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/_toctree.yml b/docs/source/_toctree.yml
index 6a835c0339..e60c30c4cb 100644
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -155,6 +155,8 @@
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 790f1cf2e6..febc884bde 100644
--- a/docs/source/environments.md
+++ b/docs/source/environments.md
@@ -294,6 +294,13 @@ The OpenEnv community has built a catalog of ready-to-run environments that cove
🤗 HF
+
+
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.
+
+
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..6949a8915f
--- /dev/null
+++ b/docs/source/environments/posttrain.md
@@ -0,0 +1,262 @@
+
+# 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
+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
+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, 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 |
+| `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`, `invalid_script`,
+`invalid_dependency`, `dataset_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
+```
+
+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, 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
+ 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 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.
+
+### 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.
+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 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"]
+# {"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
+
+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, 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.
+
+## 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
+
+| 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
+```
+
+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 |
+|---|---|
+| `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. 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
+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(
+ base_url="http://localhost:8000", message_timeout_s=3600
+).sync() as env:
+ result = env.reset(task_id="t0_artifact")
+ print(result.observation.metadata["prompt"])
+ 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"],
+ },
+ )
+ )
+ 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)
+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. 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.
+
+## 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/README.md b/envs/posttrain_env/README.md
new file mode 100644
index 0000000000..309e24c184
--- /dev/null
+++ b/envs/posttrain_env/README.md
@@ -0,0 +1,261 @@
+# 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
+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
+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, 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 |
+| `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`, `invalid_script`,
+`invalid_dependency`, `dataset_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
+```
+
+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, 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
+ 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 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.
+
+### 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.
+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 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"]
+# {"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
+
+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, 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.
+
+## 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
+
+| 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
+```
+
+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 |
+|---|---|
+| `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. 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
+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(
+ base_url="http://localhost:8000", message_timeout_s=3600
+).sync() as env:
+ result = env.reset(task_id="t0_artifact")
+ print(result.observation.metadata["prompt"])
+ 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"],
+ },
+ )
+ )
+ print(final.done, final.reward)
+ print(final.observation.metadata["service_spend"])
+```
+
+[`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.
+
+## 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. 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.
+
+## 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/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/__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..5f4b2ae02a
--- /dev/null
+++ b/envs/posttrain_env/client.py
@@ -0,0 +1,52 @@
+# 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.
+
+ 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 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"])
+ 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"])
+ ```
+ """
+
+ pass
diff --git a/envs/posttrain_env/openenv.yaml b/envs/posttrain_env/openenv.yaml
new file mode 100644
index 0000000000..10c8317f99
--- /dev/null
+++ b/envs/posttrain_env/openenv.yaml
@@ -0,0 +1,42 @@
+spec_version: 1
+name: posttrain_env
+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..0aff8c897b
--- /dev/null
+++ b/envs/posttrain_env/pyproject.toml
@@ -0,0 +1,38 @@
+# 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 = ">=3.11"
+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>=2.3.0",
+ # Checks the requirements agents pass to their jobs.
+ "packaging>=23.0",
+]
+
+[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..20ee94bcf3
--- /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="127.0.0.1", 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..ea1cc4899c
--- /dev/null
+++ b/envs/posttrain_env/server/broker.py
@@ -0,0 +1,848 @@
+# 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.
+
+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
+the per-minute rate for the minutes it actually ran.
+"""
+
+import base64
+import json
+import math
+import re
+import tempfile
+import threading
+import time
+import tomllib
+from contextlib import suppress
+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
+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 = {
+ "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_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
+ADMISSION_TIMEOUT_S = 120.0
+
+
+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
+
+
+@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 | tuple[str] | None
+ active: bool = True
+
+
+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 | tuple[str], str] = {}
+ self._hardware: dict[str, Any] | None = None
+ self._admission = threading.Condition(threading.RLock())
+ self._pending: dict[str, PendingJob] = {}
+ self._closing = False
+
+ # -- ledger -------------------------------------------------------------
+
+ 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 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:
+ with self._admission:
+ used = sum(
+ j.settled_micro_usd if j.settled else j.reserved_micro_usd
+ for j in self._charged()
+ ) + sum(
+ pending.reservation
+ for pending in self._pending.values()
+ if pending.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
+
+ 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:
+ 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."""
