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MLflow

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MLflow Tracking Serveropen in new window

# start
mlflow server --host 127.0.0.1 --port 8080

Once you started the tracking server, you can connect your local clients by set the MLFLOW_TRACKING_URI environment variable to the server’s URI, along with its scheme and port (for example, http://10.0.0.1:5000) or call [mlflow.set_tracking_uri()](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_tracking_uri).

The mlflow.start_run()open in new window, mlflow.log_param()open in new window, and mlflow.log_metric()open in new window calls then make API requests to your remote tracking server.

Artifact Storesopen in new window

Supported storage types for the Artifact Store

SFTP Server

  • To store artifacts in an SFTP server, specify a URI of the form sftp://user@host/path/to/directory. You should configure the client to be able to log in to the SFTP server without a password over SSH (e.g. public key, identity file in ssh_config, etc.).
  • The format sftp://user:pass@host/ is supported for logging in. However, for safety reasons this is not recommended.
  • When using this store, pysftp must be installed on both the server and the client. Run pip install pysftp to install the required package.

Others

  • backend store is used to store experiment and metadata (parameters, metrics, tags) in MLflow Tracking Server. MLflow supports two types of backend stores:
    • File Store
    • Database Store
  • artifact store is a storage unit for large data (e.g. S3 bucket, shared NFS file system) and is where the client records its model outputs (e.g. model files).

Code

mlflow server --backend-store-uri mysql+pymysql://user:password@host:port/mlflow --default-artifact-root sftp://user@host/path/to/directory

pkill -f "mlflow server"