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B. Kerim Tshimanga f0788b3bbc
scheduled removal of auto_move_data decorator (#9231) 
* scheduled removal of auto_move_data decorator

* update CHANGELOG.md

* remove unused import

* remove test_decorators.py

* fix missed merge conflict

Co-authored-by: thomas chaton <thomas@grid.ai>
 		2021-09-03 00:54:36 +02:00
..
accelerators Add support for CPU AMP autocast (#9084) 2021-08-25 12:18:00 +00:00
base Replace `yapf` with `black` (#7783) 2021-07-26 13:37:35 +02:00
callbacks Check `max_time` when setting defaults for min/max epochs (#9072) 2021-08-27 15:01:12 +00:00
checkpointing Remove deprecated property `ModelCheckpoint.period` in favor of `ModelCheckpoint.every_n_epochs` (#9213) 2021-08-31 10:04:29 +02:00
core scheduled removal of auto_move_data decorator (#9231) 2021-09-03 00:54:36 +02:00
deprecated_api scheduled removal of auto_move_data decorator (#9231) 2021-09-03 00:54:36 +02:00
helpers feat: Add Rich Progress Bar (#8929) 2021-08-24 02:40:36 +00:00
loggers [bugfix] Changed CometLogger to stop modifying metrics in place (#9150) 2021-08-31 08:21:16 +00:00
loops extract optimizer loop (#9191) 2021-09-02 12:40:05 +01:00
models Allow exporting to onnx when input is tuple (#8800) 2021-09-02 03:36:20 +02:00
overrides scheduled removal of auto_move_data decorator (#9231) 2021-09-03 00:54:36 +02:00
plugins Avoid wrapping LightningModule in DDP plugins when not fitting (#9096) 2021-09-02 02:23:59 +00:00
profiler removing legacy profiler arg (#9178) 2021-08-30 09:37:09 +00:00
trainer Tighten the checks for `Trainer.terminate_on_nan` (#9190) 2021-09-02 18:35:22 +02:00
tuner remove lightning module datamodule property (#9233) 2021-09-02 00:43:47 +02:00
utilities fix state extraction from batch when fault-tolerant training (#9281) 2021-09-02 11:57:40 -07:00
README.md CI: add mdformat (#8673) 2021-08-03 18:19:09 +00:00
__init__.py Replace `yapf` with `black` (#7783) 2021-07-26 13:37:35 +02:00
conftest.py Add `ShardedTensor` support in `LightningModule` (#8944) 2021-08-23 19:59:38 +00:00
mnode_tests.txt Mnodes (#5020) 2021-02-04 20:55:40 +01:00
special_tests.sh Call any trainer function from the `LightningCLI` (#7508) 2021-08-28 04:43:14 +00:00

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

git clone https://github.com/PyTorchLightning/pytorch-lightning
cd pytorch-lightning

# 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 at least 2 GPUs to run distributed tests.

Note that this setup will not run tests that require specific packages installed such as Horovod, FairScale, NVIDIA/apex, NVIDIA/DALI, etc. You can rely on our CI to make sure all these tests pass.

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-torch1.9 -f dockers/cuda-extras/Dockerfile --build-arg TORCH_VERSION=1.9 .

To build other versions, select different Dockerfile.

docker image list
docker run --rm -it pytorch_lightning:devel-torch1.9 bash
docker image rm pytorch_lightning:devel-torch1.9