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563e2ba2c6 resolving documentation warnings (#833 )

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* update badges & drop Travis/Appveyor (#826)

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Co-authored-by: William Falcon <waf2107@columbia.edu>

* add pep8speaks (#842)

* advanced profiler describe + cleaned up tests (#837)

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just to trigger CI

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Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: William Falcon <waf2107@columbia.edu>
Co-authored-by: Jeremy Jordan <13970565+jeremyjordan@users.noreply.github.com>

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models

split trainer tests (#956 )

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trainer

split trainer tests (#956 )

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__init__.py

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conftest.py

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README.md

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requirements.txt

separate requirements for logger dependencies (#792 )

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test_amp.py

Enable TPU support (#868 )

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test_cpu_models.py

Fixing tests (#936 )

2020-02-25 13:06:24 -05:00

test_gpu_models.py

Clean up dataloader logic (#926 )

2020-02-24 22:23:25 -05:00

test_loggers.py

split trainer tests (#956 )

2020-02-26 20:31:40 -05:00

test_profiler.py

resolving documentation warnings (#833 )

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test_restore_models.py

relax hparams (#919 )

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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:

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

# install module locally
pip install -e .

# install dev deps
pip install -r tests/requirements.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.
NVIDIA-apex installed.

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.txt)
pip install coverage
coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules

# print coverage stats
coverage report -m

# exporting resulys
coverage xml
codecov -t 17327163-8cca-4a5d-86c8-ca5f2ef700bc  -v