run_tests.yaml

name: Run tests
on:
  pull_request:
  push: { branches: main }

jobs:
  run-test-suite:
    name: Run test suite
    runs-on: ubuntu-latest
    container: jjerphan/numba_dpex_dev:latest
      # TODO: setup runners with a compatible gpu and activate this option:
      # options: --device=/dev/dri  # enable gpu passthrough

    steps:
    - name: Checkout code
      uses: actions/checkout@v3

      # Use a pinned commit from the `feature/engine-api` branch at
      # https://github.com/scikit-learn/scikit-learn.git to enable smooth
      # synchronization with the development of this branch.
      # Development tracker: https://github.com/scikit-learn/scikit-learn/pull/25535/
      # TODO: Remove this step when the plugin API is officially released
    - name: Install pytest, sklearn branch "feature/engine-api", and sklearn-numba-dpex
      run: pip install pytest git+https://github.com/scikit-learn/scikit-learn.git@7d52073b15ee920c6f49208c777e7ce7663ff74b#egg=scikit-learn -e .

    - name: Check device
      run: python -c "import dpctl; dpctl.select_default_device().print_device_info()"


    # TODO: once compiler optimization flags are properly balanced in `numba_dpex`,
    # remove the environment variable `NUMBA_DPEX_OPT=0` that is currently required
    # for succeeding on CPU some of the tests. So far no issue on GPUs has been
    # observed.
    # More information at https://github.com/IntelPython/numba-dpex/issues/1106
    - name: Run sklearn_numba_dpex tests
      run: pytest -v sklearn_numba_dpex/ -k "not test_kmeans_predict_centers"

    - name: Run sklearn_numba_dpex tests
      run: NUMBA_DPEX_OPT=0 pytest -v sklearn_numba_dpex/ -k test_kmeans_predict_centers

      # TODO: run those tests in a separate pipeline
      # NB: `sklearn_numba_dpex` engine set the estimators to output arrays of type
      # `dpctl.tensor.usm_ndarray` and store fitted attributes with this same type.
      # This behavior is not compatible with sklearn unit tests, that expects numpy
      # arrays or at least arrays that would closely mimic the NumPy Python API. To
      # keep compatibility with sklearn unit tests the engine must be set to a
      # different behavior where its methods are wrapped in data conversion steps so
      # that fitted attributes and outputs are numpy arrays. Currently this behavior is
      # activated when the environment variable SKLEARN_NUMBA_DPEX_TESTING_MODE is set
      # to 1.
    - name: Run sklearn test suites with sklearn_numba_dpex engines
      run: NUMBA_DPEX_OPT=0 SKLEARN_RUN_FLOAT32_TESTS=1 SKLEARN_NUMBA_DPEX_TESTING_MODE=1 pytest -v --sklearn-engine-provider sklearn_numba_dpex --pyargs sklearn.cluster.tests.test_k_means