diff --git a/.github/workflows/multi-harness-rl.yml b/.github/workflows/multi-harness-rl.yml new file mode 100644 index 00000000..50c6782f --- /dev/null +++ b/.github/workflows/multi-harness-rl.yml @@ -0,0 +1,21 @@ +name: SmolDataEnvs RL recipe +on: + pull_request: + paths: ['05-multi-harness-rl/**', '.github/workflows/multi-harness-rl.yml'] + push: + branches: [main] + paths: ['05-multi-harness-rl/**', '.github/workflows/multi-harness-rl.yml'] +permissions: + contents: read +jobs: + contracts: + runs-on: ubuntu-latest + timeout-minutes: 5 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + - run: python -m pip install pytest==9.1.1 httpx + - name: Check recipe contracts without GPUs or sandbox credentials + run: python -m pytest 05-multi-harness-rl/tests -q diff --git a/04-smoldataenvs/project.yaml b/04-smoldataenvs/project.yaml new file mode 100644 index 00000000..74d765f9 --- /dev/null +++ b/04-smoldataenvs/project.yaml @@ -0,0 +1,23 @@ +name: smoldataenvs +title: "SmolDataEnvs" +tagline: "Data-analysis tasks, verified SFT traces and a single-turn GRPO tutorial on HF Jobs." +order: 4 +status: wip +hub: + org: FineEnvs + collection: FineEnvs/smoldataenvs + datasets: + - FineEnvs/SmolDataEnvs + - FineEnvs/SmolDataEnvs-sft + - FineEnvs/SmolDataEnvs-harbor-train + - FineEnvs/SmolDataEnvs-harbor-test + - FineEnvs/SmolDataEnvs-harbor-eval + trackio: + - AdithyaSK/smoldataenvs-trackio +envs: + - name: python-sandbox + summary: "Run one generated Python program against task data and grade its answer." + backend: hf-jobs + frameworks: + trl: + verified: false diff --git a/05-multi-harness-rl/.gitignore b/05-multi-harness-rl/.gitignore new file mode 100644 index 00000000..ff8d02a0 --- /dev/null +++ b/05-multi-harness-rl/.gitignore @@ -0,0 +1,11 @@ +.runtime/ +.venv/ +__pycache__/ +.pytest_cache/ +runs/ +prepared/ +.env +.gradio/ + +temp/ +.deps/ diff --git a/05-multi-harness-rl/README.md b/05-multi-harness-rl/README.md new file mode 100644 index 00000000..96e7c0fb --- /dev/null +++ b/05-multi-harness-rl/README.md @@ -0,0 +1,164 @@ +
+ +SmolDataEnvs: one model, three ways to train + +# SmolDataEnvs: multi-harness RL + +[![Read the article](https://img.shields.io/badge/Read_the_article-Multi--harness_RL-91c5ff?style=for-the-badge&labelColor=142439)](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) +[![Collection](https://img.shields.io/badge/Collection-Models_%26_environments-FFD21E?style=for-the-badge&labelColor=142439)](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) +[![Try the playground](https://img.shields.io/badge/Try_it-SETA_playground-82d8b6?style=for-the-badge&labelColor=142439)](https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-whitebox) + +
+ +> Train a small model to solve real data tasks, then see how it behaves in different agent harnesses. + +This is the companion code for **[the multi-harness RL article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl)**. The article walks through the experiments and their results. Here you can read the training code, try the environments and run your own comparison with LFM2.5-2.6B or Qwen3.5-2B. + +The tasks come from [04: SmolDataEnvs](../04-smoldataenvs/). Each gives an agent a question, data files and a checkable answer. We keep the training tasks and reward shared while changing who runs the tool loop: TRL, native OpenCode or Harbor. + +## Start here + +| You want to… | Open | +|---|---| +| Understand the experiment and results | [Read the article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) | +| Try a data task in your browser | [SETA playground](https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-whitebox) | +| Train on HF Jobs or your own GPUs | [Step-by-step setup](REPRODUCE.md) | +| Evaluate a base model or checkpoint | [Evaluation guide](eval/README.md) | +| Find the Spaces, datasets and trained models | [Experiment collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) | + +## A look at the article's results + +[![Historical LFM training reward, held-out pass@1 and tool-call savings](assets/historical-lfm-curves.svg)](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) + +Historical LFM runs from the article. Training reward uses a trailing 50-update mean; evaluation points are unsmoothed. Hollow markers indicate incomplete evaluation coverage. Tool savings compare tasks solved by both baseline and checkpoint. + +Both policies in this figure used Harbor, including the OpenCode-only policy. This tutorial's native OpenCode mode is a separate interface. See [results and source data](RESULTS.md#historical-article-curves) for the numbers behind the figure and [validation](VALIDATION.md) for checks of the current scripts. The first panel shows training reward. + +## Three scripts, three tool loops + +Each script follows the same order: settings → tokenizer → environment → reward → TRL config → training. Read it from top to bottom, then edit its TRL config to try your own learning rate or batch size. + +| Script | Who runs the tool loop? | Trainer | +|---|---|---| +| [train/whitebox.py](train/whitebox.py) | TRL calls Python bash/SETA tools | `GRPOTrainer` | +| [train/opencode.py](train/opencode.py) | Native OpenCode runs inside a sandbox (deprecated) | `AsyncGRPOTrainer` | +| [train/multi_harness.py](train/multi_harness.py) | Harbor runs OpenCode, Claude Code, Codex or Mini-SWE-Agent | `AsyncGRPOTrainer` | + +Read whitebox first if you are new to tool-using RL. Its [environment](envs/whitebox/smoldataenv_whitebox/environment.py) shows exactly which tools the model can call and how an answer is graded. Then read the Harbor script to see what changes when an existing agent owns the conversation. The multi-harness script assigns one harness to each task; its eight rollouts all use that assignment. + +Native `opencode_env` is deprecated since OpenEnv 0.7.0 and remains here for the earlier comparison. For new runs, use Harbor. Set `HARNESSES = ("opencode",)` in `train/multi_harness.py` for OpenCode-only training, as in the article's LFM run. + +The installer uses **TRL main and OpenEnv main**, including the merged typed training-trace integration. `check_setup.py` verifies the required APIs before training. Each run records the resolved commits; see [VALIDATION.md](VALIDATION.md) for the tested versions and remaining GPU checks. + +## Folder layout + +```text +train/ Three readable TRL training scripts + whitebox.py + opencode.py + multi_harness.py +eval/evaluate.py Fixed pass@1 evaluation and tool/token metrics +envs/ Environment implementations and deployment + whitebox/ Standalone SETA Space and Python package + opencode/ Standalone native OpenCode Space and package + harbor/ Standalone Harbor Space with its rollout UI +jobs/ HF Jobs, Slurm and train/reload smoke commands +data/ Fixed task identities +prepare.py Download and check the task files +``` + +## What is being trained? + +The model receives a question and can inspect data in a Daytona sandbox. Its answer is compared with a held-out answer using the task's deterministic grader. A correct answer earns a small extra reward when it uses fewer tools: + +```python +bonus = 0.1 * 15 / (15 + tool_calls) if tool_calls > 0 else 0 +reward = correctness * (1 + bonus) +``` + +A correct answer with 15 tool calls earns `1.05`; a wrong answer earns `0`. Missing tool-count evidence gets no bonus. A failed environment without a grade is excluded, not scored as wrong. The code counts native tool actions, not model requests or training rows. + +Whitebox gives TRL the tools and lets it generate each turn. Blackbox gives TRL an OpenEnv session. OpenEnv returns the actual engine token IDs, behavior logprobs and loss masks through `fetch_training_trace()`. TRL trains on the eligible model tokens. Prompts and tool outputs are context. + +```python +# Whitebox: TRL owns generation and tool execution. +trainer = GRPOTrainer( + model=model, + args=config, + train_dataset=dataset, + reward_funcs=[], + environment_factory=BashEnvironment, +) + +# Blackbox: the agent owns generation; OpenEnv captures its tokens. +worker = HarnessRolloutWorker( + harness_session_factory=factory, + harness_adapter=None, + rollout_reward_fn=reward, + # See either blackbox script for the model and sampling arguments. +) +trainer = AsyncGRPOTrainer( + model=model, + args=config, + train_dataset=dataset, + rollout_worker=worker, +) +``` + +These are the boundaries to compare. The complete calls, including every training parameter, are in the scripts above. + +## Run a small experiment + +Use Python 3.12 and two H100 or H200 GPUs: one for vLLM, one for training. HF Jobs can supply them; you do not need a GPU on your laptop. OpenEnv runs inside the job. Daytona supplies the task sandboxes. Each [environment folder](envs/README.md) is also a standalone CPU Space: its local Dockerfile and application files are uploaded unchanged. + +Follow [REPRODUCE.md](REPRODUCE.md) for installation, a two-step smoke, a 100-step comparison, and checkpoint evaluation. From a prepared local environment: + +```bash +# Two updates, save both checkpoints, then reload checkpoint 2 for evaluation. +python jobs/smoke.py --mode multi_harness --output runs/harbor-smoke +``` + +Use `--mode opencode` for native OpenCode, or `--mode whitebox` for bash/SETA. Evaluation uses the same driver for all three training modes: + +| Training mode | Evaluation interface | Full pass@1 evaluation | +|---|---|---| +| SETA whitebox | Its own bash/SETA tools | 250 attempts | +| Native OpenCode | OpenCode, Claude Code, Codex and Mini-SWE-Agent through Harbor | 1,000 attempts | +| Harbor multi-harness | The same four Harbor harnesses | 1,000 attempts | + +The two blackbox policies share an evaluation protocol so you can compare transfer across harnesses. SETA is reported separately. The [evaluation guide](eval/README.md) explains local or Space endpoints, checkpoint loading, coverage and retrying failed pairs. + +## Shared settings + +| Setting | Value | +|---|---| +| Models | LFM2.5-2.6B or Qwen3.5-2B, non-thinking | +| Training tasks | 1,000 fixed tasks: 400 medium, 600 hard | +| Test tasks | 250 fixed tasks: 33 easy, 118 medium, 99 hard | +| Learning rate | `3e-6`, constant, no warmup | +| Sampling | Temperature `0.8`, full vocabulary | +| Rollouts per task group | 8 | +| Async limits | 32 inflight, maximum staleness 4 | +| Async batching | Batch 4, accumulation 4, packed row budget 40,960 tokens | +| Whitebox batching | Batch 1, accumulation 16 | +| Training length | Up to 1,000 optimizer updates | +| Save / evaluate | Save every 50; evaluate checkpoints 100, 200, … separately | +| Evaluation | Pass@1, default concurrency 35, TP1/DP2 | + +A step is an optimizer update, not a task. Async sampling and packing can use a variable number of prompts per step. This tutorial uses the upstream sampler and does not reproduce the archive's custom finite curriculum, exact committed-group resume, or whole-rollout weighting. Whitebox consumes 16 rollouts, or two groups of eight, per update. Blackbox histories can fork into multiple rows; long rows beyond the packing budget are dropped by upstream TRL. Inspect its drop and staleness metrics. + +Both models use a 131,072-token serving context and up to 4,096 tokens per model call. Whitebox and Harbor cap the agent at 17 model turns. The public native OpenCode API has a 600-second timeout and per-call token cap, but no equivalent strict turn limit. These limits are not interchangeable. LFM's packed convolution boundary handling is explicit in each async script and checked on the GPU before training. + +## Where to look next + +- [envs/](envs/README.md): actual environments, OpenEnv servers and clients, Docker image and Space deployment. +- [eval/evaluate.py](eval/evaluate.py): individual pass@1 results, coverage, correctness, tool calls and token usage. Failed pairs remain visible and can be retried without rerunning graded pairs. +- [jobs/](jobs/): installation, service startup, HF submission and a Slurm template. No hidden training configuration. +- [prepare.py](prepare.py) and [data/](data/): fixed task selection and checked dataset revisions. +- [RESULTS.md](RESULTS.md): earlier experiment results and artifact locations. + +Training keeps local Trackio data and checkpoints. Add `--space-id your-org/your-trackio-space` to also publish charts. It does not automatically write to the historical dashboard. + +## Earlier experiments + +The full experimental implementation remains on the [archived branch](https://github.com/adithya-s-k/FineEnvs/tree/archive/data-agent-experiments-20260930/04-data-agent). Earlier recipe code is also recoverable from this branch's git history. diff --git a/05-multi-harness-rl/REPRODUCE.md b/05-multi-harness-rl/REPRODUCE.md new file mode 100644 index 00000000..a59d13c3 --- /dev/null +++ b/05-multi-harness-rl/REPRODUCE.md @@ -0,0 +1,121 @@ +# Run the tutorial + +[Read the article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) · [Collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Tutorial](README.md) + +Start with one mode and a two-step smoke. Check that the checkpoint reloads and the evaluation grades every requested pair before spending time on a longer run. + +## 1. Install and prepare the tasks + +From the repository root: + +```bash +cd 05-multi-harness-rl +uv venv --python 3.12 --seed .venv +source .venv/bin/activate +bash jobs/install.sh +hf auth login +export HF_TOKEN="$(hf auth token)" +export DAYTONA_API_KEY="your-key" +python prepare.py +``` + +The installer uses **TRL main**, **OpenEnv main** and Transformers main. It refreshes the OpenEnv checkout on each installation and loads the environment examples from that checkout's `envs/` directory. No installed source files are patched. Each run keeps the resolved versions and commits in `dependencies.json`. + +TRL's typed OpenEnv integration is merged. Both local and HF Jobs installations use upstream main without a PR checkout. The installer checks the required APIs before starting; [VALIDATION.md](VALIDATION.md) records the versions tested here. + +Task preparation pins the corrected train/test dataset revisions and verifies instructions, graders and split separation. It downloads all 1,250 tasks into ignored `prepared/`. Credentials belong in the environment, never in source files. Create a [Daytona account](https://www.daytona.io/) and an HF bucket you can write to before using HF Jobs. + +## 2. Submit from a laptop with HF Jobs + +The submitter needs only `huggingface_hub`. It uploads the tutorial source, then installs dependencies and prepares tasks inside the job. It mounts an existing bucket at `/outputs` so checkpoints and logs survive the job. + +```bash +pip install 'huggingface_hub>=1.29.0' +hf auth login +export DAYTONA_API_KEY="your-key" +export RUN_BUCKET="your-org/smoldataenv-runs" + +python jobs/hf_job.py smoke --mode multi_harness \ + --name lfm-harbor-smoke --bucket "$RUN_BUCKET" \ + --timeout 1h +``` + +This prints the plan. Add `--submit` to launch. Use `h200x2` (default). Current upstream AsyncGRPO selects FlashAttention 3, so this tutorial does not patch in A100 support. + +The smoke trains for two updates, saves both checkpoints, stops the trainer, then reloads checkpoint 2 into vLLM for evaluation. Its report is `/outputs/lfm-harbor-smoke/smoke.json`; training and evaluation have separate subfolders. + +Repeat with `--mode opencode` and `--mode whitebox`, giving each job a fresh name. For Qwen, add `--model Qwen/Qwen3.5-2B`. A completed two-step smoke establishes execution, not learning. Check `reward`, `grad_norm`, checkpoint files and evaluation coverage. All-equal rewards can legitimately yield zero gradient. + +## 3. Run the same commands locally or with Slurm + +For a local two-GPU machine, activate the environment and run: + +```bash +CUDA_VISIBLE_DEVICES=0,1 python jobs/smoke.py --mode opencode \ + --output runs/opencode-smoke +``` + +For Slurm, choose your cluster's H100/H200 partition. Submit from this folder after installing dependencies and preparing tasks: + +```bash +sbatch --partition=YOUR_PARTITION jobs/train.slurm smoke \ + --mode multi_harness --output runs/harbor-smoke + +sbatch --partition=YOUR_PARTITION jobs/train.slurm train \ + --mode multi_harness --steps 100 --output runs/harbor-pilot + +# After checkpoint 100 has been saved: +sbatch --partition=YOUR_PARTITION jobs/train.slurm eval \ + --mode multi_harness --checkpoint runs/harbor-pilot/checkpoint-100 \ + --step 100 --tasks 25 --concurrency 35 --output runs/harbor-pilot-eval +``` + +One allocation owns one trainer and one inference engine. Evaluation gets a **different allocation**, with two inference replicas and no trainer. Shared sandbox quotas and bucket bandwidth still need headroom. The launcher chooses unused local service ports and records them in `services.json`. Keep each allocation on its own assigned GPUs and use a different output directory. + +The bounded smoke uses two optimizer updates, two rollouts per group and checkpointing each update. It checks checkpoint contents and reloads checkpoint 2 for two held-out tasks, across four harnesses for blackbox or SETA for whitebox. Normal runs retain eight rollouts per group and the settings in the training scripts. + +The launcher starts the actual [environment servers](envs/README.md) inside the allocation. Each folder under `envs/` is a standalone Space with its own Dockerfile, dependencies, data preparation and implementation. That page links the independent local and Hub instructions. + +## 4. Compare baseline with checkpoint 100 + +Use the same 25 fixed test tasks and interfaces for both measurements. Start an evaluation job without `--checkpoint` for the base model, then train for 100 updates, then evaluate `checkpoint-100`. + +```bash +python jobs/hf_job.py eval --mode multi_harness --tasks 25 \ + --name lfm-baseline --bucket "$RUN_BUCKET" --submit + +python jobs/hf_job.py train --mode multi_harness --steps 100 \ + --name lfm-harbor-100 --bucket "$RUN_BUCKET" --submit + +# Submit this only after checkpoint-100 has finished saving. +python jobs/hf_job.py eval --mode multi_harness --tasks 25 \ + --checkpoint /outputs/lfm-harbor-100/checkpoint-100 --step 100 \ + --name lfm-harbor-100-eval --bucket "$RUN_BUCKET" --submit +``` + +Both blackbox policies use four evaluation harnesses: 25 tasks means 100 pairs. Whitebox uses its native tools: 25 tasks means 25 episodes. The [evaluation guide](eval/README.md) maps each training mode to its evaluation interface and shows how to use a deployed environment. The subset is deterministic, taken from the sorted fixed test list. It is not the older pilot's stratified subset, so generate a new baseline. Omit `--tasks` for all 250 tasks. + +For a longer run, omit `--steps 100`. Checkpoints are saved every 50 updates. Submit independent evaluations at steps 100, 200, … using the corresponding path and `--step`. The trainer does not wait for them. Checkpoint serving uses vLLM's eager safetensors loader to avoid random reads from bucket and network filesystems. This tutorial keeps submission explicit rather than claiming an untested automatic checkpoint watcher. + +## 5. Read the outputs + +| Output | Contents | +|---|---| +| `train.log` / `eval.log` | Trainer or evaluator output | +| `vllm.log`, `whitebox.log` / `opencode.log` / `harbor.log` | Inference and environment service logs | +| `training_config.json` | Resolved TRL hyperparameters | +| `dependencies.json`, `packages.txt` | Installed commits and package versions | +| `task_names.json` | Selected training task identities | +| `checkpoint-50/`, `checkpoint-100/`, … | Standard TRL/Transformers training checkpoints | +| `final/` | Final model weights and tokenizer | +| `trackio/` | Local Trackio database or append-only logs on network storage | +| Eval `pairs/*.json` | One result per task/harness, including ungraded errors | +| Eval `summary.json` | Coverage, observed pass@1, reward, tool/token usage, harness and difficulty breakdowns | + +Add `--space-id your-org/your-trackio-space` for online Trackio too. Evaluation uses a separate named run and records the checkpoint identity. `pass_at_1_observed` excludes ungraded pairs; always report it together with coverage. A partial evaluation exits with status 2. Rerun the same eval command and output directory to retry missing/ungraded pairs. Graded successes **and graded failures** are retained. + +For direct experimentation, start the services once with the settings in [jobs/serve_model.py](jobs/serve_model.py) and [jobs/run.py](jobs/run.py), then run a training file directly. Native OpenCode additionally needs `SANDBOX_VLLM_URL` and `SANDBOX_VLLM_KEY`: the launcher creates an authenticated inference-only tunnel. Harbor uses OpenEnv's capture tunnel. Remote sandboxes cannot reach your machine's localhost directly. + +## What differs from the archive? + +The three training scripts use public trainers. They do not import custom trainer classes, download archived modules, replace installed source text, or assume that a worker seed is a task index. Training uses the same task lists, learning rate, reward formula, sampling and principal batch settings, but upstream async scheduling/row packing differs from the old custom curriculum. Exact resume, atomic rollout weighting and long-run automatic evaluation need separate qualification before claiming an identical experiment. diff --git a/05-multi-harness-rl/RESULTS.md b/05-multi-harness-rl/RESULTS.md new file mode 100644 index 00000000..77476ee1 --- /dev/null +++ b/05-multi-harness-rl/RESULTS.md @@ -0,0 +1,85 @@ +# Historical results + +[Read the article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) · [Collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Tutorial](README.md) + +These are checks and pilot results from the earlier pinned recipe at `4d9c040`, before the tutorial rewrite. They do not qualify the current training scripts. Two blackbox pilots completed 100 updates and evaluation; the whitebox pilot stopped at step 31 after an HTTP read timeout. Those pilots used the previous runtime pins. The merged-contract smoke qualification is recorded separately in [VALIDATION.md](VALIDATION.md). Earlier experiment scores are kept on the [archived branch](https://github.com/adithya-s-k/FineEnvs/tree/archive/data-agent-experiments-20260930/04-data-agent). + +## Completed checks + +The merged-contract HF smokes completed two updates and checkpoint reload in all three modes. Reload evaluation graded native OpenCode 4/4 pairs, Harbor 4/4 pairs and whitebox 1/1 task. [Earlier validation](https://github.com/adithya-s-k/FineEnvs/blob/4d9c040a28695c484de75bc23d6a70a99beda258/05-multi-harness-rl/VALIDATION.md) records pins and evidence. The qualification table below predates that migration. + +| Check | Result | Evidence | +|---|---|---| +| Local training, LFM and Qwen × all three modes | All six completed two updates and saved checkpoints | [Validation record](VALIDATION.md) | +| HF native OpenCode, LFM on A100 | Two updates; both checkpoints persisted | Internal job record `6abcc3d9` | +| HF Harbor, LFM on A100 | Two updates; checkpoint reload; 8/8 pairs graded with TiTO and verified tool counts | Internal job record `6abcc72d` | +| HF whitebox, LFM on A100 | Corrected preflight completed training and reload evaluation before starting its pilot | Internal job record `6abcd472`; the subsequent pilot failed at step 31 | + +The Harbor reload smoke solved 5/8 pairs on two medium tasks. This is a functional check, not a benchmark. Both standalone HF blackbox training smokes had zero reward contrast; they validated execution and checkpointing, not learning. + +## LFM 100-step pilots + +Snapshot: **2026-09-30 17:45 UTC**, read from the saved bucket summaries. Each pilot uses the same 25 held-out tasks (3 easy, 12 medium, 10 hard), baseline evaluation, 100 training updates, saves at 50/100 and final evaluation. Evaluation concurrency is 35. Blackbox evaluations have 100 task/harness pairs; whitebox has 25 native episodes. + +| Mode | Baseline pass@1 | Checkpoint-100 pass@1 | Training status | +|---|---|---|---| +| Native OpenCode | 24/100 = 24% | 20/100 = 20% | Completed 100 updates | +| Harbor multi-harness | 17/100 = 17% | 32/100 = 32% | Completed 100 updates | +| Whitebox SETA | 7/25 = 28% | Not available | Stopped at step 31; HTTP read timeout | + +Scores come from complete `baseline/eval/summary.json` and `checkpoint-100/eval/summary.json` files. These are separate stochastic baseline samples at temperature 0.8, so the initial 24% versus 17% difference is not a training effect. Whitebox uses a different native evaluation interface. With only 25 tasks per harness, treat changes as pilot observations, not evidence of a reliable model ranking. + +The jobs used immutable uploaded source snapshots; the new contract migration does not modify these results. The whitebox timeout remains a limitation of that earlier pilot, not a completed 100-step comparison. + +| Baseline evaluation harness | Native OpenCode pilot | Harbor pilot | +|---|---:|---:| +| OpenCode | 2/25 (8%) | 3/25 (12%) | +| Claude Code | 5/25 (20%) | 4/25 (16%) | +| Codex | 4/25 (16%) | 2/25 (8%) | +| Mini-SWE-Agent | 13/25 (52%) | 8/25 (32%) | + +The 10:17 UTC artifact sample contains 2 correct answers among 51 native OpenCode rollouts, 4 among 32 Harbor rollouts, and 0 among 80 whitebox episodes. These include collected work and are not counts of optimizer-consumed samples. They explain why useful gradient updates are sparse; they are not held-out scores. + +## Locate an artifact + +Internal test and pilot outputs use the **private** bucket `FineEnvs/data-agent-daytona-artifacts`, under these run prefixes: + +- `pilot-lfm-opencode-v2` +- `pilot-lfm-multi-harness-v2` +- `pilot-lfm-whitebox-v1` + +Each prefix contains `pilot.json`, `baseline/`, `train/`, `checkpoint-100/` and, after completion, `comparison.json`. Checkpoints contain model weights, optimizer and RNG state. Logs include local Trackio data, scalar metrics and rollout/audit evidence. These paths are maintainer records, not publicly accessible artifacts. No public trained-model artifact is claimed for these pilots. + +The [historical Trackio Space](https://huggingface.co/spaces/FineEnvs/data-agent-training-comparison-trackio) does not automatically contain these new pilots. Their current config keeps Trackio offline in the bucket. Maintainers can inspect the saved HF job logs. The tutorial explains how to enable your own online Trackio Space. + +## Remaining checks + +- Complete a whitebox 100-step pilot with the updated runtime. The earlier pilot failed before checkpoint 50. +- Confirm sustained learning signal, especially in native OpenCode and whitebox. +- Test separate evaluation jobs reading newly saved bucket checkpoints before enabling a long-run watcher. The completed smoke tested reload within one job. +- Treat Qwen HF execution and hardware combinations not tested here as needing their own smoke. + +Detailed pins, failure diagnoses, test counts and original smoke evidence are in [VALIDATION.md](VALIDATION.md). + +## Historical article curves + +The [multi-harness RL article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) covers the earlier LFM and Qwen experiments on [SmolDataEnvs](../04-smoldataenvs/). The README figure shows its two LFM runs, both trained with correctness plus tool efficiency and evaluated on 250 tasks across four harnesses. + +| Historical LFM run | Step-1,000 pass@1 | Graded pairs | Tool-call savings on matched successes | +|---|---:|---:|---:| +| Harbor, OpenCode only | 52.3% | 1,000/1,000 | 11.39% | +| Harbor, multi-harness | 54.2% | 1,000/1,000 | 31.07% | + +The recorded baseline is 421/998 graded pairs (42.18%); two pairs were ungraded. Some early checkpoints also have incomplete coverage, marked with hollow points in the figure. These are observed pass@1 scores, not confidence intervals. The two final scores alone do not establish a statistically significant difference between runs. + +Both historical runs used Harbor. The current tutorial instead uses native OpenCode for its `opencode` mode and explicitly non-thinking serving for both models. These curves do not validate the new pilot configuration. The article also includes Qwen results, whose historical reward was correctness only. + +The figure uses a [pinned copy of the article data](https://huggingface.co/spaces/FineEnvs/multi-harness-rl/blob/a1e03a6c7d8fd0d51d0cf7361ba51a469cc6defc/app/src/content/assets/data/training-results.json). The [local excerpt](assets/historical-lfm-curves.json) keeps the plotted values, evaluation coverage, source URL and source SHA-256. The [Trackio dashboard](https://huggingface.co/spaces/FineEnvs/data-agent-training-comparison-trackio) provides the historical run charts. + +To redraw the figure from this folder, without downloading data or starting a job: + +```bash +uv run --no-project --with matplotlib==3.11.1 python assets/plot_history.py +``` + +Training reward is a trailing 50-update mean, with raw values shown faintly. Evaluation is unsmoothed. Tool savings use task/harness pairs solved by both baseline and checkpoint; positive values mean fewer calls. diff --git a/05-multi-harness-rl/VALIDATION.md b/05-multi-harness-rl/VALIDATION.md new file mode 100644 index 00000000..befa5656 --- /dev/null +++ b/05-multi-harness-rl/VALIDATION.md @@ -0,0 +1,87 @@ +# Validation of the tutorial + +[Read the article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) · [Collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Tutorial](README.md) + +This is the validation record for the `train/`, `eval/` and `envs/` rewrite. Historical pilots in [RESULTS.md](RESULTS.md) and the [article](../content/articles/multi-harness-rl/) used an earlier implementation. + +## Runtime and CPU checks + +The final review passed 44 runtime/contract tests, including two new checks that Whitebox and Harbor preflight do not import deprecated native OpenCode. The article build, 105 external article links, 69 tutorial file links, section anchors and desktop/mobile rendering also passed. + +- **42 tests passed** against OpenEnv main `7ee88d590d36ae1ac3daf2cc11bc551bca7a804f` on 1 October 2026. Tests cover task identity, grading, cleanup, service routing, tokenizer templates and the typed training contract, including partial and zero loss masks. +- A clean Python 3.12 CI environment passed 12 lightweight tests and skipped 30 runtime tests. Lint, formatting, shell syntax and the repository index also passed. +- A separate CPU installation, with **neither Torch nor TRL installed**, started all three environment servers. Their `/health`, `/web/` and Task API requests succeeded. Whitebox and native OpenCode exposed the 250 test tasks. Harbor's UI check used an empty registry; GPU checks use the prepared task registry. +- Evaluation tests exercise both execution modes, verify routing from all three training modes, retain graded wrong answers, retry only ungraded pairs and reject a different checkpoint in the same output directory. The latest test logs and documentation previews are in `experiments/smoldataenv-docs-20261001/`. +- All 1,000 training and 250 test tasks were prepared with instruction/grader hash checks and no notebook overlap. +- LFM's GPU check produced identical packed and separate-sequence logits, then completed backward. The scripts run this guard before async training. +- A real Harbor/Daytona upload passed from the cluster's user ID with `TAR_OPTIONS=--no-same-owner`. + +TRL's typed consumer merged in [PR #6947](https://github.com/huggingface/trl/pull/6947). All 42 runtime/contract tests also passed against merged TRL main `52fb144e7ece97e26aabd3c025740022838e46b5` and OpenEnv main `7ee88d590d36ae1ac3daf2cc11bc551bca7a804f`. Local and HF installers now use both upstream main branches, without a PR revision override. Earlier GPU checks below used the reviewed TRL commit `910138e9392dda75d3a9766e7c494d44f1d82723`; they are not fresh GPU tests of the merge commit. + +The fresh GPU environment uses Python 3.12, Torch 2.11.0, vLLM 0.25.1, Transformers main `d6c1e71bd717bf092f8293f0c3c9bd4a5ac5401a`, and Harbor 0.23.0. `dependencies.json` and `packages.txt` record what each installation resolved. + +```bash +python -m pytest tests -q # Lightweight checks +python -m pytest tests -q --contract # Installed runtime and tokenizer checks +python jobs/smoke.py --mode multi_harness --output runs/harbor-smoke +``` + +## Standalone Space deployments + +Each environment was copied outside this tutorial and started in the CPU-only installation. All three passed health, UI and API checks without importing a sibling environment. The uploaded source files were also compared byte for byte with their local folders. + +| Environment | Public UI | Hardware | Task API | +|---|---|---|---| +| SETA whitebox | [Open](https://fineenvs-smoldataenv-multi-harness-whitebox.hf.space/web/) | CPU Basic | 1,000 train / 250 test | +| Native OpenCode | [Open](https://fineenvs-smoldataenv-multi-harness-opencode.hf.space/web/) | CPU Basic | 1,000 train / 250 test | +| Harbor multi-harness | [Open](https://fineenvs-smoldataenv-multi-harness-harbor.hf.space/web/) | CPU Basic | 1,000 train / 250 test | + +Browser checks on 1 October 2026 verified the three UIs and their root redirects, with no JavaScript errors. Harbor uses the current OpenEnv task browser, rollout controls and trace UI. Whitebox has a task picker, Bash workspace, execution history and score cards. Native OpenCode uses OpenEnv's playground with environment-specific connection examples. A real whitebox Space session staged task data in Daytona, executed one tool call, graded an intentionally incorrect answer as zero and released the sandbox. + +The existing 1,024-session limits are preserved. This is a configuration limit, not a measured throughput claim. Native OpenCode needs a reachable token-capturing inference endpoint before model rollouts; the Space browser check does not establish GPU training readiness. Local evidence is in `experiments/standalone-env-spaces-20261001/`, including source hashes, browser checks and the sandbox probe. The whitebox UI tests cover separate browser sessions, restart and expiry cleanup, failed submissions, unscored attempts and escaped tool output. The deployed browser smoke computed a task answer from its CSV with one command and received correctness `1.0` and reward `1.09375`. A second browser remained isolated, and the 390px mobile layout had no horizontal overflow. Screenshots and logs are in `experiments/whitebox-playground-20261001/`. Previous Space bundles are retained in ignored local archives and the Space's `.archive/` directory. + +The three Spaces were renamed to `smoldataenv-multi-harness-*` and grouped in the [SmolDataEnvs Multi-harness RL collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212). Their history, public visibility, CPU Basic hardware and concurrency settings were preserved. Checks of the new URLs passed, including the whitebox task above, browser isolation and mobile layout. The merged-main installation, test results and deployment checks are recorded in `experiments/smoldataenv-migration-main-20261001/`. + +## GPU smoke progress + +Snapshot: **1 October 2026, 07:47 UTC**. Each smoke requests two optimizer updates, saves both checkpoints, and reloads checkpoint 2 for two test tasks. Blackbox evaluation covers all four harnesses, so it requires eight graded pairs. These tiny samples check execution, not benchmark quality. + +| Platform | Model / mode | Training | Checkpoint reload evaluation | +|---|---|---|---| +| Slurm, two H100s | LFM / whitebox | Two updates and checkpoints verified | 2/2 graded on latest main | +| Slurm, two H100s | Qwen / whitebox | Two updates; persistent Trackio verified | 2/2 graded | +| Slurm, two H100s | LFM / native OpenCode | Two updates; persistent Trackio verified | 8/8 graded on latest main | +| Slurm, two H100s | Qwen / native OpenCode | Two updates and checkpoints verified | 8/8 graded on latest main | +| Slurm, two H100s | LFM / Harbor | Two updates and checkpoints verified | 8/8 graded on latest main | +| Slurm, two H100s | Qwen / Harbor | Two updates and checkpoints verified | 8/8 graded on latest main | +| HF Jobs, two H200s | Qwen / whitebox | Two updates; persistent Trackio present | Reload timed out during bucket weight loading; sequential-loading retry submitted | +| HF Jobs, two H200s | Qwen / native OpenCode | Earlier attempt saved both checkpoints; replacement queued | Pending | +| HF Jobs, two H200s | LFM / whitebox, native OpenCode, Harbor; Qwen / Harbor | Queued for hardware | Pending | + +The native OpenCode reloads were separate retries against their saved checkpoints; they were not single uninterrupted smoke invocations. Their `checkpoint-reload-verification.json` records both source snapshots. The whitebox and Harbor checks completed through `jobs/smoke.py`. + +The local two-update groups had no reward contrast and zero gradient. An earlier HF native Qwen attempt had reward 0.2574 and gradient norm 3.672 at update 2, but failed its logging-location assertion after training. It is not counted as a completed smoke. No claim of reward improvement follows from these checks. + +## Issues caught and corrected + +- **Packed LFM convolutions:** the default kernel path ignored sequence boundaries. The scripts explicitly enable the compatible version-2 kernel on the GPU and check packed logits before training. +- **Qwen whitebox context:** the current GRPO tool loop reads the outer config's context limit. The script copies it from Qwen's text config using public model configuration. +- **Trackio persistence:** set `TRACKIO_DIR` before importing TRL/Trackio. Accept SQLite or append-only JSONL storage; FSx uses the latter. Replacement jobs keep logs beside their checkpoints. +- **Harbor routing:** register task metadata routes outside Harbor's root app, while preserving its lifespan, UI and capture routes. +- **Reward selection:** the pinned tasks include JSON `correctness` and scalar `reward` outputs. OpenEnv's public `correctness,reward` fallback reads both, so valid grades are not discarded. +- **HF checkpoint reload:** both DP workers stalled in memory-mapped bucket weight loading. Checkpoint serving now requests vLLM's public `eager` loader. A local two-replica reload loaded each weight shard in about seven seconds and graded 2/2 tasks. The HF retry is queued; its saved checkpoints are retained. +- **Daytona archive ownership:** cluster file owners may be outside the sandbox's user namespace. Task preparation sets tar to retain the sandbox owner. The grader and questions are unchanged; installed libraries are not patched. + +## Evidence and remaining checks + +The tables above summarize the checks for readers. The underlying logs are internal maintainer records, not part of this repository. Local evidence is under `experiments/tutorial-gpu-smoke-20261001/`: immutable `source-v*` snapshots, `local/` checkpoints and evaluation results, CPU UI checks, the packed-kernel probe and the real Daytona upload check. Repository-local test logs are in ignored `runs/qualification-20261001/`. + +Internal HF outputs use the **private** bucket `FineEnvs/data-agent-daytona-artifacts`. Qwen whitebox checkpoints are in `tutorial-main-smoke-qwen-whitebox-v4`; their reload retry is `tutorial-main-eval-qwen-whitebox-v8`. Other refreshed source snapshots use `tutorial-main-smoke-lfm-whitebox-v8`, `tutorial-main-smoke-{lfm,qwen}-opencode-v8`, and `tutorial-main-smoke-{lfm,qwen}-multi_harness-v8`. Each smoke keeps `train/`, `reload-eval/` and, only after success, `smoke.json`. + +Before a long run, finish the outstanding HF GPU/reload checks on merged TRL main and verify reward contrast over a larger sample. The public Space deployments and contract tests above validate the environment and trainer interfaces; the separate HF GPU smoke matrix remains pending hardware. + +Native `opencode_env` remains deprecated upstream. Its timeout is not Harbor's strict turn cap. Upstream async packing may drop rows above 40,960 tokens and split one rollout into several rows. This tutorial does not reproduce the archive's exact committed-group resume or whole-rollout weighting. + +Earlier code remains in git history and in ignored `temp/pre-tutorial-rewrite/`. Credentials, prepared tasks and run artifacts are excluded from the PR. + +The tutorial intentionally installs TRL and OpenEnv from main and records the resolved commits. It is not a dependency lockfile. Native OpenCode can stop building after upstream removal. The standalone sandbox image also uses a mutable tag in a personal Docker Hub repository; moving it to an organization registry with a fixed digest remains a reproducibility follow-up. diff --git a/05-multi-harness-rl/assets/banner.svg b/05-multi-harness-rl/assets/banner.svg new file mode 100644 index 00000000..8d33ca01 --- /dev/null +++ b/05-multi-harness-rl/assets/banner.svg @@ -0,0 +1,38 @@ + + SmolDataEnvs: multi-harness RL + Train a small model with SETA, native OpenCode or Harbor, then evaluate it on the same held-out tasks. + + + + + + + + + FINEENVS / THE MULTI-HARNESS RL TUTORIAL + SmolDataEnvs + One model. Three ways to train. + Real data tasks, tool use and a checkable answer. + + Read · Run · Compare + + + + + SETA whitebox + TRL runs the tools + + + + + Native OpenCode + OpenCode runs its own loop + + + + + Harbor multi-harness + Four harnesses share one model + + + diff --git a/05-multi-harness-rl/assets/historical-lfm-curves.json b/05-multi-harness-rl/assets/historical-lfm-curves.json new file mode 100644 index 00000000..25a65fd3 --- /dev/null +++ b/05-multi-harness-rl/assets/historical-lfm-curves.json @@ -0,0 +1 @@ +{"source_url":"https://huggingface.co/spaces/FineEnvs/multi-harness-rl/blob/a1e03a6c7d8fd0d51d0cf7361ba51a469cc6defc/app/src/content/assets/data/training-results.json","source_sha256":"50f7a07f51c1feaffaeb4d27fba5e64698a93d06fdedec0a332c8aa7e057f749","source_note":"2026-09-24 run audit: data/evaluations.csv, lfm_evaluation_all_metrics.csv, lfm_eval_tokens.json, lfm_eval_efficiency.json","runs":{"lfm_harbor_opencode":{"label":"Harbor OpenCode 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diff --git a/05-multi-harness-rl/assets/historical-lfm-curves.svg b/05-multi-harness-rl/assets/historical-lfm-curves.svg new file mode 100644 index 00000000..a7afcb3e --- /dev/null +++ b/05-multi-harness-rl/assets/historical-lfm-curves.svg @@ -0,0 +1,4381 @@ + + + + + + + + image/svg+xml + + + Matplotlib v3.11.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 0 + + + + + + + + + + + + + 200 + + + + + + + + + + + + + 400 + + + + + + + + + + + + + 600 + + + + + + + + + + + + + 800 + + + + + + + + + + + + + 1000 + + + + Optimizer step / checkpoint + + + + + + + + + + + + + + + + + 0.0 + + + + + + + + + + + + + 0.2 + + + + + + + + + + + + + 0.4 + + + + + + + + + + + + + 0.6 + + + + + + + + + + + + + 0.8 + + + + + + + + + + + + + 1.0 + + + + Correctness + efficiency bonus + + + + + + + + + + + + + + + + + + + + + + Training reward + + + + + + + + + + + + + + + + + + 0 + + + + + + + + + + + + + 200 + + + + + + + + + + + + + 400 + + + + + + + + + + + + + 600 + + + + + + + + + + + + + 800 + + + + + + + + + + + + + 1000 + + + + Optimizer step / checkpoint + + + + + + + + + + + + + + 35 + + + + + + + + + + + + + 40 + + + + + + + + + + + + + 45 + + + + + + + + + + + + + 50 + + + + + + + + + + + + + 55 + + + + + + + + + + + + + 60 + + + + Pass@1 on graded pairs (%) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 52.3% + + + 54.2% + + + Held-out answer quality + + + + + + + + + + + + + + + + + + 0 + + + + + + + + + + + + + 200 + + + + + + + + + + + + + 400 + + + + + + + + + + + + + 600 + + + + + + + + + + + + + 800 + + + + + + + + + + + + + 1000 + + + + Optimizer step / checkpoint + + + + + + + + + + + + + + 0 + + + + + + + + + + + + + 5 + + + + + + + + + + + + + 10 + + + + + + + + + + + + + 15 + + + + + + + + + + + + + 20 + + + + + + + + + + + + + 25 + + + + + + + + + + + + + 30 + + + + + + + + + + + + + 35 + + + + Savings on matched successes (%) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 11.4% + + + 31.1% + + + Tool-call savings + + + + LFM2.5-2.6B: earlier multi-harness RL results + + + Historical article runs | 250 test tasks x 4 harnesses | Pass@1 + + + Training: trailing 50-update mean; faint lines are raw rewards. Evaluation: measured checkpoints, no smoothing. + + + Hollow markers: incomplete coverage. Savings use pairs solved by baseline and checkpoint. Both runs used Harbor. + + + Source: multi-harness RL article, pinned snapshot a1e03a6. These are not the new non-thinking pilot results. + + + + + + + Harbor OpenCode only + + + + + + Harbor multi-harness + + + + + + + + + + + + + + + diff --git a/05-multi-harness-rl/assets/plot_history.py b/05-multi-harness-rl/assets/plot_history.py new file mode 100644 index 00000000..af8aabd7 --- /dev/null +++ b/05-multi-harness-rl/assets/plot_history.py @@ -0,0 +1,157 @@ +"""Rebuild the article comparison from its pinned data excerpt.""" + +import json +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + + +def main(): + root = Path(__file__).resolve().parent + data = json.loads((root / "historical-lfm-curves.json").read_text()) + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 11, + "axes.spines.top": False, + "axes.spines.right": False, + "axes.edgecolor": "#cbd5e1", + "text.color": "#172033", + "axes.labelcolor": "#475569", + "svg.fonttype": "none", + "svg.hashsalt": "smoldataenvs-history", + } + ) + fig, axes = plt.subplots(1, 3, figsize=(15, 5.8)) + fig.subplots_adjust(left=0.065, right=0.975, top=0.69, bottom=0.26, wspace=0.30) + fig.text( + 0.065, + 0.94, + "LFM2.5-2.6B: earlier multi-harness RL results", + fontsize=21, + weight="bold", + ) + fig.text( + 0.065, + 0.885, + "Historical article runs | 250 test tasks x 4 harnesses | Pass@1", + color="#475569", + ) + for (key, run), color in zip(data["runs"].items(), ["#2563eb", "#d97706"]): + steps, rewards = run["train"]["step"], run["train"]["reward"] + smooth = [ + sum(rewards[max(0, i - 49) : i + 1]) / min(i + 1, 50) + for i in range(len(rewards)) + ] + axes[0].plot(steps, rewards, color=color, alpha=0.08, linewidth=0.6) + axes[0].plot(steps, smooth, color=color, linewidth=2, label=run["label"]) + points = run["eval"] + axes[1].plot( + [p["step"] for p in points], + [p["overall"] for p in points], + color=color, + linewidth=2, + ) + for p in points: + axes[1].plot( + p["step"], + p["overall"], + "o", + color=color, + markerfacecolor=color if p["complete"] else "white", + markersize=5, + ) + p = points[-1] + axes[1].annotate( + f"{p['overall']:.1f}%", + (p["step"], p["overall"]), + xytext=(0, 10 if key.endswith("multi") else -18), + textcoords="offset points", + ha="right", + color=color, + weight="bold", + ) + savings = run["efficiency"] + axes[2].plot( + [p["step"] for p in savings], + [p["tool_savings"] for p in savings], + color=color, + linewidth=2, + ) + coverage = {p["step"]: p["complete"] for p in points} + for p in savings: + axes[2].plot( + p["step"], + p["tool_savings"], + "o", + color=color, + markerfacecolor=color if coverage[p["step"]] else "white", + markersize=5, + ) + p = savings[-1] + axes[2].annotate( + f"{p['tool_savings']:.1f}%", + (p["step"], p["tool_savings"]), + xytext=(0, 10), + textcoords="offset points", + ha="right", + color=color, + weight="bold", + ) + for ax, title, ylabel in zip( + axes, + ["Training reward", "Held-out answer quality", "Tool-call savings"], + [ + "Correctness + efficiency bonus", + "Pass@1 on graded pairs (%)", + "Savings on matched successes (%)", + ], + ): + ax.set_title(title, loc="left", weight="bold", pad=14) + ax.set_xlabel("Optimizer step / checkpoint") + ax.set_ylabel(ylabel) + ax.grid(alpha=0.18) + ax.set_axisbelow(True) + ax.set_xlim(-20, 1030) + axes[0].set_ylim(0, 1.15) + axes[1].set_ylim(35, 60) + axes[2].set_ylim(-2, 36) + fig.legend( + *axes[0].get_legend_handles_labels(), + loc="upper left", + bbox_to_anchor=(0.06, 0.845), + ncol=2, + frameon=False, + ) + fig.text( + 0.065, + 0.15, + "Training: trailing 50-update mean; faint lines are raw rewards. Evaluation: measured checkpoints, no smoothing.", + fontsize=10, + ) + fig.text( + 0.065, + 0.105, + "Hollow markers: incomplete coverage. Savings use pairs solved by baseline and checkpoint. Both runs used Harbor.", + fontsize=10, + ) + fig.text( + 0.065, + 0.06, + "Source: multi-harness RL article, pinned snapshot a1e03a6. These are not the new non-thinking pilot results.", + fontsize=10, + color="#64748b", + ) + output = root / "historical-lfm-curves.svg" + fig.savefig(output, metadata={"Date": None}) + output.write_text( + "\n".join(line.rstrip() for line in output.read_text().splitlines()) + "\n" + ) + plt.close(fig) + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/check_setup.py b/05-multi-harness-rl/check_setup.py new file mode 100644 index 00000000..591e74ef --- /dev/null +++ b/05-multi-harness-rl/check_setup.py @@ -0,0 +1,58 @@ +"""Check the required public APIs before requesting GPUs or creating sandboxes.""" + +import argparse +import inspect +import json +from importlib.metadata import version + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--mode", + choices=["all", "whitebox", "opencode", "multi_harness"], + default="all", + ) + args = parser.parse_args() + from openenv.core.harness import TrainingTrace + from transformers.models.lfm2 import modeling_lfm2 + from trl import GRPOTrainer + from trl.experimental.async_grpo import AsyncGRPOTrainer, openenv_harness + + if args.mode != "whitebox" and "fetch_training_trace" not in inspect.getsource( + openenv_harness + ): + raise RuntimeError( + "The installed TRL lacks the TrainingTrace consumer. Run bash jobs/install.sh to update from main." + ) + if "seq_idx" not in inspect.getsource(modeling_lfm2.Lfm2ShortConv): + raise RuntimeError( + "This LFM implementation lacks sequence boundaries for packed training; install Transformers main." + ) + if "environment_factory" not in inspect.signature(GRPOTrainer).parameters: + raise RuntimeError("GRPOTrainer.environment_factory is required") + assert callable(TrainingTrace) and callable(AsyncGRPOTrainer) + if args.mode in ("all", "multi_harness"): + from harbor_env.harness import HarborSession + + assert callable(HarborSession) + if args.mode in ("all", "opencode"): + from opencode_env.harness import OpenCodeSessionFactory + + assert callable(OpenCodeSessionFactory) + print( + json.dumps( + { + name: version(name) + for name in ("trl", "openenv", "transformers", "vllm") + }, + indent=2, + ) + ) + print( + "Required APIs are present. GPU training and sandbox connectivity still need a smoke test." + ) + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/data/test.json b/05-multi-harness-rl/data/test.json new file mode 100644 index 00000000..25cd82ed --- /dev/null +++ b/05-multi-harness-rl/data/test.json @@ -0,0 +1,252 @@ +[ + {"name": "0000_324_324293_qa_2", "source": "0000/324/324293.ipynb_qa_2", "notebook": "0000/324/324293.ipynb", "difficulty": "medium", "instruction_sha256": "03bd60bb664c4cc96b3ab85db3e3a18039dcfbfee06d3935696f002a2bb26bc5"}, + {"name": "0000_340_340599_qa_1", "source": "0000/340/340599.ipynb_qa_1", "notebook": "0000/340/340599.ipynb", "difficulty": "medium", "instruction_sha256": "dd7911b3032aa469ef4f7cd9c71748c62cb560dcfc2bcc897e6111f4197f0383"}, + {"name": "0000_340_340599_qa_3", "source": "0000/340/340599.ipynb_qa_3", "notebook": "0000/340/340599.ipynb", "difficulty": "easy", "instruction_sha256": "dac579f315382a7411af51143da96cfddafa1a68a0271d8e80e7da09a2e49d8c"}, + 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"0076/259/76259625.ipynb_qa_4", "notebook": "0076/259/76259625.ipynb", "difficulty": "hard", "instruction_sha256": "1cff4c1b3495bd193958157ea96a1b1354c9ec9ef8380d46d1e2bffc85941534"} +] diff --git a/05-multi-harness-rl/envs/README.md b/05-multi-harness-rl/envs/README.md new file mode 100644 index 00000000..2192011f --- /dev/null +++ b/05-multi-harness-rl/envs/README.md @@ -0,0 +1,23 @@ +# Three standalone environments + +[Read the article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) · [Collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Tutorial](../README.md) + +Each directory is a complete Docker Space. Its local files are the files deployed to the Hub. There is no shared environment implementation or generated application bundle. + +| Environment | Local source | Space | +|---|---|---| +| SETA whitebox | [whitebox/](whitebox/) | [Open UI](https://fineenvs-smoldataenv-multi-harness-whitebox.hf.space/web/) | +| Native OpenCode | [opencode/](opencode/) | [Open UI](https://fineenvs-smoldataenv-multi-harness-opencode.hf.space/web/) | +| Harbor multi-harness | [harbor/](harbor/) | [Open UI](https://fineenvs-smoldataenv-multi-harness-harbor.hf.space/web/) | + +The three Spaces are listed in the [SmolDataEnvs collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-6ab4f2f6e09b7cb872ebc867) and the [SmolDataEnvs Multi-harness RL collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212). + +Each contains its own `Dockerfile`, `requirements.txt`, `install.sh`, `start.sh`, `prepare.py`, task manifests, server, and environment package. To run one, enter its directory and follow its README. To deploy it, run its `deploy.py`; it uploads that directory directly. The same Docker build works locally and on a Space. + +The Harbor app uses OpenEnv main's rollout and live-trace UI. Opening the Space redirects to `/web/`; `/docs`, the Task API, and capture endpoints remain available. Its Space card enables Hugging Face sign-in and provider selection. + +`jobs/run.py` starts these same installed packages inside a GPU allocation. The training scripts import their corresponding environment package. A Space serves the environment; the model can run elsewhere and the task sandboxes run in Daytona. + +Native OpenCode's upstream SDK is deprecated. It remains a separate environment here because the comparison explicitly includes the native interface. + +[VALIDATION.md](../VALIDATION.md) records tested deployments and GPU checks. Earlier deployment files are preserved in ignored archives and Hub history. diff --git a/05-multi-harness-rl/envs/__init__.py b/05-multi-harness-rl/envs/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/05-multi-harness-rl/envs/harbor/.dockerignore b/05-multi-harness-rl/envs/harbor/.dockerignore new file mode 100644 index 00000000..7433d48e --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/.dockerignore @@ -0,0 +1,10 @@ +.deps/ +.venv/ +prepared/ +__pycache__/ +*.egg-info/ +.pytest_cache/ +.env +.gradio/ +.archive/ +.git/ diff --git a/05-multi-harness-rl/envs/harbor/.gitattributes b/05-multi-harness-rl/envs/harbor/.gitattributes new file mode 100644 index 00000000..a6344aac --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/05-multi-harness-rl/envs/harbor/.gitignore b/05-multi-harness-rl/envs/harbor/.gitignore new file mode 100644 index 00000000..27ce9e82 --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/.gitignore @@ -0,0 +1,9 @@ +.deps/ +.venv/ +prepared/ +__pycache__/ +*.egg-info/ +.pytest_cache/ +.env +.gradio/ +.archive/ diff --git a/05-multi-harness-rl/envs/harbor/Dockerfile b/05-multi-harness-rl/envs/harbor/Dockerfile new file mode 100644 index 00000000..77695d9e --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/Dockerfile @@ -0,0 +1,9 @@ +FROM python:3.12-slim +RUN apt-get update && apt-get install -y --no-install-recommends git curl && rm -rf /var/lib/apt/lists/* +WORKDIR /app +COPY . /app +RUN bash install.sh && useradd --create-home --uid 1000 user && chown -R user:user /app +USER user +ENV HOME=/home/user HF_HOME=/home/user/.cache/huggingface PYTHONUNBUFFERED=1 PORT=7860 ENABLE_WEB_INTERFACE=true +EXPOSE 7860 +CMD ["bash", "start.sh"] diff --git a/05-multi-harness-rl/envs/harbor/README.md b/05-multi-harness-rl/envs/harbor/README.md new file mode 100644 index 00000000..c244c858 --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/README.md @@ -0,0 +1,65 @@ +--- +title: SmolDataEnvs Multi-harness | Harbor +emoji: 🧪 +colorFrom: blue +colorTo: green +sdk: docker +app_port: 7860 +hf_oauth: true +hf_oauth_scopes: + - inference-api +--- + +# SmolDataEnvs: Harbor multi-harness + +**[Read the multi-harness RL article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl)** · [Collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Training tutorial](https://github.com/adithya-s-k/FineEnvs/tree/main/05-multi-harness-rl) + +Harbor runs OpenCode, Claude Code, Codex or Mini-SWE-Agent. Its OpenEnv UI shows rollouts, live traces and downloadable training captures. + +Use the task browser to choose a problem, connect a model and watch an agent solve it. This environment also serves the four-harness evaluation used to compare both blackbox policies. + +This folder contains the complete Space source. You can run it locally or deploy it on its own. + +[Open the deployed UI](https://fineenvs-smoldataenv-multi-harness-harbor.hf.space/web/) · [Space](https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-harbor) + +## Read the code + +- [`smoldataenv_harbor/environment.py`](smoldataenv_harbor/environment.py): task assignment and tool counts. +- [`smoldataenv_harbor/server.py`](smoldataenv_harbor/server.py): OpenEnv server and UI. +- [`prepare.py`](prepare.py) and [`data/`](data/): the pinned 1,000 train / 250 test tasks, with grader and split checks. +- [`Dockerfile`](Dockerfile): exactly what the Space builds. + +## Run locally + +Use Python 3.12. From this directory: + +```bash +uv venv --python 3.12 --seed .venv +source .venv/bin/activate +bash install.sh +hf auth login +export HF_TOKEN="$(hf auth token)" +export DAYTONA_API_KEY="your-key" +bash start.sh +``` + +Open **http://localhost:7860/**. It opens the UI at `/web/`; the API is at `/docs` and health at `/health`. `install.sh` installs OpenEnv from main and its environment examples from the same checkout. This server needs no GPU or TRL installation. + +The UI supports Hugging Face sign-in and provider selection. You can also supply an OpenAI-compatible endpoint for a rollout. Set `OPENENV_LLM_URL` and `OPENENV_MODEL` only if you want a default endpoint. Training requires a token-capturing endpoint; hosted providers may support evaluation only. + +## Deploy this folder + +```bash +python deploy.py --repo YOUR_ORG/smoldataenv-multi-harness-harbor --public +``` + +Add `HF_TOKEN` and `DAYTONA_API_KEY` as Space secrets. The uploader sends the files in this directory, without generating or swapping application code. It enables the UI and preserves an existing concurrency limit. For a new Space the limit defaults to 40; use `--concurrency` to change it. CPU Basic serves the API; actual tasks run in Daytona. + +You can test the same image locally: + +```bash +docker build -t smoldataenv-harbor . +docker run --rm -p 7860:7860 -e HF_TOKEN -e DAYTONA_API_KEY smoldataenv-harbor +``` + +Runtime downloads, local credentials and old deployment archives are excluded from uploads and Docker builds. 