# PyTorch-Lightning Tests

Most of the tests in PyTorch Lightning train a [BoringModel](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/tests/helpers/boring_model.py) under various trainer conditions (ddp, ddp2+amp, etc...). Want to add a new test case and not sure how? [Talk to us!](https://www.pytorchlightning.ai/community)

## Running tests

**Local:** Testing your work locally will help you speed up the process since it allows you to focus on particular (failing) test-cases.
To setup a local development environment, install both local and test dependencies:

```bash
# clone the repo
git clone https://github.com/PyTorchLightning/pytorch-lightning
cd pytorch-lightning

# install required depedencies
python -m pip install ".[dev, examples]"
# install pre-commit (optional)
python -m pip install pre-commit
pre-commit install
```

Additionally, for testing backward compatibility with older versions of PyTorch Lightning, you also need to download all saved version-checkpoints from the public AWS storage. Run the following script to get all saved version-checkpoints:

```bash
wget https://pl-public-data.s3.amazonaws.com/legacy/checkpoints.zip -P legacy/
unzip -o legacy/checkpoints.zip -d legacy/
```

Note: These checkpoints are generated to set baselines for maintaining backward compatibility with legacy versions of PyTorch Lightning. Details of checkpoints for back-compatibility can be found [here](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/legacy/README.md).

You can run the full test suite in your terminal via this make script:

```bash
make test
```

Note: if your computer does not have multi-GPU or TPU these tests are skipped.

**GitHub Actions:** For convenience, you can also use your own GHActions building which will be triggered with each commit.
This is useful if you do not test against all required dependency versions.

**Docker:** Another option is to utilize the [pytorch lightning cuda base docker image](https://hub.docker.com/repository/docker/pytorchlightning/pytorch_lightning/tags?page=1&name=cuda). You can then run:

```bash
python -m pytest pytorch_lightning tests pl_examples -v
```

You can also run a single test as follows:

```bash
python -m pytest -v tests/trainer/test_trainer_cli.py::test_default_args
```

### Conditional Tests

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.

### Standalone Tests

There are certain standalone tests, which you can run using:

```bash
PL_RUN_STANDALONE_TESTS=1 python -m pytest -v tests/trainer/
# or
./tests/standalone_tests.sh tests/trainer
```

## Running Coverage

Make sure to run coverage on a GPU machine with at least 2 GPUs and NVIDIA apex installed.

```bash
cd pytorch-lightning

# generate coverage (coverage is also installed as part of dev dependencies under requirements/devel.txt)
coverage run --source pytorch_lightning -m pytest pytorch_lightning tests pl_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.

```bash
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.

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