M3LEO

Installation

Install Miniconda from here and then run the following commands to create the m3leo-env environment:

conda env create -f environment.yml

conda activate m3leo-env

Next, install the package:

pip install -e .

or if you want development dependencies as well:

pip install -e .[dev]

Optional, but highly recommended

Install pre-commit by running the following command to automatically run code formatting and linting before each commit:

pre-commit install

If using pre-commit, each time you commit, your code will be formatted, linted, checked for imports, merge conflicts, and more. If any of these checks fail, the commit will be aborted.

Adding a new package

To add a new package to the environment, open pyproject.toml file and add the package name to "dependencies" list. Then, run the following command to install the new package:

pip install -e . # or .[dev]

Cache data

Data will be stored in .cache inside the folder from where you run the script. If you want to change the cache_dir location, you can set the environment variable CACHE_DIR to the desired location. To do so, create a .env file and add inside it the following line:

CACHE_DIR=/path/to/cache/dir

Running train.py

Our training script is fully hydra integrated. To run experiments, set up configuration files following the example provided under <configs/example-config>.

The training script can then be run using

python train.py --config-path /path/to/config