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{"name": "0001_527_1527054_qa_2", "source": "0001/527/1527054.ipynb_qa_2", "notebook": "0001/527/1527054.ipynb", "difficulty": "medium", "instruction_sha256": "3e1ec2b4e0ab278dbb876521fb4b1551ad0b79a63a53d29852ffda60fd78e033"}, + {"name": "0106_834_106834319_qa_1", "source": "0106/834/106834319.ipynb_qa_1", "notebook": "0106/834/106834319.ipynb", "difficulty": "medium", "instruction_sha256": "637da8811aee08d64f5c6e19ecc9e0083464a3c6f2bedcfe9a6b7ad37fa746f2"}, + {"name": "0023_626_23626093_qa_1", "source": "0023/626/23626093.ipynb_qa_1", "notebook": "0023/626/23626093.ipynb", "difficulty": "hard", "instruction_sha256": "3fa61ab7b1454faf531802086d701d246c0b232c1bd724a715714445462bc3ec"}, + {"name": "0021_121_21121885_qa_3", "source": "0021/121/21121885.ipynb_qa_3", "notebook": "0021/121/21121885.ipynb", "difficulty": "hard", "instruction_sha256": "098920eaef9216d6d5d10fa2e607c19126e1336a6509c29b612f596fd46ccdbf"}, + {"name": "0085_309_85309051_qa_1", "source": 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"difficulty": "medium", "instruction_sha256": "fbc3a4777712e9e1de7f8199e0c7bb0a50d80c9579d53e472a6d80d1f1f5805d"}, + {"name": "0083_191_83191425_qa_3", "source": "0083/191/83191425.ipynb_qa_3", "notebook": "0083/191/83191425.ipynb", "difficulty": "hard", "instruction_sha256": "88c6821b7b33ffcdaaddde57e4e701b73291a2cd3888010a2f69b86cc2286f83"}, + {"name": "0119_666_119666168_qa_2", "source": "0119/666/119666168.ipynb_qa_2", "notebook": "0119/666/119666168.ipynb", "difficulty": "medium", "instruction_sha256": "133f73998e7587118d354b8fd0dd9171cb41df81684a052a06a53e0a0cdc2f9e"}, + {"name": "0122_390_122390551_qa_4", "source": "0122/390/122390551.ipynb_qa_4", "notebook": "0122/390/122390551.ipynb", "difficulty": "medium", "instruction_sha256": "65b7c9fd25993e900ac938312bbf40001e94b5a96c2a8dcbc6016ffdccda7687"}, + {"name": "0132_865_132865792_qa_4", "source": "0132/865/132865792.ipynb_qa_4", "notebook": "0132/865/132865792.ipynb", "difficulty": "hard", "instruction_sha256": "f6b16926f5640b7629204c2e484163c4c6e82083f77a75b21730cd9c1e06f04a"}, + {"name": "0122_517_122517438_qa_3", "source": "0122/517/122517438.ipynb_qa_3", "notebook": "0122/517/122517438.ipynb", "difficulty": "hard", "instruction_sha256": "4b3e13801dec3a4d66e9accbb769f2f5efd7f5d70ab682c7e9bb34535e895af6"}, + {"name": "0076_259_76259625_qa_4", "source": "0076/259/76259625.ipynb_qa_4", "notebook": "0076/259/76259625.ipynb", "difficulty": "hard", "instruction_sha256": "1cff4c1b3495bd193958157ea96a1b1354c9ec9ef8380d46d1e2bffc85941534"} +] diff --git a/05-multi-harness-rl/envs/harbor/deploy.py b/05-multi-harness-rl/envs/harbor/deploy.py new file mode 100644 index 00000000..39eabc18 --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/deploy.py @@ -0,0 +1,68 @@ +"""Upload this standalone directory to a Docker Space.""" + +import argparse +from pathlib import Path + +from huggingface_hub import HfApi + +ROOT = Path(__file__).resolve().parent + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo", required=True) + parser.add_argument("--public", action="store_true") + parser.add_argument("--concurrency", type=int) + args = parser.parse_args() + if args.concurrency is not None and args.concurrency < 1: + parser.error("Concurrency must be positive") + api = HfApi() + api.create_repo( + args.repo, + repo_type="space", + space_sdk="docker", + private=not args.public, + exist_ok=True, + ) + variables = api.get_space_variables(args.repo) + concurrency = args.concurrency or ( + int(variables["MAX_CONCURRENT_ENVS"].value) + if "MAX_CONCURRENT_ENVS" in variables + else 40 + ) + api.add_space_variable(args.repo, "MAX_CONCURRENT_ENVS", str(concurrency)) + api.add_space_variable(args.repo, "ENABLE_WEB_INTERFACE", "true") + api.upload_folder( + repo_id=args.repo, + repo_type="space", + folder_path=ROOT, + allow_patterns=[ + "*.py", + "*.json", + "*.toml", + "*.txt", + "*.sh", + "*.yaml", + "README.md", + "Dockerfile", + ".dockerignore", + ".gitignore", + ".gitattributes", + ], + ignore_patterns=[ + ".deps/**", + ".venv/**", + "prepared/**", + ".archive/**", + "**/__pycache__/**", + "**/*.egg-info/**", + ".env", + ".git/**", + ], + commit_message="Deploy SmolDataEnvs Multi-harness | Harbor", + ) + print(f"https://huggingface.co/spaces/{args.repo}") + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/envs/harbor/install.sh b/05-multi-harness-rl/envs/harbor/install.sh new file mode 100644 index 00000000..16fdf9ca --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/install.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash +set -euo pipefail +cd "$(dirname "$0")" +mkdir -p .deps +if [[ ! -d .deps/OpenEnv/.git ]]; then + git clone --depth 1 --branch main https://github.com/huggingface/OpenEnv.git .deps/OpenEnv +else + git -C .deps/OpenEnv pull --ff-only origin main +fi +python -m pip install -e .deps/OpenEnv -r requirements.txt +python -m pip install --no-deps -e . +python - <<'SETUP' +from pathlib import Path +import site +Path(site.getsitepackages()[0], "openenv_examples.pth").write_text( + str(Path(".deps/OpenEnv/envs").resolve()) + "\n" +) +SETUP diff --git a/05-multi-harness-rl/envs/harbor/openenv.yaml b/05-multi-harness-rl/envs/harbor/openenv.yaml new file mode 100644 index 00000000..252897b9 --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/openenv.yaml @@ -0,0 +1,6 @@ +spec_version: 1 +name: smoldataenv_harbor +type: space +runtime: fastapi +app: smoldataenv_harbor.server:app +port: 7860 diff --git a/05-multi-harness-rl/envs/harbor/prepare.py b/05-multi-harness-rl/envs/harbor/prepare.py new file mode 100644 index 00000000..1b558b9a --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/prepare.py @@ -0,0 +1,138 @@ +"""Materialize the fixed train/test tasks from corrected, pinned Harbor datasets.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import shutil +from pathlib import Path + +import tomllib + +ROOT = Path(__file__).resolve().parent + + +def task_rows(split): + return json.loads((ROOT / "data" / f"{split}.json").read_text()) + + +def write_json(path, data): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(data, indent=2) + "\n") + + +DATASETS = { + "train": ( + "FineEnvs/SmolDataEnvs-harbor-train", + "4719635555de1666f374d847baebb500d368e493", + ), + "test": ( + "FineEnvs/SmolDataEnvs-harbor-test", + "b130595b579fc6026ad5762925a9a3b40e72f5bb", + ), +} +GRADER_SHA256 = "16f906e9248fa8b7e1141939e425e88e59d52d65e98e360bbacef3720a43629f" + + +def prepare(destination, sources=None): + import tomli_w + from huggingface_hub import snapshot_download + + destination = Path(destination).resolve() + manifests = {} + for split in ("train", "test"): + rows = task_rows(split) + repo, revision = DATASETS[split] + source = (sources or {}).get(split) + if source is None: + source = snapshot_download( + repo, + repo_type="dataset", + revision=revision, + allow_patterns=[f"tasks/{r['name']}/*" for r in rows], + max_workers=16, + ) + source = Path(source) + tasks = [] + for row in rows: + folder = source / "tasks" / row["name"] + instruction = (folder / "instruction.md").read_bytes() + if hashlib.sha256(instruction).hexdigest() != row["instruction_sha256"]: + raise ValueError( + f"Historical task instruction changed: {split}/{row['name']}" + ) + grader = (folder / "tests/grader.py").read_bytes() + if hashlib.sha256(grader).hexdigest() != GRADER_SHA256: + raise ValueError( + f"Expected corrected numeric-only Harbor grader: {folder}" + ) + target = destination / "datasets" / split / "tasks" / row["name"] + target.mkdir(parents=True, exist_ok=True) + for rel in ( + "instruction.md", + "task.toml", + "environment/Dockerfile", + "environment/pull_bucket.py", + "tests/grader.py", + "tests/test.sh", + ): + dst = target / rel + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(folder / rel, dst) + spec = tomllib.loads((target / "task.toml").read_text()) + spec["environment"]["memory_mb"] = 4096 + spec["environment"]["cpus"] = 1 + # Cluster user IDs may not exist inside Daytona's user namespace. + spec["environment"].setdefault("env", {})["TAR_OPTIONS"] = "--no-same-owner" + (target / "task.toml").write_text(tomli_w.dumps(spec)) + hashes = { + str(f.relative_to(destination / "datasets" / split)): hashlib.sha256( + f.read_bytes() + ).hexdigest() + for f in sorted(target.rglob("*")) + if f.is_file() + } + tasks.append({**row, "file_hashes": hashes}) + local = (sources or {}).get(split) + manifest = { + "source_dataset": repo, + "source_revision": None if local else revision, + "expected_hub_revision": revision, + "local_source": str(local) if local else None, + "task_count": len(tasks), + "tasks": tasks, + } + write_json(destination / f"{split}_manifest.json", manifest) + manifests[split] = manifest + train = {r["source"] for r in manifests["train"]["tasks"]} + test = {r["source"] for r in manifests["test"]["tasks"]} + notebooks = [ + {r.get("notebook") for r in manifests[s]["tasks"]} - {None} + for s in ("train", "test") + ] + if train & test or notebooks[0] & notebooks[1]: + raise ValueError("Train/test overlap") + write_json( + destination / "ready.json", + { + "train": len(train), + "test": len(test), + "grader_sha256": GRADER_SHA256, + "train_test_overlap": 0, + }, + ) + return manifests + + +if __name__ == "__main__": + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--output", type=Path, default=ROOT / "prepared") + p.add_argument("--train-source", type=Path) + p.add_argument("--test-source", type=Path) + a = p.parse_args() + result = prepare( + a.output, + {s: v for s, v in (("train", a.train_source), ("test", a.test_source)) if v}, + ) + print(json.dumps({s: m["task_count"] for s, m in result.items()})) diff --git a/05-multi-harness-rl/envs/harbor/pyproject.toml b/05-multi-harness-rl/envs/harbor/pyproject.toml new file mode 100644 index 00000000..5968a96d --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/pyproject.toml @@ -0,0 +1,11 @@ +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" + +[project] +name = "smoldataenv-harbor" +version = "0.1.0" +requires-python = ">=3.12" + +[tool.setuptools.packages.find] +include = ["smoldataenv_harbor*"] diff --git a/05-multi-harness-rl/envs/harbor/requirements.txt b/05-multi-harness-rl/envs/harbor/requirements.txt new file mode 100644 index 00000000..e8639802 --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/requirements.txt @@ -0,0 +1,9 @@ +# OpenEnv main is installed by install.sh. No trainer or GPU packages are needed. +harbor[daytona]>=0.22.0 +daytona>=0.203.0 +fastmcp>=2 +uvicorn[standard] +gradio +huggingface-hub +math-verify +tomli-w diff --git a/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/__init__.py b/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/environment.py b/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/environment.py new file mode 100644 index 00000000..1e49d633 --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/environment.py @@ -0,0 +1,85 @@ +"""Assign SmolDataEnvs tasks to Harbor harnesses and count verified native actions.""" + +import json +from pathlib import Path + +from harbor_env import HarborEnv +from harbor_env.harness import HarborSession + +HARNESSES = ("opencode", "claude-code", "codex", "mini-swe-agent") + + +def count_tools(path): + try: + trajectory = json.loads(path.read_text()) + if not trajectory["schema_version"].startswith("ATIF-"): + raise ValueError("Unknown trajectory format") + ids = [ + call["tool_call_id"] + for step in trajectory["steps"] + if step.get("source") == "agent" + for call in step.get("tool_calls", []) or [] + ] + if any(not value for value in ids) or len(set(ids)) != len(ids): + raise ValueError("Missing or repeated tool action IDs") + calls = len(ids) + except (OSError, ValueError, KeyError, TypeError): + calls = None + return calls + + +class TaskSession(HarborSession): + def fetch_training_trace(self): + trace = super().fetch_training_trace() + # ATIF counts actual tool actions, not model requests or training rows. + path = self.trial_root / self.result.trial_name / "agent/trajectory.json" + if getattr(self, "server", None): + import httpx + + response = httpx.get( + self.server.rstrip("/") + + f"/smoldataenv/trials/{self.result.trial_name}/tool-count", + timeout=30, + ) + response.raise_for_status() + calls = response.json()["native_tool_calls"] + else: + calls = count_tools(path) + for turn in trace.turns: + turn.metadata["native_tool_calls"] = calls + return trace + + +class TaskFactory: + def __init__(self, args, tasks, *, sampling): + import httpx + + self.args, self.sampling = args, sampling + response = httpx.get( + args.server.rstrip("/") + "/smoldataenv/splits", timeout=30 + ) + response.raise_for_status() + self.split = response.json()["train"] + self.tasks = {task["instruction"]: (i, task) for i, task in enumerate(tasks)} + + def create(self, task, seed=None, episode_id=None): + # Match by prompt, not by the worker seed: seeds can change after a restart. + index, row = self.tasks[task[-1]["content"]] + session = TaskSession( + env=HarborEnv(self.args.server, message_timeout_s=1800), + owns_env=True, + split=self.split, + task_index=index, + instruction=row["instruction"], + harness=HARNESSES[index % len(HARNESSES)], + sandbox="daytona", + llm_url=getattr(self.args, "env_llm_url", None) or self.args.vllm_url, + model=self.args.model, + sampling=self.sampling, + reward_key="correctness,reward", + agent_step_limit=17, + agent_timeout_sec=600, + ) + session.trial_root = Path(self.args.trials) + session.server = self.args.server + return session diff --git a/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/server.py b/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/server.py new file mode 100644 index 00000000..e61e9ce2 --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/server.py @@ -0,0 +1,57 @@ +"""SmolDataEnvs task serving, capture and UI through OpenEnv's Harbor service.""" + +import os +from contextlib import asynccontextmanager +from pathlib import Path + +from fastapi import FastAPI + +# Both local jobs and Spaces prepare exactly these train/test manifests. +data = Path(os.environ.get("SMOLDATA_DATA", "prepared")).resolve() +os.environ.setdefault( + "OPENENV_DATASETS", ",".join(str(data / "datasets" / s) for s in ("train", "test")) +) +os.environ.setdefault("OPENENV_MAX_OUTPUT_TOKENS", "4096") +os.environ.setdefault("MAX_CONCURRENT_ENVS", "40") +os.environ.setdefault("OPENENV_HARBOR_REWARD_KEY", "correctness,reward") +os.environ.setdefault( + "OPENENV_HARBOR_AGENT_VERSIONS", + '{"opencode":"1.18.31","claude-code":"2.1.270","codex":"0.154.0","mini-swe-agent":"2.4.6"}', +) + +# This public app owns the capture proxy, task API, sandbox lifecycle and trace UI. +from harbor_env.server.app import app as harbor_app + + +@asynccontextmanager +async def lifespan(app): + async with harbor_app.router.lifespan_context(harbor_app): + yield + + +# Register task metadata before Harbor's UI, which is mounted at the root. +app = FastAPI(lifespan=lifespan, docs_url=None, redoc_url=None, openapi_url=None) + + +@app.get("/smoldataenv/splits") +def split_paths(): + return {s: str(data / "datasets" / s) for s in ("train", "test")} + + +@app.get("/smoldataenv/trials/{trial_name}/tool-count") +def tool_count(trial_name: str): + from fastapi import HTTPException + + from .environment import count_tools + + if Path(trial_name).name != trial_name or trial_name in {".", ".."}: + raise HTTPException(status_code=400, detail="Invalid trial name") + root = Path( + os.environ.get("OPENENV_HARBOR_TRIALS_DIR", "/tmp/openenv-harbor-trials") + ) + return { + "native_tool_calls": count_tools(root / trial_name / "agent/trajectory.json") + } + + +app.mount("/", harbor_app) diff --git a/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/tasks.py b/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/tasks.py new file mode 100644 index 00000000..54e166a5 --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/smoldataenv_harbor/tasks.py @@ -0,0 +1,23 @@ +"""Stage public task files in a sandbox; run the held-out grader on the host.""" + +from pathlib import Path + +import tomllib + + +def load_tasks(data, split): + root = Path(data).resolve() / "datasets" / split / "tasks" + tasks = [] + for folder in sorted(root.iterdir()): + spec = tomllib.loads((folder / "task.toml").read_text()) + tasks.append( + { + "name": folder.name, + "folder": str(folder), + "instruction": (folder / "instruction.md").read_text(), + "difficulty": spec["metadata"]["difficulty_tier"], + } + ) + if not tasks or len({t["instruction"] for t in tasks}) != len(tasks): + raise ValueError("Tasks must have unique instructions; run prepare.py first") + return tasks diff --git a/05-multi-harness-rl/envs/harbor/start.sh b/05-multi-harness-rl/envs/harbor/start.sh new file mode 100644 index 00000000..0f7093fa --- /dev/null +++ b/05-multi-harness-rl/envs/harbor/start.sh @@ -0,0 +1,5 @@ +#!/usr/bin/env bash +set -euo pipefail +cd "$(dirname "$0")" +python prepare.py --output "${SMOLDATA_DATA:-prepared}" +exec uvicorn smoldataenv_harbor.server:app --host 0.0.0.0 --port "${PORT:-7860}" --ws-ping-timeout 1800 diff --git a/05-multi-harness-rl/envs/opencode/.dockerignore b/05-multi-harness-rl/envs/opencode/.dockerignore new file mode 100644 index 00000000..7433d48e --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/.dockerignore @@ -0,0 +1,10 @@ +.deps/ +.venv/ +prepared/ +__pycache__/ +*.egg-info/ +.pytest_cache/ +.env +.gradio/ +.archive/ +.git/ diff --git a/05-multi-harness-rl/envs/opencode/.gitattributes b/05-multi-harness-rl/envs/opencode/.gitattributes new file mode 100644 index 00000000..a6344aac --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/05-multi-harness-rl/envs/opencode/.gitignore b/05-multi-harness-rl/envs/opencode/.gitignore new file mode 100644 index 00000000..27ce9e82 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/.gitignore @@ -0,0 +1,9 @@ +.deps/ +.venv/ +prepared/ +__pycache__/ +*.egg-info/ +.pytest_cache/ +.env +.gradio/ +.archive/ diff --git a/05-multi-harness-rl/envs/opencode/Dockerfile b/05-multi-harness-rl/envs/opencode/Dockerfile new file mode 100644 index 00000000..77695d9e --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/Dockerfile @@ -0,0 +1,9 @@ +FROM python:3.12-slim +RUN apt-get update && apt-get install -y --no-install-recommends git curl && rm -rf /var/lib/apt/lists/* +WORKDIR /app +COPY . /app +RUN bash install.sh && useradd --create-home --uid 1000 user && chown -R user:user /app +USER user +ENV HOME=/home/user HF_HOME=/home/user/.cache/huggingface PYTHONUNBUFFERED=1 PORT=7860 ENABLE_WEB_INTERFACE=true +EXPOSE 7860 +CMD ["bash", "start.sh"] diff --git a/05-multi-harness-rl/envs/opencode/README.md b/05-multi-harness-rl/envs/opencode/README.md new file mode 100644 index 00000000..a4184c35 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/README.md @@ -0,0 +1,62 @@ +--- +title: SmolDataEnvs Multi-harness | Native OpenCode +emoji: 🧪 +colorFrom: blue +colorTo: green +sdk: docker +app_port: 7860 +--- + +# SmolDataEnvs: Native OpenCode + +**[Read the multi-harness RL article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl)** · [Collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Training tutorial](https://github.com/adithya-s-k/FineEnvs/tree/main/05-multi-harness-rl) + +Native OpenCode runs inside the sandbox. The server grades its answer and returns a typed token trace for AsyncGRPO. + +`opencode_env` is deprecated since OpenEnv 0.7.0. This environment remains available for the historical comparison; new runs should use the [Harbor Space](https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-harbor) with `harness="opencode"`. Rebuilding this standalone Space from main will stop working once upstream removes `opencode_env`. + +This folder contains the complete Space source. You can run it locally or deploy it on its own. + +[Open the deployed UI](https://fineenvs-smoldataenv-multi-harness-opencode.hf.space/web/) · [Space](https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-opencode) + +## Read the code + +- [`smoldataenv_opencode/environment.py`](smoldataenv_opencode/environment.py): task execution and grading. +- [`smoldataenv_opencode/server.py`](smoldataenv_opencode/server.py): OpenEnv server and UI. +- [`prepare.py`](prepare.py) and [`data/`](data/): the pinned 1,000 train / 250 test tasks, with grader and split checks. +- [`Dockerfile`](Dockerfile): exactly what the Space builds. + +## Run locally + +Use Python 3.12. From this directory: + +```bash +uv venv --python 3.12 --seed .venv +source .venv/bin/activate +bash install.sh +hf auth login +export HF_TOKEN="$(hf auth token)" +export DAYTONA_API_KEY="your-key" +bash start.sh +``` + +Open **http://localhost:7860/**. It opens the UI at `/web/`; the API is at `/docs` and health at `/health`. `install.sh` installs OpenEnv from main and its environment examples from the same checkout. This server needs no GPU or TRL installation. + +For agent rollouts, set `SANDBOX_VLLM_URL` and the secret `SANDBOX_VLLM_KEY` to an inference endpoint reachable by the sandbox. Training requires engine token IDs and logprobs. + +## Deploy this folder + +```bash +python deploy.py --repo YOUR_ORG/smoldataenv-multi-harness-opencode --public +``` + +Add `HF_TOKEN` and `DAYTONA_API_KEY` as Space secrets. The uploader sends the files in this directory, without generating or swapping application code. It enables the UI and preserves an existing concurrency limit. For a new Space the limit defaults to 40; use `--concurrency` to change it. CPU Basic serves the API; actual tasks run in Daytona. + +You can test the same image locally: + +```bash +docker build -t smoldataenv-opencode . +docker run --rm -p 7860:7860 -e HF_TOKEN -e DAYTONA_API_KEY smoldataenv-opencode +``` + +Runtime downloads, local credentials and old deployment archives are excluded from uploads and Docker builds. Task questions and held-out answers are never bundled into the image; the pinned tasks are prepared at startup. diff --git a/05-multi-harness-rl/envs/opencode/data/test.json b/05-multi-harness-rl/envs/opencode/data/test.json new file mode 100644 index 00000000..25cd82ed --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/data/test.json @@ -0,0 +1,252 @@ +[ + {"name": "0000_324_324293_qa_2", "source": "0000/324/324293.ipynb_qa_2", "notebook": "0000/324/324293.ipynb", "difficulty": "medium", "instruction_sha256": "03bd60bb664c4cc96b3ab85db3e3a18039dcfbfee06d3935696f002a2bb26bc5"}, + {"name": "0000_340_340599_qa_1", "source": "0000/340/340599.ipynb_qa_1", "notebook": "0000/340/340599.ipynb", "difficulty": "medium", "instruction_sha256": "dd7911b3032aa469ef4f7cd9c71748c62cb560dcfc2bcc897e6111f4197f0383"}, + {"name": "0000_340_340599_qa_3", "source": "0000/340/340599.ipynb_qa_3", "notebook": "0000/340/340599.ipynb", "difficulty": "easy", "instruction_sha256": 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{"name": "0001_527_1527054_qa_2", "source": "0001/527/1527054.ipynb_qa_2", "notebook": "0001/527/1527054.ipynb", "difficulty": "medium", "instruction_sha256": "3e1ec2b4e0ab278dbb876521fb4b1551ad0b79a63a53d29852ffda60fd78e033"}, + {"name": "0106_834_106834319_qa_1", "source": "0106/834/106834319.ipynb_qa_1", "notebook": "0106/834/106834319.ipynb", "difficulty": "medium", "instruction_sha256": "637da8811aee08d64f5c6e19ecc9e0083464a3c6f2bedcfe9a6b7ad37fa746f2"}, + {"name": "0023_626_23626093_qa_1", "source": "0023/626/23626093.ipynb_qa_1", "notebook": "0023/626/23626093.ipynb", "difficulty": "hard", "instruction_sha256": "3fa61ab7b1454faf531802086d701d246c0b232c1bd724a715714445462bc3ec"}, + {"name": "0021_121_21121885_qa_3", "source": "0021/121/21121885.ipynb_qa_3", "notebook": "0021/121/21121885.ipynb", "difficulty": "hard", "instruction_sha256": "098920eaef9216d6d5d10fa2e607c19126e1336a6509c29b612f596fd46ccdbf"}, + {"name": "0085_309_85309051_qa_1", "source": "0085/309/85309051.ipynb_qa_1", "notebook": "0085/309/85309051.ipynb", "difficulty": "hard", "instruction_sha256": "2a9357a973f34e9c864046771c0c2b3e1b6f85052a24000a14b5310bd012a37a"}, + {"name": "0113_768_113768809_qa_5", "source": "0113/768/113768809.ipynb_qa_5", "notebook": "0113/768/113768809.ipynb", "difficulty": "hard", "instruction_sha256": "edf0e83dfac4ae49f5a612f167576c8b5d8128a94447838fed67c499299607af"}, + {"name": "0054_065_54065855_qa_5", "source": "0054/065/54065855.ipynb_qa_5", "notebook": "0054/065/54065855.ipynb", "difficulty": "hard", "instruction_sha256": "dc30fa15604f467cd7443abc7e601fe4ac6a3d0cca7e4bae4416f45492ed0f40"}, + {"name": "0040_288_40288007_qa_4", "source": "0040/288/40288007.ipynb_qa_4", "notebook": "0040/288/40288007.ipynb", "difficulty": "hard", "instruction_sha256": "c6434e6c5b002a021f382e21ea804e02087cbdf60f669b62e902513bd8b713ef"}, + {"name": "0044_412_44412230_qa_1", "source": "0044/412/44412230.ipynb_qa_1", "notebook": "0044/412/44412230.ipynb", "difficulty": "medium", "instruction_sha256": "fbc3a4777712e9e1de7f8199e0c7bb0a50d80c9579d53e472a6d80d1f1f5805d"}, + {"name": "0083_191_83191425_qa_3", "source": "0083/191/83191425.ipynb_qa_3", "notebook": "0083/191/83191425.ipynb", "difficulty": "hard", "instruction_sha256": "88c6821b7b33ffcdaaddde57e4e701b73291a2cd3888010a2f69b86cc2286f83"}, + {"name": "0119_666_119666168_qa_2", "source": "0119/666/119666168.ipynb_qa_2", "notebook": "0119/666/119666168.ipynb", "difficulty": "medium", "instruction_sha256": "133f73998e7587118d354b8fd0dd9171cb41df81684a052a06a53e0a0cdc2f9e"}, + {"name": "0122_390_122390551_qa_4", "source": "0122/390/122390551.ipynb_qa_4", "notebook": "0122/390/122390551.ipynb", "difficulty": "medium", "instruction_sha256": "65b7c9fd25993e900ac938312bbf40001e94b5a96c2a8dcbc6016ffdccda7687"}, + {"name": "0132_865_132865792_qa_4", "source": "0132/865/132865792.ipynb_qa_4", "notebook": "0132/865/132865792.ipynb", "difficulty": "hard", "instruction_sha256": "f6b16926f5640b7629204c2e484163c4c6e82083f77a75b21730cd9c1e06f04a"}, + {"name": "0122_517_122517438_qa_3", "source": "0122/517/122517438.ipynb_qa_3", "notebook": "0122/517/122517438.ipynb", "difficulty": "hard", "instruction_sha256": "4b3e13801dec3a4d66e9accbb769f2f5efd7f5d70ab682c7e9bb34535e895af6"}, + {"name": "0076_259_76259625_qa_4", "source": "0076/259/76259625.ipynb_qa_4", "notebook": "0076/259/76259625.ipynb", "difficulty": "hard", "instruction_sha256": "1cff4c1b3495bd193958157ea96a1b1354c9ec9ef8380d46d1e2bffc85941534"} +] diff --git a/05-multi-harness-rl/envs/opencode/deploy.py b/05-multi-harness-rl/envs/opencode/deploy.py new file mode 100644 index 00000000..0c677c56 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/deploy.py @@ -0,0 +1,68 @@ +"""Upload this standalone directory to a Docker Space.""" + +import argparse +from pathlib import Path + +from huggingface_hub import HfApi + +ROOT = Path(__file__).resolve().parent + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo", required=True) + parser.add_argument("--public", action="store_true") + parser.add_argument("--concurrency", type=int) + args = parser.parse_args() + if args.concurrency is not None and args.concurrency < 1: + parser.error("Concurrency must be positive") + api = HfApi() + api.create_repo( + args.repo, + repo_type="space", + space_sdk="docker", + private=not args.public, + exist_ok=True, + ) + variables = api.get_space_variables(args.repo) + concurrency = args.concurrency or ( + int(variables["MAX_CONCURRENT_ENVS"].value) + if "MAX_CONCURRENT_ENVS" in variables + else 40 + ) + api.add_space_variable(args.repo, "MAX_CONCURRENT_ENVS", str(concurrency)) + api.add_space_variable(args.repo, "ENABLE_WEB_INTERFACE", "true") + api.upload_folder( + repo_id=args.repo, + repo_type="space", + folder_path=ROOT, + allow_patterns=[ + "*.py", + "*.json", + "*.toml", + "*.txt", + "*.sh", + "*.yaml", + "README.md", + "Dockerfile", + ".dockerignore", + ".gitignore", + ".gitattributes", + ], + ignore_patterns=[ + ".deps/**", + ".venv/**", + "prepared/**", + ".archive/**", + "**/__pycache__/**", + "**/*.egg-info/**", + ".env", + ".git/**", + ], + commit_message="Deploy SmolDataEnvs Multi-harness | Native OpenCode", + ) + print(f"https://huggingface.co/spaces/{args.repo}") + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/envs/opencode/install.sh b/05-multi-harness-rl/envs/opencode/install.sh new file mode 100644 index 00000000..16fdf9ca --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/install.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash +set -euo pipefail +cd "$(dirname "$0")" +mkdir -p .deps +if [[ ! -d .deps/OpenEnv/.git ]]; then + git clone --depth 1 --branch main https://github.com/huggingface/OpenEnv.git .deps/OpenEnv +else + git -C .deps/OpenEnv pull --ff-only origin main +fi +python -m pip install -e .deps/OpenEnv -r requirements.txt +python -m pip install --no-deps -e . +python - <<'SETUP' +from pathlib import Path +import site +Path(site.getsitepackages()[0], "openenv_examples.pth").write_text( + str(Path(".deps/OpenEnv/envs").resolve()) + "\n" +) +SETUP diff --git a/05-multi-harness-rl/envs/opencode/openenv.yaml b/05-multi-harness-rl/envs/opencode/openenv.yaml new file mode 100644 index 00000000..c458ff89 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/openenv.yaml @@ -0,0 +1,6 @@ +spec_version: 1 +name: smoldataenv_opencode +type: space +runtime: fastapi +app: smoldataenv_opencode.server:app +port: 7860 diff --git a/05-multi-harness-rl/envs/opencode/prepare.py b/05-multi-harness-rl/envs/opencode/prepare.py new file mode 100644 index 00000000..1b558b9a --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/prepare.py @@ -0,0 +1,138 @@ +"""Materialize the fixed train/test tasks from corrected, pinned Harbor datasets.