if not backend_store_uri:
backend_store_uri = DEFAULT_LOCAL_FILE_AND_ARTIFACT_PATH
if not default_artifact_root:
if _is_local_uri(backend_store_uri):
default_artifact_root = backend_store_uri
else:
eprint("Option 'default-artifact-root' is required, when backend store is not "
"local file based.")
sys.exit(1)
try:
_run_server(backend_store_uri, default_artifact_root, host, port, workers, static_prefix,
gunicorn_opts)
except ShellCommandException:
eprint("Running the mlflow server failed. Please see the logs above for details.")
sys.exit(1)
cli.add_command(mlflow.pyfunc.cli.commands)
cli.add_command(mlflow.rfunc.cli.commands)
cli.add_command(mlflow.sagemaker.cli.commands)
cli.add_command(mlflow.experiments.commands)
cli.add_command(mlflow.store.cli.commands)
cli.add_command(mlflow.azureml.cli.commands)
cli.add_command(mlflow.runs.commands)
cli.add_command(mlflow.db.commands)
if __name__ == '__main__':
cli()
def server(file_store, default_artifact_root, host, port, workers,
static_prefix, gunicorn_opts):
"""
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).
"""
try:
_run_server(file_store, default_artifact_root, host, port, workers,
static_prefix, gunicorn_opts)
except ShellCommandException:
print(
"Running the mlflow server failed. Please see the logs above for details.",
file=sys.stderr)
sys.exit(1)
cli.add_command(mlflow.sklearn.commands)
cli.add_command(mlflow.data.download)
cli.add_command(mlflow.pyfunc.cli.commands)
cli.add_command(mlflow.rfunc.cli.commands)
cli.add_command(mlflow.sagemaker.cli.commands)
cli.add_command(mlflow.azureml.cli.commands)
cli.add_command(mlflow.experiments.commands)
cli.add_command(mlflow.store.cli.commands)
if __name__ == '__main__':
cli()
"local file based.")
sys.exit(1)
try:
initialize_backend_stores(backend_store_uri, default_artifact_root)
except Exception as e: # pylint: disable=broad-except
_logger.error("Error initializing backend store")
_logger.exception(e)
sys.exit(1)
try:
_run_server(backend_store_uri, default_artifact_root, host, port,
static_prefix, workers, gunicorn_opts, waitress_opts)
except ShellCommandException:
eprint(
"Running the mlflow server failed. Please see the logs above for details."
)
sys.exit(1)
cli.add_command(mlflow.models.cli.commands)
cli.add_command(mlflow.sagemaker.cli.commands)
cli.add_command(mlflow.experiments.commands)
cli.add_command(mlflow.store.artifact.cli.commands)
cli.add_command(mlflow.azureml.cli.commands)
cli.add_command(mlflow.runs.commands)
cli.add_command(mlflow.db.commands)
if __name__ == '__main__':
cli()