* added neptune integration
* added tests for NeptuneLogger, added neptune to docs
* updated link to neptune support
* fixed docstrings, fixed try/except in tests, changed append_tags input
* fixed docstrings line lenght
* bumped epoch nr in model restore tests
* added tags support for single strings
* fixed passing neptune token to backend
* fixed project name in offline mode
* added save_top_k=-1 to checkpoint callback
* reformated initialization of neptune in online mode
* bumped epoch nr to 4 in test_load_model_from_checkpoint
* bumped epoch nr to 5
Co-authored-by: William Falcon <waf2107@columbia.edu>
* fix dangling gradients
make sure only the gradients of the current optimizer's paramaters are calculated in the training step.
* add note about multiple optimizer gradient update
* Update training_loop.py
* Run AMP tests in their own process
With opt_level="O1" (the default), AMP patches many
torch functions, which breaks any tests that run afterwards.
This patch introduces a pytest extension that lets
tests be marked with @pytest.mark.spawn so that they
are run in their own process using torch.multiprocessing.spawn
so that the main python interpreter stays un-patched.
Note that tests using DDP already run AMP in its own process,
so they don't need this annotation.
* Fix AMP tests
Since AMP defaults to O1 now, DP tests no longer throw exceptions.
Since AMP patches torch functions, CPU inference no longer works.
Skip prediction step for AMP tests.
* typo
* Renamed `on_sanity_check_start` to `on_train_start` and added `on_train_end` to `ModelHooks`
* changed tests to use `on_train_start` instead of `on_sanity_check_start`
* type: debug
Calculate the adequate number of steps to run during sanity_check.
This fixes the bug when there are two or more validation dataloaders.
- Before: total=self.num_sanity_val_steps
- After: total=self.num_sanity_val_steps*len(self.get_val_dataloaders())
* type: refactor
Put total=... in the next line
* type: refactor
run flake8
* use print for INFO and lower levels summarize()
* use logging.INFO instead of magic number
* bring logging.info back for other cases
* move logging config to __init__.py
* prepend the model summary with a newline
Jirka Borovec