GitLab

* Add copyright everywhere missing

* Style

* Add early stopping patience and minimum threshold metric must improve to prevent early stopping to pytorch trainer

* Add early stopping test

* Set patience counter to 0 if best metric not defined yet

* Make early stopping a callback. Add callback event for updating the best metric for early stopping callback to trigger on.

* Run make style

* make funciton name sensible

* Improve new argument docstring wording and hope that flakey CI test passes.

* Use on_evaluation callback instead of custom. Remove some debug printing

* Move early stopping arguments and state into early stopping callback

* Run make style

* Remove old code

* Fix docs formatting. make style went rogue on me.

* Remove copied attributes and fix variable

* Add assertions on training arguments instead of mutating them. Move comment out of public docs.

* Make separate test for early stopping callback. Add test of invalid arguments.

* Run make style... I remembered before CI this time!

* appease flake8

* Add EarlyStoppingCallback to callback docs

* Make docstring EarlyStoppingCallabck match other callbacks.

* Fix typo in docs

* first attempt to add AzureML callbacks

* func arg fix

* var name fix, but still won't fix error...

* fixing as in https://discuss.huggingface.co/t/how-to-integrate-an-azuremlcallback-for-logging-in-azure/1713/2



* Avoid lint check of azureml import

* black compliance

* Make isort happy

* Fix point typo in docs

* Add AzureML to Callbacks docs

* Attempt to make sphinx happy

* Format callback docs

* Make documentation style happy

* Make docs compliant to style
Co-authored-by: Davide Fiocco <davide.fiocco@frontiersin.net>

* Add MLflow integration class

Add integration code for MLflow in integrations.py along with the code
that checks that MLflow is installed.

* Add MLflowCallback import

Add import of MLflowCallback in trainer.py

* Handle model argument

Allow the callback to handle model argument and store model config items as hyperparameters.

* Log parameters to MLflow in batches

MLflow cannot log more than a hundred parameters at once.
Code added to split the parameters into batches of 100 items and log the batches one by one.

* Fix style

* Add docs on MLflow callback

* Fix issue with unfinished runs

The "fluent" api used in MLflow integration allows only one run to be active at any given moment. If the Trainer is disposed off and a new one is created, but the training is not finished, it will refuse to log the results when the next trainer is created.

* Add MLflow integration class

Add integration code for MLflow in integrations.py along with the code
that checks that MLflow is installed.

* Add MLflowCallback import

Add import of MLflowCallback in trainer.py

* Handle model argument

Allow the callback to handle model argument and store model config items as hyperparameters.

* Log parameters to MLflow in batches

MLflow cannot log more than a hundred parameters at once.
Code added to split the parameters into batches of 100 items and log the batches one by one.

* Fix style

* Add docs on MLflow callback

* Fix issue with unfinished runs

The "fluent" api used in MLflow integration allows only one run to be active at any given moment. If the Trainer is disposed off and a new one is created, but the training is not finished, it will refuse to log the results when the next trainer is created.

* Initial callback proposal

* Finish various callbacks

* Post-rebase conflicts

* Fix tests

* Don't use something that's not set

* Documentation

* Remove unwanted print.

* Document all models can work

* Add tests + small fixes

* Update docs/source/internal/trainer_utils.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Address review comments

* Fix TF tests

* Real fix this time

* This one should work

* Fix typo

* Really fix typo
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>