Command-Line Interface

The MLflow command-line interface (CLI) provides a simple interface to various functionality in MLflow. You can use the CLI to start the tracking UI, run projects and runs, serve models to Microsoft Azure ML or Amazon SageMaker, create and list experiments, and download artifacts.

$ mlflow --help
Usage: mlflow [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  azureml      Serve models on Azure ML.
  download     Downloads the artifact at the specified DBFS...
  experiments  Tracking APIs.
  pyfunc       Serve Python models locally.
  run          Run an MLflow project from the given URI.
  sagemaker    Serve models on SageMaker.
  sklearn      Serve SciKit-Learn models.
  ui           Run the MLflow tracking UI.

Each individual command has a detailed help screen accessible via mlflow command_name --help

Azure ML

Various sub-commands to enable easy deployment on Azure ML

Download

Downloads the artifact at the specified DBFS or S3 URI into the specified local output path, or the current directory if no output path is specified.

Experiments

Sub-commands to enable creating and listing experiments.

Python Function

Sub-commands to enable serving Python models or applying them for inference.

SageMaker

Various sub-commands to enable easy deployment on SageMaker.

SciKit-Learn Models

Various sub-commands for serving SciKit-Learn models or applying them for inference.

Run

Run an MLflow project from the given URI.

If running locally (the default), the URI can be either a Git repository URI or a local path. If running on Databricks, the URI must be a Git repository.

By default, Git projects will run in a new working directory with the given parameters, while local projects will run from the project’s root directory.

UI

Run the MLflow tracking UI. The UI is served at http://localhost:5000.