7024177f7d
* Initial commit of Horovod distributed backend implementation * Update distrib_data_parallel.py * Update distrib_data_parallel.py * Update tests/models/test_horovod.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Update tests/models/test_horovod.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Fixed tests * Added six * tests * Install tox for GitHub CI * Retry tests * Catch all exceptions * Skip cache * Remove tox * Restore pip cache * Remove the cache * Restore pip cache * Remove AMP Co-authored-by: William Falcon <waf2107@columbia.edu> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: J. Borovec <jirka.borovec@seznam.cz> |
||
---|---|---|
.. | ||
base | ||
loggers | ||
models | ||
trainer | ||
Dockerfile | ||
README.md | ||
__init__.py | ||
collect_env_details.py | ||
conftest.py | ||
install_AMP.sh | ||
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.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.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