+ 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(
+ pending.reservation
+ for pending in self._pending.values()
+ if pending.role in ("agent", "dev_eval")
+ )
+ )
+ / 1e6
+ },
+ "final": not self._pending
+ and 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,
+ 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.
+
+ The script runs without network access, inside `job_launcher.py`. Its
+ dependencies, from `dependencies` and its PEP 723 header, are installed
+ from wheels before that. Each of `datasets` is mounted read-only at
+ `/datasets/`, next to `volumes`.
+ """
+ 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",
+ 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"
+ )
+ requirements = self._requirements(script, dependencies or [])
+ mounts = self._dataset_volumes(datasets or [], denied_datasets)
+ return self._start(
+ self._launcher_script(script, requirements),
+ role="agent",
+ flavor=flavor,
+ timeout_minutes=timeout_minutes,
+ dependencies=[],
+ volumes=[*(volumes or []), *mounts],
+ idempotency_key=idempotency_key,
+ )
+
+ def _start(
+ self,
+ script: str,
+ *,
+ role: str,
+ flavor: str,
+ timeout_minutes: int,
+ idempotency_key: str | tuple[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.
+ retry = None
+ with self._admission:
+ if idempotency_key:
+ 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]]
+ 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
+ )
+ 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,
+ )
+ try:
+ job = self._run(
+ script,
+ role=role,
+ 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:
+ pending.active = False
+ self._admission.notify_all()
+
+ @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.replace("\r\n", "\n"))
+ 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))
+ # packaging raises SyntaxError for some malformed markers.
+ except (InvalidRequirement, SyntaxError):
+ 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",
+ )
+ # 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=info.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` and `requirements` in the launcher."""
+ encoded = base64.b64encode(script.encode()).decode()
+ 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 header + launcher
+
+ def evaluate(
+ self,
+ source: Job,
+ *,
+ role: str,
+ script_path: Path,
+ 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`."""
+ self.require_artifact(source)
+ model = Volume(
+ type="bucket",
+ source=source.bucket,
+ path=f"{source.path}/outputs",
+ mount_path="/model",
+ read_only=True,
+ )
+ job = self._start(
+ script_path.read_text(),
+ role=role,
+ flavor=flavor,
+ timeout_minutes=timeout_minutes,
+ dependencies=[],
+ 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
+
+ def _run(
+ self,
+ script: str,
+ *,
+ role: str,
+ flavor: str,
+ timeout_minutes: int,
+ reservation: int,
+ request: str,
+ 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
+ # 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=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 []
+ 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,
+ )
+ 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": "error", "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": "error",
+ "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.
+
+ Unconfirmed creation requests keep their reservations and are reconciled
+ again on later calls. They are returned as `request:` until their
+ remote job identity can be found or their absence confirmed.
+
+ Returns:
+ `list[str]`: orphaned job ids and unresolved request labels.
+ """
+ deadline = time.monotonic() + timeout_s
+ with self._admission:
+ self._closing = True
+ self._admission.wait_for(
+ lambda: not any(p.active for p in self._pending.values()),
+ timeout=max(0, deadline - time.monotonic()),
+ )
+ unresolved = [
+ request for request, p in self._pending.items() if not p.active
+ ]
+ for request in unresolved:
+ with suppress(Exception):
+ self._reconcile(
+ request, cleanup=True, timeout_s=max(0, deadline - time.monotonic())
+ )
+ with self._admission:
+ 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
+ # Best effort: the Hub client's network errors vary by version.
+ with suppress(Exception):
+ self.cancel(job, reason)
+ while (
+ any(j.state not in TERMINAL_STATES for j in live)
+ and time.monotonic() < deadline
+ ):
+ time.sleep(min(poll_s, max(0, deadline - time.monotonic())))
+ for job in live:
+ with suppress(Exception):
+ self.refresh(job)
+ 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
+ with self._admission:
+ # Include late publications as well as unknown request identities.
+ # Their worker may still be attempting cancellation after our deadline.
+ return [f"request:{request}" for request in self._pending] + [
+ job.job_id
+ for job in self.jobs.values()
+ if job.state not in CONFIRMED_STATES
+ ]
+
+ 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..2996317c88
--- /dev/null
+++ b/envs/posttrain_env/server/eval_scripts/t0_arithmetic_format_eval.py
@@ -0,0 +1,109 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = ["torch==2.14.1", "transformers==5.19.0"]
+#
+# [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
+
+"""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.
