Defining the MLproject environment for your model

MLproject environment

In the MLproject file you just created, you need to specify one environment Chap will run your model within. The available options are uv (Python), renv environment (for R models) and docker. Below it is described how you set up each environment.

Option 1) uv environment (Python)

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}"

Option 2) R enviroment

We reccomend to use renv to handle and specify dependenices for your R-models (link) When using renv, you only need to put this line in the ML-project file:

renv_env: renv.lock

Example MLproject file with renv

name: my_model
renv_env: renv.lock
entry_points:
  train:
    parameters:
      train_data: str
      model: str
    command: "Rscript train.R {train_data} {model}"
  predict:
    parameters:
      model: str
      historic_data: str
      future_data: str
      out_file: str
    command: "Rscript predict.R {model} {historic_data} {future_data} {out_file}"

A brief introduction to renv commands

Initialize renv in your R project:
```
renv::init()
```

Install your required packages:

renv::install("dplyr")
renv::install("argparser")
# ... other packages

Create the lockfile:
```
renv::snapshot()
```

This creates renv.lock with exact versions of all dependencies, ensuring reproducible environments.

See the minimalist R model example for a complete working example.

Option 3) Docker environment

When UV or renv is not sufficent to reliably reproduce the environment, its also possible to spesificy a dokcer-env to run the model within.

Use docker_env to specify a Docker image:

For example:

docker_env:
  image: ghcr.io/dhis2-chap/docker_r_inla