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)