, timeout_s=self._tool_timeout(action, timeout_s), **kwargs ) # Ending an episode waits on HF Jobs to confirm cancels; keep that off # the event loop shared by every session. - return await asyncio.to_thread(self._finish, obs) + return await asyncio.to_thread(self._finish, obs, broker) def _step_impl( self, @@ -397,10 +407,32 @@ def state(self) -> State: return self._state def close(self) -> None: + broker = self._broker try: - self._end_jobs("environment closed") - finally: - super().close() + loop = asyncio.get_running_loop() + except RuntimeError: + self._close(broker) + else: + # Core retries failed executor cleanup directly on its event loop. + # Retain that retry without waiting for HF cancellation on the loop. + if self._close_task is None or self._close_task.done(): + self._close_task = loop.create_task(self._close_in_thread(broker)) + _CLOSE_TASKS.add(self._close_task) + self._close_task.add_done_callback(_CLOSE_TASKS.discard) + + def _close(self, broker: Broker | None) -> None: + with self._lifecycle_lock: + try: + self._end_jobs("environment closed", broker) + finally: + if broker is self._broker: + super().close() + + async def _close_in_thread(self, broker: Broker | None) -> None: + try: + await asyncio.to_thread(self._close, broker) + except Exception: + logger.exception("posttrain_env close could not confirm job cancellation") # -- tools -------------------------------------------------------------- diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py index fcdfb02c98..28276c73dc 100644 --- a/tests/envs/test_posttrain_environment.py +++ b/tests/envs/test_posttrain_environment.py @@ -9,6 +9,7 @@ from __future__ import annotations import ast +import asyncio import base64 import ctypes.util import dataclasses @@ -16,6 +17,8 @@ import re import subprocess import sys +import threading +from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait from pathlib import Path from types import SimpleNamespace @@ -863,3 +866,368 @@ def test_launcher_filter_blocks_network_sockets_but_not_unix_sockets(): [sys.executable, "-c", code], capture_output=True, text=True, check=True ) assert out.stdout.split() == ["blocked", "blocked", "blocked", "unix"] + + +@pytest.mark.parametrize("same_key", [False, True]) +@pytest.mark.parametrize("lost_response", [False, True]) +def test_concurrent_fastmcp_admission(env, api, monkeypatch, same_key, lost_response): + env.reset(task_id="t0_arithmetic_format") + api.fail_after_create = lost_response + entered = threading.Event() + release = threading.Event() + api_lock = threading.Lock() + run = api.run_uv_job + + def blocked_run(**kwargs): + entered.set() + assert release.wait(5) + with api_lock: + return run(**kwargs) + + monkeypatch.setattr(api, "run_uv_job", blocked_run) + + async def exercise(): + async with env.mcp_session() as client: + + def request(i): + return client.call_tool( + "jobs_submit", + { + "script": "print(1)", + "flavor": "a10g-small", + "timeout_minutes": 60, + "idempotency_key": "same" if same_key else str(i), + }, + ) + + calls = [asyncio.create_task(request(i)) for i in range(4)] + try: + assert await asyncio.to_thread(entered.wait, 5) + # A budget call traverses the same FastMCP thread pool while + # provider submission is pending. + budget = await client.call_tool("budget", {}) + assert budget.data["remaining_usd"] <= 3 - 60 * A10G_SMALL / 1e6 + finally: + release.set() + return [result.data for result in await asyncio.gather(*calls)] + + results = asyncio.run(exercise()) + successes = [r for r in results if "job_id" in r] + if same_key: + assert len(successes) == 4 + assert len({r["job_id"] for r in successes}) == 1 + assert len(api.runs) == 1 + else: + assert len(successes) == 2 + assert len(api.runs) == 2 + assert all( + r["error"]["type"] == "budget_exhausted" for r in results if "error" in r + ) + assert env._broker.remaining_micro_usd() >= 0 + assert ( + env._broker.spend()["reserved"]["usd"] == len(api.runs) * 60 * A10G_SMALL / 1e6 + ) + + +def test_cancelled_finish_cannot_end_reset_episode(api, monkeypatch): + env = PostTrainEnvironment(api=api, max_steps=2, poll_s=0, cancel_timeout_s=0.01) + env.reset(task_id="t0_artifact") + submit_job(env) + old_broker = env._broker + entered = threading.Event() + release = threading.Event() + cancel_all = old_broker.cancel_all + first = True + worker_finished = threading.Event() + finish = env._finish + + def blocked_cancel(*args): + nonlocal first + if first: + first = False + entered.set() + assert release.wait(5) + return cancel_all(*args) + + def finish_and_signal(*args): + try: + return finish(*args) + finally: + worker_finished.set() + + monkeypatch.setattr(old_broker, "cancel_all", blocked_cancel) + monkeypatch.setattr(env, "_finish", finish_and_signal) + + async def exercise(): + step = asyncio.create_task( + env.step_async(CallToolAction(tool_name="budget", arguments={})) + ) + assert await asyncio.to_thread(entered.wait, 5) + step.cancel() + with pytest.raises(asyncio.CancelledError): + await step + reset = asyncio.create_task( + asyncio.to_thread(env.reset, task_id="t0_artifact", episode_id="new") + ) + # Let reset finish if it can overtake the old completion. With the + # lifecycle lock it instead waits until we release that completion. + await asyncio.wait({reset}, timeout=0.05) + release.set() + obs = await reset + assert await asyncio.to_thread(worker_finished.wait, 5) + assert not obs.done + assert env.state.episode_id == "new" + assert not env._done + assert env._end == {} + + try: + asyncio.run(exercise()) + finally: + release.set() + env.close() + + +def test_session_cleanup_retry_keeps_event_loop_responsive(api, monkeypatch): + from openenv.core.env_server.http_server import HTTPEnvServer + + env = PostTrainEnvironment(api=api, poll_s=0.01, cancel_timeout_s=0.1) + env.reset(task_id="t0_artifact") + job = submit_job(env) + api.cancel_confirms = False + server = object.__new__(HTTPEnvServer) + server._shared_session_executor = None + loop_calls = [] + cancel = api.cancel_job + + def record_cancel(**kwargs): + try: + asyncio.get_running_loop() + except RuntimeError: + pass + else: + loop_calls.append(kwargs["job_id"]) + return cancel(**kwargs) + + monkeypatch.setattr(api, "cancel_job", record_cancel) + + async def exercise(): + with ThreadPoolExecutor(max_workers=1) as executor: + await server._cleanup_session_resources(env, executor) + assert not loop_calls + # If fallback scheduled another attempt, it remains owned until done. + if env._close_task is not None: + await env._close_task + assert env._broker.jobs[job["job_id"]].state == "orphaned" + api.cancel_confirms = True + await asyncio.to_thread(env.close) + assert env._broker.jobs[job["job_id"]].state == "cancelled" + + try: + asyncio.run(exercise()) + finally: + api.cancel_confirms = True + env.close() + + +@pytest.mark.parametrize("role", ["agent", "dev_eval"]) +def test_pending_admission_enforces_job_limits(env, api, monkeypatch, role): + from envs.posttrain_env.server.broker import BrokerError + + env.reset(task_id="t0_artifact") + source = env._broker.get(submit_job(env)["job_id"]) + api.finish(source.job_id) + env._broker.refresh(source) + broker = env._broker + broker.max_jobs = 2 # The source consumes the first slot. + broker.max_dev_evals = 1 + barrier = threading.Barrier(4) + entered = threading.Event() + release = threading.Event() + api_lock = threading.Lock() + run = api.run_uv_job + + def blocked_run(**kwargs): + entered.set() + assert release.wait(5) + with api_lock: + return run(**kwargs) + + monkeypatch.setattr(api, "run_uv_job", blocked_run) + + def start(): + barrier.wait(5) + try: + if role == "agent": + return broker.submit( + "print(1)", + flavor="cpu-basic", + timeout_minutes=1, + allowed_flavors=("cpu-basic",), + max_timeout_minutes=1, + ) + return broker.evaluate( + source, + role=role, + script_path=env._task.eval_script_path, + flavor="cpu-basic", + timeout_minutes=1, + env={}, + ) + except BrokerError as error: + return error.kind + + with ThreadPoolExecutor(max_workers=4) as pool: + futures = [pool.submit(start) for _ in range(4)] + try: + assert entered.wait(5) + done, _ = wait(futures, timeout=2, return_when=FIRST_COMPLETED) + assert done, ( + "Other admissions must reject while the provider call is pending" + ) + finally: + release.set() + results = [f.result(5) for f in futures] + assert results.count("limit_reached") == 3 + assert len(api.runs) == 2 + + +def test_failed_admission_releases_only_its_own_reservation(env, api, monkeypatch): + env.reset(task_id="t0_artifact") + broker = env._broker + entered = [threading.Event(), threading.Event()] + release = [threading.Event(), threading.Event()] + api_lock = threading.Lock() + run = api.run_uv_job + attempts = 0 + + def failing_run(**kwargs): + nonlocal attempts + with api_lock: + index = attempts + attempts += 1 + if index < 2: + entered[index].set() + assert release[index].wait(5) + if index == 0: + raise ConnectionError("failed before creating job") + with api_lock: + return run(**kwargs) + + monkeypatch.setattr(api, "run_uv_job", failing_run) + + async def exercise(): + async with env.mcp_session() as client: + + def start(key): + return client.call_tool( + "jobs_submit", + { + "script": "print(1)", + "flavor": "cpu-basic", + "timeout_minutes": 10, + "idempotency_key": key, + }, + ) + + first = asyncio.create_task(start("retry")) + assert await asyncio.to_thread(entered[0].wait, 5) + second = asyncio.create_task(start("other")) + try: + assert await asyncio.to_thread(entered[1].wait, 5) + release[0].set() + failed = await first + assert failed.data["error"]["type"] == "internal_error" + assert broker.remaining_micro_usd() == 50_000 - 10 * CPU_BASIC + retry = await start("retry") + assert "job_id" in retry.data + assert broker.remaining_micro_usd() == 50_000 - 20 * CPU_BASIC + finally: + for event in release: + event.set() + await asyncio.gather(first, second) + assert len(api.runs) == 2 + assert broker.spend()["reserved"]["usd"] == 20 * CPU_BASIC / 1e6 + + asyncio.run(exercise()) + + +def test_finish_queued_before_reset_keeps_episode_ownership(api, monkeypatch): + env = PostTrainEnvironment(api=api, max_steps=1) + env.reset(task_id="t0_artifact") + entered = threading.Event() + release = threading.Event() + completed = threading.Event() + finish = env._finish + + def queued_finish(*args): + entered.set() + assert release.wait(5) + try: + return finish(*args) + finally: + completed.set() + + monkeypatch.setattr(env, "_finish", queued_finish) + + async def exercise(): + step = asyncio.create_task( + env.step_async(CallToolAction(tool_name="budget", arguments={})) + ) + try: + assert await asyncio.to_thread(entered.wait, 5) + step.cancel() + with pytest.raises(asyncio.CancelledError): + await step + await asyncio.to_thread(env.reset, task_id="t0_artifact", episode_id="new") + # New episode activity must never be completed by the old worker. + env.state.step_count = 1 + finally: + release.set() + assert await asyncio.to_thread(completed.wait, 5) + assert not env._done + assert env._end == {} + assert env.state.episode_id == "new" + + try: + asyncio.run(exercise()) + finally: + release.set() + env.close() + + +def test_deferred_close_retains_the_original_broker(api, monkeypatch): + env = PostTrainEnvironment(api=api) + env.reset(task_id="t0_artifact") + old_job = submit_job(env) + entered = threading.Event() + release = threading.Event() + close = env._close + + def queued_close(broker): + entered.set() + assert release.wait(5) + close(broker) + + monkeypatch.setattr(env, "_close", queued_close) + + async def exercise(): + env.close() + owner = env._close_task + env.close() + assert env._close_task is owner + try: + assert await asyncio.to_thread(entered.wait, 5) + await asyncio.to_thread(env.reset, task_id="t0_artifact", episode_id="new") + new_job = await asyncio.to_thread(submit_job, env) + finally: + release.set() + await owner + assert api.jobs[old_job["job_id"]]["stage"] == "CANCELED" + assert api.jobs[new_job["job_id"]]["stage"] == "SCHEDULING" + assert env.mcp_server is not None + + try: + asyncio.run(exercise()) + finally: + release.set() + env.close() From 83202a09c8b014523db1369614e913fb9f72e2bc Mon Sep 17 00:00:00 2001 From: burtenshaw Date: Tue, 27 Oct 2026 14:18:48 +0100 Subject: [PATCH 20/24] Fix stale scoring ownership and retain uncertain job admissions (#1500) Capture the scoring episode before provider calls and guard result publication. Retain request labels, reservations and idempotency ownership when creation reconciliation fails. Bound admission and teardown waits, cancel late creations, and report incomplete cleanup including jobs published after its snapshot. --- envs/posttrain_env/server/broker.py | 272 +++++++++---- .../server/posttrain_environment.py | 89 +++-- tests/envs/test_posttrain_environment.py | 367 ++++++++++++++++++ 3 files changed, 626 insertions(+), 102 deletions(-) diff --git a/envs/posttrain_env/server/broker.py b/envs/posttrain_env/server/broker.py index ef85656090..0eee8552e8 100644 --- a/envs/posttrain_env/server/broker.py +++ b/envs/posttrain_env/server/broker.py @@ -61,6 +61,7 @@ MAX_ARTIFACT_FILES = 50 # Logs can lag a finished job; re-read before declaring the score missing. MAX_RESULT_READS = 3 +ADMISSION_TIMEOUT_S = 120.0 class BrokerError(Exception): @@ -115,6 +116,18 @@ def view(self) -> dict: return view +@dataclass +class PendingJob: + """A reserved request whose remote job identity is not yet confirmed.""" + + role: str + reservation: int + flavor: str + timeout_minutes: int + idempotency_key: str | None + active: bool = True + + class Broker: """ Start, track and cancel HF Jobs for one episode, against a budget. @@ -158,8 +171,7 @@ def __init__( self._idempotency: dict[str, str] = {} self._hardware: dict[str, Any] | None = None self._admission = threading.Condition(threading.RLock()) - self._pending: dict[str, tuple[str, int]] = {} - self._pending_keys: set[str] = set() + self._pending: dict[str, PendingJob] = {} self._closing = False # -- ledger ------------------------------------------------------------- @@ -168,7 +180,7 @@ def count(self, role: str) -> int: """Return how many jobs of `role` this episode has started.""" with self._admission: return sum(1 for j in self.jobs.values() if j.role == role) + sum( - 1 for pending_role, _ in self._pending.values() if pending_role == role + 1 for pending in self._pending.values() if pending.role == role ) def _charged(self) -> list[Job]: @@ -180,9 +192,9 @@ def remaining_micro_usd(self) -> int: j.settled_micro_usd if j.settled else j.reserved_micro_usd for j in self._charged() ) + sum( - cost - for role, cost in self._pending.values() - if role in ("agent", "dev_eval") + pending.reservation + for pending in self._pending.values() + if pending.role in ("agent", "dev_eval") ) return self.budget_micro_usd - used @@ -239,9 +251,9 @@ def spend(self) -> dict: "usd": ( sum(j.reserved_micro_usd for j in charged if not j.settled) + sum( - cost - for role, cost in self._pending.values() - if role in ("agent", "dev_eval") + pending.reservation + for pending in self._pending.values() + if pending.role in ("agent", "dev_eval") ) ) / 1e6 @@ -315,28 +327,67 @@ def _start( ) -> Job: # Only admission and ledger publication hold the lock. Provider calls # may block while other tools inspect or cancel existing jobs. + retry = None with self._admission: if idempotency_key: - self._admission.wait_for( - lambda: idempotency_key not in self._pending_keys + ready = self._admission.wait_for( + lambda: ( + not any( + p.idempotency_key == idempotency_key and p.active + for p in self._pending.values() + ) + ), + timeout=ADMISSION_TIMEOUT_S, ) + if not ready: + raise BrokerError( + "creation_pending", + "Remote job creation is unconfirmed; retry later", + ) if idempotency_key in self._idempotency: return self.jobs[self._idempotency[idempotency_key]] - if self._closing: - raise BrokerError("episode_over", "The episode is ending") - limit = self.max_jobs if role == "agent" else self.max_dev_evals - if role != "score" and self.count(role) >= limit: - raise BrokerError( - "limit_reached", f"This episode allows {limit} {role} jobs" + retry = next( + ( + request + for request, p in self._pending.items() + if p.idempotency_key == idempotency_key + ), + None, + ) + if retry is None: + if self._closing: + raise BrokerError("episode_over", "The episode is ending") + limit = self.max_jobs if role == "agent" else self.max_dev_evals + if role != "score" and self.count(role) >= limit: + raise BrokerError( + "limit_reached", f"This episode allows {limit} {role} jobs" + ) + reservation = self._reserve( + flavor, timeout_minutes, charged=role != "score" + ) + request = uuid4().hex + self._pending[request] = PendingJob( + role, reservation, flavor, timeout_minutes, idempotency_key ) - reservation = self._reserve( - flavor, timeout_minutes, charged=role != "score" + if retry is not None: + try: + job = self._reconcile(retry) + except Exception as error: + raise BrokerError( + "creation_pending", + "Remote job creation is unconfirmed; retry later", + ) from error + if job is not None: + return job + # A successful lookup confirmed no job, or another caller adopted it. + return self._start( + script, + role=role, + flavor=flavor, + timeout_minutes=timeout_minutes, + idempotency_key=idempotency_key, + **kwargs, ) - request = uuid4().hex - self._pending[request] = (role, reservation) - if idempotency_key: - self._pending_keys.add(idempotency_key) - job = None try: job = self._run( script, @@ -344,18 +395,86 @@ def _start( flavor=flavor, timeout_minutes=timeout_minutes, reservation=reservation, + request=request, **kwargs, ) + except Exception: + # A lost response can hide a running job. If lookup also fails, + # retain the request label, reservation and key for later recovery. + job = self._reconcile(request, creating=True) + if job is None: + raise + else: + self._publish(request, job) + return job + + def _publish(self, request: str, job: Job | None) -> None: + with self._admission: + pending = self._pending.pop(request) + if job is not None: + self.jobs[job.job_id] = job + if pending.idempotency_key: + self._idempotency[pending.idempotency_key] = job.job_id + closing = self._closing + self._admission.notify_all() + if job is not None and closing: + # A timed-out cleanup may have returned before creation completed. + # Keep the returned handle and attempt cancellation on this worker. + with suppress(Exception): + self.cancel(job, "episode ended during creation") + + def _reconcile( + self, + request: str, + *, + creating: bool = False, + cleanup: bool = False, + timeout_s: float | None = None, + ) -> Job | None: + with self._admission: + if not creating: + ready = self._admission.wait_for( + lambda: ( + request not in self._pending + or not self._pending[request].active + ), + timeout=ADMISSION_TIMEOUT_S if timeout_s is None else timeout_s, + ) + if not ready: + raise BrokerError( + "creation_pending", "Remote job creation is unconfirmed" + ) + pending = self._pending.get(request) + if pending is None: + return None + if self._closing and not (creating or cleanup): + # Cleanup owns recovery once closing starts, so a retry cannot + # publish a live job after cancel_all snapshots the known jobs. + raise BrokerError( + "creation_pending", "Remote job creation is unconfirmed" + ) + pending.active = True + try: + found = self._api.list_jobs( + labels={"openenv-request": request}, namespace=self.namespace + ) + info = next(iter(found), None) + job = ( + None + if info is None + else self._job_from_info( + info, + role=pending.role, + flavor=pending.flavor, + timeout_minutes=pending.timeout_minutes, + reservation=pending.reservation, + ) + ) + self._publish(request, job) return job finally: with self._admission: - if job is not None: - self.jobs[job.job_id] = job - if idempotency_key: - self._idempotency[idempotency_key] = job.job_id - del self._pending[request] - if idempotency_key: - self._pending_keys.remove(idempotency_key) + pending.active = False self._admission.notify_all() @staticmethod @@ -502,6 +621,7 @@ def _run( flavor: str, timeout_minutes: int, reservation: int, + request: str, dependencies: list[str], env: dict[str, str] | None = None, secrets: dict[str, str] | None = None, @@ -513,34 +633,33 @@ def _run( with tempfile.TemporaryDirectory() as tmp: path = Path(tmp) / f"{role}.py" path.write_text(script) - request = uuid4().hex - try: - info = self._api.run_uv_job( - script=str(path), - dependencies=dependencies, - env=env or {}, - secrets=secrets or {}, - flavor=flavor, - timeout=f"{timeout_minutes}m", - labels={ - "openenv-env": "posttrain_env", - "openenv-episode": self.episode_id, - "openenv-role": role, - "openenv-request": request, - }, - volumes=volumes, - namespace=self.namespace, - resource_group_id=self.resource_group_id, - ) - except Exception: - # The job can exist although the call failed, e.g. on a read - # timeout. Track it rather than leave it running unbudgeted. - found = self._api.list_jobs( - labels={"openenv-request": request}, namespace=self.namespace - ) - info = next(iter(found), None) - if info is None: - raise + info = self._api.run_uv_job( + script=str(path), + dependencies=dependencies, + env=env or {}, + secrets=secrets or {}, + flavor=flavor, + timeout=f"{timeout_minutes}m", + labels={ + "openenv-env": "posttrain_env", + "openenv-episode": self.episode_id, + "openenv-role": role, + "openenv-request": request, + }, + volumes=volumes, + namespace=self.namespace, + resource_group_id=self.resource_group_id, + ) + return self._job_from_info( + info, + role=role, + flavor=flavor, + timeout_minutes=timeout_minutes, + reservation=reservation, + ) + + @staticmethod + def _job_from_info(info, *, role, flavor, timeout_minutes, reservation) -> Job: artifacts = [ v for v in info.volumes or [] @@ -651,12 +770,29 @@ def cancel_all(self, reason: str, timeout_s: float, poll_s: float) -> list[str]: their full reservation, since their real cost is unknown. Orphans are retried on every later call until HF Jobs confirms they ended. + Unconfirmed creation requests keep their reservations and are reconciled + again on later calls. They are returned as `request:
-
Post-training
-

posttrain_env lets an agent post-train a model on HF Jobs through a broker that holds the credentials and enforces a per-rollout budget. A grader-only evaluation on a hidden split is the reward. Prototype for RFC 014.