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import shutil +from pathlib import Path + +import tomllib + +ROOT = Path(__file__).resolve().parent + + +def task_rows(split): + return json.loads((ROOT / "data" / f"{split}.json").read_text()) + + +def write_json(path, data): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(data, indent=2) + "\n") + + +DATASETS = { + "train": ( + "FineEnvs/SmolDataEnvs-harbor-train", + "4719635555de1666f374d847baebb500d368e493", + ), + "test": ( + "FineEnvs/SmolDataEnvs-harbor-test", + "b130595b579fc6026ad5762925a9a3b40e72f5bb", + ), +} +GRADER_SHA256 = "16f906e9248fa8b7e1141939e425e88e59d52d65e98e360bbacef3720a43629f" + + +def prepare(destination, sources=None): + import tomli_w + from huggingface_hub import snapshot_download + + destination = Path(destination).resolve() + manifests = {} + for split in ("train", "test"): + rows = task_rows(split) + repo, revision = DATASETS[split] + source = (sources or {}).get(split) + if source is None: + source = snapshot_download( + repo, + repo_type="dataset", + revision=revision, + allow_patterns=[f"tasks/{r['name']}/*" for r in rows], + max_workers=16, + ) + source = Path(source) + tasks = [] + for row in rows: + folder = source / "tasks" / row["name"] + instruction = (folder / "instruction.md").read_bytes() + if hashlib.sha256(instruction).hexdigest() != row["instruction_sha256"]: + raise ValueError( + f"Historical task instruction changed: {split}/{row['name']}" + ) + grader = (folder / "tests/grader.py").read_bytes() + if hashlib.sha256(grader).hexdigest() != GRADER_SHA256: + raise ValueError( + f"Expected corrected numeric-only Harbor grader: {folder}" + ) + target = destination / "datasets" / split / "tasks" / row["name"] + target.mkdir(parents=True, exist_ok=True) + for rel in ( + "instruction.md", + "task.toml", + "environment/Dockerfile", + "environment/pull_bucket.py", + "tests/grader.py", + "tests/test.sh", + ): + dst = target / rel + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(folder / rel, dst) + spec = tomllib.loads((target / "task.toml").read_text()) + spec["environment"]["memory_mb"] = 4096 + spec["environment"]["cpus"] = 1 + # Cluster user IDs may not exist inside Daytona's user namespace. + spec["environment"].setdefault("env", {})["TAR_OPTIONS"] = "--no-same-owner" + (target / "task.toml").write_text(tomli_w.dumps(spec)) + hashes = { + str(f.relative_to(destination / "datasets" / split)): hashlib.sha256( + f.read_bytes() + ).hexdigest() + for f in sorted(target.rglob("*")) + if f.is_file() + } + tasks.append({**row, "file_hashes": hashes}) + local = (sources or {}).get(split) + manifest = { + "source_dataset": repo, + "source_revision": None if local else revision, + "expected_hub_revision": revision, + "local_source": str(local) if local else None, + "task_count": len(tasks), + "tasks": tasks, + } + write_json(destination / f"{split}_manifest.json", manifest) + manifests[split] = manifest + train = {r["source"] for r in manifests["train"]["tasks"]} + test = {r["source"] for r in manifests["test"]["tasks"]} + notebooks = [ + {r.get("notebook") for r in manifests[s]["tasks"]} - {None} + for s in ("train", "test") + ] + if train & test or notebooks[0] & notebooks[1]: + raise ValueError("Train/test overlap") + write_json( + destination / "ready.json", + { + "train": len(train), + "test": len(test), + "grader_sha256": GRADER_SHA256, + "train_test_overlap": 0, + }, + ) + return manifests + + +if __name__ == "__main__": + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--output", type=Path, default=ROOT / "prepared") + p.add_argument("--train-source", type=Path) + p.add_argument("--test-source", type=Path) + a = p.parse_args() + result = prepare( + a.output, + {s: v for s, v in (("train", a.train_source), ("test", a.test_source)) if v}, + ) + print(json.dumps({s: m["task_count"] for s, m in result.items()})) diff --git a/05-multi-harness-rl/envs/opencode/pyproject.toml b/05-multi-harness-rl/envs/opencode/pyproject.toml new file mode 100644 index 00000000..b7b585ed --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/pyproject.toml @@ -0,0 +1,11 @@ +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" + +[project] +name = "smoldataenv-opencode" +version = "0.1.0" +requires-python = ">=3.12" + +[tool.setuptools.packages.find] +include = ["smoldataenv_opencode*"] diff --git a/05-multi-harness-rl/envs/opencode/requirements.txt b/05-multi-harness-rl/envs/opencode/requirements.txt new file mode 100644 index 00000000..e8639802 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/requirements.txt @@ -0,0 +1,9 @@ +# OpenEnv main is installed by install.sh. No trainer or GPU packages are needed. +harbor[daytona]>=0.22.0 +daytona>=0.203.0 +fastmcp>=2 +uvicorn[standard] +gradio +huggingface-hub +math-verify +tomli-w diff --git a/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/__init__.py b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/catalog.py b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/catalog.py new file mode 100644 index 00000000..b11166ef --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/catalog.py @@ -0,0 +1,39 @@ +"""The Task API serves names and instructions, never private verifier answers.""" + +import os +from functools import lru_cache + +from .tasks import load_tasks + + +@lru_cache +def tasks(split): + if split not in {"train", "test"}: + raise ValueError("Choose train or test") + return load_tasks(os.environ.get("SMOLDATA_DATA", "prepared"), split) + + +def task_by_name(split, name): + return next(row for row in tasks(split) if row["name"] == name) + + +class TaskCatalog: + def list_splits(self): + return ["train", "test"] + + def num_tasks(self, split): + return len(tasks(split)) + + def get_task(self, split, index): + return { + key: value for key, value in tasks(split)[index].items() if key != "folder" + } + + def get_task_range(self, split, start=None, stop=None): + return [ + self.get_task(split, i) + for i in range(*slice(start, stop).indices(self.num_tasks(split))) + ] + + def list_tasks(self, split): + return self.get_task_range(split) diff --git a/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/client.py b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/client.py new file mode 100644 index 00000000..ff5a6f88 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/client.py @@ -0,0 +1,68 @@ +"""The public OpenEnv training session contract over a remote native OpenCode server.""" + +from pathlib import Path + +from openenv.core.harness import ( + ResourceSession, + ToolResult, + TrainingTrace, + VerifyResult, +) +from openenv.core.mcp_client import MCPToolClient + + +class RemoteSession(ResourceSession): + def __init__(self, server, row, model, sampling): + self.client = MCPToolClient(server, message_timeout_s=1800).sync() + self.row, self.model, self.sampling = row, model, sampling + self.result = None + + def initial_messages(self): + return [{"role": "user", "content": self.row["instruction"]}] + + def list_tools(self): + return [] + + def call_tool(self, name, arguments): + return ToolResult(error="OpenCode owns its tool loop") + + def wait_for_completion(self, timeout_s=None): + self.client.reset() + folder = Path(self.row["folder"]) + self.result = self.client.call_tool( + "run_rollout", + split=folder.parent.parent.name, + task_name=folder.name, + model=self.model, + sampling=self.sampling, + ) + return 0 + + def fetch_training_trace(self): + if self.result is None: + raise RuntimeError("Rollout has not completed") + if "capture_error" in self.result: + raise ValueError(self.result["capture_error"]) + return TrainingTrace.model_validate(self.result["training_trace"]) + + def verify(self, transcript, final_state=None): + return VerifyResult( + env_reward=self.result["correctness"] if self.result else None, done=True + ) + + def close(self): + self.client.close() + + +class RemoteTaskFactory: + def __init__(self, args, tasks, *, sampling): + self.args, self.sampling = args, sampling + self.tasks = {row["instruction"]: row for row in tasks} + + def create(self, task, seed=None, episode_id=None): + return RemoteSession( + self.args.server, + self.tasks[task[-1]["content"]], + self.args.model, + self.sampling, + ) diff --git a/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/daytona.py b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/daytona.py new file mode 100644 index 00000000..f0b36d96 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/daytona.py @@ -0,0 +1,144 @@ +"""Daytona implementation of the standalone OpenCode sandbox protocol.""" + +from __future__ import annotations + +import math +import os +import shlex +import time +import uuid +from functools import lru_cache + +from opencode_env.sandbox.base import ExecResult + + +@lru_cache(maxsize=1) +def client(): + from daytona import Daytona + + return Daytona() + + +class DaytonaBgJob: + def __init__(self, handle, directory, pid): + self.handle, self.directory, self._pid = handle, directory, pid + + @property + def pid(self): + return self._pid + + def wait(self, timeout=None): + deadline = time.monotonic() + timeout if timeout is not None else float("inf") + while time.monotonic() < deadline: + if self.handle.exists(self.directory + "/exit"): + return int(self.handle.read_text(self.directory + "/exit").strip()) + time.sleep(min(0.5, max(0, deadline - time.monotonic()))) + raise TimeoutError("Background command exceeded its deadline") + + def kill(self): + self.handle.exec(f"kill -TERM -- -{self.pid} 2>/dev/null || true", timeout=10) + + +class DaytonaSandboxHandle: + def __init__(self, sandbox): + self._sandbox = sandbox + self._deleted = False + + @property + def sandbox_id(self): + return self._sandbox.id + + def exec(self, cmd, *, envs=None, cwd=None, timeout=60): + # The SDK's exec is not a shell. Quote the complete script once and let bash + # interpret it; neither user text nor environment values are interpolated. + result = self._sandbox.process.exec( + "bash -lc " + shlex.quote(cmd), + cwd=cwd, + env=envs, + timeout=max(1, math.ceil(timeout)) if timeout is not None else None, + ) + code = result.exit_code + return ExecResult(124 if code is None else int(code), result.result or "", "") + + def write_text(self, path, content): + parent = path.rsplit("/", 1)[0] or "." + result = self.exec("mkdir -p " + shlex.quote(parent)) + if result.exit_code: + raise RuntimeError("Could not create sandbox directory") + self._sandbox.fs.upload_file(content.encode(), path) + + def read_text(self, path): + return self._sandbox.fs.download_file(path).decode() + + def exists(self, path): + return self.exec("test -e " + shlex.quote(path), timeout=15).exit_code == 0 + + def start_bg(self, cmd, *, envs=None, cwd=None): + directory = "/tmp/openenv-process-" + uuid.uuid4().hex + self.write_text( + directory + "/run.sh", + "#!/bin/bash\n(\n" + + cmd + + "\n)" + + "\ncode=$?\nprintf '%s' \"$code\" > " + + shlex.quote(directory + "/exit") + + "\n", + ) + result = self.exec( + "setsid bash " + + shlex.quote(directory + "/run.sh") + + " > " + + shlex.quote(directory + "/output") + + " 2>&1 < /dev/null & echo $!", + envs=envs, + cwd=cwd, + timeout=15, + ) + if result.exit_code: + raise RuntimeError("Could not launch background command") + return DaytonaBgJob( + self, directory, int(result.stdout.strip().splitlines()[-1]) + ) + + def kill(self): + if not self._deleted: + client().delete(self._sandbox, timeout=60, wait=True) + self._deleted = True + + +class DaytonaSandboxBackend: + def __init__(self, *, image, snapshot=None): + self.image, self.snapshot = ( + image, + snapshot or os.environ.get("DATA_AGENT_DAYTONA_SNAPSHOT"), + ) + + def create(self, *, timeout_s=900, envs=None, metadata=None): + from daytona import ( + CreateSandboxFromImageParams, + CreateSandboxFromSnapshotParams, + Resources, + ) + + options = { + "language": "python", + "os_user": "root", + "env_vars": envs, + "labels": { + **(metadata or {}), + "openenv_component": "blackbox-opencode", + "openenv_owner": os.environ.get("RUN_OWNER", "local"), + }, + "public": False, + "auto_stop_interval": max(30, math.ceil(timeout_s / 60)), + "auto_delete_interval": 0, + } + if self.snapshot: + params = CreateSandboxFromSnapshotParams(snapshot=self.snapshot, **options) + else: + params = CreateSandboxFromImageParams( + image=self.image, + resources=Resources(cpu=1, memory=4, disk=5), + **options, + ) + return DaytonaSandboxHandle(client().create(params, timeout=300)) diff --git a/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/environment.py b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/environment.py new file mode 100644 index 00000000..e72f13be --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/environment.py @@ -0,0 +1,98 @@ +"""Native OpenCode sessions on the fixed SmolDataEnvs tasks.""" + +import json +import os + +from opencode_env.config import OpenCodeConfig +from opencode_env.harness import OpenCodeSessionFactory +from opencode_env.task import OpenCodeTask +from openenv.core.harness import VerifyResult + +from .daytona import DaytonaSandboxBackend +from .tasks import grade_answer, stage_task + + +class TaskSession: + def __init__(self, session): + self.session = session + + def __getattr__(self, name): + return getattr(self.session, name) + + def fetch_training_trace(self): + trace = self.session.fetch_training_trace() + try: + events = [ + json.loads(line) + for line in self.session.fetch_trace().splitlines() + if line.strip() + ] + parts = [ + event["part"] for event in events if event.get("type") == "tool_use" + ] + ids = [part["callID"] for part in parts] + if not any(event.get("type") == "step_finish" for event in events): + raise ValueError("Incomplete OpenCode event stream") + if any(not value for value in ids) or len(set(ids)) != len(ids): + raise ValueError("Missing or repeated tool action IDs") + if any( + part.get("state", {}).get("status") not in {"completed", "error"} + for part in parts + ): + raise ValueError("Unfinished tool action") + calls = len(ids) + except (OSError, ValueError, KeyError, TypeError): + calls = None + for turn in trace.turns: + turn.metadata["native_tool_calls"] = calls + return trace + + +def verify(sandbox, task): + return VerifyResult( + env_reward=grade_answer(sandbox, task.metadata["folder"]), done=True + ) + + +class TaskFactory: + def __init__(self, args, tasks, *, sampling): + self.tasks = {task["instruction"]: task for task in tasks} + config = OpenCodeConfig( + base_url=os.environ["SANDBOX_VLLM_URL"].rstrip("/") + "/v1", + api_key=os.environ["SANDBOX_VLLM_KEY"], + model=args.model, + opencode_version="1.18.31", + sandbox_home="/root", + proxy_disable_thinking=True, + proxy_max_tokens_cap=4096, + agent_timeout_s=600, + run_format="json", + extra_setup_shell="mkdir -p /workdir && rmdir /root/workdir && ln -s /workdir /root/workdir", + disabled_tools=["webfetch", "question", "task"], + extra_opencode_json={"permission": {"*": "allow"}}, + ) + self.factory = OpenCodeSessionFactory( + config=config, + sampling=sampling, + verifier=verify, + mode="transparent_proxy", + sandbox_backend=DaytonaSandboxBackend( + image="docker.io/savatar101/env-data-agent-train:base" + ), + ) + + def create(self, task, seed=None, episode_id=None): + row = self.tasks[task[-1]["content"]] + task = OpenCodeTask( + instruction=row["instruction"], metadata={"folder": row["folder"]} + ) + session = self.factory.create( + task, seed=seed, episode_id=episode_id, start_agent=False + ) + try: + stage_task(session.sandbox, row["folder"]) + session.start_agent() + return TaskSession(session) + except BaseException: + session.close() + raise diff --git a/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/server.py b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/server.py new file mode 100644 index 00000000..9a3626c9 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/server.py @@ -0,0 +1,75 @@ +"""Run native OpenCode on SmolDataEnvs through the OpenEnv protocol.""" + +import os +from types import SimpleNamespace + +from fastmcp import FastMCP +from openenv.core.env_server import create_app +from openenv.core.env_server.mcp_environment import MCPEnvironment +from openenv.core.env_server.mcp_types import CallToolAction, CallToolObservation +from openenv.core.env_server.types import Observation, State + +from .catalog import TaskCatalog, task_by_name +from .environment import TaskFactory +from .ui import build_ui + + +class OpenCodeEnvironment(TaskCatalog, MCPEnvironment): + SUPPORTS_CONCURRENT_SESSIONS = True + + def __init__(self): + self._state = State() + mcp = FastMCP("smoldataenv-opencode") + + @mcp.tool + def run_rollout(split: str, task_name: str, model: str, sampling: dict) -> dict: + """Run OpenCode, grade its answer, and export its typed training trace.""" + row = task_by_name(split, task_name) + factory = TaskFactory( + SimpleNamespace(model=model), [row], sampling=sampling + ) + session = factory.create([{"role": "user", "content": row["instruction"]}]) + try: + session.wait_for_completion(timeout_s=900) + try: + trace = session.fetch_training_trace() + except (ValueError, TypeError, KeyError) as exc: + return {"capture_error": str(exc)} + grade = session.verify([]).env_reward + return { + "training_trace": trace.model_dump(mode="json"), + "correctness": grade, + } + finally: + session.close() + + super().__init__(mcp) + + def reset(self, seed=None, episode_id=None, **kwargs): + self._state = State(episode_id=episode_id) + return Observation(metadata={"status": "Call run_rollout with a task name"}) + + @property + def state(self): + return self._state + + def _step_impl(self, action, **kwargs): + raise ValueError("Use an MCP tool action") + + def step(self, action, timeout_s=None, **kwargs): + return super().step(action, timeout_s=timeout_s or 1800, **kwargs) + + async def step_async(self, action, timeout_s=None, **kwargs): + return await super().step_async(action, timeout_s=timeout_s or 1800, **kwargs) + + +os.environ.setdefault("ENABLE_WEB_INTERFACE", "true") +app = create_app( + OpenCodeEnvironment, + CallToolAction, + CallToolObservation, + gradio_builder=build_ui, + show_default_tab=False, + env_name="smoldataenv_opencode", + max_concurrent_envs=int(os.environ.get("MAX_CONCURRENT_ENVS", "40")), +) diff --git a/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/tasks.py b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/tasks.py new file mode 100644 index 00000000..217a4241 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/tasks.py @@ -0,0 +1,75 @@ +"""Stage public task files in a sandbox; run the held-out grader on the host.""" + +import json +import os +import subprocess +import sys +from pathlib import Path + +import tomllib + + +def load_tasks(data, split): + root = Path(data).resolve() / "datasets" / split / "tasks" + tasks = [] + for folder in sorted(root.iterdir()): + spec = tomllib.loads((folder / "task.toml").read_text()) + tasks.append( + { + "name": folder.name, + "folder": str(folder), + "instruction": (folder / "instruction.md").read_text(), + "difficulty": spec["metadata"]["difficulty_tier"], + } + ) + if not tasks or len({t["instruction"] for t in tasks}) != len(tasks): + raise ValueError("Tasks must have unique instructions; run prepare.py first") + return tasks + + +def stage_task(sandbox, folder): + folder = Path(folder) + spec = tomllib.loads((folder / "task.toml").read_text()) + # Only data-download credentials enter the sandbox, never verifier answers. + env = { + key: os.path.expandvars(value) + for key, value in spec["environment"]["env"].items() + } + if any("${" in value for value in env.values()): + raise ValueError("Set HF_TOKEN before staging task data") + sandbox.write_text( + "/opt/pull_bucket.py", (folder / "environment/pull_bucket.py").read_text() + ) + result = sandbox.exec( + "mkdir -p /workdir /home/user/input && python3 /opt/pull_bucket.py " + '&& test -n "$(ls -A /home/user/input)"', + envs=env, + timeout=300, + ) + if result.exit_code: + raise RuntimeError("Task data download failed") + + +def grade_answer(sandbox, folder): + folder = Path(folder) + spec = tomllib.loads((folder / "task.toml").read_text()) + answer = ( + sandbox.read_text("/workdir/answer.txt") + if sandbox.exists("/workdir/answer.txt") + else "" + ) + result = subprocess.run( + [sys.executable, str(folder / "tests/grader.py"), "--json"], + input=answer, + text=True, + capture_output=True, + timeout=120, + check=False, + env={**os.environ, **spec["verifier"]["env"]}, + ) + if result.returncode: + raise RuntimeError("Task grader failed") + value = json.loads(result.stdout)["correctness"] + if value not in (0, 1): + raise ValueError("Expected a binary correctness grade") + return float(value) diff --git a/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/ui.py b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/ui.py new file mode 100644 index 00000000..972a2215 --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/smoldataenv_opencode/ui.py @@ -0,0 +1,34 @@ +"""Use OpenEnv's playground with connection examples for this environment.""" + +from openenv.core.env_server.gradio_ui import build_gradio_app + +QUICK_START = """ +This environment runs native OpenCode in Daytona and returns its grade and +training trace. Configure `SANDBOX_VLLM_URL` and `SANDBOX_VLLM_KEY` on the server +before a rollout. The endpoint must return token IDs and logprobs for training. + +Click **Reset**, set Type to `call_tool`, and use Tool Name `run_rollout`. +Arguments require `split`, `task_name`, `model` and a `sampling` object. +Use the Task API at `/docs` to find a task name. + +From Python: + +```python +import requests +from openenv.core.mcp_client import MCPToolClient + +url = "https://fineenvs-smoldataenv-multi-harness-opencode.hf.space" +task = requests.post(url + "/smoldataenv_opencode/task", + json={"split": "test", "index": 0}).json()["task"] +with MCPToolClient(url, message_timeout_s=1800).sync() as env: + env.reset() + result = env.call_tool("run_rollout", split="test", task_name=task["name"], + model="YOUR_SERVED_MODEL", sampling={"temperature": 0.8}) +``` +""" + + +def build_ui(manager, fields, metadata, is_chat, title, quick_start): + return build_gradio_app( + manager, fields, metadata, is_chat, title=title, quick_start_md=QUICK_START + ) diff --git a/05-multi-harness-rl/envs/opencode/start.sh b/05-multi-harness-rl/envs/opencode/start.sh new file mode 100644 index 00000000..bd43178a --- /dev/null +++ b/05-multi-harness-rl/envs/opencode/start.sh @@ -0,0 +1,5 @@ +#!/usr/bin/env bash +set -euo pipefail +cd "$(dirname "$0")" +python prepare.py --output "${SMOLDATA_DATA:-prepared}" +exec uvicorn smoldataenv_opencode.server:app --host 0.0.0.0 --port "${PORT:-7860}" --ws-ping-timeout 1800 diff --git a/05-multi-harness-rl/envs/whitebox/.dockerignore b/05-multi-harness-rl/envs/whitebox/.dockerignore new file mode 100644 index 00000000..7433d48e --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/.dockerignore @@ -0,0 +1,10 @@ +.deps/ +.venv/ +prepared/ +__pycache__/ +*.egg-info/ +.pytest_cache/ +.env +.gradio/ +.archive/ +.git/ diff --git a/05-multi-harness-rl/envs/whitebox/.gitattributes b/05-multi-harness-rl/envs/whitebox/.gitattributes new file mode 100644 index 00000000..a6344aac --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/05-multi-harness-rl/envs/whitebox/.gitignore b/05-multi-harness-rl/envs/whitebox/.gitignore new file mode 100644 index 00000000..27ce9e82 --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/.gitignore @@ -0,0 +1,9 @@ +.deps/ +.venv/ +prepared/ +__pycache__/ +*.egg-info/ +.pytest_cache/ +.env +.gradio/ +.archive/ diff --git a/05-multi-harness-rl/envs/whitebox/Dockerfile b/05-multi-harness-rl/envs/whitebox/Dockerfile new file mode 100644 index 00000000..77695d9e --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/Dockerfile @@ -0,0 +1,9 @@ +FROM python:3.12-slim +RUN apt-get update && apt-get install -y --no-install-recommends git curl && rm -rf /var/lib/apt/lists/* +WORKDIR /app +COPY . /app +RUN bash install.sh && useradd --create-home --uid 1000 user && chown -R user:user /app +USER user +ENV HOME=/home/user HF_HOME=/home/user/.cache/huggingface PYTHONUNBUFFERED=1 PORT=7860 ENABLE_WEB_INTERFACE=true +EXPOSE 7860 +CMD ["bash", "start.sh"] diff --git a/05-multi-harness-rl/envs/whitebox/README.md b/05-multi-harness-rl/envs/whitebox/README.md new file mode 100644 index 00000000..7436692d --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/README.md @@ -0,0 +1,69 @@ +--- +title: SmolDataEnvs Multi-harness | SETA Whitebox +emoji: 🧪 +colorFrom: blue +colorTo: green +sdk: docker +app_port: 7860 +--- + +# SmolDataEnvs: SETA whitebox + +**[Read the multi-harness RL article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl)** · [Collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Training tutorial](https://github.com/adithya-s-k/FineEnvs/tree/main/05-multi-harness-rl) + +TRL drives the bash, file and submit-solution tools. Each WebSocket session owns one sandbox. + +This is the hands-on companion to the article: explore a dataset, run a command and submit an answer. Training uses these same tools with TRL driving the model. + +This folder contains the complete Space source. You can run it locally or deploy it on its own. + +[Open the deployed UI](https://fineenvs-smoldataenv-multi-harness-whitebox.hf.space/web/) · [Space](https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-whitebox) + +## Read the code + +- [`smoldataenv_whitebox/environment.py`](smoldataenv_whitebox/environment.py): task execution and grading. +- [`smoldataenv_whitebox/server.py`](smoldataenv_whitebox/server.py): OpenEnv API. +- [`smoldataenv_whitebox/ui.py`](smoldataenv_whitebox/ui.py) and [`playground.py`](smoldataenv_whitebox/playground.py): browser layout and per-visitor sandbox lifecycle. +- [`prepare.py`](prepare.py) and [`data/`](data/): the pinned 1,000 train / 250 test tasks, with grader and split checks. +- [`Dockerfile`](Dockerfile): exactly what the Space builds. + +## Run locally + +Use Python 3.12. From this directory: + +```bash +uv venv --python 3.12 --seed .venv +source .venv/bin/activate +bash install.sh +hf auth login +export HF_TOKEN="$(hf auth token)" +export DAYTONA_API_KEY="your-key" +bash start.sh +``` + +Open **http://localhost:7860/**. It opens the UI at `/web/`; the API is at `/docs` and health at `/health`. `install.sh` installs OpenEnv from main and its environment examples from the same checkout. This server needs no GPU or TRL installation. + +The playground has three steps: + +1. Choose a split, difficulty and task, then click **Start this task**. +2. Run Bash or Python commands against `/home/user/input`. The file-list and CSV-preview buttons prepare example commands for you. +3. Enter your answer and click **Submit and score**. Correctness, tool calls and total reward appear above the workspace. + +Each browser session gets its own sandbox. **Release sandbox**, grading, or session expiry closes it. Execution history keeps your commands and outputs. The UI calls the same `BashEnvironment` used by training; no model endpoint is needed to try it yourself. + +## Deploy this folder + +```bash +python deploy.py --repo YOUR_ORG/smoldataenv-multi-harness-whitebox --public +``` + +Add `HF_TOKEN` and `DAYTONA_API_KEY` as Space secrets. The uploader sends the files in this directory, without generating or swapping application code. It enables the UI and preserves an existing concurrency limit. For a new Space the limit defaults to 40; use `--concurrency` to change it. CPU Basic serves the API; actual tasks run in Daytona. + +You can test the same image locally: + +```bash +docker build -t smoldataenv-whitebox . +docker run --rm -p 7860:7860 -e HF_TOKEN -e DAYTONA_API_KEY smoldataenv-whitebox +``` + +Runtime downloads, local credentials and old deployment archives are excluded from uploads and Docker builds. 