* Revert "deprecated: epoch indexing from 1 (#2206)"
This reverts commit f94b919b
* chlog
* grad index
* Apply suggestions from code review
* tests
* fix
* test
* deal with NotImplementedError raised by torchtext
* deal with NotImplementedError raised by torchtext
* Added tests for dataloader which raise NotImplementedError in __len__()
* Fixed some typos
Co-authored-by: Thomas Schaaf <tschaaf@cs.cmu.edu>
* move backward
* refactor backward to remove 16 bit from user override
* refactor backward to remove 16 bit from user override
* Update pytorch_lightning/core/hooks.py
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* Fixed the load_from_checkpoint path detected as URL bug
* Fixed the load_from_checkpoint path detected as URL bug
* fixed Caps lock typo
* Added .absolute() to checkpoint path to force hard drive prefix in string
* init the port using a seed that matches process id for ddp
* init the port using a seed that matches process id for ddp
* init the port using a seed that matches process id for ddp
* init the port using a seed that matches process id for ddp
* init the port using a seed that matches process id for ddp
* init the port using a seed that matches process id for ddp
* init the port using a seed that matches process id for ddp
Co-authored-by: Zhaofeng Wu <zfw7@cs.washington.edu>
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* drop train_percent_check
* chlog
* deprecated
* deprecated
* deprecated
* tests
* tests
* Apply suggestions from code review
* tests
* hydra support
* tests
* hydra support
* hydra support
* hydra support
* tests
* typo
* typo
* Update test_dataloaders.py
* docs
* docs
* docs
* docs
Co-authored-by: Jirka <jirka@pytorchlightning.ai>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* fixed percent check for val/test
* fixed percent check for val/test
* fixed percent check for val/test
* fixed percent check for val/test
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* overfit_pct now uses train loaders for val and test and does not shuffle
* add on fit_start on fit_end hooks
* add on fit_start on fit_end hooks
* add on fit_start on fit_end hooks
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* First attempt at auto-moving data for inference
* Correct my copypaste errors
* Correct for if device is CPU
* Get rid of the WIP code I accidentally added
* Add tests
* Make tests more foolproof
* Make sure we stick with pep8 formatting
* Clarify docs a little
* Apply suggestions from code review
* Get everything working again hopefully
* refactor and added hook
variant a
variant b
add test
revert rename
add changelog
docs
* move changelog entry to top
* Move data transfer to utilities
* Add back in warnings for autotransfer
* Get rid of the test code I ended up accidentally commiting again
* Add docs any changelog
* Correct PR number in Changelog
* Correct changelog
* Update data.py
* Update test_cpu.py
* make a decorator
* type hint
* changelog
* changelog
* remove old function
* import
* test for decorator
* fix test
* remove old test
* doctest
* apply decorator directly
* convert doctest to code block
* prevent side effects in tests
* fix merge
* update forward docs
* update docs
* added docs in section "deployment / prediction"
* update changelog
Co-authored-by: Hengjian Jia <henryjia18@gmail.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: William Falcon <waf2107@columbia.edu>
* Add ckpt_path option to LightningModule.test()
If ckpt_path is "best" (default), it loads the best weights saved by ModelCheckpoint for the test loop.
If ckpt_path is a path to a checkpoint file, it loads the weights from the file for the test loop.
If ckpt_path is None, it uses the weights from the end of training for the test loop.
If model parameter is set, ckpt_path is ignored.
* Update test_set.rst
Co-authored-by: William Falcon <waf2107@columbia.edu>
* log row might be a bottleneck depending on network. 50 unblocks this and is small enough for small datasets
* log row might be a bottleneck depending on network. 50 unblocks this and is small enough for small datasets
* past checkpoints
* omegaConf save
* enforce type
* resolve=True
Co-authored-by: Omry Yadan <omry@fb.com>
* test omegaconf
* tests
* test past
Co-authored-by: Omry Yadan <omry@fb.com>