* 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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