Mlflow integration callback #8016
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Add integration code for MLflow in integrations.py along with the code that checks that MLflow is installed.
Add import of MLflowCallback in trainer.py
Allow the callback to handle model argument and store model config items as hyperparameters.
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.
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 integration code for MLflow in integrations.py along with the code that checks that MLflow is installed.
Add import of MLflowCallback in trainer.py
Allow the callback to handle model argument and store model config items as hyperparameters.
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.
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.
…e-field/transformers into mlflow-integration-callback
sgugger
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Oct 26, 2020
fabiocapsouza
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Nov 15, 2020
* 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.
fabiocapsouza
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Nov 15, 2020
This reverts commit 5b13249.
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What does this PR do?
This PR adds Trainer integration with MLflow.
It is implemented in roughly the same way as other integration callbacks (CometML, wandb) and gets added to the list of Trainer callbacks automatically when mlflow is installed. All the mlflow parameters are configured with env variables, as described in the library documentation. This PR adds an additional environment variable,
HF_MLFLOW_LOG_ARTIFACTS, which controls whether to use mlflow artifact logging facility to save artifacts generated after training (it doesn't make much sense if mlflow is used locally).Fixes #7698
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