+
Post-training (experimental)
+

posttrain_env lets an agent post-train a model on HF Jobs through a broker that holds the credentials and enforces a per-rollout budget. A grader-only evaluation on a hidden split is the reward. Eval-first prototype for RFC 014; private deployments only, excluded from Spaces releases and HF collections.

📄 Docs
diff --git a/docs/source/environments/posttrain.md b/docs/source/environments/posttrain.md index cc71964fef..6949a8915f 100644 --- a/docs/source/environments/posttrain.md +++ b/docs/source/environments/posttrain.md @@ -1,9 +1,11 @@ -# Post-training Environment +# Post-training Environment (experimental) `posttrain_env` is an MCP environment where the agent post-trains a model on [HF Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs). It is the -prototype for [RFC 014: Environment Services](https://github.com/huggingface/OpenEnv/blob/main/rfcs/014-environment-services.md). +experimental, eval-first prototype for [RFC 014: Environment Services +(PR #1508)](https://github.com/openenv/OpenEnv/pull/1508); discussion in +[#1382](https://github.com/openenv/OpenEnv/issues/1382). The agent never holds a credential. It submits training scripts through MCP tools, and the server's broker starts them on HF Jobs with its own token. Each @@ -115,17 +117,40 @@ while a job is still running or a cancellation was never confirmed. ## Episode end -An episode ends when the agent calls `submit`, after `max_steps` tool calls -(default 100) or after the task's time limit. The last two give reward 0. If -the score job itself fails, times out or is cancelled, the episode ends +Through `step(CallToolAction(...))`, an episode ends when the agent calls +`submit`, after `max_steps` tool calls (default 100) or after the task's time limit. +The step and time limits give reward 0. + +Direct `/mcp` calls, including `env.call_tool`, bypass the step and time limits +and do not return the final step observation with reward and `service_spend`. + +If the score job itself fails, times out or is cancelled, the episode ends unscored: `reward` is `None` and `episode_end.reason` is `score_failed`, since the fault is the grader's. Trainers should drop such a rollout, not count it as -zero. Ending the episode cancels every live job. `reset` cancels the previous +zero, subject to acceptance of RFC 013 (#1256) and its final unscored-rollout +semantics. Ending the episode cancels every live job. `reset` cancels the previous episode's jobs first and raises if HF Jobs does not confirm a cancellation, since that job may still be running and billing. -`submit` waits for the score job, so it can take several minutes. `reset` -reports `score_timeout_s`; set the client's `message_timeout_s` above it. +## Using it with a trainer + +This environment is eval-first today. Post-training episodes can take hours, +beyond the staleness window of asynchronous GRPO trainers. Use it for evaluation; +training integration needs a service that fits the trainer's staleness window or +a stand-in for the slow service. + +`submit` blocks for up to `score_timeout_s`, reported in reset metadata (2700 s +for `t0_arithmetic_format`). Size the client's tool/message timeout above that +value, with room for cancellation and transport overhead; the examples use +`message_timeout_s=3600`. + +Read `final.reward` and `final.observation.metadata["service_spend"]` from the +final `step` result. For RFC 013 integration, forward one settled amount per +rollout, for example `service_spend["settled"]["usd"]`, as `costs` in the trainer's +chosen unit. Spend is accounting, never a sampling weight. `final: true` means +all jobs have settled, even mid-episode; it does not mean the episode is done. +Use the step result's `done` for that. An orphan keeps spend `final: false` and +may still bill; retain that accounting even when reward is `None`. ## Tasks @@ -142,6 +167,11 @@ cd envs/posttrain_env uv run --project . server ``` +The local entry point binds `127.0.0.1:8000`. The Docker command explicitly +binds `0.0.0.0:8000` so a private host can reach the published port. This +experimental env is excluded from automatic Spaces releases via +`SKIP_HF_DEPLOYMENT`; do not publish it to Spaces or add it to HF collections. + Configuration, all read by the server only: | Variable | Meaning | @@ -158,6 +188,7 @@ isolation guarantee for the credential it holds. ## Client ```python +from openenv.core.env_server.mcp_types import CallToolAction from posttrain_env import PostTrainEnv with PostTrainEnv( @@ -165,14 +196,43 @@ with PostTrainEnv( ).sync() as env: result = env.reset(task_id="t0_artifact") print(result.observation.metadata["prompt"]) - job = env.call_tool( - "jobs_submit", - script=open("train.py").read(), - flavor="cpu-basic", - timeout_minutes=5, + result = env.step( + CallToolAction( + tool_name="jobs_submit", + arguments={ + "script": open("train.py").read(), + "flavor": "cpu-basic", + "timeout_minutes": 5, + }, + ) + ) + assert result.observation.error is None + job = result.observation.result["data"] + assert "error" not in job, job + status = "queued" + while status in {"queued", "running"}: + result = env.step( + CallToolAction( + tool_name="jobs_wait", + arguments={ + "job_id": job["job_id"], + "timeout_s": 300, + }, + ) + ) + assert not result.done, result.observation.metadata + status = result.observation.result["data"]["state"] + assert status == "succeeded", result.observation.result + final = env.step( + CallToolAction( + tool_name="submit", + arguments={ + "job_id": job["job_id"], + }, + ) ) - env.call_tool("jobs_wait", job_id=job["job_id"], timeout_s=300) - print(env.call_tool("submit", job_id=job["job_id"])) + print(final.done, final.reward) + print(final.observation.metadata["service_spend"]) ``` [`examples/posttrain_env_agent.py`](https://github.com/huggingface/OpenEnv/blob/main/examples/posttrain_env_agent.py) @@ -187,7 +247,8 @@ it never needs the broker's token. - Job folders stay in the `jobs-artifacts` bucket, and the size of what a job writes there is not capped or charged to the budget. - `max_steps` and the time limit are checked in `step`; a client calling the - MCP endpoint directly bypasses them, though not the budget. + MCP endpoint directly bypasses them, though not the budget. Core enforcement + is tracked in [#1507](https://github.com/openenv/OpenEnv/issues/1507). - `torch.distributed` over localhost TCP is blocked in agent jobs, so tasks use single-GPU flavors. diff --git a/envs/posttrain_env/README.md b/envs/posttrain_env/README.md index 54d0f4a2d7..309e24c184 100644 --- a/envs/posttrain_env/README.md +++ b/envs/posttrain_env/README.md @@ -1,8 +1,10 @@ -# Post-training Environment +# Post-training Environment (experimental) `posttrain_env` is an MCP environment where the agent post-trains a model on [HF Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs). It is the -prototype for [RFC 014: Environment Services](../../rfcs/014-environment-services.md). +experimental, eval-first prototype for [RFC 014: Environment Services +(PR #1508)](https://github.com/openenv/OpenEnv/pull/1508); discussion in +[#1382](https://github.com/openenv/OpenEnv/issues/1382). The agent never holds a credential. It submits training scripts through MCP tools, and the server's broker starts them on HF Jobs with its own token. Each @@ -114,17 +116,40 @@ while a job is still running or a cancellation was never confirmed. ## Episode end -An episode ends when the agent calls `submit`, after `max_steps` tool calls -(default 100) or after the task's time limit. The last two give reward 0. If -the score job itself fails, times out or is cancelled, the episode ends +Through `step(CallToolAction(...))`, an episode ends when the agent calls +`submit`, after `max_steps` tool calls (default 100) or after the task's time limit. +The step and time limits give reward 0. + +Direct `/mcp` calls, including `env.call_tool`, bypass the step and time limits +and do not return the final step observation with reward and `service_spend`. + +If the score job itself fails, times out or is cancelled, the episode ends unscored: `reward` is `None` and `episode_end.reason` is `score_failed`, since the fault is the grader's. Trainers should drop such a rollout, not count it as -zero. Ending the episode cancels every live job. `reset` cancels the previous +zero, subject to acceptance of RFC 013 (#1256) and its final unscored-rollout +semantics. Ending the episode cancels every live job. `reset` cancels the previous episode's jobs first and raises if HF Jobs does not confirm a cancellation, since that job may still be running and billing. -`submit` waits for the score job, so it can take several minutes. `reset` -reports `score_timeout_s`; set the client's `message_timeout_s` above it. +## Using it with a trainer + +This environment is eval-first today. Post-training episodes can take hours, +beyond the staleness window of asynchronous GRPO trainers. Use it for evaluation; +training integration needs a service that fits the trainer's staleness window or +a stand-in for the slow service. + +`submit` blocks for up to `score_timeout_s`, reported in reset metadata (2700 s +for `t0_arithmetic_format`). Size the client's tool/message timeout above that +value, with room for cancellation and transport overhead; the examples use +`message_timeout_s=3600`. + +Read `final.reward` and `final.observation.metadata["service_spend"]` from the +final `step` result. For RFC 013 integration, forward one settled amount per +rollout, for example `service_spend["settled"]["usd"]`, as `costs` in the trainer's +chosen unit. Spend is accounting, never a sampling weight. `final: true` means +all jobs have settled, even mid-episode; it does not mean the episode is done. +Use the step result's `done` for that. An orphan keeps spend `final: false` and +may still bill; retain that accounting even when reward is `None`. ## Tasks @@ -141,6 +166,11 @@ cd envs/posttrain_env uv run --project . server ``` +The local entry point binds `127.0.0.1:8000`. The Docker command explicitly +binds `0.0.0.0:8000` so a private host can reach the published port. This +experimental env is excluded from automatic Spaces releases via +`SKIP_HF_DEPLOYMENT`; do not publish it to Spaces or add it to HF collections. + Configuration, all read by the server only: | Variable | Meaning | @@ -157,6 +187,7 @@ isolation guarantee for the credential it holds. ## Client ```python +from openenv.core.env_server.mcp_types