import urllib
from typing import Optional
from urllib.parse import urlparse
from mlflow.environment_variables import MLFLOW_DEPLOYMENTS_TARGET
from mlflow.exceptions import MlflowException
from mlflow.utils.uri import append_to_uri_path
_deployments_target: Optional[str] = None
def parse_target_uri(target_uri):
"""Parse out the deployment target from the provided target uri"""
parsed = urllib.parse.urlparse(target_uri)
if not parsed.scheme:
if parsed.path:
# uri = 'target_name' (without :/<path>)
return parsed.path
raise MlflowException(
f"Not a proper deployment URI: {target_uri}. "
+ "Deployment URIs must be of the form 'target' or 'target:/suffix'"
)
return parsed.scheme
def _is_valid_uri(uri: str) -> bool:
"""
Evaluates the basic structure of a provided uri to determine if the scheme and
netloc are provided
"""
try:
parsed = urlparse(uri)
return bool(parsed.scheme and parsed.netloc)
except ValueError:
return False
def resolve_endpoint_url(base_url: str, endpoint: str) -> str:
"""Performs a validation on whether the returned value is a fully qualified url
or requires the assembly of a fully qualified url by appending `endpoint`.
Args:
base_url: The base URL. Should include the scheme and domain, e.g.,
``http://127.0.0.1:6000``.
endpoint: The endpoint to be appended to the base URL, e.g., ``/api/2.0/endpoints/`` or,
in the case of Databricks, the fully qualified url.
Returns:
The complete URL, either directly returned or formed and returned by joining the
base URL and the endpoint path.
"""
return endpoint if _is_valid_uri(endpoint) else append_to_uri_path(base_url, endpoint)
[docs]def set_deployments_target(target: str):
"""Sets the target deployment client for MLflow deployments
Args:
target: The full uri of a running MLflow deployments server or, if running on
Databricks, "databricks".
"""
if not _is_valid_target(target):
raise MlflowException.invalid_parameter_value(
"The target provided is not a valid uri or 'databricks'"
)
global _deployments_target
_deployments_target = target
[docs]def get_deployments_target() -> str:
"""
Returns the currently set MLflow deployments target iff set.
If the deployments target has not been set by using ``set_deployments_target``, an
``MlflowException`` is raised.
"""
global _deployments_target
if _deployments_target is not None:
return _deployments_target
elif uri := MLFLOW_DEPLOYMENTS_TARGET.get():
return uri
else:
raise MlflowException(
"No deployments target has been set. Please either set the MLflow deployments target"
" via `mlflow.deployments.set_deployments_target()` or set the environment variable "
f"{MLFLOW_DEPLOYMENTS_TARGET} to the running deployment server's uri"
)
def _is_valid_target(target: str):
"""
Evaluates the basic structure of a provided target to determine if the scheme and
netloc are provided
"""
if target == "databricks":
return True
return _is_valid_uri(target)