GitLab

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* [examples] reorganize files

* remove run_tpu_glue.py as superseded by TPU support in Trainer

* Bugfix: int, not tuple

* move files around

* doc

* [tests] Add sample files for a regression task

* [HUGE] Trainer

* Feedback from @sshleifer

* Feedback from @thomwolf + logging tweak

* [file_utils] when downloading concurrently, get_from_cache will use the cached file for subsequent processes

* [glue] Use default max_seq_length of 128 like before

* [glue] move DataTrainingArguments around

* [ner] Change interface of InputExample, and align run_{tf,pl}

* Re-align the pl scripts a little bit

* ner

* [ner] Add integration test

* Fix language_modeling with API tweak

* [ci] Tweak loss target

* Don't break console output

* amp.initialize: model must be on right device before

* [multiple-choice] update for Trainer

* Re-align to 827d6d6e

* Start cleaning examples

* Fixup

* Using loaded checkpoint with --do_predict

Without this fix, I'm getting near-random validation performance for a trained model, and the validation performance differs per validation run. I think this happens since the `model` variable isn't set with the loaded checkpoint, so I'm using a randomly initialized model. Looking at the model activations, they differ each time I run evaluation (but they don't with this fix).

* Update checkpoint loading

* Fixing model loading

* ✨ Alter base pl transformer to use automodels

* 🐛 Add batch size env variable to function call

* 💄 Apply black code style from Makefile

* 🚚 Move lightning base out of ner directory

* ✨ Add lightning glue example

* 💄 self

* move _feature_file to base class

* ✨ Move eval logging to custom callback

* 💄 Apply black code style

* 🐛 Add parent to pythonpath, remove copy command

* 🐛 Add missing max_length kwarg

* 1. seqeval required by ner pl example. install from examples/requirements. 2. unrecognized arguments: save_steps

* pl checkpoint callback filenotfound error: make directory and pass

* #3159 pl checkpoint path difference

* 1. Updated Readme for pl 2. pl script now also correct displays logs 3. pass gpu ids compared to number of gpus

* Updated results in readme

* 1. updated readme 2. removing deprecated pl methods 3. finalizing scripts

* comment length check

* using deprecated validation_end for stable results

* style related changes

* changes to allow for tpu training

* black

* tpu

* tpu

* initial pytorch lightning commit

* tested multigpu

* Fix learning rate schedule

* black formatting

* fix flake8

* isort

* isort

* .
Co-authored-by: Check your git settings! <chris@chris-laptop>