import CallToolAction from posttrain_env import PostTrainEnv with PostTrainEnv( @@ -164,14 +195,43 @@ with PostTrainEnv( ).sync() as env: result = env.reset(task_id="t0_artifact") print(result.observation.metadata["prompt"]) - job = env.call_tool( - "jobs_submit", - script=open("train.py").read(), - flavor="cpu-basic", - timeout_minutes=5, + result = env.step( + CallToolAction( + tool_name="jobs_submit", + arguments={ + "script": open("train.py").read(), + "flavor": "cpu-basic", + "timeout_minutes": 5, + }, + ) + ) + assert result.observation.error is None + job = result.observation.result["data"] + assert "error" not in job, job + status = "queued" + while status in {"queued", "running"}: + result = env.step( + CallToolAction( + tool_name="jobs_wait", + arguments={ + "job_id": job["job_id"], + "timeout_s": 300, + }, + ) + ) + assert not result.done, result.observation.metadata + status = result.observation.result["data"]["state"] + assert status == "succeeded", result.observation.result + final = env.step( + CallToolAction( + tool_name="submit", + arguments={ + "job_id": job["job_id"], + }, + ) ) - env.call_tool("jobs_wait", job_id=job["job_id"], timeout_s=300) - print(env.call_tool("submit", job_id=job["job_id"])) + print(final.done, final.reward) + print(final.observation.metadata["service_spend"]) ``` [`examples/posttrain_env_agent.py`](../../examples/posttrain_env_agent.py) @@ -186,7 +246,8 @@ it never needs the broker's token. - Job folders stay in the `jobs-artifacts` bucket, and the size of what a job writes there is not capped or charged to the budget. - `max_steps` and the time limit are checked in `step`; a client calling the - MCP endpoint directly bypasses them, though not the budget. + MCP endpoint directly bypasses them, though not the budget. Core enforcement + is tracked in [#1507](https://github.com/openenv/OpenEnv/issues/1507). - `torch.distributed` over localhost TCP is blocked in agent jobs, so tasks use single-GPU flavors. diff --git a/envs/posttrain_env/SKIP_HF_DEPLOYMENT b/envs/posttrain_env/SKIP_HF_DEPLOYMENT new file mode 100644 index 0000000000..6a16aa4e88 --- /dev/null +++ b/envs/posttrain_env/SKIP_HF_DEPLOYMENT @@ -0,0 +1,2 @@ +Experimental credential-holding broker: private deployments only. +Do not release to Spaces or add to HF collections (RFC 014). diff --git a/envs/posttrain_env/client.py b/envs/posttrain_env/client.py index 903366c11a..5f4b2ae02a 100644 --- a/envs/posttrain_env/client.py +++ b/envs/posttrain_env/client.py @@ -9,23 +9,43 @@ class PostTrainEnv(MCPToolClient): """ Client for the post-training environment. - All interaction happens through MCP tools: `budget`, `jobs_submit`, + Dispatch tools with `step(CallToolAction(...))` to count episode steps and + receive the final reward and `service_spend`. Direct `call_tool` bypasses + episode step/time limits and does not return that final observation. + Available tools are: `budget`, `jobs_submit`, `jobs_status`, `jobs_wait`, `jobs_logs`, `jobs_cancel`, `dev_evaluate` and `submit`. `submit` ends the episode and can take several minutes, so give - the client a message timeout of at least the `score_timeout_s` reported by - `reset`. + the client a message timeout above the `score_timeout_s` reported by + `reset`, with room for cancellation and transport overhead (2700 s for + `t0_arithmetic_format`; the example uses 3600 s). Examples: ```python + from openenv.core.env_server.mcp_types import CallToolAction from posttrain_env import PostTrainEnv with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync() as env: result = env.reset(task_id="t0_artifact") print(result.observation.metadata["prompt"]) - job = env.call_tool("jobs_submit", script=script, flavor="cpu-basic", timeout_minutes=5) - env.call_tool("jobs_wait", job_id=job["job_id"], timeout_s=300) - env.call_tool("submit", job_id=job["job_id"]) + result = env.step(CallToolAction(tool_name="jobs_submit", arguments={ + "script": script, "flavor": "cpu-basic", "timeout_minutes": 5, + })) + assert result.observation.error is None + job = result.observation.result["data"] + assert "error" not in job, job + status = "queued" + while status in {"queued", "running"}: + result = env.step(CallToolAction(tool_name="jobs_wait", arguments={ + "job_id": job["job_id"], "timeout_s": 300, + })) + assert not result.done, result.observation.metadata + status = result.observation.result["data"]["state"] + assert status == "succeeded", result.observation.result + final = env.step(CallToolAction(tool_name="submit", arguments={ + "job_id": job["job_id"], + })) + print(final.reward, final.observation.metadata["service_spend"]) ``` """ diff --git a/envs/posttrain_env/server/app.py b/envs/posttrain_env/server/app.py index d2ff813efe..20ee94bcf3 100644 --- a/envs/posttrain_env/server/app.py +++ b/envs/posttrain_env/server/app.py @@ -25,7 +25,7 @@ def main(): import uvicorn - uvicorn.run(app, host="0.0.0.0", port=8000) + uvicorn.run(app, host="127.0.0.1", port=8000) if __name__ == "__main__": diff --git a/rfcs/014-environment-services.md b/rfcs/014-environment-services.md deleted file mode 100644 index fcd1e649e3..0000000000 --- a/rfcs/014-environment-services.md +++ /dev/null @@ -1,216 +0,0 @@ -# RFC: Environment Services - -**Status**: In Review -**Created**: 2026-10-16 -**Authors**: @rycerzes -**RFC ID**: 014 - -Discussion: [#1382](https://github.com/openenv/OpenEnv/issues/1382). Prototype: [`envs/posttrain_env`](../envs/posttrain_env/). - -## Summary - -More environments give the agent real services instead of in-env tools: post-training tasks that train models on rented GPUs, hill-climbing tasks that call an evaluator on dedicated hardware, simulators and render farms. Today each environment decides alone who holds the credentials, who pays, what happens to a job that outlives the episode and whether the agent can reach the grader. - -This RFC defines **Environment Services**: a brokered layer between the agent and external services, with three rules. The agent never holds a real credential. The agent never reaches the grader. Nothing is spent without a reservation. v1 covers two service profiles, **batch job** and **evaluator**, and a **per-rollout budget**, which is enough for post-training and kernel optimisation. `envs/posttrain_env` implements v1 on HF Jobs without core changes. - -## Motivation - -### Problem Statement - -Environments answer five questions ad hoc: - -1. **Who holds the credential?** Raw keys reach the agent environment. Examples: Harbor `environment.env`, Prime sandbox `secrets`, and OpenHands exporting secrets into the agent's shell. -2. **Who pays?** Existing caps cover LLM tokens only and are checked after spending, so they overshoot (OpenHands `max_budget_per_run`, LiteLLM MCP cost, RSIGym). Nothing defines a budget per rollout, per group or per run. -3. **What happens to a job that outlives the episode?** There is no job handle, no cancel on reset and no orphan handling. `kernrl` runs its GPU evaluator inside `step`. -4. **Can the agent reach the grader?** [ROLL](https://github.com/alibaba/ROLL/blob/581046abce143e643221f8e292895c46a3b20112/roll/pipeline/agentic/env/rock/sanbox_manager.py#L1230-L1332) and [rLLM](https://github.com/rllm-org/rllm/blob/4e828c81680e5a85b638ceedeaf09d1e90109ca8/rllm/eval/script_evaluator.py#L73-L94) upload tests into the agent's sandbox after the episode; in AReaL a rollout's own key can set its reward. METR measured [reward hacking in 30.4% of RE-Bench runs vs 0.7% on HCAST](https://metr.org/blog/2025-06-05-recent-reward-hacking). #1232 and #1357 raise the same concern for OpenEnv. -5. **What was called, and what did it produce?** No framework links service calls to trajectory steps. - -### Goals - -1. A service contract an environment declares, instead of per-env plumbing. -2. Credentials that stay with a broker; the agent and its jobs hold none. -3. A per-rollout budget that reserves before spending and settles after, published in a stable form so trainers can account for spend and plan budgets and concurrency. -4. Jobs with handles, cancellation at episode end and a defined orphan state. -5. Evaluation split into an agent-callable dev endpoint and a grader-only score. - -### Non-Goals - -- Hosting services. Brokers wrap existing ones (HF Jobs first). -- Profiles beyond batch job and evaluator in v1 (see [Future Work](#future-work)). -- Changing `/mcp` or the core types in this RFC. - -## Design - -### Architecture Overview - -``` -agent ──MCP tools──▶ environment server = broker (holds the HF token) - (no credentials) ledger · job handles · artifacts - │ run_uv_job, no secrets - ▼ - HF Jobs: agent jobs (no network) · dev_evaluate · score - │ - score job ◀────┘ artifact mounted read-only; its score is the reward -``` - -In v1 the environment server is the broker, as in `posttrain_env`. Moving the credential to openenvd (RFC 009) is later work. - -### Core Abstractions - -**Service manifest.** An environment declares its services by profile: - -```yaml -services: - - {name: jobs, profile: batch_job, surface: agent, flavors: [a10g-small], max_timeout: 60m, meter: [usd, gpu_s]} - - {name: kernel_eval, profile: evaluator, surface: {agent: dev_evaluate, grader: score}, - hardware: {class: h100, exclusive: true}} -budget: {usd: 3.0, scope: rollout} -``` - -`posttrain_env` keeps this in its task definitions (`server/tasks.py`); the manifest becomes a core type in step 2 of the plan below. - -**Service profiles (v1).** - -| Profile | Examples | Adds to the contract | -|---|---|---| -| batch job | HF Jobs; later Modal, Slurm, render farms | job handle, worst-case reservation, cancel, orphans | -| evaluator | held-out model eval, kernel timing | `dev_evaluate` vs `score`; exclusive hardware and caching for timing (not in the prototype) | - -**Budget ledger.