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gnu: python-keras: Enable parallel tests. 

* (python-keras)[phases]: Delete trailing #t.
{check}: Honor TESTS?.  Run the tests in parallel.  Disable PEP8 lint.
master
Maxim Cournoyer 2021-10-08 22:34:32 -04:00
parent 92c3d1c3d0
commit 11e7a534f7
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1 changed files with 17 additions and 16 deletions
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33
gnu/packages/machine-learning.scm
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@ -2521,24 +2521,26 @@ with image data, text data, and sequence data.")
(delete-file "keras/backend/theano_backend.py")
(delete-file "keras/backend/cntk_backend.py")
(delete-file "tests/keras/backend/backend_test.py")
;; FIXME: This doesn't work because Tensorflow is missing the
;; coder ops library.
(delete-file "tests/keras/test_callbacks.py")
#t))
(delete-file "tests/keras/test_callbacks.py")))
(replace 'check
(lambda _
;; These tests attempt to download data files from the internet.
(delete-file "tests/integration_tests/test_datasets.py")
(delete-file "tests/integration_tests/imagenet_utils_test.py")
;; Backport https://github.com/keras-team/keras/pull/12479.
(substitute* "tests/keras/engine/test_topology.py"
(("np.ones\\(\\(3, 2\\)\\)")
"1."))
(invoke "python" "-m" "pytest"
;; The following test fail only in the build container;
;; skip it.
"-k" "not test_selu"))))))
(lambda* (#:key tests? #:allow-other-keys)
(when tests?
;; These tests attempt to download data files from the internet.
(delete-file "tests/integration_tests/test_datasets.py")
(delete-file "tests/integration_tests/imagenet_utils_test.py")
;; Backport https://github.com/keras-team/keras/pull/12479.
(substitute* "tests/keras/engine/test_topology.py"
(("np.ones\\(\\(3, 2\\)\\)")
"1."))
(invoke "python" "-m" "pytest" "tests"
"-p" "no:pep8"
;; FIXME: python-build-system lacks PARALLEL-TESTS?
"-n" (number->string (parallel-job-count))
;; The following test fail only in the build container;
;; skip it.
"-k" "not test_selu")))))))
(propagated-inputs
`(("python-h5py" ,python-h5py)
("python-keras-applications" ,python-keras-applications)
@ -2565,7 +2567,6 @@ with image data, text data, and sequence data.")
and capable of running on top of TensorFlow. It was developed with a focus on
enabling fast experimentation. Use Keras if you need a deep learning library
that:
@itemize
@item Allows for easy and fast prototyping (through user friendliness,
modularity, and extensibility).