+
+Only the weights come from the checkpoint. The tokenizer, chat template, model
+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
+job's timeout.
+"""
+
+import json
+import os
+import random
+import re
+
+import torch
+from transformers import (
+ AutoConfig,
+ AutoModelForCausalLM,
+ AutoTokenizer,
+ GenerationConfig,
+)
+
+BASE_MODEL = "/base_model"
+N_PROMPTS = {"dev": 50, "test": 100}
+
+
+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])]
+
+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:
+ # 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,
+ )
+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["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.
+model.generation_config = generation_config
+model.eval()
+
+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, generation_config=generation_config)
+ 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/job_launcher.py b/envs/posttrain_env/server/job_launcher.py
new file mode 100644
index 0000000000..c6ef20a591
--- /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 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;
+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
+`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
+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
+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 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:
+ """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))
+ 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, AGENT_UID, AGENT_UID)
+ install_filter()
+ os.closerange(3, os.sysconf("SC_OPEN_MAX"))
+ os.setgroups([])
+ os.setgid(AGENT_UID)
+ os.setuid(AGENT_UID)
+ except Exception as e:
+ 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"}
+ python = str(VENV / "bin" / "python")
+ os.execve(python, [python, 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
new file mode 100644
index 0000000000..8e171339cb
--- /dev/null
+++ b/envs/posttrain_env/server/posttrain_environment.py
@@ -0,0 +1,677 @@
+# 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 asyncio
+import logging
+import os
+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
+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__)
+
+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
+# Keep asynchronous close fallbacks alive after core releases the session.
+_CLOSE_TASKS: set[asyncio.Task] = set()
+
+
+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._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):
+ # 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")
+ )
+ 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:
+ """
+ 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,
+ datasets: 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 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 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.
+ """
+ ownership = self._episode()
+ broker, task, _, _ = ownership
+ return self._call(
+ lambda: self._job_view(
+ broker.submit(
+ script,
+ flavor=flavor,
+ timeout_minutes=timeout_minutes,
+ allowed_flavors=task.flavors,
+ max_timeout_minutes=task.max_timeout_minutes,
+ dependencies=dependencies,
+ datasets=datasets,
+ denied_datasets=task.denied_datasets,
+ volumes=task.volumes(),
+ idempotency_key=idempotency_key,
+ ),
+ broker=broker,
+ ),
+ ownership,
+ )
+
+ @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`.
+ """
+ ownership = self._episode()
+ broker, task, nonce, _ = ownership
+ return self._call(
+ lambda: self._job_view(
+ self._evaluate(
+ job_id, "dev_eval", broker=broker, task=task, nonce=nonce
+ ),
+ broker=broker,
+ ),
+ ownership,
+ )
+
+ @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`.
+ """
+ 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)
+
+ # -- 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.
+ """
+ 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
+ )
+ 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
+ self._score_source_job_id = None
+ self._score_lock = threading.Lock()
+ 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, broker: Broker | None) -> None:
+ if broker is None:
+ return
+ 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"{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, 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
+
+ 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:
+ 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,
+ action: Action,
+ timeout_s: Optional[float] = None,
+ **kwargs: Any,
+ ) -> Observation:
+ 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,
+ 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:
+ broker = self._broker
+ try:
+ 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.
