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CONTRIBUTING.md

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Contributing to pyscf-ipu

This project is still evolving but at the moment is focused around a high-performance and easily hackable implementation of Gaussian basis set DFT. We hope this is useful for the generation of large-scale datasets needed for training machine-learning models. We are interested in hearing any and all feedback so feel free to raise any questions, bugs encountered, or enhancement requests as Issues.

Setting up a development environment

We recommend using the conda package manager as this can automatically enable the Graphcore Poplar SDK. This is particularly useful in VS Code which can automatically activate the conda environment in a variety of scenarios:

  • visual debugging
  • running quick experiments in an interactive Jupyter window
  • using VS code for Jupyter notebook development.

The following assumes that you have already set up an install of conda and that the conda command is available on your system path. Refer to your preferred conda installer:

  1. Create a new conda environment with the same python version as required by the Poplar SDK. For example, on ubuntu 20 use python=3.8.10

    conda create -n pyscf-ipu python=3.8.10

  2. Confirm that you have the Poplar SDK installed on your machine and store the location in a temporary shell variable. The following will test that the SDK is found and configured correctly:

    TMP_POPLAR_SDK=/path/to/sdk
source $TMP_POPLAR_SDK/enable
gc-monitor

  3. Activate the environment and make POPLAR_SDK a persistent environment variable.

    conda activate pyscf-ipu
conda env config vars set POPLAR_SDK=$TMP_POPLAR_SDK

  4. You have to reactivate the conda environment to use the $POPLAR_SDK variable in the environment.

    conda deactivate
conda activate pyscf-ipu

  5. Setup the conda environment to automatically enable the Poplar SDK whenever the environment is activated.

    mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo "source $POPLAR_SDK/enable" > $CONDA_PREFIX/etc/conda/activate.d/enable.sh

  6. Check that everything is working by once reactivating the pyscf-ipu environment in a new shell and calling gc-monitor:

    conda deactivate
conda activate pyscf-ipu
gc-monitor

  7. Install all required packages for developing JAX DFT:

    pip install -e ".[ipu,test]"

  8. Install the pre-commit hooks

    pre-commit install

  9. Create a feature branch, make changes, and when you commit them the pre-commit hooks will run.

    git checkout -b feature
...
git push --set-upstream origin feature

    The last command will prints a link that you can follow to open a PR.

Testing

Run all the tests using pytest

pytest

We also use the nbmake package to check our notebooks work in the IpuModel environment. These checks can also be run on IPU hardware equiped machines e.g.:

pytest --nbmake --nbmake-timeout=3000 notebooks/nanoDFT-demo.ipynb