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* Create utils.py * Create __init__.py * redo sklearn metrics * add some more metrics * add sklearn metrics * Create __init__.py * redo sklearn metrics * New metric classes (#1326) * Create metrics package * Create metric.py * Create utils.py * Create __init__.py * add tests for metric utils * add docstrings for metrics utils * add function to recursively apply other function to collection * add tests for this function * update test * Update pytorch_lightning/metrics/metric.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * update metric name * remove example docs * fix tests * add metric tests * fix to tensor conversion * fix apply to collection * Update CHANGELOG.md * Update pytorch_lightning/metrics/metric.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * remove tests from init * add missing type annotations * rename utils to convertors * Create metrics.rst * Update index.rst * Update index.rst * Update pytorch_lightning/metrics/convertors.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Update pytorch_lightning/metrics/convertors.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * add doctest example * rename file and fix imports * added parametrized test * replace lambda with inlined function * rename apply_to_collection to apply_func * Separated class description from init args * Apply suggestions from code review Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * adjust random values * suppress output when seeding * remove gpu from doctest * Add requested changes and add ellipsis for doctest * forgot to push these files... * add explicit check for dtype to convert to * fix ddp tests * remove explicit ddp destruction Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * add sklearn metrics * start adding sklearn tests * fix typo * return x and y only for curves * fix typo * add missing tests for sklearn funcs * imports * __all__ * imports * fix sklearn arguments * fix imports * update requirements * Update CHANGELOG.md * Update test_sklearn_metrics.py * formatting * formatting * format * fix all warnings and formatting problems * Update environment.yml * Update requirements-extra.txt * Update environment.yml * Update requirements-extra.txt * fix all warnings and formatting problems * Update CHANGELOG.md * docs * inherit * docs inherit. * docs * Apply suggestions from code review Co-authored-by: Nicki Skafte <skaftenicki@gmail.com> * docs * req * min * Apply suggestions from code review Co-authored-by: Tullie Murrell <tulliemurrell@gmail.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Jirka <jirka@pytorchlightning.ai> Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> Co-authored-by: Nicki Skafte <skaftenicki@gmail.com> Co-authored-by: Tullie Murrell <tulliemurrell@gmail.com> |
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| base | ||
| callbacks | ||
| loggers | ||
| metrics | ||
| models | ||
| trainer | ||
| utilities | ||
| Dockerfile | ||
| README.md | ||
| __init__.py | ||
| collect_env_details.py | ||
| conftest.py | ||
| install_AMP.sh | ||
| requirements-devel.txt | ||
| requirements.txt | ||
| test_deprecated.py | ||
| 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 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
# 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