def server( backend_store_uri, default_artifact_root, serve_artifacts, artifacts_only, artifacts_destination, host, port, workers, static_prefix, gunicorn_opts, waitress_opts, expose_prometheus, ): """ Run the MLflow tracking server. The server which listen on http://localhost:5000 by default, and only accept connections from the local machine. To let the server accept connections from other machines, you will need to pass ``--host 0.0.0.0`` to listen on all network interfaces (or a specific interface address). """ from mlflow.server import _run_server from mlflow.server.handlers import initialize_backend_stores _validate_server_args(gunicorn_opts=gunicorn_opts, workers=workers, waitress_opts=waitress_opts) # Ensure that both backend_store_uri and default_artifact_uri are set correctly. if not backend_store_uri: backend_store_uri = DEFAULT_LOCAL_FILE_AND_ARTIFACT_PATH default_artifact_root = resolve_default_artifact_root( serve_artifacts, default_artifact_root, backend_store_uri ) try: initialize_backend_stores(backend_store_uri, default_artifact_root) except Exception as e: _logger.error("Error initializing backend store") _logger.exception(e) sys.exit(1) try: _run_server( backend_store_uri, default_artifact_root, serve_artifacts, artifacts_only, artifacts_destination, host, port, static_prefix, workers, gunicorn_opts, waitress_opts, expose_prometheus, ) except ShellCommandException: eprint("Running the mlflow server failed. Please see the logs above for details.") sys.exit(1)
def ui(backend_store_uri, default_artifact_root, serve_artifacts, artifacts_destination, port, host): """ Launch the MLflow tracking UI for local viewing of run results. To launch a production server, use the "mlflow server" command instead. The UI will be visible at http://localhost:5000 by default, and only accept connections from the local machine. To let the UI server accept connections from other machines, you will need to pass ``--host 0.0.0.0`` to listen on all network interfaces (or a specific interface address). """ from mlflow.server import _run_server from mlflow.server.handlers import initialize_backend_stores # Ensure that both backend_store_uri and default_artifact_uri are set correctly. if not backend_store_uri: backend_store_uri = DEFAULT_LOCAL_FILE_AND_ARTIFACT_PATH default_artifact_root = resolve_default_artifact_root( serve_artifacts, default_artifact_root, backend_store_uri, resolve_to_local=True) try: initialize_backend_stores(backend_store_uri, default_artifact_root) except Exception as e: _logger.error("Error initializing backend store") _logger.exception(e) sys.exit(1) # TODO: We eventually want to disable the write path in this version of the server. try: _run_server( backend_store_uri, default_artifact_root, serve_artifacts, False, artifacts_destination, host, port, None, 1, ) except ShellCommandException: eprint( "Running the mlflow server failed. Please see the logs above for details." ) sys.exit(1)