from functools import partial
from typing import Optional
from mlflow.environment_variables import MLFLOW_REGISTRY_URI
from mlflow.store.db.db_types import DATABASE_ENGINES
from mlflow.store.model_registry.databricks_workspace_model_registry_rest_store import (
DatabricksWorkspaceModelRegistryRestStore,
)
from mlflow.store.model_registry.file_store import FileStore
from mlflow.store.model_registry.rest_store import RestStore
from mlflow.tracking._model_registry.registry import ModelRegistryStoreRegistry
from mlflow.tracking._tracking_service.utils import (
_resolve_tracking_uri,
)
from mlflow.utils._spark_utils import _get_active_spark_session
from mlflow.utils.credentials import get_default_host_creds
from mlflow.utils.databricks_utils import (
get_databricks_host_creds,
is_in_databricks_serverless_runtime,
warn_on_deprecated_cross_workspace_registry_uri,
)
from mlflow.utils.uri import (
_DATABRICKS_UNITY_CATALOG_SCHEME,
_OSS_UNITY_CATALOG_SCHEME,
construct_db_uc_uri_from_profile,
get_db_info_from_uri,
is_databricks_uri,
)
# NOTE: in contrast to tracking, we do not support the following ways to specify
# the model registry URI:
# - via environment variables like MLFLOW_TRACKING_URI, MLFLOW_TRACKING_USERNAME, ...
# We do support specifying it
# - via the ``model_registry_uri`` parameter when creating an ``MlflowClient`` or
# ``ModelRegistryClient``.
# - via a utility method ``mlflow.set_registry_uri``
# - by not specifying anything: in this case we assume the model registry store URI is
# the same as the tracking store URI. This means Tracking and Model Registry are
# backed by the same backend DB/Rest server. However, note that we access them via
# different ``Store`` classes (e.g. ``mlflow.store.tracking.SQLAlchemyStore`` &
# ``mlflow.store.model_registry.SQLAlchemyStore``).
# This means the following combinations are not supported:
# - Tracking RestStore & Model Registry RestStore that use different credentials.
_registry_uri = None
[docs]def set_registry_uri(uri: str) -> None:
"""Set the registry server URI. This method is especially useful if you have a registry server
that's different from the tracking server.
Args:
uri: An empty string, or a local file path, prefixed with ``file:/``. Data is stored
locally at the provided file (or ``./mlruns`` if empty). An HTTP URI like
``https://my-tracking-server:5000`` or ``http://my-oss-uc-server:8080``. A Databricks
workspace, provided as the string "databricks" or, to use a Databricks CLI
`profile <https://github.com/databricks/databricks-cli#installation>`_,
"databricks://<profileName>".
.. code-block:: python
:caption: Example
import mflow
# Set model registry uri, fetch the set uri, and compare
# it with the tracking uri. They should be different
mlflow.set_registry_uri("sqlite:////tmp/registry.db")
mr_uri = mlflow.get_registry_uri()
print(f"Current registry uri: {mr_uri}")
tracking_uri = mlflow.get_tracking_uri()
print(f"Current tracking uri: {tracking_uri}")
# They should be different
assert tracking_uri != mr_uri
.. code-block:: text
:caption: Output
Current registry uri: sqlite:////tmp/registry.db
Current tracking uri: file:///.../mlruns
"""
global _registry_uri
_registry_uri = uri
if uri:
# Set 'MLFLOW_REGISTRY_URI' environment variable
# so that subprocess can inherit it.
MLFLOW_REGISTRY_URI.set(_registry_uri)
def _get_registry_uri_from_spark_session():
session = _get_active_spark_session()
if session is None:
return None
if is_in_databricks_serverless_runtime():
# Connected to Serverless
return "databricks-uc"
from pyspark.sql.utils import AnalysisException
try:
return session.conf.get("spark.mlflow.modelRegistryUri", None)
except AnalysisException:
# In serverless clusters, session.conf.get() is unsupported
# and raises an AnalysisException. We may encounter this case
# when DBConnect is used to connect to a serverless cluster,
# in which case the prior `is_in_databricks_serverless_runtime()`
# check will have returned false (as of 2025-06-07, it checks
# an environment variable that isn't set by DBConnect)
return None
def _get_registry_uri_from_context():
if _registry_uri is not None:
return _registry_uri
elif (uri := MLFLOW_REGISTRY_URI.get()) or (uri := _get_registry_uri_from_spark_session()):
return uri
return _registry_uri
def _get_default_registry_uri_for_tracking_uri(tracking_uri: Optional[str]) -> Optional[str]:
"""
Get the default registry URI for a given tracking URI.
