ml-std

The standard library for Brunost ML and RL tasks.

One pinned set of packages. The Judge's ML sandbox image installs exactly one release of ml-std; a contestant installs the same release and has the Judge's environment on their own machine:

pip install ml-std==2.0.0
python -m ml_std check      # "environment matches the Judge runtime"

On Linux x86_64 add --extra-index-url https://download.pytorch.org/whl/cpu to get the CPU build of PyTorch (the Judge runs CPU workers; the default PyPI wheel drags in CUDA libraries). macOS and aarch64 wheels are CPU-only.

Nothing else is importable inside the Judge (no network, no pip), so this package's dependency list is the complete answer to "what can I use?".

What is in it

The contest environment of IOAI 2025 (Python 3.12.7, requirements.txt), reduced to the ML-relevant packages and pinned at exactly their versions, so training for Brunost is training for the Olympiad:

Deviations from IOAI 2025, both forced by Python 3.13 wheels: scipy 1.14.1 (IOAI 1.13.1) and sentencepiece 0.2.1 (IOAI 0.2.0). Left out on purpose: GPU-only packages (bitsandbytes, diffusers), notebooks (jupyter, ipykernel) and API clients, which are not part of a judged solution.

Releasing a new runtime

1. Change the pins in pyproject.toml, bump version there and __version__.
2. python -m ml_std catalog > src/ml_std/runtimes/<version>.json (keep the old files: earlier runtimes stay known).
3. Set RUNTIME to the new runtime name (python-3.13-ml-v2, ...).
4. Build and publish the wheel; build the Judge image with ML_STD_SPEC=ml-std==<version> and map the new runtime in BRUNOST_JUDGE_SANDBOX_IMAGES.

Tasks already published keep their runtime; new tasks default to the newest. python -m ml_std runtimes prints every runtime this release knows about, which is what the Judge's GET /v1/runtimes serves to the task editor.