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472f394788
* remove unnecessary pass statements * use isinstance for type checks * remove unnecessary else/elif after return * remove unnecessary return statements * move doc string to top * merge isinstance calls * remove unnecessary else/elif after raise * use list comprehension * do not use len without comparison * add missing shebang * revert isinstance check back to type broke tests, because bool is actually subclass of int * add missing period to doc string * remove unnecessary pass statements * use isinstance for type checks * remove unnecessary else/elif after return * remove unnecessary return statements * move doc string to top * merge isinstance calls * remove unnecessary else/elif after raise * use list comprehension * do not use len without comparison * add missing shebang * revert isinstance check back to type broke tests, because bool is actually subclass of int * add missing period to doc string * Fix default ckpt path when logger exists (#771) * rename logging -> loggers (#767) * move logging >> loggers * add warning * fix tests * logging alias * formatting * formatting * use isinstance for type checks * revert isinstance check back to type broke tests, because bool is actually subclass of int * add more detail to tbptt example (#755) * add more detail to tbptt example * warn user about new arg in training_step Co-authored-by: Vadim Bereznyuk <kuynzereb@gmail.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Jeremy Jordan <13970565+jeremyjordan@users.noreply.github.com>
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 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
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