** Money is integer micro-USD. Before a job starts the broker reserves its worst case, the flavor's per-minute price times the job's timeout, and refuses the job with a typed `budget_exhausted` error if it does not fit. When the job ends the reservation is replaced by the real cost, the per-minute price times the minutes the job ran, rounded up to whole minutes as HF Jobs bills. Budgets are per rollout and set by the orchestrator, so every rollout in a GRPO group faces the same limit. The agent sees its remaining budget as data, never as reward. The ledger is the broker's estimate; the provider's bill is authoritative, and a provider-side cap is the backstop (D1). - -Every observation carries a stable spend entry. It is for accounting, and for budgets, concurrency and rollout allocation, never a sampling weight: dividing a task's sampling weight by its cost would starve expensive, hard tasks. RFC 013's sampler never reads the environment; the trainer reads this entry from the final observation and can report one value per rollout as `costs` to `update(task, rewards, costs)`. RFC 013 takes costs for every group or for none, so an episode that ends unscored still reports its spend. - -```python -obs.metadata["service_spend"] = { - "version": 1, - "settled": {"usd": 0.62, "gpu_s": 410.0, "jobs": 2, "calls": 1}, # cumulative for the episode - "reserved": {"usd": 0.0}, # open reservations; empty once every job has settled - "final": True, # false while a job is live or was orphaned -} -``` - -v1 reports `usd`, `gpu_s`, `jobs` and `calls`. `jobs` counts agent jobs and `calls` counts dev evaluations. Grader evaluations are not charged to the agent. - -**Jobs.** Tools return a handle. States are `queued → running → succeeded | failed | cancelled`, plus `cancelling` while a cancel is pending and `orphaned` when the provider does not confirm one in time. An orphaned job is settled at its full reservation, keeps `final: false` and blocks the next `reset` with an error, because it may still be running and billing. `reset`, `close`, `submit`, the step limit and the time limit cancel the episode's live jobs. Submissions take an idempotency key; a retry with the same key returns the same job and makes no second reservation. - -**Network.** Agent jobs run without network access. A job that can reach the internet can download a model already trained for the task, or the evaluation's own test data; [PostTrainBench](https://arxiv.org/abs/2603.08640) reports both. Inputs come as read-only mounts instead: the task's base model at a pinned revision, and the public, ungated Hub datasets the agent names, at their current revision, minus a per-task denylist. Packages are installed into a fresh venv from wheels only, which runs none of their code, before the agent's code runs. HF Jobs has no egress control, and its containers can't create network namespaces or firewall rules (checked on `cpu-basic` and `a10g-small`). So `posttrain_env` runs the agent's script inside a launcher that installs a seccomp filter refusing every socket except `AF_UNIX`, and io_uring and cross-process memory access, then closes inherited file descriptors and drops to a uid no other process in the job uses; systemd's `RestrictAddressFamilies=` works the same way. This limits a job to declared, recorded inputs, but doesn't make them clean: a public dataset or a PyPI wheel can still hold weights or test data. Tasks whose test data is public also need a contamination check after the run. - -**Evaluator.** Two endpoints. `dev_evaluate` runs on the public dev split, is rate-limited and charged to the agent, and returns at most five metrics and short feedback. `score` runs on the hidden split as a grader-only job and is the reward; the hidden split's seed reaches it as a secret. Both run the environment's evaluator script as their own job, with the artifact under test mounted read-only. The evaluator prints one `POSTTRAIN_SCORE {"score": ...}` line; a checkpoint it can't use scores 0 as `invalid`. The evaluator takes only data from the artifact, never code or anything that shapes the evaluator's own input: `t0_arithmetic_format` loads safetensors weights into the base model's config, with the base tokenizer, chat template and generation settings from the base model mount, since a chat template could compute the answer into the prompt. Its dependencies are pinned too, so scores don't change with a new release. A score job that fails, times out, is cancelled or prints no valid score ends the episode unscored (`reward` is `None`, `episode_end.reason` is `score_failed`): the fault is the grader's, and RFC 013's `update` takes `None` for a rollout the environment did not score, which samplers leave out rather than count as zero. - -**Artifacts.** An artifact is the output folder of a succeeded job, exposed as `openenv-artifact:////outputs` with each file's path, size and content hash. Graders mount the folder read-only; nothing is copied through the agent. - -### Key Design Decisions - -#### D1. The broker is the only credential holder - -- **Chosen approach**: The broker starts jobs with `HfApi.run_uv_job`, with explicit flavor, namespace, timeout and labels. Agent jobs get an empty `secrets` map. It never runs `hf jobs uv run`, whose script header can [request secrets that resolve from the caller's environment](https://github.com/huggingface/huggingface_hub/blob/841c22b46cdc25b55a2bfb1c2ad97f418162c282/src/huggingface_hub/cli/jobs.py#L311-L323), `HF_TOKEN` included, and set the namespace; the Python API [ignores that header](https://github.com/huggingface/huggingface_hub/blob/841c22b46cdc25b55a2bfb1c2ad97f418162c282/src/huggingface_hub/hf_api.py#L12901-L12902). -- **Rationale**: A fine-grained token with only `job.write` cannot touch repos, but a job holding it can start further jobs that skip the broker's budget checks (verified on HF Jobs). So the token never enters a job. -- **Broker token**: "Start and manage Jobs" plus write access to the namespace's `jobs-artifacts` bucket. A `job.write`-only token is refused (HTTP 403) when the broker uploads the job script to that bucket. -- **Backstop**: A token has no spend limit of its own; spend limits are set on a [Resource Group](https://huggingface.co/docs/hub/en/enterprise-resource-groups) (Enterprise). Billing broker jobs to a group with a Jobs spend limit caps the total; over the limit, HF refuses new jobs and cancels running ones. - -#### D2. Artifacts move through the HF Jobs artifacts bucket - -- **Chosen approach**: Follow `run_uv_job`'s own convention. It uploads the script to the namespace's private `jobs-artifacts` bucket and mounts that job's folder read-write at `/data`. Jobs write outputs to `/data/outputs/`. Evaluator jobs mount `/outputs` read-only at `/model`. -- **Rationale**: No token inside the job, no extra repository, and HF's existing behaviour. Content hashes come from the bucket listing (Xet hashes). -- **Trade-offs**: Every agent job and evaluation creates a bucket folder; cleanup follows the bucket's retention, and what a job writes there is neither capped nor charged to the budget. - -#### D3. Tool names use an underscore prefix - -- **Chosen approach**: `jobs_submit`, `jobs_status`, `jobs_wait`, `jobs_logs`, `jobs_cancel`, `dev_evaluate`, `submit` and `budget`. -- **Rationale**: OpenAI-compatible function names allow only `[a-zA-Z0-9_-]`, and most trainers convert MCP tools to that form, so `jobs.submit` breaks them. The reserved-name prefix rule in core (step 2) covers `jobs_`. - -#### D4. Long calls block with a declared timeout - -- **Chosen approach**: `submit` waits for the score job and returns the reward in the same step; `jobs_wait` waits up to a capped number of seconds. The environment raises its tool timeout for these calls and reports `score_timeout_s` at `reset` so clients can set their message deadline (the same need as #1026). -- **Rationale**: The reward stays inside the environment (RFC 004) and the episode ends with it, without a side channel. - -### Decisions from the discussion - -1. **Long-running services and GRPO.** A service runs live at train time only if it finishes within the trainer's staleness window. Asynchronous GRPO trainers drop samples generated more than a few weight updates ago, so an episode has minutes, not hours. Slower services declare a stand-in and the real service runs only at eval. Post-training tasks are multi-hour episodes, so their near-term use is evaluation. -2. **HF Spaces.** No services on Spaces for now. A Space gives no isolation guarantee, so it must not hold a broker credential. - -### Not allowed - -- Raw provider keys in the agent sandbox or in agent jobs. -- Network access from agent jobs. -- Grading in the agent's sandbox or jobs. -- Reward writes the agent can reach. -- Passing the broker's token into jobs. -- Building on MCP sampling or payment protocols; MCP has no official cost or payment extension. - -## Examples - -`posttrain_env` with task `t0_arithmetic_format`, driven by any agent loop: - -```python -from posttrain_env import PostTrainEnv - -with PostTrainEnv(base_url="http://localhost:8000", message_timeout_s=3600).sync() as env: - result = env.reset(task_id="t0_arithmetic_format") - print(result.observation.metadata["prompt"]) - print(env.call_tool("budget")) - # {'remaining_usd': 3.0, 'flavors': {'t4-small': {'usd_per_hour': 0.4}, ...}, 'jobs_left': 6, ...} - - job = env.call_tool("jobs_submit", script=train_py, flavor="t4-small", timeout_minutes=30) - status = env.call_tool("jobs_wait", job_id=job["job_id"], timeout_s=300) - # {'state': 'succeeded', 'cost_usd': 0.04, - # 'artifact': {'uri': 'openenv-artifact:////outputs', 'files': [...]}} - - dev = env.call_tool("dev_evaluate", job_id=job["job_id"]) - env.call_tool("jobs_wait", job_id=dev["job_id"], timeout_s=300) # result: {'score': ...} - - final = env.call_tool("submit", job_id=job["job_id"]) # ends the episode; reward = hidden-split score -``` - -The final observation carries `reward`, `metadata["service_spend"]` and `metadata["episode_end"]`. - -## Prototype results - -`envs/posttrain_env` runs this design on HF Jobs. Its unit tests replace HF Jobs with an in-memory fake, with no network access. Live runs: - -| Date | Task | Driver | Agent jobs' network | Dev eval | Reward | Rollout spend | -|---|---|---|---|---|---|---| -| 2026-10-16 | `t0_artifact` | script | open | 1.0 | 1.0 | $0.000334 | -| 2026-10-16 | `t0_arithmetic_format` | script: SFT on `t4-small`, 333 s | open | 0.94 | 0.95 | $0.041 (`gpu_s` 333) | -| 2026-10-26 | `t0_arithmetic_format` | Qwen3.8 through HF Inference Providers, 25 and 29 turns | open | 1.0 | 1.0, 1.0 | $0.21, $0.34 | -| 2026-10-27 | `t0_artifact` | script | none | | 1.0 | $0.000167 | -| 2026-10-27 | `t0_arithmetic_format` | script: SFT on `t4-small`, 333 s | none | 0.94 | 0.93 | $0.041 (`gpu_s` 333) | -| 2026-10-27 | `t0_arithmetic_format` | Qwen3.8 through HF Inference Providers, 23 turns | none | 1.0 | 1.0 | $0.12 | - -Spend is the agent's HF Jobs spend; LLM tokens are not included. Every run ended with no orphaned jobs and `final: true`, and score jobs were not charged to the agent. A run with a `job.write`-only broker token was refused with HTTP 403 when the script upload reached the `jobs-artifacts` bucket (D1). - -- **No network.