Files
pytorch-lightning/tests
4c5e82c065 Skepticleo trainer argparser (#1023)
* Added default parser for trainer and class method to construct trainer from default args

* Removed print statement

* Added test for constructing Trainer from command line args

* Removed extra line

* Removed redundant imports, removed whitespace from empty lines

* Fixed typo

* Updated default parser creation to get class attributes automatically

* Updated default parser creation to get class attributes automatically

* Added method to get default args for trainer

* Trimmed trainer get default args method

* Updated from argparse method to not return trainer with static arguments

* Update trainer get default args to classmethod

* adjustment

* fix

* Fixed variable name

* Update trainer.py

* Update test_trainer.py

* Update trainer.py

* Update tests/trainer/test_trainer.py

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* Update trainer.py

* Update test_trainer.py

* Update trainer.py

* Update test_trainer.py

* Update tests/trainer/test_trainer.py

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* Update pytorch_lightning/trainer/trainer.py

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* Update trainer.py

* Update test_trainer.py

Co-authored-by: Mudit Tanwani <mudittanwani@gmail.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
2020-03-03 09:32:15 -05:00
..
2020-03-02 20:49:14 -05:00
2020-03-02 21:05:38 -05:00
2019-07-24 21:32:31 -04:00
2020-01-05 14:34:25 -05:00
2020-02-17 16:01:20 -05:00
2020-02-25 13:06:24 -05:00

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:

  1. At least 2 GPUs.
  2. 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