wehub-resource-sync
28 lines
1.2 KiB
ReStructuredText
28 lines
1.2 KiB
ReStructuredText
.. _cli:
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Command-Line Interface
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======================
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The MLflow command-line interface (CLI) provides a simple interface to various functionality in MLflow. You can use the CLI to run projects, start the tracking UI, create and list experiments, download run artifacts,
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serve MLflow Python Function and scikit-learn models, serve MLflow Python Function and scikit-learn models, and serve models on
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`Microsoft Azure Machine Learning <https://azure.microsoft.com/en-us/services/machine-learning-service/>`_
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and `Amazon SageMaker <https://aws.amazon.com/sagemaker/>`_.
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Each individual command has a detailed help screen accessible via ``mlflow command_name --help``.
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.. attention::
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It is advisable to set the ``MLFLOW_TRACKING_URI`` environment variable by default,
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as the CLI does not automatically connect to a tracking server. Without this,
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the CLI will default to using the local filesystem where the command is executed,
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rather than connecting to a localhost or remote HTTP server.
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Setting ``MLFLOW_TRACKING_URI`` to the URL of your desired tracking server is required for most of the commands below.
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.. contents:: Table of Contents
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:local:
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:depth: 2
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.. click:: mlflow.cli:cli
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:prog: mlflow
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:show-nested:
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