* memory benchmark rss
* have both forward pass and line-by-line mem tracing
* cleaned up tracing
* refactored and cleaning up API
* no f-strings yet...
* add GPU mem logging
* fix GPU memory monitoring
* style and quality
* clean up and doc
* update with comments
* Switching to python 3.6+
* fix quality
* Create self-hosted.yml
* Update self-hosted.yml
* Update self-hosted.yml
* Update self-hosted.yml
* Update self-hosted.yml
* Update self-hosted.yml
* do not run slow tests, for now
* [ci] For comparison with circleci, let's also run CPU-tests
* [ci] reorganize
* clearer filenames
* [ci] Final tweaks before merging
* rm slow tests on circle ci
* Trigger CI
* On GPU this concurrency was way too high
Tensorflow 2.1.0 introduce a new dependency model where pip install tensorflow would install tf with GPU support.
Before it would just install with CPU support, thus CircleCI is looking for NVidia driver version at initialization of the
tensorflow related tests but fails as their is no NVidia Driver running.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
This prevents transformers from being importable simply because the CWD
is the root of the git repository, while not being importable from other
directories. That led to inconsistent behavior, especially in examples.
Once you fetch this commit, in your dev environment, you must run:
$ pip uninstall transformers
$ pip install -e .
These libraries aren't always installed in the virtual environment where
isort is running. Declaring them properly avoids mixing these
third-party imports with local imports.
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.
Harutaka Kawamura