309ce7a966
* Raise if scheduler interval not 'step' or 'epoch' * Add test for unknown 'interval' value in scheduler * Use BoringModel instead of EvalModelTemplate Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Fix import order * Apply yapf in test_datamodules * Add missing imports to test_datamodules * Fix too long comment * Update pytorch_lightning/trainer/optimizers.py * Fix unused imports and exception message * Fix failing test Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> |
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| .. | ||
| accelerators | ||
| base | ||
| callbacks | ||
| checkpointing | ||
| core | ||
| deprecated_api | ||
| helpers | ||
| loggers | ||
| metrics | ||
| models | ||
| overrides | ||
| plugins | ||
| trainer | ||
| tuner | ||
| utilities | ||
| README.md | ||
| __init__.py | ||
| collect_env_details.py | ||
| conftest.py | ||
| mnode_tests.txt | ||
| special_tests.sh | ||
| test_profiler.py | ||
README.md
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:
Install Open MPI or another MPI implementation. Learn how to install Open MPI on this page.
git clone https://github.com/PyTorchLightning/pytorch-lightning
cd pytorch-lightning
# install AMP support
bash requirements/install_AMP.sh
# install dev deps
pip install -r 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_OPERATIONS=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 requirements/devel.txt)
coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v
# 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-torch1.4 -f dockers/cuda-extras/Dockerfile --build-arg TORCH_VERSION=1.4 .
To build other versions, select different Dockerfile.
docker image list
docker run --rm -it pytorch_lightning:devel-torch1.4 bash
docker image rm pytorch_lightning:devel-torch1.4