Tarteel-ML is an open-source project, which means you can help us make it better!
Check out the Issues tab to see open issues.
You're welcome to start with those issues that are tagged with Good First Issue,
tackle other issues, or create your own issues.
Thank you for considering contributing to Tarteel-ML! Here are step-by-step instructions.
Before starting, you will need to install a few dependencies. We use the Anaconda Python distribution for dependency management, and our instructions assume you use it to. You can download it at this link.
Once you have installed Anaconda and verified it is being used, download and cd into the
Tarteel-ML repository and run the following commands to install all dependencies.
conda env create -f environment.yml
After this, activate the tarteel environment.
source activate tarteel-ml
You should now be ready to contribute to Tarteel-ML! When you are done, remember to deactivate the environment.
source deactivate tarteel-ml
Use the conda install command to add any new dependencies and ensure that the environment
resolves. Pull requests with new dependencies that break the existing environment for others will be
rejected.
After adding your new dependencies in Anaconda, add it (with the version number) in environment.yml
under dependencies.
For any dependencies not present in Anaconda, there is a way to install with pip, the default
Python library manager. Use the command
which pip
ensure that your pip binary is the one installed by Anaconda (the output of the command should be
similar to /anaconda3/envs/tarteel/bin/pip). You should then pip install the library and add it
to the file environment.yml under pip:.
Whenever submitting a new PR, create a new branch named using the convention <username>/<issue>.
Make sure to include descriptive and clear commit messages, while also referencing any issues your
PR addresses. Your pull request will be reviewed by the maintainers of this repository, and upon
approval, will be merged into the master branch.
Tarteel-ML requires that your code be well-commented and that you explain clearly what your changes are doing. Insufficiently commented code may be rejected if it is unclear to reviewers. Take a look at existing code to see what is expected!