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{"name": "0001_527_1527054_qa_2", "source": "0001/527/1527054.ipynb_qa_2", "notebook": "0001/527/1527054.ipynb", "difficulty": "medium", "instruction_sha256": "3e1ec2b4e0ab278dbb876521fb4b1551ad0b79a63a53d29852ffda60fd78e033"}, + {"name": "0106_834_106834319_qa_1", "source": "0106/834/106834319.ipynb_qa_1", "notebook": "0106/834/106834319.ipynb", "difficulty": "medium", "instruction_sha256": "637da8811aee08d64f5c6e19ecc9e0083464a3c6f2bedcfe9a6b7ad37fa746f2"}, + {"name": "0023_626_23626093_qa_1", "source": "0023/626/23626093.ipynb_qa_1", "notebook": "0023/626/23626093.ipynb", "difficulty": "hard", "instruction_sha256": "3fa61ab7b1454faf531802086d701d246c0b232c1bd724a715714445462bc3ec"}, + {"name": "0021_121_21121885_qa_3", "source": "0021/121/21121885.ipynb_qa_3", "notebook": "0021/121/21121885.ipynb", "difficulty": "hard", "instruction_sha256": "098920eaef9216d6d5d10fa2e607c19126e1336a6509c29b612f596fd46ccdbf"}, + {"name": "0085_309_85309051_qa_1", "source": 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"difficulty": "medium", "instruction_sha256": "fbc3a4777712e9e1de7f8199e0c7bb0a50d80c9579d53e472a6d80d1f1f5805d"}, + {"name": "0083_191_83191425_qa_3", "source": "0083/191/83191425.ipynb_qa_3", "notebook": "0083/191/83191425.ipynb", "difficulty": "hard", "instruction_sha256": "88c6821b7b33ffcdaaddde57e4e701b73291a2cd3888010a2f69b86cc2286f83"}, + {"name": "0119_666_119666168_qa_2", "source": "0119/666/119666168.ipynb_qa_2", "notebook": "0119/666/119666168.ipynb", "difficulty": "medium", "instruction_sha256": "133f73998e7587118d354b8fd0dd9171cb41df81684a052a06a53e0a0cdc2f9e"}, + {"name": "0122_390_122390551_qa_4", "source": "0122/390/122390551.ipynb_qa_4", "notebook": "0122/390/122390551.ipynb", "difficulty": "medium", "instruction_sha256": "65b7c9fd25993e900ac938312bbf40001e94b5a96c2a8dcbc6016ffdccda7687"}, + {"name": "0132_865_132865792_qa_4", "source": "0132/865/132865792.ipynb_qa_4", "notebook": "0132/865/132865792.ipynb", "difficulty": "hard", "instruction_sha256": "f6b16926f5640b7629204c2e484163c4c6e82083f77a75b21730cd9c1e06f04a"}, + {"name": "0122_517_122517438_qa_3", "source": "0122/517/122517438.ipynb_qa_3", "notebook": "0122/517/122517438.ipynb", "difficulty": "hard", "instruction_sha256": "4b3e13801dec3a4d66e9accbb769f2f5efd7f5d70ab682c7e9bb34535e895af6"}, + {"name": "0076_259_76259625_qa_4", "source": "0076/259/76259625.ipynb_qa_4", "notebook": "0076/259/76259625.ipynb", "difficulty": "hard", "instruction_sha256": "1cff4c1b3495bd193958157ea96a1b1354c9ec9ef8380d46d1e2bffc85941534"} +] diff --git a/05-multi-harness-rl/envs/whitebox/deploy.py b/05-multi-harness-rl/envs/whitebox/deploy.py new file mode 100644 index 00000000..d78d7db9 --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/deploy.py @@ -0,0 +1,68 @@ +"""Upload this standalone directory to a Docker Space.""" + +import argparse +from pathlib import Path + +from huggingface_hub import HfApi + +ROOT = Path(__file__).resolve().parent + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo", required=True) + parser.add_argument("--public", action="store_true") + parser.add_argument("--concurrency", type=int) + args = parser.parse_args() + if args.concurrency is not None and args.concurrency < 1: + parser.error("Concurrency must be positive") + api = HfApi() + api.create_repo( + args.repo, + repo_type="space", + space_sdk="docker", + private=not args.public, + exist_ok=True, + ) + variables = api.get_space_variables(args.repo) + concurrency = args.concurrency or ( + int(variables["MAX_CONCURRENT_ENVS"].value) + if "MAX_CONCURRENT_ENVS" in variables + else 40 + ) + api.add_space_variable(args.repo, "MAX_CONCURRENT_ENVS", str(concurrency)) + api.add_space_variable(args.repo, "ENABLE_WEB_INTERFACE", "true") + api.upload_folder( + repo_id=args.repo, + repo_type="space", + folder_path=ROOT, + allow_patterns=[ + "*.py", + "*.json", + "*.toml", + "*.txt", + "*.sh", + "*.yaml", + "README.md", + "Dockerfile", + ".dockerignore", + ".gitignore", + ".gitattributes", + ], + ignore_patterns=[ + ".deps/**", + ".venv/**", + "prepared/**", + ".archive/**", + "**/__pycache__/**", + "**/*.egg-info/**", + ".env", + ".git/**", + ], + commit_message="Deploy SmolDataEnvs Multi-harness | SETA Whitebox", + ) + print(f"https://huggingface.co/spaces/{args.repo}") + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/envs/whitebox/install.sh b/05-multi-harness-rl/envs/whitebox/install.sh new file mode 100644 index 00000000..16fdf9ca --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/install.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash +set -euo pipefail +cd "$(dirname "$0")" +mkdir -p .deps +if [[ ! -d .deps/OpenEnv/.git ]]; then + git clone --depth 1 --branch main https://github.com/huggingface/OpenEnv.git .deps/OpenEnv +else + git -C .deps/OpenEnv pull --ff-only origin main +fi +python -m pip install -e .deps/OpenEnv -r requirements.txt +python -m pip install --no-deps -e . +python - <<'SETUP' +from pathlib import Path +import site +Path(site.getsitepackages()[0], "openenv_examples.pth").write_text( + str(Path(".deps/OpenEnv/envs").resolve()) + "\n" +) +SETUP diff --git a/05-multi-harness-rl/envs/whitebox/openenv.yaml b/05-multi-harness-rl/envs/whitebox/openenv.yaml new file mode 100644 index 00000000..26ef1d65 --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/openenv.yaml @@ -0,0 +1,6 @@ +spec_version: 1 +name: smoldataenv_whitebox +type: space +runtime: fastapi +app: smoldataenv_whitebox.server:app +port: 7860 diff --git a/05-multi-harness-rl/envs/whitebox/prepare.py b/05-multi-harness-rl/envs/whitebox/prepare.py new file mode 100644 index 00000000..1b558b9a --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/prepare.py @@ -0,0 +1,138 @@ +"""Materialize the fixed train/test tasks from corrected, pinned Harbor datasets.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import shutil +from pathlib import Path + +import tomllib + +ROOT = Path(__file__).resolve().parent + + +def task_rows(split): + return json.loads((ROOT / "data" / f"{split}.json").read_text()) + + +def write_json(path, data): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(data, indent=2) + "\n") + + +DATASETS = { + "train": ( + "FineEnvs/SmolDataEnvs-harbor-train", + "4719635555de1666f374d847baebb500d368e493", + ), + "test": ( + "FineEnvs/SmolDataEnvs-harbor-test", + "b130595b579fc6026ad5762925a9a3b40e72f5bb", + ), +} +GRADER_SHA256 = "16f906e9248fa8b7e1141939e425e88e59d52d65e98e360bbacef3720a43629f" + + +def prepare(destination, sources=None): + import tomli_w + from huggingface_hub import snapshot_download + + destination = Path(destination).resolve() + manifests = {} + for split in ("train", "test"): + rows = task_rows(split) + repo, revision = DATASETS[split] + source = (sources or {}).get(split) + if source is None: + source = snapshot_download( + repo, + repo_type="dataset", + revision=revision, + allow_patterns=[f"tasks/{r['name']}/*" for r in rows], + max_workers=16, + ) + source = Path(source) + tasks = [] + for row in rows: + folder = source / "tasks" / row["name"] + instruction = (folder / "instruction.md").read_bytes() + if hashlib.sha256(instruction).hexdigest() != row["instruction_sha256"]: + raise ValueError( + f"Historical task instruction changed: {split}/{row['name']}" + ) + grader = (folder / "tests/grader.py").read_bytes() + if hashlib.sha256(grader).hexdigest() != GRADER_SHA256: + raise ValueError( + f"Expected corrected numeric-only Harbor grader: {folder}" + ) + target = destination / "datasets" / split / "tasks" / row["name"] + target.mkdir(parents=True, exist_ok=True) + for rel in ( + "instruction.md", + "task.toml", + "environment/Dockerfile", + "environment/pull_bucket.py", + "tests/grader.py", + "tests/test.sh", + ): + dst = target / rel + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(folder / rel, dst) + spec = tomllib.loads((target / "task.toml").read_text()) + spec["environment"]["memory_mb"] = 4096 + spec["environment"]["cpus"] = 1 + # Cluster user IDs may not exist inside Daytona's user namespace. + spec["environment"].setdefault("env", {})["TAR_OPTIONS"] = "--no-same-owner" + (target / "task.toml").write_text(tomli_w.dumps(spec)) + hashes = { + str(f.relative_to(destination / "datasets" / split)): hashlib.sha256( + f.read_bytes() + ).hexdigest() + for f in sorted(target.rglob("*")) + if f.is_file() + } + tasks.append({**row, "file_hashes": hashes}) + local = (sources or {}).get(split) + manifest = { + "source_dataset": repo, + "source_revision": None if local else revision, + "expected_hub_revision": revision, + "local_source": str(local) if local else None, + "task_count": len(tasks), + "tasks": tasks, + } + write_json(destination / f"{split}_manifest.json", manifest) + manifests[split] = manifest + train = {r["source"] for r in manifests["train"]["tasks"]} + test = {r["source"] for r in manifests["test"]["tasks"]} + notebooks = [ + {r.get("notebook") for r in manifests[s]["tasks"]} - {None} + for s in ("train", "test") + ] + if train & test or notebooks[0] & notebooks[1]: + raise ValueError("Train/test overlap") + write_json( + destination / "ready.json", + { + "train": len(train), + "test": len(test), + "grader_sha256": GRADER_SHA256, + "train_test_overlap": 0, + }, + ) + return manifests + + +if __name__ == "__main__": + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--output", type=Path, default=ROOT / "prepared") + p.add_argument("--train-source", type=Path) + p.add_argument("--test-source", type=Path) + a = p.parse_args() + result = prepare( + a.output, + {s: v for s, v in (("train", a.train_source), ("test", a.test_source)) if v}, + ) + print(json.dumps({s: m["task_count"] for s, m in result.items()})) diff --git a/05-multi-harness-rl/envs/whitebox/pyproject.toml b/05-multi-harness-rl/envs/whitebox/pyproject.toml new file mode 100644 index 00000000..f70ef1c4 --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/pyproject.toml @@ -0,0 +1,11 @@ +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" + +[project] +name = "smoldataenv-whitebox" +version = "0.1.0" +requires-python = ">=3.12" + +[tool.setuptools.packages.find] +include = ["smoldataenv_whitebox*"] diff --git a/05-multi-harness-rl/envs/whitebox/requirements.txt b/05-multi-harness-rl/envs/whitebox/requirements.txt new file mode 100644 index 00000000..62af9823 --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/requirements.txt @@ -0,0 +1,8 @@ +# OpenEnv main is installed by install.sh. No trainer or GPU packages are needed. +daytona>=0.203.0 +fastmcp>=2 +uvicorn[standard] +gradio +huggingface-hub +math-verify +tomli-w diff --git a/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/__init__.py b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/catalog.py b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/catalog.py new file mode 100644 index 00000000..b11166ef --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/catalog.py @@ -0,0 +1,39 @@ +"""The Task API serves names and instructions, never private verifier answers.""" + +import os +from functools import lru_cache + +from .tasks import load_tasks + + +@lru_cache +def tasks(split): + if split not in {"train", "test"}: + raise ValueError("Choose train or test") + return load_tasks(os.environ.get("SMOLDATA_DATA", "prepared"), split) + + +def task_by_name(split, name): + return next(row for row in tasks(split) if row["name"] == name) + + +class TaskCatalog: + def list_splits(self): + return ["train", "test"] + + def num_tasks(self, split): + return len(tasks(split)) + + def get_task(self, split, index): + return { + key: value for key, value in tasks(split)[index].items() if key != "folder" + } + + def get_task_range(self, split, start=None, stop=None): + return [ + self.get_task(split, i) + for i in range(*slice(start, stop).indices(self.num_tasks(split))) + ] + + def list_tasks(self, split): + return self.get_task_range(split) diff --git a/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/client.py b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/client.py new file mode 100644 index 00000000..9ae23791 --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/client.py @@ -0,0 +1,101 @@ +"""TRL's tool interface backed by the local server or the same server on a Space.""" + +import math +from pathlib import Path + +from openenv.core.mcp_client import MCPToolClient + + +class RemoteBashEnvironment: + def __init__(self, server): + self._client = MCPToolClient(server, message_timeout_s=900).sync() + self._calls = 0 + self._submitted = False + + def reset(self, folder, **kwargs): + folder = Path(folder) + result = self._client.reset( + split=folder.parent.parent.name, task_name=folder.name + ) + self._calls, self._submitted = 0, False + return result.observation.metadata["instruction"] + + def bash(self, command: str) -> str: + """Run a shell command in /workdir. + + Args: + command: The shell command to execute. + """ + return self._client.call_tool("bash", command=command) + + def read(self, path: str) -> str: + """Read a text file. + + Args: + path: Absolute path of the file. + """ + return self._client.call_tool("read", path=path) + + def write(self, path: str, content: str) -> str: + """Write a text file, creating parent directories. + + Args: + path: Absolute path of the file. + content: Text to write. + """ + return self._client.call_tool("write", path=path, content=content) + + def edit(self, path: str, old: str, new: str) -> str: + """Replace one exact occurrence in a file. + + Args: + path: Absolute path of the file. + old: Text that must occur exactly once. + new: Replacement text. + """ + return self._client.call_tool("edit", path=path, old=old, new=new) + + def ls(self, path: str = "/workdir") -> str: + """List a directory. + + Args: + path: Directory to list. + """ + return self._client.call_tool("ls", path=path) + + def grep(self, pattern: str, path: str) -> str: + """Search text files recursively. + + Args: + pattern: Regular expression to search for. + path: File or directory to search. + """ + return self._client.call_tool("grep", pattern=pattern, path=path) + + def glob(self, pattern: str, path: str = "/workdir") -> str: + """Find files by name. + + Args: + pattern: Filename pattern, for example *.csv. + path: Directory to search. + """ + return self._client.call_tool("glob", pattern=pattern, path=path) + + def submit_solution(self, answer: str) -> str: + """Save your final answer and finish the episode. + + Args: + answer: The answer itself, not a command. + """ + result = self._client.call_tool("submit_solution", answer=answer) + self._submitted = True + return result + + def get_reward(self): + result = self._client.call_tool("grade") + self._calls = result["tool_calls"] + self._correctness = result["correctness"] + return result["reward"] if result["reward"] is not None else math.nan + + def _close(self): + self._client.close() diff --git a/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/daytona.py b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/daytona.py new file mode 100644 index 00000000..f52168ef --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/daytona.py @@ -0,0 +1,150 @@ +"""Daytona sandbox lifecycle and shell commands for SETA.""" + +from __future__ import annotations + +import math +import os +import shlex +import time +import uuid +from dataclasses import dataclass +from functools import lru_cache + + +@dataclass +class ExecResult: + exit_code: int + stdout: str + stderr: str + + +@lru_cache(maxsize=1) +def client(): + from daytona import Daytona + + return Daytona() + + +class DaytonaBgJob: + def __init__(self, handle, directory, pid): + self.handle, self.directory, self._pid = handle, directory, pid + + @property + def pid(self): + return self._pid + + def wait(self, timeout=None): + deadline = time.monotonic() + timeout if timeout is not None else float("inf") + while time.monotonic() < deadline: + if self.handle.exists(self.directory + "/exit"): + return int(self.handle.read_text(self.directory + "/exit").strip()) + time.sleep(min(0.5, max(0, deadline - time.monotonic()))) + raise TimeoutError("Background command exceeded its deadline") + + def kill(self): + self.handle.exec(f"kill -TERM -- -{self.pid} 2>/dev/null || true", timeout=10) + + +class DaytonaSandboxHandle: + def __init__(self, sandbox): + self._sandbox = sandbox + self._deleted = False + + @property + def sandbox_id(self): + return self._sandbox.id + + def exec(self, cmd, *, envs=None, cwd=None, timeout=60): + # The SDK's exec is not a shell. Quote the complete script once and let bash + # interpret it; neither user text nor environment values are interpolated. + result = self._sandbox.process.exec( + "bash -lc " + shlex.quote(cmd), + cwd=cwd, + env=envs, + timeout=max(1, math.ceil(timeout)) if timeout is not None else None, + ) + code = result.exit_code + return ExecResult(124 if code is None else int(code), result.result or "", "") + + def write_text(self, path, content): + parent = path.rsplit("/", 1)[0] or "." + result = self.exec("mkdir -p " + shlex.quote(parent)) + if result.exit_code: + raise RuntimeError("Could not create sandbox directory") + self._sandbox.fs.upload_file(content.encode(), path) + + def read_text(self, path): + return self._sandbox.fs.download_file(path).decode() + + def exists(self, path): + return self.exec("test -e " + shlex.quote(path), timeout=15).exit_code == 0 + + def start_bg(self, cmd, *, envs=None, cwd=None): + directory = "/tmp/openenv-process-" + uuid.uuid4().hex + self.write_text( + directory + "/run.sh", + "#!/bin/bash\n(\n" + + cmd + + "\n)" + + "\ncode=$?\nprintf '%s' \"$code\" > " + + shlex.quote(directory + "/exit") + + "\n", + ) + result = self.exec( + "setsid bash " + + shlex.quote(directory + "/run.sh") + + " > " + + shlex.quote(directory + "/output") + + " 2>&1 < /dev/null & echo $!", + envs=envs, + cwd=cwd, + timeout=15, + ) + if result.exit_code: + raise RuntimeError("Could not launch background command") + return DaytonaBgJob( + self, directory, int(result.stdout.strip().splitlines()[-1]) + ) + + def kill(self): + if not self._deleted: + client().delete(self._sandbox, timeout=60, wait=True) + self._deleted = True + + +class DaytonaSandboxBackend: + def __init__(self, *, image, snapshot=None): + self.image, self.snapshot = ( + image, + snapshot or os.environ.get("DATA_AGENT_DAYTONA_SNAPSHOT"), + ) + + def create(self, *, timeout_s=900, envs=None, metadata=None): + from daytona import ( + CreateSandboxFromImageParams, + CreateSandboxFromSnapshotParams, + Resources, + ) + + options = { + "language": "python", + "os_user": "root", + "env_vars": envs, + "labels": { + **(metadata or {}), + "openenv_component": "whitebox-seta", + "openenv_owner": os.environ.get("RUN_OWNER", "local"), + }, + "public": False, + "auto_stop_interval": max(30, math.ceil(timeout_s / 60)), + "auto_delete_interval": 0, + } + if self.snapshot: + params = CreateSandboxFromSnapshotParams(snapshot=self.snapshot, **options) + else: + params = CreateSandboxFromImageParams( + image=self.image, + resources=Resources(cpu=1, memory=4, disk=5), + **options, + ) + return DaytonaSandboxHandle(client().create(params, timeout=300)) diff --git a/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/environment.py b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/environment.py new file mode 100644 index 00000000..f16221b3 --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/environment.py @@ -0,0 +1,149 @@ +"""SETA tools, sandbox lifecycle and correctness grading for SmolDataEnvs.""" + +import math +import shlex +import time +from pathlib import Path + +from .daytona import DaytonaSandboxBackend +from .tasks import grade_answer, stage_task + +SYSTEM = "Use the sandbox tools to solve the task. Call submit_solution with the final answer itself." + + +class BashEnvironment: + def __init__(self): + self._sandbox = None + self._calls = 0 + self._deadline = 0 + self._submitted = False + + def reset(self, folder, **kwargs): + self._close() + self._folder = Path(folder) + self._calls, self._submitted = 0, False + self._sandbox = DaytonaSandboxBackend( + image="docker.io/savatar101/env-data-agent-train:base" + ).create(timeout_s=900) + try: + stage_task(self._sandbox, self._folder) + except BaseException: + self._close() + raise + self._deadline = time.monotonic() + 600 + return (self._folder / "instruction.md").read_text() + + def _check(self): + if self._submitted or time.monotonic() >= self._deadline: + raise RuntimeError("Episode is finished") + + def _execute(self, command): + self._check() + self._calls += 1 + result = self._sandbox.exec( + command, + cwd="/workdir", + timeout=min(60, max(1, self._deadline - time.monotonic())), + ) + return (result.stdout + result.stderr)[-16000:] + + def bash(self, command: str) -> str: + """Run a shell command in /workdir. + + Args: + command: The shell command to execute. + """ + return self._execute(command) + + def read(self, path: str) -> str: + """Read a text file. + + Args: + path: Absolute path of the file. + """ + return self._execute("cat -- " + shlex.quote(path)) + + def write(self, path: str, content: str) -> str: + """Write a text file, creating parent directories. + + Args: + path: Absolute path of the file. + content: Text to write. + """ + self._check() + self._calls += 1 + self._sandbox.write_text(path, content) + return "Saved." + + def edit(self, path: str, old: str, new: str) -> str: + """Replace one exact occurrence in a file. + + Args: + path: Absolute path of the file. + old: Text that must occur exactly once. + new: Replacement text. + """ + self._check() + self._calls += 1 + text = self._sandbox.read_text(path) + if text.count(old) != 1: + return "The old text must occur exactly once." + self._sandbox.write_text(path, text.replace(old, new, 1)) + return "Saved." + + def ls(self, path: str = "/workdir") -> str: + """List a directory. + + Args: + path: Directory to list. + """ + return self._execute("ls -la -- " + shlex.quote(path)) + + def grep(self, pattern: str, path: str) -> str: + """Search text files recursively. + + Args: + pattern: Regular expression to search for. + path: File or directory to search. + """ + return self._execute( + "grep -rn -- " + shlex.quote(pattern) + " " + shlex.quote(path) + ) + + def glob(self, pattern: str, path: str = "/workdir") -> str: + """Find files by name. + + Args: + pattern: Filename pattern, for example *.csv. + path: Directory to search. + """ + return self._execute( + "find " + shlex.quote(path) + " -name " + shlex.quote(pattern) + ) + + def submit_solution(self, answer: str) -> str: + """Save your final answer and finish the episode. + + Args: + answer: The answer itself, not a command. + """ + self._check() + self._sandbox.write_text("/workdir/answer.txt", answer) + self._submitted = True + return "Answer saved. End your response now." + + # Submitting the answer does not count as a tool action. + def get_reward(self): + try: + self._correctness = grade_answer(self._sandbox, self._folder) + bonus = 0.1 * 15 / (15 + self._calls) if self._calls > 0 else 0.0 + return self._correctness * (1 + bonus) + except (OSError, RuntimeError, ValueError, TimeoutError): + return math.nan # No grade is different from a wrong answer. + finally: + self._close() + + def _close(self): + if self._sandbox is not None: + self._sandbox.kill() + self._sandbox = None diff --git a/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/playground.py b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/playground.py new file mode 100644 index 00000000..6af9305e --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/playground.py @@ -0,0 +1,85 @@ +"""One browser session, using the same tools and grader as training.""" + +import math +import threading +import time + +from .catalog import task_by_name +from .environment import BashEnvironment + + +class PlaygroundSession: + def __init__(self): + self.environment = BashEnvironment() + self.lock = threading.RLock() + self.task = None + self.history = [] + self.output = "Start a task to open your workspace." + self.status = "Ready when you are." + self.reward = None + self.correctness = None + self.active = False + + def __deepcopy__(self, memo): + # Gradio copies the empty initial state for each browser session. + return type(self)() + + def start(self, split, name): + with self.lock: + self.close() + self.task = task_by_name(split, name) + self.history = [] + self.reward = self.correctness = None + self.output = "" + self.environment.reset(self.task["folder"]) + self.active = True + self.status = "Sandbox ready. Explore the files, then submit your answer." + + def run(self, command): + with self.lock: + if not self.active: + raise ValueError("Start a task before running a command.") + if not command.strip(): + raise ValueError("Enter a command first.") + started = time.monotonic() + self.output = self.environment.bash(command) + self.history.append( + { + "command": command[:4000], + "output": self.output[-4000:], + "seconds": time.monotonic() - started, + } + ) + self.history = self.history[-100:] + self.status = "Command finished. Continue exploring or submit your answer." + + def grade(self, answer): + with self.lock: + if not self.active: + raise ValueError("Start a task before submitting an answer.") + if not answer.strip(): + raise ValueError("Write your final answer first.") + try: + self.environment.submit_solution(answer) + value = self.environment.get_reward() + self.reward = value if math.isfinite(value) else None + self.correctness = ( + self.environment._correctness if self.reward is not None else None + ) + self.status = ( + "Answer graded. Your sandbox has been released." + if self.reward is not None + else "Grading was unavailable. This attempt is unscored." + ) + finally: + self.close() + + def close(self): + with self.lock: + self.active = False + self.environment._close() + + +def close_session(session): + if session is not None: + session.close() diff --git a/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/server.py b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/server.py new file mode 100644 index 00000000..aebc6713 --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/server.py @@ -0,0 +1,88 @@ +"""Serve the same SETA environment used by GRPO, one sandbox per WebSocket session.""" + +import inspect +import math +import os + +from fastmcp import FastMCP +from openenv.core.env_server import create_app +from openenv.core.env_server.mcp_environment import MCPEnvironment +from openenv.core.env_server.mcp_types import CallToolAction, CallToolObservation +from openenv.core.env_server.types import Observation, State + +from .catalog import TaskCatalog, task_by_name +from .environment import BashEnvironment +from .ui import build_ui + + +class WhiteboxEnvironment(TaskCatalog, MCPEnvironment): + SUPPORTS_CONCURRENT_SESSIONS = True + + def __init__(self): + self._state = State() + self.episode = BashEnvironment() + mcp = FastMCP("smoldataenv-whitebox") + for name, method in inspect.getmembers(self.episode, inspect.ismethod): + if not name.startswith("_") and name not in {"reset", "get_reward"}: + mcp.tool(method) + + @mcp.tool + def start_task(split: str = "test", index: int = 0) -> dict: + """Select a task and stage its files in a new sandbox.""" + task = self.get_task(split, index) + observation = self.reset(split=split, task_name=task["name"]) + return observation.metadata + + @mcp.tool + def grade() -> dict: + """Grade the final answer and release the sandbox.""" + value = self.episode.get_reward() + return { + "reward": value if math.isfinite(value) else None, + "correctness": getattr(self.episode, "_correctness", None), + "tool_calls": self.episode._calls, + } + + super().__init__(mcp) + + def reset( + self, seed=None, episode_id=None, split="train", task_name=None, **kwargs + ): + if task_name is None: + return Observation(metadata={"status": "Choose a task from the Task API"}) + task = task_by_name(split, task_name) + instruction = self.episode.reset(folder=task["folder"]) + return Observation( + metadata={"instruction": instruction, "task_name": task_name} + ) + + @property + def state(self): + return self._state + + def _step_impl(self, action, **kwargs): + raise ValueError("Use an MCP tool action") + + def step(self, action, timeout_s=None, **kwargs): + return super().step(action, timeout_s=timeout_s or 180, **kwargs) + + async def step_async(self, action, timeout_s=None, **kwargs): + return await super().step_async(action, timeout_s=timeout_s or 180, **kwargs) + + def close(self): + try: + self.episode._close() + finally: + super().close() + + +os.environ.setdefault("ENABLE_WEB_INTERFACE", "true") +app = create_app( + WhiteboxEnvironment, + CallToolAction, + CallToolObservation, + gradio_builder=build_ui, + show_default_tab=False, + env_name="smoldataenv_whitebox", + max_concurrent_envs=int(os.environ.get("MAX_CONCURRENT_ENVS", "40")), +) diff --git a/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/tasks.py b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/tasks.py new file mode 100644 index 00000000..217a4241 --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/tasks.py @@ -0,0 +1,75 @@ +"""Stage public task files in a sandbox; run the held-out grader on the host.""" + +import json +import os +import subprocess +import sys +from pathlib import Path + +import tomllib + + +def load_tasks(data, split): + root = Path(data).resolve() / "datasets" / split / "tasks" + tasks = [] + for folder in sorted(root.iterdir()): + spec = tomllib.loads((folder / "task.toml").read_text()) + tasks.append( + { + "name": folder.name, + "folder": str(folder), + "instruction": (folder / "instruction.md").read_text(), + "difficulty": spec["metadata"]["difficulty_tier"], + } + ) + if not tasks or len({t["instruction"] for t in tasks}) != len(tasks): + raise ValueError("Tasks must have unique instructions; run prepare.py first") + return tasks + + +def stage_task(sandbox, folder): + folder = Path(folder) + spec = tomllib.loads((folder / "task.toml").read_text()) + # Only data-download credentials enter the sandbox, never verifier answers. + env = { + key: os.path.expandvars(value) + for key, value in spec["environment"]["env"].items() + } + if any("${" in value for value in env.values()): + raise ValueError("Set HF_TOKEN before staging task data") + sandbox.write_text( + "/opt/pull_bucket.py", (folder / "environment/pull_bucket.py").read_text() + ) + result = sandbox.exec( + "mkdir -p /workdir /home/user/input && python3 /opt/pull_bucket.py " + '&& test -n "$(ls -A /home/user/input)"', + envs=env, + timeout=300, + ) + if result.exit_code: + raise RuntimeError("Task data download failed") + + +def grade_answer(sandbox, folder): + folder = Path(folder) + spec = tomllib.loads((folder / "task.toml").read_text()) + answer = ( + sandbox.read_text("/workdir/answer.txt") + if sandbox.exists("/workdir/answer.txt") + else "" + ) + result = subprocess.run( + [sys.executable, str(folder / "tests/grader.py"), "--json"], + input=answer, + text=True, + capture_output=True, + timeout=120, + check=False, + env={**os.environ, **spec["verifier"]["env"]}, + ) + if result.returncode: + raise RuntimeError("Task grader failed") + value = json.loads(result.stdout)["correctness"] + if value not in (0, 1): + raise ValueError("Expected a binary correctness grade") + return float(value) diff --git a/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/ui.py b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/ui.py new file mode 100644 index 00000000..c298e9bd --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/smoldataenv_whitebox/ui.py @@ -0,0 +1,323 @@ +"""Explore, solve and score a SmolDataEnvs task in the browser.""" + +import html +import logging + +import gradio as gr + +from .catalog import tasks +from .playground import PlaygroundSession, close_session + +log = logging.getLogger(__name__) +HEADER = """ + +
+
FINEENVS / INTERACTIVE ENVIRONMENT
+

Small data. Real problems.

