MNT Synchronize with sklearn 1.4 #542
Workflow file for this run
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| 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/fcharras/scikit-learn.git@80f58bf10d2f8b8cb43f6253bbe13413985a1413#egg=scikit-learn -e . | |
| - name: Check device | |
| run: python -c "import dpctl; dpctl.select_default_device().print_device_info()" | |
| # HACK: We force the test `test_need_to_workaround_numba_dpex_906` to run | |
| # in a separate process. Indeed, the test only requires action if it fails | |
| # in all circumstances, but should be ignored as long as it fails only | |
| # occasionally. With recent updates the test started to fail when ran | |
| # along all the other tests, while still succeeding when singled out. See | |
| # https://github.com/IntelPython/numba-dpex/issues/906 for more context | |
| # on the issue. | |
| - name: Run sklearn_numba_dpex tests | |
| run: pytest -v sklearn_numba_dpex/ -k "not test_need_to_workaround_numba_dpex_906" | |
| - name: Run sklearn_numba_dpex tests | |
| run: pytest -v sklearn_numba_dpex/ -k test_need_to_workaround_numba_dpex_906 | |
| # 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: 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 |