** In the 27 Oct runs the agent's script could not open a socket and ran as an unprivileged uid. CUDA training, DataLoader workers and writing `/data/outputs/` worked as before. Qwen3.8 loaded the base model from `/base_model` from its first script on; its three failed jobs were its own bugs and one CUDA allocation error, none from the network policy. Loading the base model from its mount instead of the Hub also cut the score job from about 20 minutes to 140 s. -- **T0 measures plumbing, not training skill.** Qwen3.8 trained on all 10,000 `(a, b)` pairs, so a hidden split of an input space that small hides nothing. Real tasks need inputs that cannot be enumerated, or test data the agent cannot reach. -- **A failed score job is not a zero.** One Qwen3.8 run scored 1.0 on dev, and its score job printed 1.0 and then hit its timeout; the episode got reward 0. Such episodes now end unscored (`score_failed`), and the evaluator installs CPU-only torch. - -## Plan - -1. **This PR**: the RFC and `envs/posttrain_env`, with no core changes and no network in agent jobs. Two T0 tasks that check the plumbing: `t0_artifact` for a fraction of a cent, and `t0_arithmetic_format`, a small SFT task with a hidden test split. -2. **Core types**: manifest, ledger, job and artifact models outside `server/`; the reserved-name prefix rule for `jobs_`; cleanup that surfaces cancel failures (`_cleanup_session_resources` swallows errors today) and the idle reaper on when services are declared. -3. **openenvd integration** (RFC 009): credential custody and the grader zone. - -## Future Work - -Profiles added when an environment needs one: - -| Profile | Examples | Adds | -|---|---|---| -| model call | inference, LLM/VLM judge | token metering | -| training session | Tinker-compatible APIs | session state, checkpoints as artifacts | -| interactive simulator | Blender, Isaac, browser, VM | lease, snapshot/fork, hardware capability | -| sub-sandbox | E2B, Daytona | nested isolation, per-service network policy | -| data access | search, paper/chem DBs | licensing, contamination audit, caching | -| licensed tool | FlexLM EDA, quantum queues | non-money leases (seats, daily quotas) | -| real-world actuation | cloud lab, robot, deploy | irreversible spend, orchestrator approval | -| human in the loop | expert review | hours-to-days latency, scarcity | -| artifact registry | HF Hub push | write-scoped credentials, provenance | -| peer agent | A2A | budget passed down to the peer (#779) | - -Also: shared brokers outside the environment with per-rollout tokens ([RFC 8693](https://www.rfc-editor.org/rfc/rfc8693) token exchange), the MCP 2026-07-28 transport through an RFC 003 amendment, audit records keyed to `(episode, step, tool_call_id)` as OpenTelemetry GenAI/MCP spans, signed checkpoints (OpenSSF model signing), and exclusive-hardware leases for timing evaluators. - -## Related - -- RFC 009 openenvd (#1053, #1354, #1355): the base for credential custody and the grader zone. -- RFC 013 task sampling (#1256): `service_spend` is one source of the trainer's `costs`, never a sampling weight; an unscored episode's `None` reward is left out of the sampler's estimates. -- RFC 004: `score` feeds rubrics. RFC 005/006: LLM proxying is the future model-call profile. RFC 008: contract checks for declared services, plus #1357's adversarial panel. RFC 012: separate-verifier precedent. -- Issues: #898, #1026, #1232, #1299, #1357, #779. diff --git a/rfcs/README.md b/rfcs/README.md index 836a05f962..8c2824b2a5 100644 --- a/rfcs/README.md +++ b/rfcs/README.md @@ -101,9 +101,6 @@ Each RFC should include the following sections: ### World Modeling - [010-echo-env-token-world-model.md](./010-echo-env-token-world-model.md) - Env-token World Modeling (ECHO): trajectory token-role masks + an optimizer world-loss seam -### Environment Services -- [014-environment-services.md](./014-environment-services.md) - Environment Services: brokered, budgeted access to external services (HF Jobs batch jobs and evaluators in v1) - ### Discovery & Distribution - [011-ard-catalog-discovery.md](./011-ard-catalog-discovery.md) - Versioned environment metadata, ARD interchange, and a read-only producer-consumer path diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py index 451cf3a044..fdaa18f3e7 100644 --- a/tests/envs/test_posttrain_environment.py +++ b/tests/envs/test_posttrain_environment.py @@ -1666,3 +1666,78 @@ def blocked_cancel(**kwargs): submission.result(timeout=5) env.close() assert broker.spend()["final"] + + +@pytest.mark.parametrize("source", ["readme", "client_docstring"]) +def test_documented_client_episode_returns_reward_and_spend( + env, api, monkeypatch, tmp_path, source +): + """Execute the examples across server serialization and client parsing.""" + from contextlib import nullcontext + + import posttrain_env + from openenv.core.mcp_client import MCPToolClient + + if source == "readme": + text = (ENV_DIR / "README.md").read_text().split("## Client", 1)[1] + else: + text = posttrain_env.PostTrainEnv.__doc__ + code = re.search(r"```python\n(.*?)\n\s*```", text, re.S).group(1) + # The docstring is indented, while the README fence is not. + import textwrap + + code = textwrap.dedent(code) + api.on_inspect.update( + agent={"stage": "COMPLETED", "running_secs": 90}, + score={ + "stage": "COMPLETED", + "running_secs": 10, + "logs": [score_line(1.0)], + }, + ) + parser = MCPToolClient(base_url="http://localhost:8000") + results = [] + + def wire_result(obs): + payload = { + "observation": obs.model_dump(mode="json"), + "reward": obs.reward, + "done": obs.done, + } + return parser._parse_result(json.loads(json.dumps(payload))) + + def step(action): + result = wire_result(env.step(action)) + results.append(result) + return result + + client = SimpleNamespace( + reset=lambda **kwargs: wire_result(env.reset(**kwargs)), step=step + ) + monkeypatch.setattr( + posttrain_env, + "PostTrainEnv", + lambda **kwargs: SimpleNamespace(sync=lambda: nullcontext(client)), + ) + monkeypatch.chdir(tmp_path) + (tmp_path / "train.py").write_text("print('train')") + exec(compile(code, source, "exec"), {"script": "print('train')"}) + assert env.state.step_count == 3 + final = results[-1] + assert final.done is True + assert final.reward == 1.0 + spend = final.observation.metadata["service_spend"] + assert spend["final"] is True + assert spend["settled"]["usd"] == pytest.approx(2 * CPU_BASIC / 1e6) + assert spend["settled"]["jobs"] == 1 + assert spend["reserved"]["usd"] == 0 + + +def test_local_server_binds_only_loopback(monkeypatch): + import uvicorn + from envs.posttrain_env.server import app + + calls = [] + monkeypatch.setattr(uvicorn, "run", lambda *args, **kwargs: calls.append(kwargs)) + app.main() + assert calls == [{"host": "127.0.0.1", "port": 8000}] From f01e94d177ae2e71683f75096529672837332834 Mon Sep 17 00:00:00 2001 From: burtenshaw Date: Tue, 27 Oct 2026 18:12:12 +0100 Subject: [PATCH 23/24] Fix score admission and tool episode ownership in posttrain env Serialize per-episode scoring and reuse an internal broker idempotency key through uncertain creation recovery. Pin the selected artifact before creation. Capture episode ownership before MCP scheduling, validate the request marker at dispatch, and keep broker ownership in tool results. Reproduces Bugbot findings 4391723507 and 4391723523 on 834c8c2f: 11 regression cases fail before and pass after. Focused suite: 194 passed, 1 skipped. Existing adversarial probes: 15 passed. Core MCP suite: 117 passed. No real HF Jobs or spend. --- envs/posttrain_env/server/broker.py | 9 +- .../server/posttrain_environment.py | 169 ++++++++--- tests/envs/test_posttrain_environment.py | 281 ++++++++++++++++++ 3 files changed, 418 insertions(+), 41 deletions(-) diff --git a/envs/posttrain_env/server/broker.py b/envs/posttrain_env/server/broker.py index 0eee8552e8..ea1cc4899c 100644 --- a/envs/posttrain_env/server/broker.py +++ b/envs/posttrain_env/server/broker.py @@ -124,7 +124,7 @@ class PendingJob: reservation: int flavor: str timeout_minutes: int - idempotency_key: str | None + idempotency_key: str | tuple[str] | None active: bool = True @@ -168,7 +168,7 @@ def __init__( self.max_dev_evals = max_dev_evals self.resource_group_id = resource_group_id self.jobs: dict[str, Job] = {} - self._idempotency: dict[str, str] = {} + self._idempotency: dict[str | tuple[str], str] = {} self._hardware: dict[str, Any] | None = None self._admission = threading.Condition(threading.RLock()) self._pending: dict[str, PendingJob] = {} @@ -322,7 +322,7 @@ def _start( role: str, flavor: str, timeout_minutes: int, - idempotency_key: str | None = None, + idempotency_key: str | tuple[str] | None = None, **kwargs: Any, ) -> Job: # Only admission and ledger publication hold the lock. Provider calls @@ -609,6 +609,9 @@ def evaluate( env=env, secrets=secrets, volumes=[model, *(volumes or [])], + # Broker lifetime is one episode; the tuple cannot collide with an + # agent-supplied string key. Retain all uncertain-creation guards. + idempotency_key=("score",) if role == "score" else None, ) job.source_job_id = source.job_id return job diff --git a/envs/posttrain_env/server/posttrain_environment.py b/envs/posttrain_env/server/posttrain_environment.py index c9df3c366f..535c49a1bf 100644 --- a/envs/posttrain_env/server/posttrain_environment.py +++ b/envs/posttrain_env/server/posttrain_environment.py @@ -27,10 +27,13 @@ import secrets import threading import time +from contextvars import ContextVar from typing import Any, Optional from uuid import uuid4 from fastmcp import FastMCP +from fastmcp.server.middleware import Middleware +from fastmcp.tools.tool import ToolResult from huggingface_hub import HfApi from huggingface_hub.errors import HfHubHTTPError from openenv.core.env_server.mcp_environment import MCPEnvironment @@ -102,10 +105,36 @@ def __init__( self._done = False self._reward: float | None = None self._score_job_id: str | None = None + self._score_source_job_id: str | None = None + self._score_lock = threading.Lock() + self._tool_episode = ContextVar("posttrain_episode", default=None) self._end: dict = {} self._lifecycle_lock = threading.RLock() self._close_tasks: dict[Broker | None, asyncio.Task] = {} + owner = self + + class EpisodeDispatch(Middleware): + async def on_call_tool(self, context, call_next): + ownership = owner._episode() + meta = context.fastmcp_context.request_context.meta + expected = ( + None if meta is None else meta.model_dump().get("posttrain_episode") + ) + if expected is not None and expected != ownership[2]: + return ToolResult( + structured_content=BrokerError( + "episode_over", "The episode has ended" + ).as_dict() + ) + token = owner._tool_episode.set(ownership) + try: + return await call_next(context) + finally: + owner._tool_episode.reset(token) + + mcp.add_middleware(EpisodeDispatch()) + @mcp.tool def budget() -> dict: """ @@ -139,21 +168,25 @@ def jobs_submit( datasets: Public Hub dataset repo ids, mounted read-only at `/datasets/`. idempotency_key: Retrying with the same key returns the same job. """ + ownership = self._episode() + broker, task, _, _ = ownership return self._call( lambda: self._job_view( - self._broker.submit( + broker.submit( script, flavor=flavor, timeout_minutes=timeout_minutes, - allowed_flavors=self._task.flavors, - max_timeout_minutes=self._task.max_timeout_minutes, + allowed_flavors=task.flavors, + max_timeout_minutes=task.max_timeout_minutes, dependencies=dependencies, datasets=datasets, - denied_datasets=self._task.denied_datasets, - volumes=self._task.volumes(), + denied_datasets=task.denied_datasets, + volumes=task.volumes(), idempotency_key=idempotency_key, - ) - ) + ), + broker=broker, + ), + ownership, ) @mcp.tool @@ -220,8 +253,16 @@ def dev_evaluate(job_id: str) -> dict: Args: job_id: Id of a succeeded job from `jobs_submit`. """ + ownership = self._episode() + broker, task, nonce, _ = ownership return self._call( - lambda: self._job_view(self._evaluate(job_id, "dev_eval")) + lambda: self._job_view( + self._evaluate( + job_id, "dev_eval", broker=broker, task=task, nonce=nonce + ), + broker=broker, + ), + ownership, ) @mcp.tool @@ -234,7 +275,8 @@ def submit(job_id: str) -> dict: Args: job_id: Id of a succeeded job from `jobs_submit`. """ - return self._call(lambda: self._submit(job_id)) + ownership = self._episode() + return self._call(lambda: self._submit(job_id, ownership), ownership) super().__init__(mcp) self._state = State(episode_id=str(uuid4()), step_count=0) @@ -293,6 +335,8 @@ def reset( self._reward = None self._end = {} self._score_job_id = None + self._score_source_job_id = None + self._score_lock = threading.Lock() return Observation( done=False, reward=0.0, @@ -365,12 +409,18 @@ def step( timeout_s: Optional[float] = None, **kwargs: Any, ) -> Observation: - broker = self._broker - self._state.step_count += 1 - obs = super().step( - action, timeout_s=self._tool_timeout(action, timeout_s), **kwargs - ) - return self._finish(obs, broker) + with self._lifecycle_lock: + ownership = self._episode() + broker = ownership[0] + self._state.step_count += 1 + token = self._tool_episode.set(ownership) + try: + obs = super().step( + action, timeout_s=self._tool_timeout(action, timeout_s), **kwargs + ) + return self._finish(obs, broker) + finally: + self._tool_episode.reset(token) async def step_async( self, @@ -378,14 +428,28 @@ async def step_async( timeout_s: Optional[float] = None, **kwargs: Any, ) -> Observation: - broker = self._broker - self._state.step_count += 1 - obs = await super().step_async( - action, timeout_s=self._tool_timeout(action, timeout_s), **kwargs - ) - # Ending an episode waits on HF Jobs to confirm cancels; keep that off - # the event loop shared by every session. - return await asyncio.to_thread(self._finish, obs, broker) + with self._lifecycle_lock: + ownership = self._episode() + broker = ownership[0] + self._state.step_count += 1 + token = self._tool_episode.set(ownership) + try: + obs = await super().step_async( + action, timeout_s=self._tool_timeout(action, timeout_s), **kwargs + ) + # Cancellation confirmation must stay off the session event loop. + return await asyncio.to_thread(self._finish, obs, broker) + finally: + self._tool_episode.reset(token) + + async def _async_call_tool(self, tool_name: str, arguments: dict) -> Any: + # MCP runs the server in a separate task: Python context alone does not + # cross that transport. Reject a queued call after reset at dispatch. + ownership = self._episode() + async with self.mcp_session() as client: + return await client.call_tool( + tool_name, arguments, meta={"posttrain_episode": ownership[2]} + ) def _step_impl( self, @@ -444,11 +508,22 @@ async def _close_in_thread(self, broker: Broker | None) -> None: # -- tools -------------------------------------------------------------- - def _call(self, fn) -> dict: - if self._broker is None: - return BrokerError("not_started", "Call reset before using tools").as_dict() - if self._done: - return BrokerError("episode_over", "The episode has ended").as_dict() + def _episode(self): + ownership = self._tool_episode.get() + if ownership is not None: + return ownership + with self._lifecycle_lock: + return self._broker, self._task, self._nonce, self._score_lock + + def _call(self, fn, ownership=None) -> dict: + broker = (ownership if ownership is not None else self._episode())[0] + with self._lifecycle_lock: + if broker is None: + return BrokerError( + "not_started", "Call reset before using tools" + ).as_dict() + if broker is not self._broker or self._done: + return BrokerError("episode_over", "The episode has ended").as_dict() try: return fn() except BrokerError as e: @@ -482,14 +557,15 @@ def _budget(self) -> dict: / 1e6, } - def _job_view(self, job) -> dict: + def _job_view(self, job, *, broker=None) -> dict: + broker = broker if broker is not None else self._broker view = job.view() if job.role == "agent" and job.state == "succeeded": view["artifact"] = { - "uri": f"openenv-artifact://{self._state.episode_id}/{job.job_id}/outputs", - "files": self._broker.artifacts(job), + "uri": f"openenv-artifact://{broker.episode_id}/{job.job_id}/outputs", + "files": broker.artifacts(job), } - view["remaining_usd"] = self._broker.remaining_micro_usd() / 1e6 + view["remaining_usd"] = broker.remaining_micro_usd() / 1e6 return view def _logs(self, job_id: str, tail: int) -> dict: @@ -526,17 +602,34 @@ def _evaluate(self, job_id: str, role: str, *, broker=None, task=None, nonce=Non volumes=task.volumes(), ) - def _submit(self, job_id: str) -> dict: - # Snapshot episode ownership before any provider call. Reset must not - # wait on the lifecycle lock while a provider worker is hung. + def _submit(self, job_id: str, ownership=None) -> dict: + broker, task, nonce, score_lock = ownership or self._episode() + # Serialize scoring, without blocking reset/close on a provider call. + if not score_lock.acquire(timeout=TOOL_TIMEOUT_S): + raise BrokerError("creation_pending", "Scoring is in progress; retry later") + try: + return self._submit_score(job_id, broker, task, nonce) + finally: + score_lock.release() + + def _submit_score(self, job_id, broker, task, nonce) -> dict: with self._lifecycle_lock: - broker, task, nonce = self._broker, self._task, self._nonce + if broker is not self._broker: + return BrokerError("episode_over", "The episode has ended").as_dict() score_job_id = self._score_job_id - score_timeout_s = self._score_timeout_s() + job_id = self._score_source_job_id or job_id + score_timeout_s = task.eval_timeout_minutes * 60 + 900.0 if score_job_id is None: broker.require_artifact(broker.refresh(broker.get(job_id))) + with self._lifecycle_lock: + if broker is not self._broker: + return BrokerError( + "episode_over", "The episode has ended" + ).as_dict() + # Preserve the selected artifact even when create/recovery fails. + self._score_source_job_id = job_id for job in list(broker.jobs.values()): - if job.state not in TERMINAL_STATES: + if job.role != "score" and job.state not in TERMINAL_STATES: broker.cancel(job, "submitted") score_job = self._evaluate( job_id, "score", broker=broker, task=task, nonce=nonce diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py index fdaa18f3e7..d60dfb0b03 100644 --- a/tests/envs/test_posttrain_environment.py +++ b/tests/envs/test_posttrain_environment.py @@ -1741,3 +1741,284 @@ def test_local_server_binds_only_loopback(monkeypatch): monkeypatch.setattr(uvicorn, "run", lambda *args, **kwargs: calls.append(kwargs)) app.main() assert calls == [{"host": "127.0.0.1", "port": 8000}] + + +@pytest.mark.parametrize("lost_response", [False, True]) +def test_score_admission_fastmcp(env, api, monkeypatch, lost_response): + env.reset(task_id="t0_artifact") + source = submit_job(env)["job_id"] + api.finish(source) + api.on_inspect["score"] = { + "stage": "COMPLETED", + "running_secs": 1, + "logs": [score_line(0.75)], + } + entered, release = threading.Event(), threading.Event() + run, lookup = api.run_uv_job, api.list_jobs + creates = [] + second_entered = threading.Event() + submit = env._submit + submit_count = 0 + + def counted_submit(*args): + nonlocal submit_count + submit_count += 1 + if submit_count == 2: + second_entered.set() + return submit(*args) + + monkeypatch.setattr(env, "_submit", counted_submit) + + def blocked_run(**kwargs): + if kwargs["labels"]["openenv-role"] == "score": + creates.append(kwargs) + entered.set() + assert release.wait(5) + return run(**kwargs) + + monkeypatch.setattr(api, "run_uv_job", blocked_run) + + async def exercise(): + async with env.mcp_session() as client: + if lost_response: + api.fail_after_create = True + + def unavailable(**kwargs): + raise ConnectionError("lookup unavailable") + + monkeypatch.setattr(api, "list_jobs", unavailable) + release.set() + failed = await client.call_tool("submit", {"job_id": source}) + assert "error" in failed.data + for _ in range(2): + failed = await client.call_tool("submit", {"job_id": source}) + assert "error" in failed.data + assert len(creates) == 1 + assert len(env._broker._pending) == 1 + monkeypatch.setattr(api, "list_jobs", lookup) + results = [await client.call_tool("submit", {"job_id": source})] + else: + first = asyncio.create_task( + client.call_tool("submit", {"job_id": source}) + ) + assert await asyncio.to_thread(entered.wait, 5) + second = asyncio.create_task( + client.call_tool("submit", {"job_id": source}) + ) + assert await asyncio.to_thread(second_entered.wait, 5) + await client.call_tool("budget", {}) + release.set() + results = await asyncio.gather(first, second) + assert all(r.data.get("result", {}).get("score") == 0.75 for r in results) + + try: + asyncio.run(exercise()) + assert len(creates) == 