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README.md

MLflow development container (experimental)

This directory contains a set of files to set up a reproducible and disposable development environment for MLflow in Visual Studio Code using the Remote - Containers extension.

Supported features

  • Pre-installed tools/packages required for MLflow development.
  • Pre-configured VSCode settings and extensions for automatic code formatting and lint check.
  • Pre-commit hooks to avoid pushing commits that won't pass the CI checks.

Prerequisites

Getting started

  1. Build the devcontainer image.

    # This command may take a while to finish
DOCKER_BUILDKIT=1 docker build -f .devcontainer/Dockerfile.devcontainer -t ghcr.io/mlflow/mlflow-devcontainer .

# Test the image
docker run --rm -it ghcr.io/mlflow/mlflow-devcontainer zsh

# Alternatively, you can pull the pre-built image from GitHub Container Registry,
# but a GitHub personal access token is required to authenticate to ghcr.io:
echo <GITHUB_TOKEN> | docker login ghcr.io -u <GITHUB_USERNAME> --password-stdin
docker pull ghcr.io/mlflow/mlflow-devcontainer

  2. Open the MLflow repository on VSCode.

  3. Press Ctrl/Cmd+Shift+P to launch the command palette.

  4. Select Remote-Containers: Reopen in Container.

  5. Once the devcontainer is up and running, launch the command palette again.

  6. Select Terminal: Create New Terminal.

  7. Run the following commands and make sure they run successfully:

pytest tests/test_version.py

Developing in GitHub Codespaces

You can create the same development environment as your local devcontainer in a web browser with just a few clicks using GitHub Codespaces. The instructions in Creating a codespace cover how to set up a codespace.

Limitations

The following tools/packages are NOT pre-installed to avoid increasing the image size. They can be manually installed after launching the devcontainer if necessary.

  • Python ML packages such as tensorflow
  • R
  • Docker
  • conda

How to update requirements.txt

NAME="mlflow-$(uuidgen)"
docker run \
   --name $NAME \
   -w /mlflow \
   -v $(pwd)/requirements:/mlflow/requirements:ro \
   -v $(pwd)/.devcontainer/pip-compile.sh:/mlflow/pip-compile.sh \
   python:3.8.19 ./pip-compile.sh
docker cp $NAME:/tmp/requirements.txt .devcontainer/requirements.txt
docker rm -f -v $NAME