+

Explore a dataset, run a little code, and see how your answer scores.

+
SETA whiteboxYour own sandbox + The same tools used in training
+
+""" +HEADER_CSS = """ +.hero { background:#111e32; color:#f7fafc; border-radius:16px !important; padding:30px 32px; margin-bottom:12px; } +.eyebrow { color:#90bfff; font-size:11px; font-weight:650; letter-spacing:1.7px; } +.hero h1 { color:#fff; margin:12px 0 8px; font-size:34px; font-weight:650; } +.hero p { color:#c3cfdf; margin:0 0 20px; font-size:16px; } +.tags { display:flex; flex-wrap:wrap; gap:8px; } +.tags span { border:1px solid #42516a; padding:5px 10px; border-radius:30px !important; font-size:12px; color:#d6e6ff; } +@media (max-width:600px) { .hero { padding:22px; } .hero h1 { font-size:27px; } } +""" +CARD_CSS = """ +.cards { display:flex; gap:12px; margin:8px 0 16px; flex-wrap:wrap; } +.card { flex:1; min-width:100px; border:1px solid var(--border-color-primary); border-radius:12px !important; padding:16px 18px; background:var(--block-background-fill); } +.card .label { font-size:12px; color:var(--body-text-color-subdued); } +.card .value { font-size:25px; font-weight:650; color:var(--body-text-color); margin-top:4px; } +.trace { border:1px solid var(--border-color-primary); border-radius:10px !important; margin:8px 0; padding:12px; } +.trace summary { cursor:pointer; font-weight:600; } +.trace pre { white-space:pre-wrap; overflow-wrap:anywhere; font-size:12px; } +.empty { padding:16px; color:var(--body-text-color-subdued); } +""" + + +def task_question(row): + text = row["instruction"] + return ( + text.partition("Question:")[2].split("\n\n")[0].strip() or text.splitlines()[0] + ) + + +def task_choices(split, difficulty): + return [ + ( + f"{i + 1:03d} · {row['difficulty'].title()} · {task_question(row)[:90]}", + row["name"], + ) + for i, row in enumerate(tasks(split)) + if difficulty == "All" or row["difficulty"] == difficulty.lower() + ] + + +def preview(split, name): + if not name: + return "No tasks in this filter. Try another difficulty." + row = next(t for t in tasks(split) if t["name"] == name) + files = ( + row["instruction"] + .partition("Files (in /home/user/input, no subfolders):")[2] + .split("\n\n")[0] + .strip() + ) + return f"**{row['difficulty'].title()} · {split.title()} split**\n\n{task_question(row)}\n\n**Input files**\n{files or 'Use List files to see the inputs.'}\n\nFiles are in `/home/user/input`. Run your code in `/workdir`." + + +def instructions(split, name): + return next( + (row["instruction"] for row in tasks(split) if row["name"] == name), + "Choose a task first.", + ) + + +def metrics(session): + values = [ + ("Tool calls", str(session.environment._calls)), + ( + "Correctness", + "Not graded" + if session.correctness is None + else f"{session.correctness:.0%}", + ), + ( + "Total reward", + "Not graded" if session.reward is None else f"{session.reward:.3f}", + ), + ] + return ( + '
' + + "".join( + f'
{label}
{value}
' + for label, value in values + ) + + "
" + ) + + +def trace(session): + if not session.history: + return ( + '
Your commands and their outputs will appear here.
' + ) + return "".join( + f'
Command {i + 1} · {item["seconds"]:.1f}s
$ {html.escape(item["command"])}
{html.escape(item["output"])}
' + for i, item in enumerate(session.history) + ) + + +def build_ui(manager, fields, metadata, is_chat, title, quick_start): + choices = task_choices("test", "All") + first = choices[0][1] if choices else None + with gr.Blocks( + title="SmolDataEnvs Multi-harness | SETA Whitebox", analytics_enabled=False + ) as demo: + session = gr.State( + PlaygroundSession(), time_to_live=900, delete_callback=close_session + ) + gr.HTML(HEADER, css_template=HEADER_CSS, apply_default_css=False) + gr.Markdown( + "**01 Choose a task** → **02 Explore the data** → **03 Submit and score**" + ) + scores = gr.HTML( + metrics(PlaygroundSession()), css_template=CARD_CSS, apply_default_css=False + ) + with gr.Row(): + with gr.Column(scale=4, min_width=300): + with gr.Group(elem_classes=["smol-panel"]): + gr.Markdown("### 01 / Your task") + with gr.Row(): + split = gr.Dropdown( + ["test", "train"], + value="test", + label="Dataset split", + interactive=True, + ) + difficulty = gr.Dropdown( + ["All", "Easy", "Medium", "Hard"], + value="All", + label="Difficulty", + interactive=True, + ) + picker = gr.Dropdown( + choices, value=first, label="Choose a task", interactive=True + ) + start = gr.Button( + "Start this task", variant="primary", elem_id="start-task" + ) + question = gr.Markdown(preview("test", first)) + with gr.Accordion("Full agent instructions", open=False): + full_instructions = gr.Markdown(instructions("test", first)) + with gr.Accordion("How scoring works", open=False): + gr.Markdown( + "Correctness comes from the task's held-out grader. A correct answer gets a small bonus for fewer tool calls.\n\n" + "`reward = correctness × (1 + 1.5 / (15 + tool calls))`\n\n" + "The bonus applies only after at least one tool call. Submitting your answer does not count as a tool call. A grading failure is shown as unscored.\n\n" + "You have 10 minutes per task. Starting a new task releases the previous sandbox." + ) + with gr.Column(scale=6, min_width=340): + with gr.Group(elem_classes=["smol-panel"]): + gr.Markdown("### 02 / Explore the data") + status = gr.Markdown( + "Start a task to create your sandbox. No model or API key needed." + ) + with gr.Row(): + list_files = gr.Button("List files", size="sm") + inspect_data = gr.Button("Preview a CSV", size="sm") + command = gr.Code( + "ls -lh /home/user/input", + language="shell", + label="Bash command", + lines=6, + ) + with gr.Row(): + run = gr.Button( + "Run command", + variant="primary", + interactive=False, + elem_id="run-command", + ) + stop = gr.Button("Release sandbox", interactive=False) + output = gr.Textbox( + label="Command output", + value="Start a task to open your workspace.", + lines=7, + max_lines=14, + interactive=False, + ) + with gr.Group(elem_classes=["smol-panel"]): + gr.Markdown("### 03 / Submit your answer") + answer = gr.Textbox( + label="Final answer", + placeholder="Write the answer itself, not a command.", + lines=2, + ) + grade = gr.Button( + "Submit and score", + variant="primary", + interactive=False, + elem_id="grade-answer", + ) + with gr.Accordion("Execution history", open=False): + history = gr.HTML( + trace(PlaygroundSession()), + css_template=CARD_CSS, + apply_default_css=False, + ) + with gr.Accordion("Use this environment in code", open=False): + gr.Markdown( + "This playground uses the same `BashEnvironment` as the whitebox training script. Each browser session has its own sandbox.\n\n" + "[Source and local setup](https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-whitebox/tree/main) · " + "[Training tutorial](https://github.com/adithya-s-k/FineEnvs/tree/main/05-multi-harness-rl) · " + "[SmolDataEnvs collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-6ab4f2f6e09b7cb872ebc867)" + ) + + def render(state): + return ( + state, + state.status, + state.output, + metrics(state), + trace(state), + *[gr.update(interactive=state.active)] * 3, + *[gr.update(interactive=not state.active)] * 4, + ) + + def act(state, action, *args): + try: + getattr(state, action)(*args) + if action == "close": + state.status = "Sandbox released. Choose a task to start again." + except ValueError as exc: + gr.Warning(str(exc)) + except Exception: + log.exception("Whitebox playground action failed: %s", action) + state.status = ( + "The sandbox operation failed. Release it and start the task again." + ) + if not state.active: + state.output = "No active sandbox." + return render(state) + + def choose(split_name, level): + rows = task_choices(split_name, level) + name = rows[0][1] if rows else None + return ( + gr.update(choices=rows, value=name), + preview(split_name, name), + instructions(split_name, name), + gr.update(interactive=bool(rows)), + ) + + outputs = [ + session, + status, + output, + scores, + history, + run, + grade, + stop, + split, + difficulty, + picker, + start, + ] + split.change( + choose, [split, difficulty], [picker, question, full_instructions, start] + ) + difficulty.change( + choose, [split, difficulty], [picker, question, full_instructions, start] + ) + picker.change( + lambda s, n: (preview(s, n), instructions(s, n)), + [split, picker], + [question, full_instructions], + ) + start.click( + lambda s, p, n: (*act(s, "start", p, n), ""), + [session, split, picker], + outputs + [answer], + concurrency_limit=None, + ) + run.click( + lambda s, c: act(s, "run", c), + [session, command], + outputs, + concurrency_limit=None, + ) + grade.click( + lambda s, a: act(s, "grade", a), + [session, answer], + outputs, + concurrency_limit=None, + ) + stop.click(lambda s: act(s, "close"), session, outputs, concurrency_limit=None) + list_files.click(lambda: "ls -lh /home/user/input", outputs=command) + inspect_data.click( + lambda: ( + "python3 - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\np = next(Path('/home/user/input').glob('*.csv'))\ndf = pd.read_csv(p)\nprint(p.name, df.shape)\nprint(df.head().to_string(index=False))\nPY" + ), + outputs=command, + ) + return demo diff --git a/05-multi-harness-rl/envs/whitebox/start.sh b/05-multi-harness-rl/envs/whitebox/start.sh new file mode 100644 index 00000000..8c6558bb --- /dev/null +++ b/05-multi-harness-rl/envs/whitebox/start.sh @@ -0,0 +1,5 @@ +#!/usr/bin/env bash +set -euo pipefail +cd "$(dirname "$0")" +python prepare.py --output "${SMOLDATA_DATA:-prepared}" +exec uvicorn smoldataenv_whitebox.server:app --host 0.0.0.0 --port "${PORT:-7860}" --ws-ping-timeout 1800 diff --git a/05-multi-harness-rl/eval/README.md b/05-multi-harness-rl/eval/README.md new file mode 100644 index 00000000..8070e0bb --- /dev/null +++ b/05-multi-harness-rl/eval/README.md @@ -0,0 +1,65 @@ +# Evaluate a checkpoint + +[Read the article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) · [Collection](https://huggingface.co/collections/FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Tutorial](../README.md) · [Setup](../REPRODUCE.md) + +Use the same tasks, sampling settings and evaluation interfaces for the base model and every checkpoint. The shared evaluator measures pass@1, correctness plus tool-efficiency reward, tool calls and token use. + +## Which environment does it use? + +The launcher accepts the same mode names as training: + +| Training mode | Evaluation environment | Full test set | +|---|---|---| +| `whitebox` | SETA bash tools | 250 tasks × one attempt | +| `opencode` | Harbor: OpenCode, Claude Code, Codex and Mini-SWE-Agent | 250 tasks × four harnesses | +| `multi_harness` | The same four Harbor harnesses | 250 tasks × four harnesses | + +An OpenCode-trained checkpoint is tested across four harnesses to measure transfer. This evaluates OpenCode through Harbor; it does not invoke the separate native OpenCode environment. Whitebox has its own tool interface, so report that result separately. + +## Start with a small comparison + +After [installation and task preparation](../REPRODUCE.md#1-install-and-prepare-the-tasks), run these commands from `05-multi-harness-rl` on a two-GPU machine: + +```bash +# Base model: 25 fixed tasks across four harnesses, 100 attempts total. +python jobs/run.py eval --mode multi_harness --tasks 25 \ + --concurrency 35 --output runs/baseline + +# After checkpoint 100 has finished saving, repeat the same measurement. +python jobs/run.py eval --mode multi_harness --tasks 25 \ + --checkpoint runs/harbor-pilot/checkpoint-100 --step 100 \ + --concurrency 35 --output runs/checkpoint-100-eval +``` + +Use `--mode opencode` for a native OpenCode-trained policy or `--mode whitebox` for SETA. Add `--model Qwen/Qwen3.5-2B` for Qwen; the default is LFM2.5-2.6B. Omit `--tasks 25` to evaluate all 250 tasks. A tiny subset is useful for checking that a run works; use the full split for the final comparison. + +The launcher starts vLLM with TP1/DP2 and the appropriate OpenEnv server inside the allocation. The evaluator uses a different allocation from training. Follow the [HF Jobs and Slurm recipes](../REPRODUCE.md) to submit the same commands remotely. + +## Already serving the model and environment? + +Run the evaluator directly. Its two execution modes are `blackbox` and `whitebox`: + +```bash +python -m eval.evaluate --mode blackbox \ + --model LiquidAI/LFM2.5-2.6B --checkpoint YOUR_IMMUTABLE_CHECKPOINT_ID \ + --server http://127.0.0.1:8200 --vllm-url http://127.0.0.1:8000 \ + --tasks 25 --concurrency 35 --output runs/custom-eval +``` + +`--server` can point to your local server or the matching deployed Space: Harbor for `blackbox`, SETA for `whitebox`. For a remote Harbor server, also pass `--env-llm-url` with an inference endpoint reachable from that server. Its localhost is different from your machine's localhost. The model endpoint must support the token capture checks used by Harbor. + +## Read the result, then retry failures + +| File | What to look for | +|---|---| +| `summary.json` | Pass@1 and coverage, plus reward, tool and token averages; harness and difficulty breakdowns | +| `pairs/*.json` | Each task/harness result, elapsed time and any ungraded error | +| `identity.json` | Model/checkpoint, task identities, sampling and evaluator version | +| `progress.json` | Results completed so far | +| `trackio/` | Local aggregate evaluation metrics | + +Always read **coverage alongside pass@1**. An infrastructure failure has no grade and is excluded from the observed score. A graded wrong answer counts as zero. Incomplete coverage exits with status 2. + +To recover, rerun the same command with the same output directory. It retains both correct and incorrect graded attempts and retries only ungraded or missing pairs. Use a fresh directory for a different model, checkpoint or evaluator version. + +Add `--space-id your-org/your-trackio-space` to publish aggregate evaluation metrics. Per-harness and difficulty details are retained in `summary.json`. The [article](https://huggingface.co/spaces/FineEnvs/multi-harness-rl) shows how to interpret answer quality and tool savings together. diff --git a/05-multi-harness-rl/eval/__init__.py b/05-multi-harness-rl/eval/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/05-multi-harness-rl/eval/evaluate.py b/05-multi-harness-rl/eval/evaluate.py new file mode 100644 index 00000000..b533f3f3 --- /dev/null +++ b/05-multi-harness-rl/eval/evaluate.py @@ -0,0 +1,292 @@ +"""Pass@1 on fixed held-out tasks; resume only missing or ungraded pairs.""" + +import argparse +import hashlib +import inspect +import json +import math +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +from smoldataenv_harbor.tasks import load_tasks + +HARNESSES = ("opencode", "claude-code", "codex", "mini-swe-agent") + + +def write_json(path, value): + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(".tmp") + temporary.write_text(json.dumps(value, indent=2) + "\n") + temporary.replace(path) + + +def harbor_episode(args, index, task, harness): + import httpx + from harbor_env import HarborEnv + from harbor_env.harness import HarborSession + + response = httpx.get(args.server.rstrip("/") + "/smoldataenv/splits", timeout=30) + response.raise_for_status() + session = HarborSession( + env=HarborEnv(args.server, message_timeout_s=1800), + owns_env=True, + split=response.json()["test"], + task_index=index, + instruction=task["instruction"], + harness=harness, + sandbox="daytona", + llm_url=args.env_llm_url or args.vllm_url, + model=args.model, + reward_key="correctness,reward", + sampling={"temperature": 0.8, "top_p": 1.0, "top_k": -1}, + agent_step_limit=17, + agent_timeout_sec=600, + ) + try: + session.wait_for_completion(timeout_s=1200) + correctness = session.verify([]).env_reward + if correctness not in (0, 1): + raise RuntimeError("No verifier grade") + trace = ( + session.fetch_training_trace() + ) # Checks engine tokens, logprobs and masks. + response = httpx.get( + args.server.rstrip("/") + + f"/smoldataenv/trials/{session.result.trial_name}/tool-count", + timeout=30, + ) + response.raise_for_status() + calls = response.json()["native_tool_calls"] + return { + "correctness": correctness, + "tool_calls": calls, + "generated_tokens": sum(len(t.completion_token_ids) for t in trace.turns), + "prompt_tokens": sum(len(t.prompt_token_ids) for t in trace.turns), + "model_calls": len(trace.turns), + } + finally: + session.close() + + +def whitebox_episode(args, index, task, harness): + from openai import OpenAI + from smoldataenv_whitebox.client import RemoteBashEnvironment + from smoldataenv_whitebox.environment import SYSTEM, BashEnvironment + from transformers.utils import get_json_schema + + environment = ( + RemoteBashEnvironment(args.server) if args.server else BashEnvironment() + ) + messages = [{"role": "system", "content": SYSTEM}] + generated, prompt_tokens = 0, 0 + try: + messages.append( + {"role": "user", "content": environment.reset(folder=task["folder"])} + ) + tools = [ + get_json_schema(method) + for name, method in inspect.getmembers(environment, inspect.ismethod) + if not name.startswith("_") and name not in {"reset", "get_reward"} + ] + allowed = {tool["function"]["name"] for tool in tools} + with OpenAI( + base_url=args.vllm_url.rstrip("/") + "/v1", api_key="unused", timeout=600 + ) as client: + for _ in range(17): + response = client.chat.completions.create( + model=args.model, + messages=messages, + tools=tools, + temperature=0.8, + top_p=1.0, + max_tokens=4096, + extra_body={"chat_template_kwargs": {"enable_thinking": False}}, + ) + message = response.choices[0].message + generated += response.usage.completion_tokens + prompt_tokens += response.usage.prompt_tokens + messages.append(message.model_dump(exclude_none=True)) + if not message.tool_calls: + break + for call in message.tool_calls: + if call.function.name not in allowed: + value = "Unknown tool" + else: + try: + value = getattr(environment, call.function.name)( + **json.loads(call.function.arguments) + ) + except (TypeError, ValueError, RuntimeError) as exc: + value = type(exc).__name__ + messages.append( + {"role": "tool", "tool_call_id": call.id, "content": str(value)} + ) + if environment._submitted: + break + value = environment.get_reward() + if not math.isfinite(value): + raise RuntimeError("No verifier grade") + return { + "correctness": environment._correctness, + "tool_calls": environment._calls, + "generated_tokens": generated, + "prompt_tokens": prompt_tokens, + } + finally: + environment._close() + + +def summarize(rows, expected): + graded = [row for row in rows if row.get("correctness") in (0, 1)] + + def mean(key): + values = [row[key] for row in graded if row.get(key) is not None] + return sum(values) / len(values) if values else None + + return { + "expected": expected, + "graded": len(graded), + "coverage": len(graded) / expected, + "pass_at_1_observed": mean("correctness"), + "combined_reward": mean("reward"), + "mean_tool_calls": mean("tool_calls"), + "tool_count_coverage": sum(r.get("tool_calls") is not None for r in graded), + "mean_generated_tokens": mean("generated_tokens"), + "mean_prompt_tokens": mean("prompt_tokens"), + } + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--model", default="LiquidAI/LFM2.5-2.6B", help="The served model name" + ) + parser.add_argument( + "--checkpoint", + required=True, + help="Immutable model revision or checkpoint identity", + ) + parser.add_argument("--step", type=int, default=0) + parser.add_argument("--mode", choices=["blackbox", "whitebox"], default="blackbox") + parser.add_argument("--data", default="prepared") + parser.add_argument( + "--env-llm-url", help="Inference URL reachable by a remote environment server" + ) + parser.add_argument("--server", default="http://127.0.0.1:8200") + parser.add_argument("--vllm-url", default="http://127.0.0.1:8000") + parser.add_argument("--trials", default="runs/trials") + parser.add_argument("--output", required=True) + parser.add_argument("--tasks", type=int, default=250) + parser.add_argument("--concurrency", type=int, default=35) + parser.add_argument("--space-id", default=None) + args = parser.parse_args() + if not 1 <= args.tasks <= 250 or args.concurrency < 1: + parser.error("Use 1 to 250 tasks and positive concurrency") + tasks = load_tasks(args.data, "test") + # Stable subset: every model and checkpoint sees the same task/harness pairs. + selected = list(enumerate(tasks))[: args.tasks] + harnesses = ["whitebox"] if args.mode == "whitebox" else HARNESSES + output = Path(args.output) + identity = { + "protocol_source_sha256": hashlib.sha256( + Path(__file__).read_bytes() + ).hexdigest(), + "checkpoint": args.checkpoint, + "model": args.model, + "harnesses": harnesses, + "task_names": [t["name"] for _, t in selected], + "temperature": 0.8, + "instruction_hash": hashlib.sha256( + "".join(t["instruction"] for _, t in selected).encode() + ).hexdigest(), + } + identity_path = output / "identity.json" + if identity_path.exists() and json.loads(identity_path.read_text()) != json.loads( + json.dumps(identity) + ): + raise ValueError("Output already belongs to a different evaluation") + write_json(identity_path, identity) + pairs = [ + (index, task, harness) for index, task in selected for harness in harnesses + ] + + def one(pair): + index, task, harness = pair + path = output / "pairs" / f"{task['name']}--{harness}.json" + if path.exists(): + previous = json.loads(path.read_text()) + if previous.get("correctness") in (0, 1): + return previous + start = time.monotonic() + row = { + "task": task["name"], + "difficulty": task["difficulty"], + "harness": harness, + } + try: + episode = whitebox_episode if args.mode == "whitebox" else harbor_episode + row.update(episode(args, index, task, harness)) + calls = row["tool_calls"] + row["reward"] = row["correctness"] * ( + 1 + 1.5 / (15 + calls) if calls else 1 + ) + except Exception as exc: # noqa: BLE001 - persist an ungraded pair for retry + import os + + detail = str(exc) + for key, value in os.environ.items(): + if value and any( + word in key for word in ("TOKEN", "SECRET", "API_KEY") + ): + detail = detail.replace(value, "[redacted]") + row.update(correctness=None, error=type(exc).__name__, detail=detail[:1000]) + row["seconds"] = time.monotonic() - start + write_json(path, row) + return row + + rows = [] + with ThreadPoolExecutor(max_workers=args.concurrency) as pool: + for future in as_completed([pool.submit(one, pair) for pair in pairs]): + rows.append(future.result()) + write_json(output / "progress.json", summarize(rows, len(pairs))) + summary = summarize(rows, len(pairs)) + summary["harnesses"] = { + h: summarize([r for r in rows if r["harness"] == h], args.tasks) + for h in harnesses + } + summary["difficulty"] = { + d: summarize( + [r for r in rows if r["difficulty"] == d], + sum(t["difficulty"] == d for _, t in selected) * len(harnesses), + ) + for d in {t["difficulty"] for _, t in selected} + } + write_json(output / "summary.json", summary) + import os + + os.environ["TRACKIO_DIR"] = str(output.resolve() / "trackio") + import trackio + + run = trackio.init( + project="smoldataenv-rl", + name=output.name, + space_id=args.space_id, + config=identity, + ) + run.log( + { + f"eval/{key}": value + for key, value in summary.items() + if isinstance(value, (int, float)) + }, + step=args.step, + ) + run.finish() + print(json.dumps(summary, indent=2)) + if summary["graded"] != summary["expected"]: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/jobs/entrypoint.sh b/05-multi-harness-rl/jobs/entrypoint.sh new file mode 100644 index 00000000..1aec68f8 --- /dev/null +++ b/05-multi-harness-rl/jobs/entrypoint.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail +mkdir -p /workspace/tutorial +cp -a /tutorial-source/. /workspace/tutorial/ +cd /workspace/tutorial +uv venv --python 3.12 --seed .venv +export PATH="$PWD/.venv/bin:$PATH" +bash jobs/install.sh +python prepare.py +if [[ "$1" == "smoke" ]]; then + shift + exec python jobs/smoke.py "$@" +fi +exec python jobs/run.py "$@" diff --git a/05-multi-harness-rl/jobs/hf_job.py b/05-multi-harness-rl/jobs/hf_job.py new file mode 100644 index 00000000..1f9f6bc3 --- /dev/null +++ b/05-multi-harness-rl/jobs/hf_job.py @@ -0,0 +1,144 @@ +"""Submit a tutorial to HF Jobs, with source and persistent output volumes.""" + +import argparse +import json +import os +import shutil +import tempfile +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("action", choices=["train", "eval", "smoke"]) + parser.add_argument( + "--mode", + choices=["whitebox", "opencode", "multi_harness"], + default="multi_harness", + ) + parser.add_argument("--model", default="LiquidAI/LFM2.5-2.6B") + parser.add_argument("--name", required=True) + parser.add_argument( + "--bucket", + required=True, + help="An existing owner/bucket for checkpoints and logs", + ) + parser.add_argument("--flavor", default="h200x2") + parser.add_argument("--timeout", default="12h") + parser.add_argument("--steps", type=int, default=1000) + parser.add_argument("--save-steps", type=int, default=50) + parser.add_argument("--checkpoint", help="For eval: /outputs//checkpoint-100") + parser.add_argument("--step", type=int, default=0) + parser.add_argument("--tasks", type=int) + parser.add_argument("--concurrency", type=int) + parser.add_argument("--space-id") + parser.add_argument( + "--submit", action="store_true", help="Without this flag, print the job plan" + ) + args = parser.parse_args() + args.tasks = args.tasks or (2 if args.action == "smoke" else 250) + args.concurrency = args.concurrency or (4 if args.action == "smoke" else 35) + if Path(args.name).name != args.name or args.name in {".", ".."}: + parser.error("Use a single directory name for --name") + if args.flavor != "h200x2": + parser.error( + "Use h200x2 for this two-GPU HF recipe; local H100 pairs are also supported" + ) + namespace, separator, bucket = args.bucket.partition("/") + if not separator or not namespace or not bucket: + parser.error("Use owner/bucket for --bucket") + command = [ + "bash", + "/tutorial-source/jobs/entrypoint.sh", + args.action, + "--mode", + args.mode, + "--model", + args.model, + "--output", + "/outputs/" + args.name, + "--steps", + str(args.steps), + "--save-steps", + str(args.save_steps), + "--step", + str(args.step), + "--tasks", + str(args.tasks), + "--concurrency", + str(args.concurrency), + ] + if args.checkpoint: + command += ["--checkpoint", args.checkpoint] + if args.space_id: + command += ["--space-id", args.space_id] + specification = { + "image": "huggingface/trl@sha256:8433cde7eaf3b289f3daffdaefcab6bb71499ca1ff59dc5d05abba7a71603a58", + "command": command, + "namespace": namespace, + "flavor": args.flavor, + "timeout": args.timeout, + "name": args.name, + "env": { + "PYTHONUNBUFFERED": "1", + "TUTORIAL_MODE": args.mode, + }, + } + print( + json.dumps( + { + **specification, + "bucket": args.bucket, + "secrets": ["HF_TOKEN", "DAYTONA_API_KEY"], + }, + indent=2, + ) + ) + if not args.submit: + return + from huggingface_hub import Volume, get_token, run_job, sync_job_volume + + token = get_token() + if not token or not os.environ.get("DAYTONA_API_KEY"): + parser.error("Log in to HF and set DAYTONA_API_KEY") + with tempfile.TemporaryDirectory() as directory: + # An allowlist keeps credentials, prepared tasks and old experiments out of the source upload. + paths = list(ROOT.glob("*.py")) + [ROOT / "requirements.txt"] + for folder in ("jobs", "envs", "train", "eval", "data"): + paths += [ + p + for p in (ROOT / folder).rglob("*") + if p.suffix in {".py", ".sh", ".json", ".toml", ".txt", ".yaml"} + and not any( + part in {".deps", ".venv", "prepared", ".archive", "__pycache__"} + or part.endswith(".egg-info") + for part in p.relative_to(ROOT).parts + ) + ] + for source in paths: + target = Path(directory) / source.relative_to(ROOT) + target.