Torchlightning

0.4.0 is the first public release after a short period testing with public users. Thanks for all the help ironing out bugs to get Lightning to run on everything from notebooks to local to server machines.

This release includes:
- Extensively tested code.
- Cleaner API to accommodate the various research use cases

New features
- No need for experiment object in trainer.
- Training continuation (not just weights, but also epoch, global step, etc...)
- if the folder the checkpoint callback uses has weights, it loads the last weights automatically.
- training step and validation step don't reduce outputs automatically anymore. This fixes issues with reducing generated outputs for example (images, text).
- 16-bit can now be used with a single GPU (no DP or DDP in this case). bypasses issue with NVIDIA apex and PT compatibility for DP+16-bit training.

- Extra tests for CPU models.
- Experiment object is process-safe, it will only write from process_rank=0

Simplified data loader.

Added a decorator to do lazy loading internally:

Old:
python
property
def tng_dataloader(self):
if self._tng_dataloader is None:
self._tng_dataloader = DataLoader(...)
return self.tng_dataloder


Now:

python
ptl.data_loader
def tng_dataloader(self):
return DataLoader(...)
`

Fully tested!

Includes:
- Code coverage (99%)
- Full tests that run multiple models in different configs
- Full tests that test specific functionality in trainer.