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Drop Python 3.9; test on Python 3.13; drop NumPy 1.21; skip CUDA install #304
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@@ -100,8 +95,6 @@ version. | |||
- As with NumPy, type annotations and positional-only arguments may not | |||
exactly match the spec for functions that are not wrapped at all. | |||
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The minimum supported PyTorch version is 1.13. |
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Do not advertise what you don't test.
@@ -131,8 +124,6 @@ For `linalg`, several methods are missing, for example: | |||
- `matrix_rank` | |||
Other methods may only be partially implemented or return incorrect results at times. | |||
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The minimum supported Dask version is 2023.12.0. |
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Do not advertise what you don't test. Also helplessly obsolete; we know for sure that less than 2024.9.0 won't work.
@@ -24,11 +23,14 @@ classifiers = [ | |||
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[project.optional-dependencies] | |||
cupy = ["cupy"] | |||
dask = ["dask"] | |||
dask = ["dask>=2024.9.0"] |
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It may be a good idea to completely remove all mentions of minimum versions until we have unit tests in place for them (i.e. not anytime soon).
numpy = ["numpy"] | ||
# Note: array-api-compat follows scikit-learn minimum dependencies, which support | ||
# much older versions of NumPy than what SPEC0 recommends. | ||
numpy = ["numpy>=1.22"] | ||
pytorch = ["torch"] | ||
sparse = ["sparse>=0.15.1"] |
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same.
numpy-1-22-xfails.txt
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# attributes are np.float32 instead of float | ||
# (see also https://github.com/data-apis/array-api/issues/405) | ||
array_api_tests/test_data_type_functions.py::test_finfo[float32] | ||
array_api_tests/test_data_type_functions.py::test_finfo[complex64] | ||
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# Rounding error near infinity | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_clip |
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This seems to crop up only when the test is run on its own
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array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__imod__(x1, x2)] | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x, s)] | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x1, x2)] | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[remainder(x1, x2)] |
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Tested these and could reproduce none of the core dumps
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Can't say I fancy chasing segfaults down the line. OTOH we won't be able to remove these skips otherwise, so OK, let's live dangerously and see if these reappear.
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__imod__(x1, x2)] | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x, s)] | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x1, x2)] | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[remainder(x1, x2)] |
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Could reproduce none of these
@ev-br ready for review |
This reverts commit 3bc8ffa.
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Removing skips which are claimed to have caused segfaults feels a bit dangerous; let's give it a shot and see if segfaults reappear.
The rest is great, thank you @crusaderky
# type promotion issues | ||
# NOTE: some of these may not fail until one runs array-api-tests with | ||
# --max-examples 100000 |
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Huh, this is really trying hard to fish these out :-)
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__imod__(x1, x2)] | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x, s)] | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[__mod__(x1, x2)] | ||
array_api_tests/test_operators_and_elementwise_functions.py::test_remainder[remainder(x1, x2)] |
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Can't say I fancy chasing segfaults down the line. OTOH we won't be able to remove these skips otherwise, so OK, let's live dangerously and see if these reappear.
Follow-up from #266
scikit-learn
minimumscikit-learn
(closes Drop Python 3.9 #230)--max-examples 100000
on NumPy 1.22--max-examples 1000
on all other numpy versions