1 + assert len(api.runs) == 2 + assert not api.jobs["job1"].get("cancel_requested") + assert env._reward == 0.75 + assert env._end["reason"] == "submitted" + assert env._broker.spend()["settled"]["jobs"] == 1 + finally: + release.set() + monkeypatch.setattr(api, "list_jobs", lookup) + + +@pytest.mark.parametrize("tool", ["jobs_submit", "dev_evaluate"]) +@pytest.mark.parametrize("pause_at", ["call", "dispatch"]) +def test_tool_dispatch_keeps_episode_ownership(env, api, monkeypatch, tool, pause_at): + env.reset(task_id="t0_artifact", episode_id="old") + source = submit_job(env)["job_id"] + api.finish(source) + old = env._broker + entered, release = threading.Event(), threading.Event() + original = env._call if pause_at == "call" else env._async_call_tool + + if pause_at == "call": + + def blocked(*args, **kwargs): + entered.set() + assert release.wait(5) + return original(*args, **kwargs) + + monkeypatch.setattr(env, "_call", blocked) + else: + + async def blocked(*args, **kwargs): + entered.set() + assert await asyncio.to_thread(release.wait, 5) + return await original(*args, **kwargs) + + monkeypatch.setattr(env, "_async_call_tool", blocked) + + async def exercise(): + async with env.mcp_session(): + args = ( + {"job_id": source} + if tool == "dev_evaluate" + else {"script": "print(1)", "flavor": "cpu-basic", "timeout_minutes": 1} + ) + request = asyncio.create_task( + env.step_async(CallToolAction(tool_name=tool, arguments=args)) + ) + try: + assert await asyncio.to_thread(entered.wait, 5) + await asyncio.to_thread( + env.reset, task_id="t0_arithmetic_format", episode_id="new" + ) + new = env._broker + finally: + release.set() + obs = await request + assert obs.result.data["error"]["type"] == "episode_over" + assert env._broker is new and new is not old + assert not new.jobs and not new._pending and not new._closing + assert new.remaining_micro_usd() == 3_000_000 + assert env.state.step_count == 0 + assert env._score_job_id is None and not env._done and env._end == {} + assert len(api.runs) == 1 + + try: + asyncio.run(exercise()) + finally: + release.set() + + +@pytest.mark.parametrize("created", [False, True]) +def test_score_recovery_pins_source_and_key(env, api, monkeypatch, created): + env.reset(task_id="t0_artifact") + first = submit_job(env, idempotency_key="score")["job_id"] + other = submit_job(env)["job_id"] + api.finish(first) + api.finish(other) + run, lookup = api.run_uv_job, api.list_jobs + + def uncertain(**kwargs): + if created: + run(**kwargs) + raise ConnectionError("create response lost") + + def unavailable(**kwargs): + raise ConnectionError("lookup unavailable") + + monkeypatch.setattr(api, "run_uv_job", uncertain) + monkeypatch.setattr(api, "list_jobs", unavailable) + failed, _ = call(env, "submit", job_id=first) + assert "error" in failed + pending = list(env._broker._pending) + for _ in range(2): + failed, _ = call(env, "submit", job_id=other) + assert failed["error"]["type"] == "creation_pending" + assert list(env._broker._pending) == pending + assert len(api.runs) == 2 + int(created) + monkeypatch.setattr(api, "run_uv_job", run) + monkeypatch.setattr(api, "list_jobs", lookup) + api.on_inspect["score"] = { + "stage": "COMPLETED", + "running_secs": 1, + "logs": [score_line(0.5)], + } + result, obs = call(env, "submit", job_id=other) + assert result["submitted"] == first + assert obs.reward == 0.5 + assert len(api.runs) == 3 and not env._broker._pending + assert not api.jobs["job2"].get("cancel_requested") + assert api.runs[-1]["volumes"][0].path == "run0/outputs" + + +def test_queued_old_submit_cannot_cancel_new_score(env, api, monkeypatch): + env.reset(task_id="t0_artifact", episode_id="old") + source = submit_job(env)["job_id"] + api.finish(source) + entered, release = threading.Event(), threading.Event() + original = env._async_call_tool + + async def blocked(*args, **kwargs): + entered.set() + assert await asyncio.to_thread(release.wait, 5) + return await original(*args, **kwargs) + + monkeypatch.setattr(env, "_async_call_tool", blocked) + + async def exercise(): + async with env.mcp_session(): + request = asyncio.create_task( + env.step_async( + CallToolAction(tool_name="submit", arguments={"job_id": source}) + ) + ) + try: + assert await asyncio.to_thread(entered.wait, 5) + await asyncio.to_thread( + env.reset, task_id="t0_artifact", episode_id="new" + ) + broker = env._broker + job = await asyncio.to_thread( + broker.submit, + "print(1)", + flavor="cpu-basic", + timeout_minutes=1, + allowed_flavors=("cpu-basic",), + max_timeout_minutes=1, + ) + api.finish(job.job_id) + score = await asyncio.to_thread(env._evaluate, job.job_id, "score") + spend = broker.spend() + finally: + release.set() + obs = await request + assert obs.result.data["error"]["type"] == "episode_over" + assert broker.spend() == spend + assert not broker._closing and not env._done + assert not api.jobs[score.job_id].get("cancel_requested") + assert len(api.runs) == 3 + + try: + asyncio.run(exercise()) + finally: + release.set() + + +@pytest.mark.parametrize("tool", ["jobs_submit", "dev_evaluate"]) +def test_tool_result_keeps_old_budget_after_reset(env, api, monkeypatch, tool): + env.reset(task_id="t0_artifact", episode_id="old") + source = submit_job(env)["job_id"] + api.finish(source) + old = env._broker + entered, release = threading.Event(), threading.Event() + view = env._job_view + + def blocked(*args, **kwargs): + entered.set() + assert release.wait(5) + return view(*args, **kwargs) + + monkeypatch.setattr(env, "_job_view", blocked) + + async def exercise(): + async with env.mcp_session(): + args = ( + {"job_id": source} + if tool == "dev_evaluate" + else {"script": "print(1)", "flavor": "cpu-basic", "timeout_minutes": 1} + ) + request = asyncio.create_task( + env.step_async(CallToolAction(tool_name=tool, arguments=args)) + ) + try: + assert await asyncio.to_thread(entered.wait, 5) + await asyncio.to_thread( + env.reset, task_id="t0_arithmetic_format", episode_id="new" + ) + finally: + release.set() + obs = await request + assert obs.result.data["remaining_usd"] == old.remaining_micro_usd() / 1e6 + assert obs.result.data["remaining_usd"] != 3.0 + assert not env._broker.jobs and not env._broker._closing + assert env.state.step_count == 0 and not env._done + + try: + asyncio.run(exercise()) + finally: + release.set() From 15d7d0240cb8e85c922e36694ea1e7a8e56a9cf0 Mon Sep 17 00:00:00 2001 From: burtenshaw Date: Tue, 27 Oct 2026 18:19:49 +0100 Subject: [PATCH 24/24] Fix posttrain dispatch after session inherits an old episode Snapshot current episode ownership under the lifecycle lock at MCP dispatch instead of consulting the session-inherited ContextVar. Keep captured request ownership and nonce validation so stale requests remain rejected after reset. Add four deterministic regression cases for sessions opened by episode A and preopened controls, with budget and jobs_submit paused across reset. Verify fresh B creates exactly one job and stale A cannot mutate B. All 11 prior race regressions, 4 independent cases, 15 adversarial cases and 117 core MCP cases pass. Posttrain: 103 passed, 1 skipped; release/catalog/docs selection: 294 passed. No HF Jobs or spend. --- .../server/posttrain_environment.py | 9 ++- tests/envs/test_posttrain_environment.py | 59 +++++++++++++++++++ 2 files changed, 67 insertions(+), 1 deletion(-) diff --git a/envs/posttrain_env/server/posttrain_environment.py b/envs/posttrain_env/server/posttrain_environment.py index 535c49a1bf..8e171339cb 100644 --- a/envs/posttrain_env/server/posttrain_environment.py +++ b/envs/posttrain_env/server/posttrain_environment.py @@ -116,7 +116,14 @@ def __init__( class EpisodeDispatch(Middleware): async def on_call_tool(self, context, call_next): - ownership = owner._episode() + # The MCP session task may have inherited an earlier episode. + with owner._lifecycle_lock: + ownership = ( + owner._broker, + owner._task, + owner._nonce, + owner._score_lock, + ) meta = context.fastmcp_context.request_context.meta expected = ( None if meta is None else meta.model_dump().get("posttrain_episode") diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py index d60dfb0b03..34ca3b980a 100644 --- a/tests/envs/test_posttrain_environment.py +++ b/tests/envs/test_posttrain_environment.py @@ -19,6 +19,7 @@ import sys import threading from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait +from contextlib import AsyncExitStack from pathlib import Path from types import SimpleNamespace @@ -1824,6 +1825,64 @@ def unavailable(**kwargs): monkeypatch.setattr(api, "list_jobs", lookup) +@pytest.mark.parametrize("preopen", [False, True]) +@pytest.mark.parametrize("old_tool", ["budget", "jobs_submit"]) +def test_fresh_episode_dispatch_during_old_request( + env, api, monkeypatch, preopen, old_tool +): + env.reset(task_id="t0_artifact", episode_id="old") + old = env._broker + entered, release = threading.Event(), threading.Event() + original = env._call + + def blocked(*args, **kwargs): + if not entered.is_set(): + entered.set() + assert release.wait(5) + return original(*args, **kwargs) + + monkeypatch.setattr(env, "_call", blocked) + + async def exercise(): + args = {"script": "print(1)", "flavor": "cpu-basic", "timeout_minutes": 1} + async with AsyncExitStack() as stack: + if preopen: + await stack.enter_async_context(env.mcp_session()) + request = asyncio.create_task( + env.step_async( + CallToolAction( + tool_name=old_tool, + arguments={} if old_tool == "budget" else args, + ) + ) + ) + try: + assert await asyncio.to_thread(entered.wait, 5) + await asyncio.to_thread( + env.reset, task_id="t0_artifact", episode_id="new" + ) + new = env._broker + fresh = await env.step_async( + CallToolAction(tool_name="jobs_submit", arguments=args) + ) + finally: + release.set() + stale = await request + assert fresh.error is None + assert fresh.result.data["job_id"] in new.jobs + assert stale.error is None + assert stale.result.data["error"]["type"] == "episode_over" + assert env._broker is new and new is not old + assert not old.jobs and len(new.jobs) == 1 and len(api.runs) == 1 + assert env.state.step_count == 1 + assert not new._closing and not env._done and env._end == {} + + try: + asyncio.run(exercise()) + finally: + release.set() + + @pytest.mark.parametrize("tool", ["jobs_submit", "dev_evaluate"]) @pytest.mark.parametrize("pause_at", ["call", "dispatch"]) def test_tool_dispatch_keeps_episode_ownership(env, api, monkeypatch, tool, pause_at):