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Contributing to ART

Clone the repository:

git clone https://github.com/OpenPipe/ART.git
cd ART

Install the dependencies:

uv sync

Code Formatting and Linting

This project uses ruff for both code formatting and linting. Before submitting a pull request, please ensure your code passes all quality checks:

# Run all code quality checks (formatting, linting, and dependency sync)
./scripts/run_checks.sh

# Automatically fix any issues that can be fixed
./scripts/run_checks.sh --fix

The run_checks.sh script will:

  1. Check code formatting with ruff
  2. Check for linting issues with ruff
  3. Verify that uv.lock is in sync with pyproject.toml

These checks are automatically run in CI for all pull requests. If your PR fails these checks, simply run ./scripts/run_checks.sh --fix locally and commit the changes.

Release Process

To create a new release:

  1. Review merged PRs since the last release:

    • Go to the pull requests page
    • Review PRs merged since the last release to understand what changed
    • Note any breaking changes, new features, or important bug fixes

  2. Create a draft release:

    • Go to Actions
    • Click "Run workflow"
    • Select the version bump type:
      • patch: Bug fixes and minor changes (0.3.13 → 0.3.14)
      • minor: New features and non-breaking changes (0.3.13 → 0.4.0)
      • major: Breaking changes (0.3.13 → 1.0.0)

  3. Edit the draft release notes:

    • Go to the releases page
    • Click "Edit" on the draft release
    • Add release highlights, breaking changes, and curated changelog
    • The auto-generated PR list provides a starting point, but manual curation improves clarity

  4. Finalize the release:

    • Review and merge the automatically created release PR
    • This will automatically:
      • Create the git tag
      • Publish the curated release notes
      • Build and publish the package to PyPI

Then follow the SkyPilot or Local Training instructions below.

SkyPilot

Copy the .env.example file to .env and set the environment variables:

cp .env.example .env

Ensure you have a valid SkyPilot cloud available:

uv run sky check

Launch a cluster:

./scripts/launch-cluster.sh # you can pass any sky launch arguments here

Make sure you are on a machine with at least one H100 or A100-80GB GPU. Machines equipped with lower-end GPUs may work, but training will be slower.

You can now SSH into the art cluster, using either VSCode or the command line.

Connecting via Command Line

Simply run:

ssh art

Connecting via VSCode

  1. Install the Remote-SSH extension on your local machine

    • Open the extensions view by clicking on the Extensions icon in the Activity Bar on the left.
    • Search for "Remote-SSH" and install it.

  2. Configure default extensions for your remote host

    • In your VSCode settings, find "Remote.SSH: Default Extensions"
    • Add the following extensions:
      • ms-python.python
      • ms-toolsai.jupyter
      • eamodio.gitlens
      • charliermarsh.ruff

  3. Connect to the host

    • Open the command palette and run "Remote-SSH: Connect to Host..."
    • Select art

  4. Set up the host

    • Click "Open Folder"
      • Select "sky_workdir"
      • Click OK

  5. Run a notebook

    • Find 2048.ipynb and run it!

"2048" example

Now you can run the "2048" example in /examples/2048/2048.ipynb.

It has been tested with the Qwen/Qwen2.5-14B-Instruct model on a 1xH100 instance.

You can monitor training progress with Weights & Biases at https://wandb.ai/your-wandb-organization/agent-reinforcement-training.

You should see immediate improvement in val/reward after one step.

If you run into any issues, the training output is set to maximum verbosity. Copying the outputs such as the vLLM or torchtune logs, or copying/screenshotting the plotted packed tensors, may help me debug the issue.

Cleaning Up

When you're done, you can tear down the cluster with:

uv run sky down art