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(source, target) + source_volume = sync_job_volume( + directory, "/tutorial-source", namespace=namespace + ) + job = run_job( + **specification, + volumes=[ + source_volume, + Volume( + type="bucket", + source=args.bucket, + mount_path="/outputs", + read_only=False, + ), + ], + secrets={"HF_TOKEN": token, "DAYTONA_API_KEY": os.environ["DAYTONA_API_KEY"]}, + ) + print(job.url) + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/jobs/inference_proxy.py b/05-multi-harness-rl/jobs/inference_proxy.py new file mode 100644 index 00000000..a022d679 --- /dev/null +++ b/05-multi-harness-rl/jobs/inference_proxy.py @@ -0,0 +1,80 @@ +"""Expose inference to native OpenCode sandboxes without exposing weight-update routes.""" + +import argparse +import os +import secrets +import threading +import time +from contextlib import asynccontextmanager +from pathlib import Path + +import httpx +import uvicorn +from fastapi import FastAPI, HTTPException, Request +from starlette.background import BackgroundTask +from starlette.responses import StreamingResponse + + +def create_app(key, upstream="http://127.0.0.1:8000"): + @asynccontextmanager + async def lifespan(app): + async with httpx.AsyncClient(timeout=660) as client: + app.state.client = client + yield + + app = FastAPI(lifespan=lifespan) + + @app.api_route("/v1/{path:path}", methods=["GET", "POST"]) + async def forward(path: str, request: Request): + if not secrets.compare_digest( + request.headers.get("authorization", ""), "Bearer " + key + ): + raise HTTPException(401) + if (request.method, path) not in { + ("GET", "models"), + ("POST", "chat/completions"), + }: + raise HTTPException(404) + client = app.state.client + message = client.build_request( + request.method, + upstream + "/v1/" + path, + content=await request.body(), + headers={"content-type": "application/json"}, + ) + response = await client.send(message, stream=True) + return StreamingResponse( + response.aiter_raw(), + status_code=response.status_code, + media_type=response.headers.get("content-type"), + background=BackgroundTask(response.aclose), + ) + + return app + + +def main(): + from gradio.networking import setup_tunnel + + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", required=True) + parser.add_argument("--port", type=int, default=8400) + parser.add_argument("--upstream", default="http://127.0.0.1:8000") + args = parser.parse_args() + app = create_app(os.environ["SANDBOX_VLLM_KEY"], args.upstream) + server = uvicorn.Server( + uvicorn.Config(app, host="127.0.0.1", port=args.port, log_level="warning") + ) + thread = threading.Thread(target=server.run, daemon=True) + thread.start() + while not server.started: + if not thread.is_alive(): + raise RuntimeError("Inference proxy failed to start") + time.sleep(0.1) + url = setup_tunnel("127.0.0.1", args.port, secrets.token_urlsafe(24), None, None) + Path(args.output).write_text(url) + thread.join() + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/jobs/install.sh b/05-multi-harness-rl/jobs/install.sh new file mode 100644 index 00000000..474c9efd --- /dev/null +++ b/05-multi-harness-rl/jobs/install.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash +set -euo pipefail +# Run from 05-multi-harness-rl in a Python 3.12 virtual environment. +python -m pip install -r requirements.txt +python -m pip install --upgrade "trl @ git+https://github.com/huggingface/trl.git@main" +mkdir -p .deps +if [[ ! -d .deps/OpenEnv/.git ]]; then + git clone --depth 1 --branch main https://github.com/huggingface/OpenEnv.git .deps/OpenEnv +else + git -C .deps/OpenEnv pull --ff-only origin main +fi +python -m pip install -e .deps/OpenEnv +# The environment examples are source packages, outside OpenEnv's core wheel. +python - <<'PY' +from pathlib import Path +from importlib.metadata import distribution +import json +import site +import subprocess +Path(site.getsitepackages()[0], 'openenv_examples.pth').write_text(str(Path('.deps/OpenEnv/envs').resolve()) + '\n') +versions = {} +for name in ('openenv', 'trl', 'transformers', 'vllm', 'harbor'): + installed = distribution(name) + source = json.loads(installed.read_text('direct_url.json') or '{}') + versions[name] = {'version': installed.version, 'commit': source.get('vcs_info', {}).get('commit_id')} +versions['openenv']['commit'] = subprocess.check_output(['git', '-C', '.deps/OpenEnv', 'rev-parse', 'HEAD'], text=True).strip() +Path('.deps/revisions.json').write_text(json.dumps(versions, indent=2) + '\n') +PY +python -m pip install --no-deps -e envs/whitebox -e envs/opencode -e envs/harbor +python check_setup.py --mode "${TUTORIAL_MODE:-all}" diff --git a/05-multi-harness-rl/jobs/run.py b/05-multi-harness-rl/jobs/run.py new file mode 100644 index 00000000..e0ee3675 --- /dev/null +++ b/05-multi-harness-rl/jobs/run.py @@ -0,0 +1,302 @@ +"""Launch services and one tutorial script on a two-GPU machine. Training logic lives in train/.""" + +import argparse +import json +import os +import secrets +import signal +import socket +import subprocess +import sys +import time +from contextlib import ExitStack +from pathlib import Path + +import httpx + +ROOT = Path(__file__).resolve().parents[1] + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("action", choices=["train", "eval"]) + parser.add_argument( + "--mode", + choices=["whitebox", "opencode", "multi_harness"], + default="multi_harness", + ) + parser.add_argument("--model", default="LiquidAI/LFM2.5-2.6B") + parser.add_argument("--output", required=True) + parser.add_argument("--smoke", action="store_true") + parser.add_argument("--steps", type=int, default=1000) + parser.add_argument("--save-steps", type=int, default=50) + parser.add_argument("--checkpoint") + parser.add_argument("--step", type=int, default=0) + parser.add_argument("--tasks", type=int, default=250) + parser.add_argument("--concurrency", type=int, default=35) + parser.add_argument("--space-id") + args = parser.parse_args() + os.chdir(ROOT) + subprocess.run([sys.executable, "check_setup.py", "--mode", args.mode], check=True) + if not Path("prepared/ready.json").exists(): + raise RuntimeError("Run prepare.py before allocating GPUs") + output = Path(args.output).resolve() + if output.exists() and args.action == "train": + raise ValueError("Use a fresh training output directory") + output.mkdir(parents=True, exist_ok=True) + revisions = ROOT / ".deps/revisions.json" + if revisions.exists(): + (output / "dependencies.json").write_text(revisions.read_text()) + with (output / "packages.txt").open("w") as packages: + subprocess.run( + [sys.executable, "-m", "pip", "freeze"], stdout=packages, check=True + ) + devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0,1").split(",") + if len(devices) < 2: + raise RuntimeError("Allocate two GPUs") + env = dict( + os.environ, + PYTHONUNBUFFERED="1", + TRACKIO_DIR=str(output / "trackio"), + VLLM_USE_DEEP_GEMM="0", + VLLM_DEEP_GEMM_WARMUP="skip", + VLLM_USE_FLASHINFER_SAMPLER="0", + ) + with ExitStack() as stack: + sockets = [stack.enter_context(socket.socket()) for _ in range(4)] + for listener in sockets: + listener.bind(("127.0.0.1", 0)) + engine_port, server_port, capture_port, proxy_port = [ + s.getsockname()[1] for s in sockets + ] + engine_url = f"http://127.0.0.1:{engine_port}" + server_url = f"http://127.0.0.1:{server_port}" + (output / "services.json").write_text( + json.dumps({"vllm": engine_url, "openenv": server_url}) + ) + children = [] + + def spawn(command, name, extra=None): + with (output / (name + ".log")).open("a") as stream: + process = subprocess.Popen( + command, + env={**env, **(extra or {})}, + stdout=stream, + stderr=subprocess.STDOUT, + start_new_session=True, + ) + children.append(process) + return process + + def healthy(): + if any(p.poll() is not None for p in children): + raise RuntimeError("A service exited; inspect logs in " + str(output)) + + def wait(url, timeout=1200): + deadline = time.monotonic() + timeout + while time.monotonic() < deadline: + healthy() + try: + if httpx.get(url, timeout=5).status_code == 200: + return + except httpx.HTTPError: + pass + time.sleep(2) + raise TimeoutError(url) + + def stop(signum, frame): + raise KeyboardInterrupt + + signal.signal(signal.SIGTERM, stop) + try: + command = [ + sys.executable, + "jobs/serve_model.py", + "--model", + args.model, + "--output", + str(output), + "--port", + str(engine_port), + ] + if args.action == "eval": + command += ["--eval"] + if args.checkpoint: + command += ["--checkpoint", args.checkpoint] + spawn( + command, + "vllm", + { + "CUDA_VISIBLE_DEVICES": devices[0] + if args.action == "train" + else ",".join(devices[:2]) + }, + ) + wait(engine_url + "/health") + if args.mode != "whitebox" and ( + args.action == "eval" or args.mode == "multi_harness" + ): + spawn( + [ + sys.executable, + "-m", + "uvicorn", + "smoldataenv_harbor.server:app", + "--host", + "127.0.0.1", + "--port", + str(server_port), + "--ws-ping-timeout", + "1800", + ], + "harbor", + { + "OPENENV_DATASETS": ",".join( + str(Path("prepared/datasets", s).resolve()) + for s in ("train", "test") + ), + "OPENENV_LLM_URL": engine_url, + "OPENENV_MODEL": args.model, + "OPENENV_HARBOR_TRIALS_DIR": str(output / "trials"), + "OPENENV_MAX_OUTPUT_TOKENS": "4096", + "OPENENV_CAPTURE_PORT": str(capture_port), + "OPENENV_EXPOSE": "gradio", + "MAX_CONCURRENT_ENVS": str(max(32, args.concurrency) + 8), + "ENABLE_WEB_INTERFACE": "false", + "OPENENV_HARBOR_AGENT_VERSIONS": '{"opencode":"1.18.31","claude-code":"2.1.270","codex":"0.154.0","mini-swe-agent":"2.4.6"}', + }, + ) + wait(server_url + "/health") + if args.action == "train" and args.mode == "opencode": + env["SANDBOX_VLLM_KEY"] = secrets.token_urlsafe(32) + location = output / "inference-url.txt" + spawn( + [ + sys.executable, + "jobs/inference_proxy.py", + "--output", + str(location), + "--port", + str(proxy_port), + "--upstream", + engine_url, + ], + "inference-proxy", + ) + deadline = time.monotonic() + 180 + while not location.exists(): + healthy() + if time.monotonic() > deadline: + raise TimeoutError("Public inference tunnel") + time.sleep(1) + env["SANDBOX_VLLM_URL"] = location.read_text() + # Confirm that the sandbox-facing endpoint has the expected model. + response = httpx.get( + env["SANDBOX_VLLM_URL"] + "/v1/models", + headers={"Authorization": "Bearer " + env["SANDBOX_VLLM_KEY"]}, + timeout=30, + ) + response.raise_for_status() + if args.mode == "whitebox": + spawn( + [ + sys.executable, + "-m", + "uvicorn", + "smoldataenv_whitebox.server:app", + "--host", + "127.0.0.1", + "--port", + str(server_port), + ], + "whitebox", + {"ENABLE_WEB_INTERFACE": "false"}, + ) + wait(server_url + "/health") + if args.action == "train" and args.mode == "opencode": + spawn( + [ + sys.executable, + "-m", + "uvicorn", + "smoldataenv_opencode.server:app", + "--host", + "127.0.0.1", + "--port", + str(server_port), + "--ws-ping-timeout", + "1800", + ], + "opencode", + {"ENABLE_WEB_INTERFACE": "false"}, + ) + wait(server_url + "/health") + common = [ + "--model", + args.model, + "--output", + str(output), + "--vllm-url", + engine_url, + ] + common += ["--server", server_url] + if args.action == "eval" or args.mode == "multi_harness": + common += ["--trials", str(output / "trials")] + if args.space_id: + common += ["--space-id", args.space_id] + if args.action == "train": + command = [ + sys.executable, + "-m", + f"train.{args.mode}", + *common, + "--steps", + str(args.steps), + "--save-steps", + str(args.save_steps), + ] + else: + command = [ + sys.executable, + "-m", + "eval.evaluate", + *common, + "--mode", + "whitebox" if args.mode == "whitebox" else "blackbox", + "--checkpoint", + args.checkpoint or args.model, + "--step", + str(args.step), + "--tasks", + str(args.tasks), + "--concurrency", + str(args.concurrency), + ] + if args.smoke and args.action == "train": + command += ["--smoke"] + worker = spawn(command, args.action, {"CUDA_VISIBLE_DEVICES": devices[1]}) + children.remove(worker) + try: + while worker.poll() is None: + healthy() + time.sleep(2) + if worker.returncode: + raise RuntimeError( + f"{args.action} exited {worker.returncode}; inspect {output}" + ) + finally: + children.append(worker) + finally: + for process in reversed(children): + if process.poll() is None: + os.killpg(process.pid, signal.SIGTERM) + for process in reversed(children): + try: + process.wait(timeout=30) + except subprocess.TimeoutExpired: + os.killpg(process.pid, signal.SIGKILL) + process.wait() + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/jobs/serve_model.py b/05-multi-harness-rl/jobs/serve_model.py new file mode 100644 index 00000000..d0048717 --- /dev/null +++ b/05-multi-harness-rl/jobs/serve_model.py @@ -0,0 +1,83 @@ +"""Start the same non-thinking vLLM configuration for training or evaluation.""" + +import argparse +import os +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from train.whitebox import MODEL_REVISIONS, tokenizer_for + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--model", default="LiquidAI/LFM2.5-2.6B", help="Base model identity" + ) + parser.add_argument( + "--checkpoint", help="Load these weights but retain the base served-model name" + ) + parser.add_argument("--output", required=True) + parser.add_argument("--port", type=int, default=8000) + parser.add_argument("--eval", action="store_true") + args = parser.parse_args() + output = Path(args.output) + output.mkdir(parents=True, exist_ok=True) + tokenizer = tokenizer_for(args.model) + template = output / "chat_template.jinja" + template.write_text(tokenizer.chat_template) + command = [ + "vllm", + "serve", + args.checkpoint or args.model, + "--host", + "127.0.0.1", + "--port", + str(args.port), + "--served-model-name", + args.model, + "--tensor-parallel-size", + "1", + "--data-parallel-size", + "2" if args.eval else "1", + "--dtype", + "bfloat16", + "--max-model-len", + "131072", + "--gpu-memory-utilization", + "0.85", + "--enable-auto-tool-choice", + "--tool-call-parser", + "lfm2" if "lfm" in args.model.lower() else "qwen3_xml", + "--chat-template", + str(template), + "--default-chat-template-kwargs", + '{"enable_thinking":false,"preserve_thinking":true}', + "--generation-config", + "vllm", + "--logprobs-mode", + "processed_logprobs", + "--return-tokens-as-token-ids", + "--enforce-eager", + "--no-enable-prefix-caching", + ] + if not args.checkpoint: + command += ["--revision", MODEL_REVISIONS[args.model]] + else: + # Read bucket/network checkpoints sequentially instead of memory-mapping them. + command += ["--safetensors-load-strategy", "eager"] + if not args.eval: + command += ["--weight-transfer-config", '{"backend":"nccl"}'] + os.environ["VLLM_SERVER_DEV_MODE"] = "1" + if "qwen" in args.model.lower(): + command += [ + "--limit-mm-per-prompt", + '{"image":0,"video":0}', + "--gdn-prefill-backend", + "triton", + ] + os.execvp(command[0], command) + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/jobs/smoke.py b/05-multi-harness-rl/jobs/smoke.py new file mode 100644 index 00000000..ce18174c --- /dev/null +++ b/05-multi-harness-rl/jobs/smoke.py @@ -0,0 +1,117 @@ +"""Run two updates, verify the saved checkpoint, then reload it for pass@1 evaluation.""" + +import argparse +import json +import math +import os +import subprocess +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--mode", choices=["whitebox", "opencode", "multi_harness"], required=True + ) + parser.add_argument("--model", default="LiquidAI/LFM2.5-2.6B") + parser.add_argument("--output", required=True) + parser.add_argument("--tasks", type=int, default=2) + parser.add_argument("--concurrency", type=int, default=4) + parser.add_argument("--space-id") + # Accept the launcher's shared options; the smoke always uses two updates. + parser.add_argument("--steps", type=int) + parser.add_argument("--save-steps", type=int) + parser.add_argument("--step", type=int) + args = parser.parse_args() + output = Path(args.output).resolve() + output.mkdir(parents=True, exist_ok=True) + train = output / "train" + evaluation = output / "reload-eval" + checkpoint = train / "checkpoint-2" + common = ["--mode", args.mode, "--model", args.model] + if args.space_id: + common += ["--space-id", args.space_id] + subprocess.run( + [ + sys.executable, + str(ROOT / "jobs/run.py"), + "train", + *common, + "--smoke", + "--output", + str(train), + ], + check=True, + cwd=ROOT, + ) + for step in (1, 2): + path = train / f"checkpoint-{step}" + state = json.loads((path / "trainer_state.json").read_text()) + assert state["global_step"] == step + assert list(path.glob("*.safetensors")), "No saved model weights" + for filename in ( + "config.json", + "tokenizer_config.json", + "optimizer.pt", + "scheduler.pt", + ): + assert (path / filename).stat().st_size > 0, ( + f"Missing checkpoint file: {filename}" + ) + metrics = state.get("log_history", []) + updates = [row for row in metrics if "loss" in row or "grad_norm" in row] + assert updates, "No optimizer metrics were saved" + for row in updates: + for key in ("loss", "grad_norm"): + if key in row: + assert math.isfinite(row[key]), f"Non-finite {key}" + assert any( + p.stat().st_size + for p in (train / "trackio").rglob("*") + if p.suffix in {".db", ".jsonl"} + ), "No local Trackio metrics" + subprocess.run( + [ + sys.executable, + str(ROOT / "jobs/run.py"), + "eval", + *common, + "--checkpoint", + str(checkpoint), + "--step", + "2", + "--tasks", + str(args.tasks), + "--concurrency", + str(args.concurrency), + "--output", + str(evaluation), + ], + check=True, + cwd=ROOT, + ) + summary = json.loads((evaluation / "summary.json").read_text()) + assert summary["graded"] == summary["expected"] + report = { + "mode": args.mode, + "model": args.model, + "checkpoint": str(checkpoint), + "steps": 2, + "optimizer_metrics": updates, + "nonzero_gradient_updates": sum(row.get("grad_norm", 0) > 0 for row in updates), + "eval": summary, + "dependencies": json.loads((train / "dependencies.json").read_text()) + if (train / "dependencies.json").exists() + else None, + "job": os.getenv("SLURM_JOB_ID") or os.getenv("HF_JOB_ID"), + "complete": True, + } + (output / "smoke.json").write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps(report, indent=2), flush=True) + + +if __name__ == "__main__": + main() diff --git a/05-multi-harness-rl/jobs/train.slurm b/05-multi-harness-rl/jobs/train.slurm new file mode 100644 index 00000000..777952c0 --- /dev/null +++ b/05-multi-harness-rl/jobs/train.slurm @@ -0,0 +1,15 @@ +#!/usr/bin/env bash +#SBATCH --nodes=1 +#SBATCH --gpus=2 +#SBATCH --cpus-per-task=16 +#SBATCH --mem=192G +#SBATCH --time=12:00:00 +#SBATCH --output=smoldataenv-%j.log +set -euo pipefail +# Submit from 05-multi-harness-rl after installation and prepare.py. +# Activate your Python 3.12 environment before sbatch; it is inherited by the job. +if [[ "$1" == "smoke" ]]; then + shift + exec python jobs/smoke.py "$@" +fi +python jobs/run.py "$@" diff --git a/05-multi-harness-rl/prepare.py b/05-multi-harness-rl/prepare.py new file mode 100644 index 00000000..1b558b9a --- /dev/null +++ b/05-multi-harness-rl/prepare.py @@ -0,0 +1,138 @@ +"""Materialize the fixed train/test tasks from corrected, pinned Harbor datasets.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import shutil +from pathlib import Path + +import tomllib + +ROOT = Path(__file__).resolve().parent + + +def task_rows(split): + return json.loads((ROOT / "data" / f"{split}.json").read_text()) + + +def write_json(path, data): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(data, indent=2) + "\n") + + +DATASETS = { + "train": ( + "FineEnvs/SmolDataEnvs-harbor-train", + "4719635555de1666f374d847baebb500d368e493", + ), + "test": ( + "FineEnvs/SmolDataEnvs-harbor-test", + "b130595b579fc6026ad5762925a9a3b40e72f5bb", + ), +} +GRADER_SHA256 = "16f906e9248fa8b7e1141939e425e88e59d52d65e98e360bbacef3720a43629f" + + +def prepare(destination, sources=None): + import tomli_w + from huggingface_hub import snapshot_download + + destination = Path(destination).resolve() + manifests = {} + for split in ("train", "test"): + rows = task_rows(split) + repo, revision = DATASETS[split] + source = (sources or {}).get(split) + if source is None: + source = snapshot_download( + repo, + repo_type="dataset", + revision=revision, + allow_patterns=[f"tasks/{r['name']}/*" for r in rows], + max_workers=16, + ) + source = Path(source) + tasks = [] + for row in rows: + folder = source / "tasks" / row["name"] + instruction = (folder / "instruction.md").read_bytes() + if hashlib.sha256(instruction).hexdigest() != row["instruction_sha256"]: + raise ValueError( + f"Historical task instruction changed: {split}/{row['name']}" + ) + grader = (folder / "tests/grader.py").read_bytes() + if hashlib.sha256(grader).hexdigest() != GRADER_SHA256: + raise ValueError( + f"Expected corrected numeric-only Harbor grader: {folder}" + ) + target = destination / "datasets" / split / "tasks" / row["name"] + target.mkdir(parents=True, exist_ok=True) + for rel in ( + "instruction.md", + "task.toml", + "environment/Dockerfile", + "environment/pull_bucket.py", + "tests/grader.py", + "tests/test.sh", + ): + dst = target / rel + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(folder / rel, dst) + spec = tomllib.loads((target / "task.toml").read_text()) + spec["environment"]["memory_mb"] = 4096 + spec["environment"]["cpus"] = 1 + # Cluster user IDs may not exist inside Daytona's user namespace. + spec["environment"].setdefault("env", {})["TAR_OPTIONS"] = "--no-same-owner" + (target / "task.toml").write_text(tomli_w.dumps(spec)) + hashes = { + str(f.relative_to(destination / "datasets" / split)): hashlib.sha256( + f.read_bytes() + ).hexdigest() + for f in sorted(target.rglob("*")) + if f.is_file() + } + tasks.append({**row, "file_hashes": hashes}) + local = (sources or {}).get(split) + manifest = { + "source_dataset": repo, + "source_revision": None if local else revision, + "expected_hub_revision": revision, + "local_source": str(local) if local else None, + "task_count": len(tasks), + "tasks": tasks, + } + write_json(destination / f"{split}_manifest.json", manifest) + manifests[split] = manifest + train = {r["source"] for r in manifests["train"]["tasks"]} + test = {r["source"] for r in manifests["test"]["tasks"]} + notebooks = [ + {r.get("notebook") for r in manifests[s]["tasks"]} - {None} + for s in ("train", "test") + ] + if train & test or notebooks[0] & notebooks[1]: + raise ValueError("Train/test overlap") + write_json( + destination / "ready.json", + { + "train": len(train), + "test": len(test), + "grader_sha256": GRADER_SHA256, + "train_test_overlap": 0, + }, + ) + return manifests + + +if __name__ == "__main__": + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--output", type=Path, default=ROOT / "prepared") + p.add_argument("--train-source", type=Path) + p.add_argument("--test-source", type=Path) + a = p.parse_args() + result = prepare( + a.output, + {s: v for s, v in (("train", a.train_source), ("test", a.test_source)) if v}, + ) + print(json.dumps({s: m["task_count"] for s, m in result.items()})) diff --git a/05-multi-harness-rl/project.yaml b/05-multi-harness-rl/project.yaml new file mode 100644 index 00000000..d6e8ac76 --- /dev/null +++ b/05-multi-harness-rl/project.yaml @@ -0,0 +1,33 @@ +name: multi-harness-rl +title: "SmolDataEnvs: multi-harness RL" +tagline: "Train on SmolDataEnvs with SETA, native OpenCode or Harbor, using HF Jobs or Slurm." +order: 5 +status: wip +hub: + org: FineEnvs + collection: FineEnvs/smoldataenvs-multi-harness-rl-6abdfaaa8d74dacd481d5212 + datasets: + - FineEnvs/SmolDataEnvs-harbor-train + - FineEnvs/SmolDataEnvs-harbor-test + spaces: + - FineEnvs/smoldataenv-multi-harness-whitebox + - FineEnvs/smoldataenv-multi-harness-opencode + - FineEnvs/smoldataenv-multi-harness-harbor + trackio: + - FineEnvs/data-agent-training-comparison-trackio +envs: + - name: seta + summary: "Native bash/SETA tools driven by synchronous GRPO." + frameworks: + openenv: + verified: false + - name: native-opencode + summary: "Native OpenCode owns its loop; AsyncGRPO consumes captured tokens." + frameworks: + openenv: + verified: false + - name: harbor + summary: "Rotate four Harbor harnesses between task groups." + frameworks: + openenv: + verified: false diff --git a/05-multi-harness-rl/requirements.txt b/05-multi-harness-rl/requirements.txt new file mode 100644 index 00000000..3a32a1e3 --- /dev/null +++ b/05-multi-harness-rl/requirements.txt @@ -0,0 +1,16 @@ +# TRL and OpenEnv are installed from upstream main by jobs/install.sh. +vllm==0.25.1 +transformers @ git+https://github.com/huggingface/transformers.git@main +datasets +accelerate +bitsandbytes +kernels +trackio +harbor[daytona]>=0.22.0 +daytona>=0.203.0 +math-verify +tomli-w +pytest +# Compatible with Harbor's websocket dependencies and vLLM's Numba. +websockets>=15,<16 +numpy>=2.2,<2.4 diff --git a/05-multi-harness-rl/tests/conftest.py b/05-multi-harness-rl/tests/conftest.py new file mode 100644 index 00000000..80ced4be --- /dev/null +++ b/05-multi-harness-rl/tests/conftest.py @@ -0,0 +1,33 @@ +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +for mode in ("whitebox", "opencode", "harbor"): + sys.path.insert(0, str(ROOT / "envs" / mode)) + +import pytest + + +def pytest_addoption(parser): + parser.addoption( + "--contract", + action="store_true", + help="Run integration checks against installed TRL and OpenEnv", + ) + + +def pytest_configure(config): + config.addinivalue_line( + "markers", "contract: requires the tutorial's installed runtime" + ) + + +def pytest_collection_modifyitems(config, items): + if not config.getoption("--contract"): + for item in items: + if "contract" in item.keywords: + item.add_marker( + pytest.mark.skip( + reason="Use --contract after installing TRL/OpenEnv" + ) + ) diff --git a/05-multi-harness-rl/tests/test_contract.py b/05-multi-harness-rl/tests/test_contract.py new file mode 100644 index 00000000..06225fc2 --- /dev/null +++ b/05-multi-harness-rl/tests/test_contract.py @@ -0,0 +1,261 @@ +"""Run with --contract after installing the tutorial dependencies.""" + +import json +import sys +from pathlib import Path +from types import SimpleNamespace +from unittest.mock import Mock + +import pytest + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +pytestmark = pytest.mark.contract + + +@pytest.fixture +def trace(): + from openenv.core.harness import TrainingTrace, TrainingTurn + + return TrainingTrace( + turns=[ + TrainingTurn( + node_id="agent", + prompt_token_ids=[1, 2], + completion_token_ids=[3, 4, 5], + per_token_logps=[-0.1, -0.2, -0.3], + loss_mask=[0, 0, 1, 0, 1], + ), + TrainingTurn( + node_id="context", + prompt_token_ids=[1], + completion_token_ids=[2], + per_token_logps=[-0.3], + loss_mask=[0, 0], + ), + ] + ) + + +def test_native_trace_preserves_masks_and_passes_native_count_to_trl(trace): + from smoldataenv_opencode.environment import TaskSession + from trl.experimental.async_grpo.openenv_harness import _turns_from_training_trace + + from train.opencode import reward + + events = [{"type": "step_finish"}] + [ + { + "type": "tool_use", + "part": {"callID": str(i), "state": {"status": "completed"}}, + } + for i in range(6) + ] + native = Mock() + native.fetch_training_trace.return_value = trace + native.fetch_trace.return_value = "\n".join(json.dumps(e) for e in events) + result = TaskSession(native).fetch_training_trace() + assert [t.loss_mask for t in result.turns] == [[0, 0, 1, 0, 1], [0, 0]] + # Exercise the real typed consumer, including diagnostic metadata used by reward(). + records = _turns_from_training_trace(result) + entries = result.to_trace_entries() + assert records[0].output_mask == [1, 0, 1] + assert records[1].output_mask == [0] + assert reward(SimpleNamespace(env_reward=1.0, trace=entries)) == pytest.approx( + 