lightning/tests
SeanNaren a80e37b95b Add hydra experimental to correct location 2021-01-26 14:29:47 +01:00
..
backends Fix pre-commit isort failure on tests/backends/*.py (#5430) 2021-01-14 19:32:41 -05:00
base [feat] Add PyTorch Profiler. (#5560) 2021-01-26 06:48:54 -05:00
callbacks deprecate enable_pl_optimizer as it is not restored properly (#5244) 2021-01-26 14:29:46 +01:00
checkpointing deprecate enable_pl_optimizer as it is not restored properly (#5244) 2021-01-26 14:29:46 +01:00
core deprecate enable_pl_optimizer as it is not restored properly (#5244) 2021-01-26 14:29:46 +01:00
deprecated_api deprecate enable_pl_optimizer as it is not restored properly (#5244) 2021-01-26 14:29:46 +01:00
loggers [bugfix] Logging only on `not should_accumulate()` during training (#5417) 2021-01-26 14:28:47 +01:00
metrics Classification metrics overhaul: precision & recall (4/n) (#4842) 2021-01-18 03:24:13 -05:00
models Add hydra experimental to correct location 2021-01-26 14:29:47 +01:00
overrides Refactor LightningDistributedDataParallel (#5185) 2021-01-13 14:35:42 -05:00
plugins Fix pre-commit isort failure on tests/plugins/*.py (#5422) 2021-01-14 03:57:16 -05:00
trainer deprecate enable_pl_optimizer as it is not restored properly (#5244) 2021-01-26 14:29:46 +01:00
tuner refactor - check F841 (#5202) 2020-12-21 11:10:55 +05:30
utilities [BUG] Check environ before selecting a seed to prevent warning message (#4743) 2021-01-26 14:28:47 +01:00
README.md Fix pre-commit trailing-whitespace and end-of-file-fixer hooks. (#5387) 2021-01-26 14:27:56 +01:00
__init__.py tests for legacy checkpoints (#5223) 2021-01-26 14:27:56 +01:00
collect_env_details.py add copyright to tests (#5143) 2021-01-05 09:57:37 +01:00
conftest.py update isort config (#5335) 2021-01-06 12:49:23 +01:00
special_tests.sh [bug-fix] Call transfer_batch_to_device in DDPlugin (#5195) 2021-01-26 14:28:45 +01:00
test_profiler.py update isort config (#5335) 2021-01-06 12:49:23 +01:00

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 requirements/install_AMP.sh

# install dev deps
pip install -r 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_OPERATIONS=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 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-torch1.4 -f dockers/cuda-extras/Dockerfile --build-arg TORCH_VERSION=1.4 .

To build other versions, select different Dockerfile.

docker image list
docker run --rm -it pytorch_lightning:devel-torch1.4 bash
docker image rm pytorch_lightning:devel-torch1.4