+ task = self._close_tasks.get(broker)
+ if task is None or task.done():
+ task = loop.create_task(self._close_in_thread(broker))
+ self._close_tasks[broker] = task
+ _CLOSE_TASKS.add(task)
+
+ def finished(task):
+ _CLOSE_TASKS.discard(task)
+ if self._close_tasks.get(broker) is task:
+ del self._close_tasks[broker]
+
+ task.add_done_callback(finished)
+
+ 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 --------------------------------------------------------------
+
+ 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:
+ 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, *, 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://{broker.episode_id}/{job.job_id}/outputs",
+ "files": broker.artifacts(job),
+ }
+ view["remaining_usd"] = 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, *, broker=None, task=None, nonce=None):
+ with self._lifecycle_lock:
+ broker = broker if broker is not None else self._broker
+ task = task if task is not None else self._task
+ nonce = nonce if nonce is not None else self._nonce
+ source = broker.refresh(broker.get(job_id))
+ env = {"POSTTRAIN_EXPECTED": 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 broker.evaluate(
+ source,
+ role=role,
+ script_path=task.eval_script_path,
+ flavor=task.eval_flavor,
+ timeout_minutes=task.eval_timeout_minutes,
+ env=env,
+ secrets=secrets,
+ volumes=task.volumes(),
+ )
+
+ 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:
+ if broker is not self._broker:
+ return BrokerError("episode_over", "The episode has ended").as_dict()
+ score_job_id = self._score_job_id
+ 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.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
+ )
+ with self._lifecycle_lock:
+ if broker is not self._broker:
+ return BrokerError(
+ "episode_over", "The episode has ended"
+ ).as_dict()
+ self._score_job_id = score_job.job_id
+ else:
+ score_job = broker.get(score_job_id)
+ broker.wait(score_job, score_timeout_s, self._poll_s)
+ if score_job.state not in TERMINAL_STATES:
+ broker.cancel(score_job, "score timed out")
+ result = score_job.result or {"status": "timeout", "score": 0.0}
+ orphaned = broker.cancel_all("submitted", self._cancel_timeout_s, self._poll_s)
+ response = {"submitted": score_job.source_job_id, "result": result}
+ with self._lifecycle_lock:
+ if broker is not self._broker:
+ return response
+ self._done = True
+ # 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",
+ "job_id": score_job.source_job_id,
+ "score_job_id": score_job.job_id,
+ "score": result,
+ "orphaned_jobs": orphaned,
+ }
+ return response
diff --git a/envs/posttrain_env/server/tasks.py b/envs/posttrain_env/server/tasks.py
new file mode 100644
index 0000000000..2265e8db6e
--- /dev/null
+++ b/envs/posttrain_env/server/tasks.py
@@ -0,0 +1,153 @@
+# 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 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)
+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.
+ dev_seed (`int`):
+ 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
+ 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
+ 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,
+ )
+ ]
+
+
+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 = {
+ "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. " + JOB_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`, 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 "
+ "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). " + JOB_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=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
new file mode 100644
index 0000000000..7e9f207113
--- /dev/null
+++ b/envs/posttrain_env/uv.lock
@@ -0,0 +1,3192 @@
+version = 1
+revision = 5
+requires-python = ">=3.11"
+resolution-markers = [
+ "python_full_version >= '3.14' 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'",
+ "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "aiofile"
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diff --git a/examples/posttrain_env_agent.py b/examples/posttrain_env_agent.py
new file mode 100644
index 0000000000..5f892262f3
--- /dev/null
+++ b/examples/posttrain_env_agent.py
@@ -0,0 +1,152 @@
+# 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()
+
+ # 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
+ # 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()
diff --git a/tests/envs/test_posttrain_environment.py b/tests/envs/test_posttrain_environment.py
new file mode 100644
index 0000000000..34ca3b980a
--- /dev/null
+++ b/tests/envs/test_posttrain_environment.py
@@ -0,0 +1,2083 @@
+# 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 ast
+import asyncio
+import base64
+import ctypes.util
+import dataclasses
+import json
+import re
+import subprocess
+import sys
+import threading
+from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
+from contextlib import AsyncExitStack
+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 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"
+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 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."""
