chore: import upstream snapshot with attribution

docs/api_reference/source/cli.rst

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+.. _cli:
+
+Command-Line Interface
+======================
+
+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,
+serve MLflow Python Function and scikit-learn models, serve MLflow Python Function and scikit-learn models, and serve models on
+`Microsoft Azure Machine Learning <https://azure.microsoft.com/en-us/services/machine-learning-service/>`_
+and `Amazon SageMaker <https://aws.amazon.com/sagemaker/>`_.
+
+Each individual command has a detailed help screen accessible via ``mlflow command_name --help``.
+
+.. attention::
+    It is advisable to set the ``MLFLOW_TRACKING_URI`` environment variable by default, 
+    as the CLI does not automatically connect to a tracking server. Without this, 
+    the CLI will default to using the local filesystem where the command is executed, 
+    rather than connecting to a localhost or remote HTTP server. 
+    Setting ``MLFLOW_TRACKING_URI`` to the URL of your desired tracking server is required for most of the commands below.
+
+
+.. contents:: Table of Contents
+  :local:
+  :depth: 2
+
+.. click:: mlflow.cli:cli
+  :prog: mlflow
+  :show-nested: