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Jirka Borovec 9893681859
fix changelog (#1864) 
* fix chlog

* test for #1729

* hist

* update

* Document use case of passing test dataloaders to Trainer.test() (#1992)

* Issue 1990 Doc patch.

* Codeblock directive.

* Update to reflect current state of pytorch-lightning

* Final grammar cleaning. I hope these commits are squashed.

* Apply suggestions from code review

* Apply suggestions from code review

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

Co-authored-by: authman <uapatira@gmail.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
 		2020-05-31 00:48:05 -04:00
..
base replace Hparams by init args (#1896) 2020-05-24 18:59:08 -04:00
callbacks LearningRateLogger in multi-scheduler setting (#1944) 2020-05-27 22:44:46 -04:00
loggers unify tests (#1940) 2020-05-27 22:45:23 -04:00
metrics New metric classes (#1326) (#1877) 2020-05-19 11:05:07 -04:00
models replace Hparams by init args (#1896) 2020-05-24 18:59:08 -04:00
trainer unify tests (#1940) 2020-05-27 22:45:23 -04:00
utilities New metric classes (#1326) (#1877) 2020-05-19 11:05:07 -04:00
Dockerfile Tests/docker (#1573) 2020-04-23 12:52:59 -04:00
README.md Tests/docker (#1573) 2020-04-23 12:52:59 -04:00
__init__.py default test logger (#1478) 2020-04-21 20:33:10 -04:00
collect_env_details.py fix changelog (#1452) 2020-04-20 17:36:26 -04:00
conftest.py test deprecation warnings (#1470) 2020-04-23 17:34:47 -04:00
install_AMP.sh CI: split tests-examples (#990) 2020-03-25 07:46:27 -04:00
requirements-devel.txt Tests/docker (#1573) 2020-04-23 12:52:59 -04:00
requirements.txt fix codecov reports (#1867) 2020-05-18 20:34:59 -04:00
test_deprecated.py fix changelog (#1864) 2020-05-31 00:48:05 -04:00
test_profiler.py RC & Docs/changelog (#1776) 2020-05-11 21:57:53 -04:00

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:

  1. At least 2 GPUs.
  2. NVIDIA-apex installed.
  3. 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