b0a0a47a0b
- data_batch → batch - batch_i → batch_idx - dataloader_i → dataloader_idx - tng → training - training_dataloader → train_dataloader - add_log_row_interval → row_log_interval - gradient_clip → gradient_clip_val - prog → progress - tqdm_dic → tqdm_dict |
||
|---|---|---|
| .. | ||
| README.md | ||
| __init__.py | ||
| debug.py | ||
| requirements.txt | ||
| test_models.py | ||
README.md
PyTorch-Lightning Tests
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.
test_models.py
This file fits a tiny model on MNIST using these different set-ups.
- CPU only.
- Single GPU with DP.
- Multiple (2) GPUs using DP.
- Multiple (2) GPUs using DDP.
- Multiple (2) GPUs using DP + apex (for 16-bit precision).
- Multiple (2) GPUs using DDP + apex (for 16-bit precision).
For each set up it also tests:
- model saving.
- model loading.
- predicting with a loaded model.
- simulated save from HPC signal.
- simulated load from HPC signal.
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