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ec7fc97857
* Basic wandb support * refactor(wandb): remove unused variables and document logger * docs(wandb): explain how to use WandbLogger * test(wandb): add tests for WandbLogger * feat(wandb): add save_dir * fix(wandb): allow pickle of logger * fix(wandb): save logs in custom directory * test(wandb): test import * docs(wandb): simplify docstring and use doctest * test: increase number of epochs for satisfactory accuracy * test(test_load_model_from_checkpoint): ensure we load last checkpoint Co-authored-by: Chris Van Pelt <vanpelt@wandb.com> Co-authored-by: William Falcon <waf2107@columbia.edu>
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/williamFalcon/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