7c7e50ca47
* added tpu_id added tpu_id to mixins * train on individual tpu * parallel loader if tpu_id is None * removed progress_bar_refresh_rate * chlog * replaced num_tpu_cores with tpu_cores * set tpu_id to None if int * changed num_tpu_cores to tpu_cores in docs * updated docs * updated __init__.py removed self.tpu_id for ParallelLoader * Update pytorch_lightning/trainer/__init__.py * check if tpu_cores is a list Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * xla device conditional * num_tpu_cores deprecation * removed duplicate warning * fixed pep8 error * Revert "removed duplicate warning" This reverts commit 8adb0a9b * deprecated api update * fixed recursion error * fixed tests * fixed flake errors * removed current_tpu_index * Update CHANGELOG.md * Update trainer.py Co-authored-by: Jirka <jirka.borovec@seznam.cz> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: William Falcon <waf2107@columbia.edu>
PyTorch-Lightning Tests
Most PL tests train a full MNIST model under various trainer conditions (ddp, ddp2+amp, etc...). This provides testing for most combinations of important settings. The tests expect the model to perform to a reasonable degree of testing accuracy to pass.
Running tests
The automatic travis tests ONLY run CPU-based tests. Although these cover most of the use cases, run on a 2-GPU machine to validate the full test-suite.
To run all tests do the following:
git clone https://github.com/PyTorchLightning/pytorch-lightning
cd pytorch-lightning
# install AMP support
bash tests/install_AMP.sh
# install dev deps
pip install -r tests/requirements-devel.txt
# run tests
py.test -v
To test models that require GPU make sure to run the above command on a GPU machine. The GPU machine must have:
- At least 2 GPUs.
- NVIDIA-apex installed.
- Horovod with NCCL support:
HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_GPU_BROADCAST=NCCL pip install horovod
Running Coverage
Make sure to run coverage on a GPU machine with at least 2 GPUs and NVIDIA apex installed.
cd pytorch-lightning
# generate coverage (coverage is also installed as part of dev dependencies under tests/requirements-devel.txt)
coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules
# print coverage stats
coverage report -m
# exporting results
coverage xml
Building test image
You can build it on your own, note it takes lots of time, be prepared.
git clone <git-repository>
docker image build -t pytorch_lightning:devel-pt_1_4 -f tests/Dockerfile --build-arg TORCH_VERSION=1.4 .
To build other versions, select different Dockerfile.
docker image list
docker run --rm -it pytorch_lightning:devel-pt_1_4 bash
docker image rm pytorch_lightning:devel-pt_1_4