PROJECT_NAME

A template for developing an ML experimentation framework.

Setup

The first think you should do is run the post-install.sh script to configure the name of the project; this script will change every instance of PROJECT_NAME to be whatever you pass as an argument. In particular, this will change the name of the project in the pyproject.toml file and the name of the associated Conda environment.

bash scripts/post-install.sh my_project

Next, you should figure out what tensor backend you want. By default, the pyproject.toml won't install any, but it provides optional dependency groups for torch and flax, as well as a group for huggingface. You can either keep these all as optional dependencies and just decide which to use, or move the appropriate block into the main dependencies section.

Local Installation

All python dependencies are provided in pyproject.toml. Install using uv:

1. curl -LsSf https://astral.sh/uv/install.sh | sh
2. uv venv
3. source .venv/bin/activate
4. uv pip install -e .

To generate a set of locked dependencies, run

uv pip compile pyproject.toml -o requirements.txt

If you need to use Conda instead, you can do so by creating a new environment from the provided environment.yml file, which will just wrap the pyproject.toml file with pip:

conda env create -f environment.yml

Docker / Devcontainer

There's a built-in Dockerfile and devcontainer configuration to make running the project in a remote container from VSCode easy. Just install the remote containers extension and open the project in a container.