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49 lines
1.9 KiB
ReStructuredText
49 lines
1.9 KiB
ReStructuredText
.. _tune-mlflow:
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Using MLflow with Tune
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======================
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.. warning:: If you are using these MLflow integrations with :ref:`ray-client`, it is recommended that you setup a remote Mlflow tracking server instead of one that is backed by the local filesystem.
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`MLflow <https://mlflow.org/>`_ is an open source platform to manage the ML lifecycle, including experimentation,
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reproducibility, deployment, and a central model registry. It currently offers four components, including
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MLflow Tracking to record and query experiments, including code, data, config, and results.
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.. image:: /images/mlflow.png
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:height: 80px
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:alt: MLflow
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:align: center
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:target: https://www.mlflow.org/
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Ray Tune currently offers two lightweight integrations for MLflow Tracking.
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One is the :ref:`MLflowLoggerCallback <tune-mlflow-logger>`, which automatically logs
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metrics reported to Tune to the MLflow Tracking API.
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The other one is the :ref:`@mlflow_mixin <tune-mlflow-mixin>` decorator, which can be
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used with the function API. It automatically
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initializes the MLflow API with Tune's training information and creates a run for each Tune trial.
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Then within your training function, you can just use the
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MLflow like you would normally do, e.g. using ``mlflow.log_metrics()`` or even ``mlflow.autolog()``
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to log to your training process.
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Please :doc:`see here </tune/examples/mlflow_example>` for a full example on how you can use either the
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MLflowLoggerCallback or the mlflow_mixin.
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MLflow AutoLogging
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------------------
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You can also check out :doc:`here </tune/examples/mlflow_ptl_example>` for an example on how you can leverage MLflow
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autologging, in this case with Pytorch Lightning
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MLflow Logger API
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-----------------
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.. _tune-mlflow-logger:
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.. autoclass:: ray.tune.integration.mlflow.MLflowLoggerCallback
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:noindex:
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MLflow Mixin API
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----------------
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.. _tune-mlflow-mixin:
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.. autofunction:: ray.tune.integration.mlflow.mlflow_mixin
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:noindex:
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