+
+ 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 = {}
+ self.datasets = {
+ "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"}
+
+ 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,
+ )
+ 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]
+ 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 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(
+ 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})
+
+
+def launched(run):
+ """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
+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, script="print('train')", **extra
+):
+ data, _ = call(
+ env,
+ "jobs_submit",
+ script=script,
+ 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"
+ request = run["labels"].pop("openenv-request")
+ assert len(request) == 32
+ assert run["labels"] == {
+ "openenv-env": "posttrain_env",
+ "openenv-episode": "ep1",
+ "openenv-role": "agent",
+ }
+ 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\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, requirements = launched(run)
+ assert agent_script == script
+ 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")
+
+ @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)"),
+ (['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'),
+ ],
+ )
+ 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", "old/name", "../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)
+ 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,
+ "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")
+ 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"] == []
+ 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_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"])
+ 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"] == "error"
+ 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"
+ # 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"
+ 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_ends_the_episode_unscored(self, env, api):
+ env.reset(task_id="t0_artifact")
+ job = self._finished(env, api)
+ # 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.done is True
+ 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
+ )
+ 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 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
+
+ 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
+
+ @pytest.mark.parametrize("error", [http_error(409), ConnectionError("reset")])
+ def test_a_failing_cancel_call_does_not_stop_the_others(
+ self, env, api, monkeypatch, error
+ ):
+ 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 error
+ 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_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)
+ 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"] == {}
+
+
+@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"]
+
+
+@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_tasks:
+ await asyncio.gather(*env._close_tasks.values())
+ 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_close_after_reset_runs_while_old_close_is_pending(api, monkeypatch, caplog):
+ env = PostTrainEnvironment(api=api, poll_s=0, cancel_timeout_s=0)
+ env.reset(task_id="t0_artifact")
+ old_job = submit_job(env)
+ old_broker = env._broker
+ entered = threading.Event()
+ release = threading.Event()
+ close = env._close
+ closed_brokers = []
+
+ def queued_close(broker):
+ closed_brokers.append(broker)
+ if broker is old_broker:
+ entered.set()
+ assert release.wait(5)
+ close(broker)
+
+ monkeypatch.setattr(env, "_close", queued_close)
+
+ async def exercise():
+ env.close()
+ old_owner = env._close_tasks[old_broker]
+ try:
+ assert await asyncio.to_thread(entered.wait, 5)
+ await asyncio.to_thread(env.reset, task_id="t0_artifact", episode_id="new")
+ new_broker = env._broker
+ new_job = await asyncio.to_thread(submit_job, env)
+ api.cancel_confirms = False
+ env.close()
+ new_owner = env._close_tasks[new_broker]
+ assert new_owner is not old_owner
+ env.close()
+ assert env._close_tasks[new_broker] is new_owner
+ await asyncio.wait_for(asyncio.shield(new_owner), 3)
+ assert not old_owner.done()
+ assert new_broker not in env._close_tasks
+ assert old_broker in env._close_tasks
+ assert new_broker.jobs[new_job["job_id"]].state == "orphaned"
+ assert not new_broker.spend()["final"]
+ assert "close could not confirm job cancellation" in caplog.text
+
+ api.cancel_confirms = True
+ env.close()
+ retry = env._close_tasks[new_broker]
+ assert retry is not new_owner
+ await asyncio.wait_for(asyncio.shield(retry), 3)
+ assert not old_owner.done()
+ assert api.jobs[new_job["job_id"]]["stage"] == "CANCELED"
+ assert new_broker.spend()["final"]
