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* 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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| .. | ||
| callback.rst | ||
| configuration.rst | ||
| logging.rst | ||
| model.rst | ||
| optimizer_schedules.rst | ||
| output.rst | ||
| pipelines.rst | ||
| processors.rst | ||
| tokenizer.rst | ||
| trainer.rst | ||