619f984c36
* Option to provide seed to random generators to ensure reproducibility I added small function in utilities which imports torch, numpy, python random and sets seed for all of the libraries to ensure reproducibility of results. * Apply recommendations from core contributors on seeding 1. Moved the seeding code to another file 2. Make deterministic as a parameter for trainer class 3. Add assertions for seeding numpy 4. Added warnings 5. torch.manual_seed should be enough for seeding torch * Revert "Apply recommendations from core contributors on seeding" This reverts commit a213c8e6882eec8a9e7408b9418926d2db7c5461. * Revert "Revert "Apply recommendations from core contributors on seeding"" This reverts commit 59b2da53c62878de7aab0aa3feb3115e105eea06. * Change in test, for correct seeding * Allow seed equal to 0 * Allow seed to be uint32.max * Added deterministic to benchmarks * Cuda manual seed as in benchmark seeding * Seeding should be done before model initialization * cuda manual_seed is not necessary * Fixing seed test_cpu_lbfgs On some seeds seems like lbfgs doesn't converge. So I fixed the seed during testing. * rebasing issue with old reproducibility.py * Improved documentation and ability to seed before initializing Train class * Change in docs * Removed seed from trainer, update for documentation * Typo in the docs * Added seed_everything to _all_ * Fixing old changes * Model initialization should be earlier then Trainer * Update pytorch_lightning/trainer/__init__.py From Example to testcode Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Fixing according to the contributors suggestions * Moving horovod deterministic to Trainer class * deterministic flag affects horovod docs update * Improved static typing * Added deterministic to test runners of horovod It is failing on some versions, not very predictable * static seeds for horovod tests * Change for reset_seed function in tests * Seeding horovod using reset_seed from tutils * Update pytorch_lightning/trainer/__init__.py * chlog * Update trainer.py * change "testcode" to "Example" in trainer init documentation * Update pytorch_lightning/trainer/seed.py, first line in comment Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Jirka <jirka.borovec@seznam.cz> Co-authored-by: William Falcon <waf2107@columbia.edu> |
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| .. | ||
| base | ||
| callbacks | ||
| loggers | ||
| models | ||
| trainer | ||
| 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:
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