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.

• 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.

• Visual Studio Code
• Remote - Containers extension
• Docker

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

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.

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

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:3.8.15 ./pip-compile.sh
docker cp $NAME:/tmp/requirements.txt .devcontainer/requirements.txt
docker rm -f -v $NAME