+ assert closed_brokers == [old_broker, new_broker, new_broker]
+ finally:
+ api.cancel_confirms = True
+ release.set()
+ await old_owner
+ assert api.jobs[old_job["job_id"]]["stage"] == "CANCELED"
+ assert not env._close_tasks
+ assert old_owner not in posttrain_environment._CLOSE_TASKS
+ assert new_owner not in posttrain_environment._CLOSE_TASKS
+ assert retry not in posttrain_environment._CLOSE_TASKS
+
+ try:
+ asyncio.run(exercise())
+ finally:
+ release.set()
+ api.cancel_confirms = True
+ close(env._broker)
+
+
+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_tasks[env._broker]
+ env.close()
+ assert env._close_tasks[env._broker] 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()
+
+
+@pytest.mark.parametrize("pause_at", ["refresh", "evaluate", "wait", "cancel_all"])
+def test_cancelled_submit_cannot_touch_reset_episode(env, api, monkeypatch, pause_at):
+ env.reset(task_id="t0_artifact")
+ source = submit_job(env)
+ api.finish(source["job_id"])
+ old_broker = env._broker
+ if pause_at == "cancel_all":
+ api.on_inspect["score"] = {
+ "stage": "COMPLETED",
+ "running_secs": 1,
+ "logs": [score_line(1.0)],
+ }
+ entered = threading.Event()
+ release = threading.Event()
+ completed = threading.Event()
+ original = getattr(old_broker, pause_at)
+ submit = env._submit
+ first = True
+
+ def blocked(*args, **kwargs):
+ nonlocal first
+ if first:
+ first = False
+ entered.set()
+ assert release.wait(5)
+ return original(*args, **kwargs)
+
+ def signalled_submit(*args):
+ try:
+ return submit(*args)
+ finally:
+ completed.set()
+
+ monkeypatch.setattr(old_broker, pause_at, blocked)
+ monkeypatch.setattr(env, "_submit", signalled_submit)
+
+ async def exercise():
+ async with env.mcp_session():
+ request = asyncio.create_task(
+ env.step_async(
+ CallToolAction(
+ tool_name="submit", arguments={"job_id": source["job_id"]}
+ )
+ )
+ )
+ try:
+ assert await asyncio.to_thread(entered.wait, 5)
+ request.cancel()
+ with pytest.raises(asyncio.CancelledError):
+ await request
+ await asyncio.to_thread(
+ env.reset, task_id="t0_artifact", episode_id="new"
+ )
+ new_broker = env._broker
+ new_job = await env.step_async(
+ CallToolAction(
+ tool_name="jobs_submit",
+ arguments={
+ "script": "print(1)",
+ "flavor": "cpu-basic",
+ "timeout_minutes": 1,
+ },
+ )
+ )
+ job_id = new_job.result.data["job_id"]
+ finally:
+ release.set()
+ assert await asyncio.to_thread(completed.wait, 5)
+ assert env._broker is new_broker
+ assert env.state.episode_id == "new"
+ assert not env._done
+ assert env._reward is None
+ assert env._end == {}
+ assert env._score_job_id is None
+ assert not new_broker._closing
+ assert api.jobs[job_id]["stage"] == "SCHEDULING"
+ assert not api.jobs[job_id].get("cancel_requested")
+
+ try:
+ asyncio.run(exercise())
+ finally:
+ release.set()
+
+
+def test_uncertain_creation_keeps_ownership_until_reconciliation(env, api, monkeypatch):
+ from envs.posttrain_env.server.broker import BrokerError
+
+ env.reset(task_id="t0_arithmetic_format")
+ broker = env._broker
+ list_jobs = api.list_jobs
+ api.fail_after_create = True
+
+ def unavailable(**kwargs):
+ raise ConnectionError("reconciliation unavailable")
+
+ monkeypatch.setattr(api, "list_jobs", unavailable)
+ arguments = dict(
+ flavor="a10g-small",
+ timeout_minutes=60,
+ allowed_flavors=("a10g-small",),
+ max_timeout_minutes=60,
+ idempotency_key="same",
+ )
+ with pytest.raises(ConnectionError):
+ broker.submit("print(1)", **arguments)
+ reservation = 60 * A10G_SMALL
+ assert broker.remaining_micro_usd() == 3_000_000 - reservation
+ assert broker.spend()["reserved"]["usd"] == reservation / 1e6
+ assert not broker.spend()["final"]
+ assert broker.count("agent") == 1
+ for _ in range(2):
+ with pytest.raises(BrokerError, match="unconfirmed"):
+ broker.submit("print(1)", **arguments)
+ assert len(api.runs) == 1
+ # The unknown job's reservation still constrains other keys.
+ broker.submit("print(2)", **{**arguments, "idempotency_key": "other"})
+ with pytest.raises(BrokerError) as error:
+ broker.submit("print(3)", **{**arguments, "idempotency_key": "third"})
+ assert error.value.kind == "budget_exhausted"
+ assert broker.spend()["reserved"]["usd"] == 2 * reservation / 1e6
+ for cleanup in (env.close, lambda: env.reset(task_id="t0_artifact")):
+ with pytest.raises(RuntimeError, match="did not confirm cancelling"):
+ cleanup()
+ assert env._broker is broker
+ assert broker._pending
+ assert not broker.spend()["final"]
+ assert api.jobs["job0"]["stage"] == "SCHEDULING"
+ assert api.jobs["job1"]["stage"] == "CANCELED"
+ monkeypatch.setattr(api, "list_jobs", list_jobs)
+ # Once closing starts, cleanup owns recovery and cancels the recovered id.
