History

Kaushik B c33df2639f Set `dataset` attribute to `MpDeviceLoader` used in TPU Spawn (#10151 )		2021-10-27 01:23:01 +05:30
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accelerators	Set `dataset` attribute to `MpDeviceLoader` used in TPU Spawn (#10151 )	2021-10-27 01:23:01 +05:30
base	Keep global step update in the loop (#8856 )	2021-09-14 19:21:39 +05:30
callbacks	Raise `MisconfigurationException` if `trainer.eval` is missing required methods (#10016 )	2021-10-25 23:12:08 -07:00
checkpointing	Remove `optimizer_connector.py` (#10120 )	2021-10-26 00:52:43 +00:00
core	Unify checkpoint load paths [redo #9693 ] (#10061 )	2021-10-25 19:05:31 +00:00
deprecated_api	Rename `master_params` to `main_params` (#10105 )	2021-10-26 11:17:32 +02:00
helpers	Some minor CI cleanup (#10088 )	2021-10-26 13:58:20 +02:00
loggers	Don't raise DeprecationWarning for `LoggerConnector.gpus_metrics` (#9959 )	2021-10-18 22:51:09 +00:00
loops	Unify checkpoint load paths [redo #9693 ] (#10061 )	2021-10-25 19:05:31 +00:00
models	Raise `MisconfigurationException` if `trainer.eval` is missing required methods (#10016 )	2021-10-25 23:12:08 -07:00
overrides	Mark accelerator connector as protected (#10032 )	2021-10-25 19:24:54 +00:00
plugins	Raise `MisconfigurationException` if `trainer.eval` is missing required methods (#10016 )	2021-10-25 23:12:08 -07:00
profiler	Make sure file and folder exists in Profiler (#10073 )	2021-10-26 11:13:31 +00:00
trainer	Move optimizer step and clipping into the `PrecisionPlugin` (#10143 )	2021-10-26 17:26:26 +02:00
tuner	reset val dataloader for binsearch (#9975 )	2021-10-18 12:54:26 +02:00
utilities	Change default value of the `max_steps` Trainer argument from `None` to `-1` (#9460 )	2021-10-25 20:21:33 +00:00
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 support for `torch.use_deterministic_algorithms` (#9121 )	2021-09-30 04:40:09 +00:00
mnode_tests.txt	Mnodes (#5020 )	2021-02-04 20:55:40 +01:00
special_tests.sh	Skip reconciliate_processes if used within a cluster environment that creates processes externally (#9389 )	2021-09-15 11:54:17 +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

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