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Jirka Borovec c438d0dd90
increase acc (#2039) 
* increase acc

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* @pytest

* try .50

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 		2020-06-03 08:28:19 -04:00
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base increase acc (#2039) 2020-06-03 08:28:19 -04:00
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loggers fix(wandb): use same logger on multiple training loops (#2055) 2020-06-02 18:46:02 -04:00
metrics New metric classes (#1326) (#1877) 2020-05-19 11:05:07 -04:00
models data transfer model hook (+ refactor) (#1756) 2020-06-02 21:45:19 -04:00
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utilities New metric classes (#1326) (#1877) 2020-05-19 11:05:07 -04:00
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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
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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:

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:

  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

# 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