1 + 1.5 / 21 + ) + + +@pytest.mark.parametrize( + "grade,calls,expected", + [(None, 4, None), (0, 4, 0), (1, None, 1), (1, 0, 1), (1, 15, 1.05)], +) +def test_both_async_rewards_match(grade, calls, expected): + from train.multi_harness import reward as harbor + from train.opencode import reward as native + + outcome = SimpleNamespace( + env_reward=grade, trace=[{"metadata": {"native_tool_calls": calls}}] + ) + assert native(outcome) == expected + assert harbor(outcome) == expected + + +def test_harbor_assignment_depends_on_prompt_not_seed(monkeypatch): + import smoldataenv_harbor.environment as tutorial + + rows = [{"instruction": str(i)} for i in range(8)] + args = SimpleNamespace( + server="unused", + data="prepared", + vllm_url="unused", + model="test", + trials="trials", + ) + monkeypatch.setattr(tutorial, "HarborEnv", Mock()) + constructor = Mock() + monkeypatch.setattr(tutorial, "TaskSession", constructor) + monkeypatch.setattr( + "httpx.get", + Mock(return_value=Mock(json=lambda: {"train": "prepared/datasets/train"})), + ) + factory = tutorial.TaskFactory(args, rows, sampling={"temperature": 0.8}) + for seed in (0, 17, 9000): + factory.create([{"role": "user", "content": "5"}], seed=seed) + assert constructor.call_args.kwargs["task_index"] == 5 + assert constructor.call_args.kwargs["harness"] == "claude-code" + assert constructor.call_args.kwargs["sampling"] == {"temperature": 0.8} + assert constructor.call_args.kwargs["reward_key"] == "correctness,reward" + + +@pytest.mark.parametrize("grade", [0.0, 1.0]) +def test_harbor_reads_both_published_grader_formats(grade): + from openenv.harbor.rollout import _pick_reward + + assert _pick_reward({"reward": grade}, "correctness,reward")[0] == grade + assert ( + _pick_reward({"correctness": grade, "submission": 1.0}, "correctness,reward")[0] + == grade + ) + + +def test_native_setup_failure_closes_sandbox(monkeypatch): + import smoldataenv_opencode.environment as tutorial + + monkeypatch.setenv("SANDBOX_VLLM_URL", "https://example.test") + monkeypatch.setenv("SANDBOX_VLLM_KEY", "test-key") + factory = tutorial.TaskFactory( + SimpleNamespace(model="test"), + [{"instruction": "task", "folder": "unused"}], + sampling={"temperature": 0.8}, + ) + session = Mock() + factory.factory = Mock() + factory.factory.create.return_value = session + monkeypatch.setattr( + tutorial, "stage_task", Mock(side_effect=RuntimeError("staging")) + ) + with pytest.raises(RuntimeError, match="staging"): + factory.create([{"role": "user", "content": "task"}], seed=999) + session.close.assert_called_once() + session.start_agent.assert_not_called() + + +def test_whitebox_reward_and_cleanup(monkeypatch): + import smoldataenv_whitebox.environment as tutorial + + env = tutorial.BashEnvironment() + sandbox = Mock() + env._sandbox, env._folder, env._calls = sandbox, Path("unused"), 15 + monkeypatch.setattr(tutorial, "grade_answer", lambda *args: 1.0) + assert env.get_reward() == 1.05 + sandbox.kill.assert_called_once() + assert env._sandbox is None + + +@pytest.mark.parametrize( + "module", ["train.opencode", "train.multi_harness", "train.whitebox"] +) +@pytest.mark.parametrize("model", ["LiquidAI/LFM2.5-2.6B", "Qwen/Qwen3.5-2B"]) +def test_nonthinking_prompt(module, model): + import importlib + + tokenizer = importlib.import_module(module).tokenizer_for(model) + prompt = tokenizer.apply_chat_template( + [{"role": "user", "content": "Say OK"}], + tokenize=False, + add_generation_prompt=True, + enable_thinking=False, + ) + tail = prompt.rsplit("<|im_start|>assistant", 1)[-1] + assert "" in tail and tail.count("") == tail.count("") + + +def test_whitebox_public_tool_schemas_are_valid(): + import inspect + + from transformers.utils import get_json_schema + + from train.whitebox import BashEnvironment + + env = BashEnvironment() + tools = [ + get_json_schema(method)["function"]["name"] + for name, method in inspect.getmembers(env, inspect.ismethod) + if not name.startswith("_") and name not in {"reset", "get_reward"} + ] + assert set(tools) == { + "bash", + "read", + "write", + "edit", + "ls", + "grep", + "glob", + "submit_solution", + } + + +def test_inference_proxy_blocks_unauthorized_and_admin_routes(): + from fastapi.testclient import TestClient + + from jobs.inference_proxy import create_app + + with TestClient(create_app("test-secret")) as client: + assert client.get("/v1/models").status_code == 401 + assert ( + client.get( + "/v1/models", headers={"Authorization": "Bearer wrong"} + ).status_code + == 401 + ) + assert ( + client.post( + "/v1/update_weights", headers={"Authorization": "Bearer test-secret"} + ).status_code + == 404 + ) + assert ( + client.post( + "/update_weights", headers={"Authorization": "Bearer test-secret"} + ).status_code + == 404 + ) + + +def test_public_config_signatures_accept_tutorial_keywords(): + import ast + import inspect + + from trl import GRPOConfig + from trl.experimental.async_grpo import AsyncGRPOConfig + + for name, cls in [ + ("whitebox", GRPOConfig), + ("opencode", AsyncGRPOConfig), + ("multi_harness", AsyncGRPOConfig), + ]: + tree = ast.parse((ROOT / "train" / f"{name}.py").read_text()) + calls = [ + node + for node in ast.walk(tree) + if isinstance(node, ast.Call) + and isinstance(node.func, ast.Name) + and node.func.id == cls.__name__ + ] + assert len(calls) == 1 + assert {kw.arg for kw in calls[0].keywords} <= set( + inspect.signature(cls).parameters + ) + + +def test_whitebox_does_not_count_submission(monkeypatch): + import time + + from train.whitebox import BashEnvironment + + env = BashEnvironment() + env._sandbox, env._deadline, env._calls = Mock(), time.monotonic() + 60, 3 + env.submit_solution("42") + assert env._calls == 3 + with pytest.raises(RuntimeError, match="finished"): + env.bash("echo retry") diff --git a/05-multi-harness-rl/tests/test_data_and_eval.py b/05-multi-harness-rl/tests/test_data_and_eval.py new file mode 100644 index 00000000..663641f0 --- /dev/null +++ b/05-multi-harness-rl/tests/test_data_and_eval.py @@ -0,0 +1,62 @@ +import json +import sys +from pathlib import Path +from types import SimpleNamespace + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +from smoldataenv_whitebox.tasks import stage_task + +from eval.evaluate import summarize, write_json + + +def test_fixed_split_is_disjoint_and_keeps_the_training_mix(): + train = json.loads((ROOT / "data/train.json").read_text()) + test = json.loads((ROOT / "data/test.json").read_text()) + assert len(train) == 1000 and len(test) == 250 + assert sum(row["difficulty"] == "medium" for row in train) == 400 + assert sum(row["difficulty"] == "hard" for row in train) == 600 + for key in ("source", "notebook", "instruction_sha256"): + assert not {t[key] for t in train} & {t[key] for t in test} + + +def test_ungraded_is_excluded_from_pass_at_one_but_visible_in_coverage(): + result = summarize( + [ + {"correctness": 1, "tool_calls": 6}, + {"correctness": 0}, + {"correctness": None}, + ], + 4, + ) + assert result["pass_at_1_observed"] == 0.5 + assert result["coverage"] == 0.5 + assert result["mean_tool_calls"] == 6 + assert result["tool_count_coverage"] == 1 + assert summarize([], 4)["pass_at_1_observed"] is None + + +def test_staging_does_not_upload_gold_or_leave_tokens_in_files(tmp_path, monkeypatch): + monkeypatch.setenv("HF_TOKEN", "test-download-token") + (tmp_path / "environment").mkdir() + (tmp_path / "environment/pull_bucket.py").write_text("print('download')") + (tmp_path / "task.toml").write_text( + '[environment.env]\nHF_TOKEN="${HF_TOKEN}"\n[verifier.env]\nEXPECTED_ANSWER="hidden-gold"\n' + ) + writes, commands = [], [] + sandbox = SimpleNamespace( + write_text=lambda *x: writes.append(x), + exec=lambda *x, **kw: commands.append((x, kw)) or SimpleNamespace(exit_code=0), + ) + stage_task(sandbox, tmp_path) + assert "hidden-gold" not in str(writes) + str(commands) + assert "test-download-token" not in str(writes) + assert commands[0][1]["envs"] == {"HF_TOKEN": "test-download-token"} + + +def test_atomic_results_are_readable_after_replacement(tmp_path): + target = tmp_path / "pairs" / "a.json" + write_json(target, {"correctness": None}) + write_json(target, {"correctness": 1}) + assert json.loads(target.read_text())["correctness"] == 1 + assert not target.with_suffix(".tmp").exists() diff --git a/05-multi-harness-rl/tests/test_environment_servers.py b/05-multi-harness-rl/tests/test_environment_servers.py new file mode 100644 index 00000000..8fd07f6e --- /dev/null +++ b/05-multi-harness-rl/tests/test_environment_servers.py @@ -0,0 +1,150 @@ +"""Verify the deployment boundary without paying for a sandbox.""" + +import json +from unittest.mock import Mock + +import pytest + +pytestmark = pytest.mark.contract + + +def exchange(ws, kind, data): + ws.send_json({"type": kind, "data": data}) + message = ws.receive_json() + assert message["type"] == "observation", message + return message["data"]["observation"] + + +def tool(ws, name, **arguments): + observation = exchange( + ws, "step", {"type": "call_tool", "tool_name": name, "arguments": arguments} + ) + assert not observation.get("error"), observation + result = observation["result"] + if isinstance(result, str): + return json.loads(result) + return result.get("data", result) + + +def test_whitebox_websocket_retains_episode_and_closes_sandbox(monkeypatch): + monkeypatch.setenv("ENABLE_WEB_INTERFACE", "false") + from fastapi.testclient import TestClient + from smoldataenv_whitebox import server + + monkeypatch.setattr( + server, "task_by_name", lambda split, name: {"folder": "/test/tasks/demo"} + ) + monkeypatch.setattr( + server.BashEnvironment, "reset", lambda self, folder: "Solve demo" + ) + monkeypatch.setattr( + server.WhiteboxEnvironment, + "get_task", + lambda self, split, index: {"name": "demo"}, + ) + sandbox = Mock() + + def grade(self): + self._calls, self._correctness = 15, 1.0 + self._sandbox = sandbox + self._close() + return 1.05 + + monkeypatch.setattr(server.BashEnvironment, "get_reward", grade) + with TestClient(server.app) as client: + assert client.get("/health").status_code == 200 + with client.websocket_connect("/ws") as ws: + initial = exchange(ws, "reset", {"split": "test", "task_name": "demo"}) + assert initial["metadata"]["instruction"] == "Solve demo" + selected = tool(ws, "start_task", split="test", index=0) + assert selected["instruction"] == "Solve demo" + grade = tool(ws, "grade") + assert grade == {"reward": 1.05, "correctness": 1.0, "tool_calls": 15} + sandbox.kill.assert_called_once() + + +def test_native_server_exports_typed_trace_and_closes_session(monkeypatch): + monkeypatch.setenv("ENABLE_WEB_INTERFACE", "false") + from fastapi.testclient import TestClient + from openenv.core.harness import TrainingTrace, TrainingTurn + from smoldataenv_opencode import server + + trace = TrainingTrace( + turns=[ + TrainingTurn( + node_id="agent", + prompt_token_ids=[1], + completion_token_ids=[2, 3], + per_token_logps=[-0.1, -0.2], + loss_mask=[0, 1, 0], + ) + ] + ) + session = Mock() + session.fetch_training_trace.return_value = trace + session.verify.return_value.env_reward = 1.0 + monkeypatch.setattr( + server, + "TaskFactory", + Mock(return_value=Mock(create=Mock(return_value=session))), + ) + monkeypatch.setattr( + server, "task_by_name", lambda split, name: {"instruction": "demo"} + ) + with TestClient(server.app) as client: + assert client.get("/health").status_code == 200 + with client.websocket_connect("/ws") as ws: + exchange(ws, "reset", {}) + result = tool( + ws, + "run_rollout", + split="test", + task_name="demo", + model="model", + sampling={"temperature": 0.8}, + ) + exported = TrainingTrace.model_validate(result["training_trace"]) + assert exported.turns[0].loss_mask == [0, 1, 0] + assert result["correctness"] == 1.0 + session.close.assert_called_once() + + +def test_remote_native_capture_failure_stays_a_contract_error(): + from smoldataenv_opencode.client import RemoteSession + + session = RemoteSession.__new__(RemoteSession) + session.result = {"capture_error": "mask does not match sampled tokens"} + with pytest.raises(ValueError, match="mask does not match"): + session.fetch_training_trace() + + +def test_harbor_metadata_routes_precede_root_ui(monkeypatch, tmp_path): + monkeypatch.setenv("ENABLE_WEB_INTERFACE", "false") + monkeypatch.setenv("SPACE_HOST", "smoke.hf.space") + monkeypatch.setenv("OPENENV_DATASETS", "") + monkeypatch.setenv("OPENENV_LLM_URL", "") + monkeypatch.setenv("OPENENV_HARBOR_TRIALS_DIR", str(tmp_path)) + from fastapi.testclient import TestClient + from smoldataenv_harbor import server + + trajectory = tmp_path / "trial" / "agent" / "trajectory.json" + trajectory.parent.mkdir(parents=True) + trajectory.write_text( + json.dumps( + { + "schema_version": "ATIF-v1.0", + "steps": [ + {"source": "agent", "tool_calls": [{"tool_call_id": "call-1"}]} + ], + } + ) + ) + with TestClient(server.app) as client: + assert client.get("/health").status_code == 200 + assert "/{env_name}/task" in client.get("/openapi.json").json()["paths"] + splits = client.get("/smoldataenv/splits") + assert splits.status_code == 200 + assert set(splits.json()) == {"train", "test"} + counted = client.get("/smoldataenv/trials/trial/tool-count") + assert counted.status_code == 200 + assert counted.json() == {"native_tool_calls": 1} diff --git a/05-multi-harness-rl/tests/test_evaluation.py b/05-multi-harness-rl/tests/test_evaluation.py new file mode 100644 index 00000000..ae24b0eb --- /dev/null +++ b/05-multi-harness-rl/tests/test_evaluation.py @@ -0,0 +1,99 @@ +"""Check complete evaluations and retry behavior without model or sandbox services.""" + +import json +import sys +from pathlib import Path +from types import SimpleNamespace +from unittest.mock import Mock + +import pytest + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from eval import evaluate + + +@pytest.mark.parametrize("mode", ["whitebox", "blackbox"]) +def test_evaluation_keeps_grades_and_retries_only_missing_pairs( + mode, tmp_path, monkeypatch +): + tasks = [ + {"name": "a", "difficulty": "medium", "instruction": "first task"}, + {"name": "b", "difficulty": "hard", "instruction": "second task"}, + ] + monkeypatch.setattr(evaluate, "load_tasks", lambda *args: tasks) + logger = Mock() + monkeypatch.setitem( + sys.modules, "trackio", SimpleNamespace(init=lambda **kw: logger) + ) + harnesses = ["whitebox"] if mode == "whitebox" else list(evaluate.HARNESSES) + missing = ("b", harnesses[-1]) + calls = [] + fail = True + + def episode(args, index, task, harness): + pair = (task["name"], harness) + calls.append(pair) + if fail and pair == missing: + raise RuntimeError("Sandbox temporarily unavailable") + return { + "correctness": float(index), + "tool_calls": 15, + "generated_tokens": 10, + "prompt_tokens": 20, + } + + selected = "whitebox_episode" if mode == "whitebox" else "harbor_episode" + other = "harbor_episode" if mode == "whitebox" else "whitebox_episode" + monkeypatch.setattr(evaluate, selected, episode) + monkeypatch.setattr( + evaluate, other, Mock(side_effect=AssertionError("Wrong evaluation interface")) + ) + argv = [ + "evaluate.py", + "--mode", + mode, + "--checkpoint", + "checkpoint-100", + "--tasks", + "2", + "--concurrency", + "4", + "--output", + str(tmp_path), + ] + monkeypatch.setattr(sys, "argv", argv) + with pytest.raises(SystemExit) as error: + evaluate.main() + assert error.value.code == 2 + partial = json.loads((tmp_path / "summary.json").read_text()) + expected = 2 * len(harnesses) + assert partial["graded"] == expected - 1 + assert partial["expected"] == expected + assert set(partial["harnesses"]) == set(harnesses) + saved_wrong = tmp_path / "pairs" / f"a--{harnesses[0]}.json" + original = saved_wrong.read_bytes() + + fail = False + calls.clear() + evaluate.main() + assert calls == [missing] + assert saved_wrong.read_bytes() == original + complete = json.loads((tmp_path / "summary.json").read_text()) + assert complete["graded"] == expected + assert complete["coverage"] == 1.0 + assert complete["pass_at_1_observed"] == 0.5 + assert complete["combined_reward"] == pytest.approx(0.525) + assert complete["mean_generated_tokens"] == 10 + assert complete["mean_prompt_tokens"] == 20 + assert complete["difficulty"]["medium"]["pass_at_1_observed"] == 0 + assert complete["difficulty"]["hard"]["pass_at_1_observed"] == 1 + logger.log.assert_called() + + calls.clear() + evaluate.main() + assert not calls + argv[argv.index("checkpoint-100")] = "checkpoint-200" + with pytest.raises(ValueError, match="different evaluation"): + evaluate.main() + assert not calls diff --git a/05-multi-harness-rl/tests/test_launch.py b/05-multi-harness-rl/tests/test_launch.py new file mode 100644 index 00000000..43006a0f --- /dev/null +++ b/05-multi-harness-rl/tests/test_launch.py @@ -0,0 +1,76 @@ +"""Exercise service wiring without allocating GPUs or starting real subprocesses.""" + +import sys +from pathlib import Path +from types import SimpleNamespace +from unittest.mock import Mock + +import pytest + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + + +@pytest.mark.parametrize("action", ["train", "eval"]) +@pytest.mark.parametrize("mode", ["whitebox", "opencode", "multi_harness"]) +def test_each_mode_gets_the_right_services_and_gpu(tmp_path, monkeypatch, action, mode): + pytest.importorskip("httpx") + from jobs import run + + monkeypatch.chdir(tmp_path) + monkeypatch.setattr(run, "ROOT", tmp_path) + (tmp_path / "prepared").mkdir() + (tmp_path / "prepared/ready.json").write_text("{}") + monkeypatch.setattr( + sys, "argv", ["run.py", action, "--mode", mode, "--output", "outputs"] + ) + monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "2,3") + monkeypatch.setattr(run.subprocess, "run", Mock()) + monkeypatch.setattr(run.signal, "signal", Mock()) + monkeypatch.setattr(run.os, "killpg", Mock()) + monkeypatch.setattr( + run.httpx, + "get", + Mock( + return_value=SimpleNamespace(status_code=200, raise_for_status=lambda: None) + ), + ) + calls = [] + + def spawn(command, **kwargs): + calls.append((command, kwargs["env"])) + is_worker = command[1] == "-m" and ( + command[2].startswith("train.") or command[2] == "eval.evaluate" + ) + if command[1] == "jobs/inference_proxy.py": + Path(command[command.index("--output") + 1]).write_text( + "https://sandbox-inference.test" + ) + return SimpleNamespace( + poll=lambda: 0 if is_worker else None, + returncode=0, + pid=1234, + wait=lambda **kw: 0, + ) + + monkeypatch.setattr(run.subprocess, "Popen", spawn) + run.main() + engine, worker = calls[0], calls[-1] + assert engine[1]["CUDA_VISIBLE_DEVICES"] == ("2" if action == "train" else "2,3") + assert worker[1]["CUDA_VISIBLE_DEVICES"] == "3" + engine_port = engine[0][engine[0].index("--port") + 1] + assert ( + worker[0][worker[0].index("--vllm-url") + 1] + == f"http://127.0.0.1:{engine_port}" + ) + has_harbor = any("smoldataenv_harbor.server:app" in command for command, _ in calls) + assert has_harbor == ( + mode != "whitebox" and (mode == "multi_harness" or action == "eval") + ) + assert any("jobs/inference_proxy.py" in command for command, _ in calls) == ( + action == "train" and mode == "opencode" + ) + assert worker[0][2] == (f"train.{mode}" if action == "train" else "eval.evaluate") + if action == "eval": + assert worker[0][worker[0].index("--mode") + 1] == ( + "whitebox" if mode == "whitebox" else "blackbox" + ) diff --git a/05-multi-harness-rl/tests/test_setup.py b/05-multi-harness-rl/tests/test_setup.py new file mode 100644 index 00000000..f58eee67 --- /dev/null +++ b/05-multi-harness-rl/tests/test_setup.py @@ -0,0 +1,23 @@ +import builtins +import runpy +import sys +from pathlib import Path + +import pytest + + +@pytest.mark.contract +@pytest.mark.parametrize("mode", ["whitebox", "multi_harness"]) +def test_setup_does_not_require_native_opencode(monkeypatch, mode): + original_import = builtins.__import__ + + def without_opencode(name, *args, **kwargs): + if name == "opencode_env" or name.startswith("opencode_env."): + raise ModuleNotFoundError("Native OpenCode is unavailable") + return original_import(name, *args, **kwargs) + + monkeypatch.setattr(builtins, "__import__", without_opencode) + monkeypatch.setattr(sys, "argv", ["check_setup.py", "--mode", mode]) + runpy.run_path( + str(Path(__file__).parents[1] / "check_setup.py"), run_name="__main__" + ) diff --git a/05-multi-harness-rl/tests/test_whitebox_playground.py b/05-multi-harness-rl/tests/test_whitebox_playground.py new file mode 100644 index 00000000..f853c6e2 --- /dev/null +++ b/05-multi-harness-rl/tests/test_whitebox_playground.py @@ -0,0 +1,89 @@ +"""Browser sessions must remain independent and use the training reward unchanged.""" + +from copy import deepcopy +from unittest.mock import Mock + +import pytest + +pytestmark = pytest.mark.contract + + +@pytest.fixture +def playground(monkeypatch): + from smoldataenv_whitebox import playground + + monkeypatch.setattr( + playground, "task_by_name", lambda split, name: {"folder": "/tasks/" + name} + ) + return playground + + +def fake_episode(state, reward=1.05): + episode = Mock(_calls=0, _correctness=1.0) + episode.get_reward.return_value = reward + episode.bash.return_value = "two rows" + state.environment = episode + return episode + + +def test_browsers_do_not_share_sandboxes_or_history(playground): + initial = playground.PlaygroundSession() + first, second = deepcopy(initial), deepcopy(initial) + a, b = fake_episode(first), fake_episode(second) + first.start("test", "one") + second.start("test", "two") + first.run("ls") + assert len(first.history) == 1 and second.history == [] + first.grade("answer") + assert first.reward == 1.05 and first.correctness == 1.0 + assert not first.active and second.active + assert b._close.call_count == 1 # Only its own initial reset. + a.submit_solution.assert_called_once_with("answer") + b.submit_solution.assert_not_called() + + +def test_restart_and_state_expiry_close_sandbox(playground): + state = playground.PlaygroundSession() + episode = fake_episode(state) + state.start("test", "one") + state.run("ls") + state.start("test", "two") + assert episode._close.call_count == 2 + assert not state.history and state.reward is None + playground.close_session(state) + assert episode._close.call_count == 3 and not state.active + + +def test_ungraded_attempt_does_not_show_wrong_answer(playground): + state = playground.PlaygroundSession() + fake_episode(state, float("nan")) + state.start("test", "one") + state.grade("answer") + assert state.reward is None and state.correctness is None + assert "unscored" in state.status and not state.active + + +def test_failed_submission_still_releases_sandbox(playground): + state = playground.PlaygroundSession() + episode = fake_episode(state) + state.start("test", "one") + episode.submit_solution.side_effect = RuntimeError("expired") + with pytest.raises(RuntimeError): + state.grade("answer") + assert not state.active and episode._close.call_count == 2 + + +def test_browser_trace_escapes_tool_output(playground): + from smoldataenv_whitebox.ui import trace + + state = playground.PlaygroundSession() + state.history = [ + { + "command": "", + "output": "", + "seconds": 0.1, + } + ] + rendered = trace(state) + assert " +``` + +### Step 4: Integrate in MDX + +Import and use the `HtmlEmbed` component: + +```mdx +import HtmlEmbed from '../../components/HtmlEmbed.astro'; + + +``` + +#### HtmlEmbed props + +| Prop | Type | Description | +|------|------|-------------| +| `src` | string | Path to HTML file in `embeds/` (required) | +| `title` | string | Title above the card | +| `desc` | string | Description below (supports HTML) | +| `frameless` | boolean | Removes card background/border | +| `wide` | boolean | Wide layout (~1100px) | +| `data` | string or string[] | Path(s) to data files | +| `config` | object | JSON config passed via `data-config` attribute | + +#### Usage examples + +```mdx + + + + + + + + + + + + + + +``` + +## Key conventions (quick reference) + +Full details in the directives file. The critical ones: + +1. **Colors**: Use `window.ColorPalettes.getColors('categorical', n)` — never hardcode palettes +2. **CSS variables**: `--text-color`, `--surface-bg`, `--border-color`, `--axis-color`, `--tick-color`, `--grid-color` +3. **Dark mode**: Check `document.documentElement.getAttribute('data-theme') === 'dark'` +4. **Mount guard**: Always set `container.dataset.mounted = 'true'` +5. **Data loading**: Try `/data/` first, then `./assets/data/` — use `fetchFirstAvailable()` +6. **Responsiveness**: `ResizeObserver` on container, recompute on resize +7. **Legend**: HTML-based, title "Legend", swatch 14x14px +8. **Controls**: HTML only (no SVG UI), selects labeled "Metric" when applicable +9. **Tooltip**: Single `.d3-tooltip` absolutely positioned inside container +10. **No globals**: Everything in IIFE, nothing on `window` + +## Data files + +- Store data in: `app/src/content/assets/data/` +- Served from: `/data/` (public) at build time +- Formats: CSV (preferred for tabular), JSON (for nested/hierarchical) + +## Post-creation checklist + +After creating the embed, verify against the **Agent Checklist** (section 14.1) and **Definition of Done** (section 14.2) in [directives.md](directives.md). diff --git a/content/articles/multi-harness-rl/.ai/skills/create-html-embed/directives.md b/content/articles/multi-harness-rl/.ai/skills/create-html-embed/directives.md new file mode 100644 index 00000000..329833c8 --- /dev/null +++ b/content/articles/multi-harness-rl/.ai/skills/create-html-embed/directives.md @@ -0,0 +1,504 @@ +## Embed Chart Authoring Guidelines + +### Quickstart (TL;DR) +- Create a single self-contained HTML fragment: root div + scoped style + IIFE script. +- Draw marks/axes in SVG; render UI (legend and controls) in HTML. +- Place legend and controls BELOW the chart (header appended after the chart). Include a legend title "Legend" and a select labeled "Metric" when relevant. +- Load data from public `/data` first, then fall back to `assets/data`. +- Use `window.ColorPalettes` for colors; stick to CSS variables for theming. + +Minimal header markup: +```html +
+
Legend
+
+ +
+
+
+ + +
+ +
+``` + +See also: `d3-line-simple.html`, `d3-line-quad.html`, `d3-benchmark.html`. + +Authoring rules for creating a new interactive chart as a single self-contained `.html` file under `src/content/embeds/`. These conventions are derived from `d3-bar.html`, `d3-comparison.html`, `d3-neural.html`, `d3-line.html`, and `d3-pie.html`. + +### A) Colors & palettes (MANDATORY) +- Always obtain color arrays from `window.ColorPalettes`; do not hardcode palettes. +- Use the categorical/sequential/diverging helpers and the current primary color. +- If you change `--primary-color` dynamically, call `window.ColorPalettes.refresh()` so listeners update. + +Usage: +```js +// Usage (with explicit counts) +const cat = window.ColorPalettes.getColors('categorical', 8); +const seq = window.ColorPalettes.getColors('sequential', 8); +const div = window.ColorPalettes.getColors('diverging', 7); + +// For current primary color string +const primaryHex = window.ColorPalettes.getPrimary(); + +// If you change --primary-color dynamically, call refresh to notify listeners +document.documentElement.style.setProperty('--primary-color', '#6D4AFF'); +window.ColorPalettes.refresh(); +``` + +Notes: +- Keep chart accents (lines, markers, selection) aligned with `--primary-color`. +- Prefer CSS variables for fills/strokes when possible; derive series colors via `ColorPalettes`. +- Provide a graceful fallback to CSS variables if `window.ColorPalettes` is unavailable. + +### B) Layout & form elements (HTML-only) +- All UI controls (labels, selects, sliders, buttons, toggles) must be plain HTML inside the root container. +- Do not draw controls with SVG; style them consistently (rounded 8px, custom caret, focus ring). +- Use `