Make Python-models compatible with Chap

CHAP supports multiple environment options for Python models, including Docker, MLflow/Conda, and uv.

Environment options

Docker environment

Use docker_env to specify a Docker image:

docker_env:
  image: python:3.11

MLflow/Conda environment

Use python_env to specify a conda/pip environment file (uses MLflow to manage):

python_env: python_env.yml

uv environment

Use uv_env to specify a pyproject.toml for uv-managed environments. This is useful for models that use uv for dependency management:

uv_env: pyproject.toml

Commands will be executed via uv run, which automatically handles the virtual environment. Make sure your model directory contains a valid pyproject.toml with dependencies specified. See the example uv model for a complete example.

Example MLproject file with uv:

name: my_model
uv_env: pyproject.toml
entry_points:
  train:
    parameters:
      train_data: str
      model: str
    command: "python main.py train {train_data} {model}"
  predict:
    parameters:
      model: str
      historic_data: str
      future_data: str
      out_file: str
    command: "python main.py predict {model} {historic_data} {future_data} {out_file}"

Next steps

• For prediction length constraints and configurable model parameters, see Additional Configuration