If the tracking URI starts with "databricks", returns "databricks-uc" with profile if present.
Otherwise, returns the tracking URI itself.
Args:
tracking_uri: The tracking URI to get the default registry URI for
Returns:
The default registry URI
"""
if tracking_uri is not None and is_databricks_uri(tracking_uri):
# If the tracking URI is "databricks", we impute the registry URI as "databricks-uc"
# corresponding to Databricks Unity Catalog Model Registry, which is the recommended
# model registry offering on Databricks
if tracking_uri == "databricks":
return _DATABRICKS_UNITY_CATALOG_SCHEME
else:
# Extract profile from tracking URI and construct databricks-uc URI
profile, key_prefix = get_db_info_from_uri(tracking_uri)
if profile:
# Reconstruct the profile string including key_prefix if present
profile_string = f"{profile}:{key_prefix}" if key_prefix else profile
return construct_db_uc_uri_from_profile(profile_string)
else:
return _DATABRICKS_UNITY_CATALOG_SCHEME
# For non-databricks tracking URIs, use the tracking URI as the registry URI
return tracking_uri
[docs]def get_registry_uri() -> str:
"""Get the current registry URI. If none has been specified, defaults to the tracking URI.
Returns:
The registry URI.
.. code-block:: python
# Get the current model registry uri
mr_uri = mlflow.get_registry_uri()
print(f"Current model registry uri: {mr_uri}")
# Get the current tracking uri
tracking_uri = mlflow.get_tracking_uri()
print(f"Current tracking uri: {tracking_uri}")
# They should be the same
assert mr_uri == tracking_uri
.. code-block:: text
Current model registry uri: file:///.../mlruns
Current tracking uri: file:///.../mlruns
"""
return _resolve_registry_uri()
def _resolve_registry_uri(
registry_uri: Optional[str] = None, tracking_uri: Optional[str] = None
) -> Optional[str]:
"""
Resolve the registry URI following the same logic as get_registry_uri().
"""
return (
registry_uri
or _get_registry_uri_from_context()
or _get_default_registry_uri_for_tracking_uri(_resolve_tracking_uri(tracking_uri))
)
def _get_sqlalchemy_store(store_uri):
from mlflow.store.model_registry.sqlalchemy_store import SqlAlchemyStore
return SqlAlchemyStore(store_uri)
def _get_rest_store(store_uri, **_):
return RestStore(partial(get_default_host_creds, store_uri))
def _get_databricks_rest_store(store_uri, **_):
warn_on_deprecated_cross_workspace_registry_uri(registry_uri=store_uri)
return DatabricksWorkspaceModelRegistryRestStore(partial(get_databricks_host_creds, store_uri))
# We define the global variable as `None` so that instantiating the store does not lead to circular
# dependency issues.
_model_registry_store_registry = None
def _get_file_store(store_uri, **_):
return FileStore(store_uri)
def _get_store_registry():
global _model_registry_store_registry
from mlflow.store._unity_catalog.registry.rest_store import UcModelRegistryStore
from mlflow.store._unity_catalog.registry.uc_oss_rest_store import UnityCatalogOssStore
if _model_registry_store_registry is not None:
return _model_registry_store_registry
_model_registry_store_registry = ModelRegistryStoreRegistry()
_model_registry_store_registry.register("databricks", _get_databricks_rest_store)
# Register a placeholder function that raises if users pass a registry URI with scheme
# "databricks-uc"
_model_registry_store_registry.register(_DATABRICKS_UNITY_CATALOG_SCHEME, UcModelRegistryStore)
_model_registry_store_registry.register(_OSS_UNITY_CATALOG_SCHEME, UnityCatalogOssStore)
for scheme in ["http", "https"]:
_model_registry_store_registry.register(scheme, _get_rest_store)
for scheme in DATABASE_ENGINES:
_model_registry_store_registry.register(scheme, _get_sqlalchemy_store)
for scheme in ["", "file"]:
_model_registry_store_registry.register(scheme, _get_file_store)
_model_registry_store_registry.register_entrypoints()
return _model_registry_store_registry
def _get_store(store_uri=None, tracking_uri=None):
return _get_store_registry().get_store(store_uri, tracking_uri)
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