+ with pytest.raises(BrokerError, match="unconfirmed"):
+ broker.submit("print(1)", **arguments)
+ env.close()
+ assert "job0" in broker.jobs
+ assert len(api.runs) == 2
+ assert all(job["stage"] == "CANCELED" for job in api.jobs.values())
+ assert not broker._pending
+ assert broker.spend()["final"]
+
+
+@pytest.mark.parametrize("found", [False, True])
+def test_uncertain_creation_retry_recovers_without_duplicate(
+ env, api, monkeypatch, found
+):
+ from envs.posttrain_env.server.broker import BrokerError
+
+ env.reset(task_id="t0_arithmetic_format")
+ broker = env._broker
+ run = api.run_uv_job
+ list_jobs = api.list_jobs
+ lookups = []
+
+ def lost_response(**kwargs):
+ if found:
+ run(**kwargs)
+ raise ConnectionError("response lost")
+
+ def unavailable(**kwargs):
+ lookups.append(kwargs["labels"]["openenv-request"])
+ raise ConnectionError("list unavailable")
+
+ monkeypatch.setattr(api, "run_uv_job", lost_response)
+ monkeypatch.setattr(api, "list_jobs", unavailable)
+ arguments = dict(
+ flavor="a10g-small",
+ timeout_minutes=60,
+ allowed_flavors=("a10g-small",),
+ max_timeout_minutes=60,
+ idempotency_key="retry",
+ )
+ with pytest.raises(ConnectionError):
+ broker.submit("print(1)", **arguments)
+ with pytest.raises(BrokerError, match="unconfirmed"):
+ broker.submit("print(1)", **arguments)
+ assert lookups == [lookups[0], lookups[0]]
+ assert list(broker._pending) == [lookups[0]]
+ monkeypatch.setattr(api, "run_uv_job", run)
+ monkeypatch.setattr(api, "list_jobs", list_jobs)
+
+ async def exercise():
+ async with env.mcp_session() as client:
+ replies = await asyncio.gather(
+ *(
+ client.call_tool(
+ "jobs_submit",
+ {
+ "script": "print(1)",
+ "flavor": "a10g-small",
+ "timeout_minutes": 60,
+ "idempotency_key": "retry",
+ },
+ )
+ for _ in range(4)
+ )
+ )
+ assert all(reply.data["job_id"] == "job0" for reply in replies)
+
+ asyncio.run(exercise())
+ assert len(api.runs) == 1
+ assert not broker._pending
+ assert broker.count("agent") == 1
+ assert broker.spend()["reserved"]["usd"] == 60 * A10G_SMALL / 1e6
+ assert broker.remaining_micro_usd() == 3_000_000 - 60 * A10G_SMALL
+
+
+@pytest.mark.parametrize(
+ "cleanup", ["close", "reset", "max_steps", "time_limit", "duplicate_key"]
+)
+def test_hung_creation_waits_have_deadlines(env, api, monkeypatch, cleanup):
+ from envs.posttrain_env.server import broker as broker_module
+ from envs.posttrain_env.server.broker import BrokerError
+
+ env.reset(task_id="t0_artifact")
+ broker = env._broker
+ monkeypatch.setattr(broker_module, "ADMISSION_TIMEOUT_S", 0.02, raising=False)
+ entered = threading.Event()
+ release = threading.Event()
+ run = api.run_uv_job
+
+ def blocked_run(**kwargs):
+ entered.set()
+ assert release.wait(5)
+ return run(**kwargs)
+
+ monkeypatch.setattr(api, "run_uv_job", blocked_run)
+ arguments = dict(
+ flavor="cpu-basic",
+ timeout_minutes=10,
+ allowed_flavors=("cpu-basic",),
+ max_timeout_minutes=10,
+ idempotency_key="hung",
+ )
+
+ def operation():
+ if cleanup == "duplicate_key":
+ with pytest.raises(BrokerError) as error:
+ broker.submit("print(1)", **arguments)
+ assert error.value.kind == "creation_pending"
+ elif cleanup in ("max_steps", "time_limit"):
+ if cleanup == "max_steps":
+ env._max_steps = 1
+ else:
+ env._started_at -= 60 * env._task.max_episode_minutes
+ obs = env.step(CallToolAction(tool_name="budget", arguments={}))
+ assert obs.done
+ assert obs.metadata["episode_end"]["orphaned_jobs"]
+ assert not obs.metadata["service_spend"]["final"]
+ else:
+ with pytest.raises(RuntimeError, match="did not confirm cancelling"):
+ if cleanup == "close":
+ env.close()
+ else:
+ env.reset(task_id="t0_artifact", episode_id="new")
+
+ with ThreadPoolExecutor(max_workers=2) as pool:
+ submission = pool.submit(broker.submit, "print(1)", **arguments)
+ try:
+ assert entered.wait(5)
+ # The worker stays blocked until all deadline assertions are done.
