We're already using as many processes in parallel as we have CPU cores.
Furthermore, the number of core may be incorrectly calculated as 36
(we've seen this in pytest-xdist) which make compound the problem.
PyTorch performance craters without this.
Set the number of CPUs manually based on the Circle CI resource class,
or else we're getting 36 CPUs, which is far too much (perhaps that's
the underlying hardware and not what Circle CI allocates to us).
Don't parallelize the custom tokenizers tests because they take less
than one second to run and parallelization actually makes them slower.
Since the file is written to the filesystem, a filesystem lock is the
way to go here. Add a dependency on the third-party filelock library to
get cross-platform functionality.
Caching models across test cases and across runs of the test suite makes
slow tests somewhat more bearable.
Use gettempdir() instead of /tmp in tests. This makes it easier to
change the location of the cache with semi-standard TMPDIR/TEMP/TMP
environment variables.
Fix#2222.
This allows moving the file instead of copying it, which is more
reliable. Also it avoids writing large amounts of data to /tmp,
which may not be large enough to accomodate it.
Refs #2222.
* Switch to plain unittest for skipping slow tests.
Add a RUN_SLOW environment variable for running them.
* Switch to plain unittest for PyTorch dependency.
* Switch to plain unittest for TensorFlow dependency.
* Avoid leaking open files in the test suite.
This prevents spurious warnings when running tests.
* Fix unicode warning on Python 2 when running tests.
The warning was:
UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
* Support running PyTorch tests on a GPU.
Reverts 27e015bd.
* Tests no longer require pytest.
* Make tests pass on cuda
Aymeric Augustin