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Getting all cuml tests to pass with cudf.pandas enabled #5876
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Maybe the first thing to do is to add a additional workflow/CI run that is optional (it failing doesn't block merging) that runs Once all tests pass we can then make it a required workflow so that we don't regress. |
cc @betatim This PR adds the CI jobs proposed in #5876 Authors: - Dante Gama Dessavre (https://github.com/dantegd) Approvers: - Ray Douglass (https://github.com/raydouglass) URL: #5881
FAILED test_input_utils.py::test_convert_matrix_order_cuml_array[K-C-pandas-float32] - AssertionError: assert 'F' == 'C'
FAILED test_input_utils.py::test_convert_matrix_order_cuml_array[K-C-pandas-float64] - AssertionError: assert 'F' == 'C'
FAILED test_input_utils.py::test_convert_matrix_order_cuml_array[K-F-pandas-float32] - AssertionError: assert 'F' == 'C'
FAILED test_input_utils.py::test_convert_matrix_order_cuml_array[K-F-pandas-float64] - AssertionError: assert 'F' == 'C' are fixed in PR #5882 |
This failure was just a testing failure, expectint identical pointers of actual dataframes, as opposed to wrapped objects. Contributes to fixing #5876 cc @betatim Authors: - Dante Gama Dessavre (https://github.com/dantegd) Approvers: - Divye Gala (https://github.com/divyegala) URL: #5885
…5882) Error came from the fact that pandas and cudf convert to numpy by default with different order. Towards fixing #5876 Authors: - Dante Gama Dessavre (https://github.com/dantegd) Approvers: - Divye Gala (https://github.com/divyegala) - Tim Head (https://github.com/betatim) URL: #5882
This issue is the result of running the cuml tests with
pytest -p cudf.pandas --ignore=dask -m "not memleak"
. The goal is to find out how well cuml works withcudf.pandas
activated. As far as I can tell this actually means at least two things: (1) cudf's "pandas compatibility mode" is activated and (2) the cudf.pandas accelerator is doing its magic. We could look at (1) separately, for example users of cuml might turn on this option even without using the pandas accelerator.The goal of this issue is to have a central list of all failing tests so we can coordinate working on getting them fixed. I think it would make sense to open one PR per test file or even one PR for a set of tests in one file. That way the diff stays manageable for the reviewer and we don't have too many PRs. Post in this issue if you are working on one of these so we can avoid duplicate effort.
The failures in
test_one_hot_encoder.py
might be a bit harder to solve than the other ones, so I singled them out.I used 6d3bb0d and a conda environment setup today.
The tests in
test_one_hot_encoder.py
cause trouble withcudf.pandas
activated. Several of them lead toMemoryError
orCUDARuntimeError
s. Example output below.Full list of failed tests in `test_one_hot_encoder.py`
example test with `CUDARuntimeError`
example test with `MemoryError`
For the rest of the tests the results look like this:
test_compose.py::test_column_transformer_index
test_doctest.py::test_docstring
test_holtwinters.py::test_singlets_holtwinters
test_input_utils.py::test_convert_matrix_order_cuml_array
Fixcudf.pandas
failure ontest_convert_matrix_order_cuml_array
#5882test_input_utils.py::test_convert_input_dtype
Fixcudf.pandas
failure ontest_convert_input_dtype
#5885test_label_encoder.py::test_inverse_transform
test_label_encoder.py::test_empty_input
test_label_encoder.py::test_inverse_transform_cupy_numpy
test_metrics.py::test_sklearn_search
test_module_config.py::test_default_global_output_type
test_ordinal_encoder.py::test_ordinal_encoder_df
test_ordinal_encoder.py::test_ordinal_encoder_array
test_ordinal_encoder.py::test_output_type
test_target_encoder.py::test_targetencoder_random
test_target_encoder.py::test_targetencoder_median
test_tsne.py::test_tsne_knn_graph_used
test_tsne.py::test_tsne
explainer/test_sampling.py::test_kmeans_input
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