+ attempt = pool.submit(operation)
+ attempt.result(timeout=0.5)
+ assert not release.is_set()
+ assert not submission.done()
+ assert env._broker is broker
+ assert broker.spend()["reserved"]["usd"] == 10 * CPU_BASIC / 1e6
+ assert not broker.spend()["final"]
+ assert broker.count("agent") == 1
+ finally:
+ release.set()
+ job = submission.result(timeout=5)
+ assert len(api.runs) == 1
+ if cleanup != "duplicate_key":
+ assert api.jobs[job.job_id]["stage"] == "CANCELED"
+ env.close()
+ assert broker.spend()["final"]
+
+
+def test_hung_score_creation_does_not_hold_lifecycle_lock(env, api, monkeypatch):
+ env.reset(task_id="t0_artifact")
+ source = submit_job(env)
+ api.finish(source["job_id"])
+ broker = env._broker
+ entered = threading.Event()
+ release = threading.Event()
+ run = api.run_uv_job
+
+ def blocked_score(**kwargs):
+ if kwargs["labels"]["openenv-role"] == "score":
+ entered.set()
+ assert release.wait(5)
+ return run(**kwargs)
+
+ monkeypatch.setattr(api, "run_uv_job", blocked_score)
+ with ThreadPoolExecutor(max_workers=2) as pool:
+ scoring = pool.submit(env._submit, source["job_id"])
+ try:
+ assert entered.wait(5)
+
+ def cleanup():
+ with pytest.raises(RuntimeError, match="did not confirm cancelling"):
+ env.close()
+
+ pool.submit(cleanup).result(timeout=0.5)
+ assert not scoring.done()
+ assert not broker.spend()["final"]
+ assert broker.count("score") == 1
+ finally:
+ release.set()
+ scoring.result(timeout=5)
+ assert len(api.runs) == 2
+ assert api.jobs["job1"]["stage"] == "CANCELED"
+ assert broker.spend()["final"]
+
+
+def test_cleanup_reports_job_published_after_its_live_snapshot(env, api, monkeypatch):
+ env.reset(task_id="t0_artifact")
+ submit_job(env)
+ broker = env._broker
+ creating = threading.Event()
+ release_creation = threading.Event()
+ cancelling = [threading.Event(), threading.Event()]
+ release_cancel = [threading.Event(), threading.Event()]
+ run = api.run_uv_job
+ cancel = api.cancel_job
+
+ def blocked_run(**kwargs):
+ creating.set()
+ assert release_creation.wait(5)
+ return run(**kwargs)
+
+ def blocked_cancel(**kwargs):
+ index = int(kwargs["job_id"][-1])
+ cancelling[index].set()
+ assert release_cancel[index].wait(5)
+ return cancel(**kwargs)
+
+ monkeypatch.setattr(api, "run_uv_job", blocked_run)
+ monkeypatch.setattr(api, "cancel_job", blocked_cancel)
+ with ThreadPoolExecutor(max_workers=2) as pool:
+ submission = pool.submit(
+ broker.submit,
+ "print(1)",
+ flavor="cpu-basic",
+ timeout_minutes=1,
+ allowed_flavors=("cpu-basic",),
+ max_timeout_minutes=1,
+ )
+ try:
+ assert creating.wait(5)
+ closing = pool.submit(env.close)
+ # Cleanup has snapshotted job0, then the late worker publishes job1.
+ assert cancelling[0].wait(5)
+ release_creation.set()
+ assert cancelling[1].wait(5)
+ release_cancel[0].set()
+ with pytest.raises(RuntimeError, match="job1"):
+ closing.result(timeout=0.5)
+ assert not broker.spend()["final"]
+ finally:
+ release_creation.set()
+ for event in release_cancel:
+ event.set()
+ 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}]
+
+
+@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("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):
+ 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()