- 27 Oct, 2020 1 commit
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Davide Fiocco authored
* 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>
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- 26 Oct, 2020 1 commit
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noise-field authored
* 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.
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- 13 Oct, 2020 1 commit
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Tiger authored
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- 07 Oct, 2020 1 commit
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Sylvain Gugger authored
* 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:
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