0.5.1

Simpler interface

All trainers now have a default logger, early stopping and checkpoint object. To modify the behavior, pass in your own versions of those.

• Removed collisions with logger versions by tying it to job id.

Features

• Added new DDP implementation. It uses DP in a node but allows multiple nodes. Useful for models which need negative samples, etc...

Trainer(distributed_backend='ddp2')

• support for LBFGS. If you pass in LBFGS Lightning handles the closure for you automatically.
• No longer need to set master port, Lightning does it for you using the job id.

Minor changes

• training_step and validation_end now return two separate dicts, one for the progress bar and one for logging.

• Added options to memory printing: 'min_max' logs only the max/min memory use. 'all' logs all the GPUs on the root node.