def create_or_patch(
request: Request,
project: str,
endpoint_id: str,
model_endpoint: ModelEndpoint,
auth_verifier: mlrun.api.api.deps.AuthVerifier = Depends(
mlrun.api.api.deps.AuthVerifier),
db_session: Session = Depends(mlrun.api.api.deps.get_db_session),
) -> Response:
"""
Either create or updates the kv record of a given ModelEndpoint object
"""
access_key = get_access_key(request.headers)
if project != model_endpoint.metadata.project:
raise MLRunConflictError(
f"Can't store endpoint of project {model_endpoint.metadata.project} into project {project}"
)
if endpoint_id != model_endpoint.metadata.uid:
raise MLRunConflictError(
f"Mismatch between endpoint_id {endpoint_id} and ModelEndpoint.metadata.uid {model_endpoint.metadata.uid}."
f"\nMake sure the supplied function_uri, and model are configured as intended"
)
ModelEndpoints.create_or_patch(
db_session=db_session,
access_key=access_key,
model_endpoint=model_endpoint,
leader_session=auth_verifier.auth_info.session,
)
return Response(status_code=HTTPStatus.NO_CONTENT.value)
async def create_or_patch(request: Request, project: str, endpoint_id: str,
model_endpoint: ModelEndpoint) -> Response:
"""
Either create or updates the kv record of a given ModelEndpoint object
"""
access_key = get_access_key(request.headers)
if project != model_endpoint.metadata.project:
raise MLRunConflictError(
f"Can't store endpoint of project {model_endpoint.metadata.project} into project {project}"
)
if endpoint_id != model_endpoint.metadata.uid:
raise MLRunConflictError(
f"Mismatch between endpoint_id {endpoint_id} and ModelEndpoint.metadata.uid {model_endpoint.metadata.uid}."
f"\nMake sure the supplied function_uri, and model are configured as intended"
)
await ModelEndpoints.create_or_patch(access_key=access_key,
model_endpoint=model_endpoint)
return Response(status_code=HTTPStatus.NO_CONTENT.value)
def create_or_patch(
project: str,
endpoint_id: str,
model_endpoint: ModelEndpoint,
auth_info: mlrun.api.schemas.AuthInfo = Depends(
mlrun.api.api.deps.authenticate_request),
db_session: Session = Depends(mlrun.api.api.deps.get_db_session),
) -> Response:
"""
Either create or updates the kv record of a given ModelEndpoint object
"""
mlrun.api.utils.auth.verifier.AuthVerifier(
).query_project_resource_permissions(
mlrun.api.schemas.AuthorizationResourceTypes.model_endpoint,
project,
endpoint_id,
mlrun.api.schemas.AuthorizationAction.store,
auth_info,
)
# get_access_key will validate the needed auth (which is used later) exists in the request
mlrun.api.crud.ModelEndpoints().get_access_key(auth_info)
if project != model_endpoint.metadata.project:
raise MLRunConflictError(
f"Can't store endpoint of project {model_endpoint.metadata.project} into project {project}"
)
if endpoint_id != model_endpoint.metadata.uid:
raise MLRunConflictError(
f"Mismatch between endpoint_id {endpoint_id} and ModelEndpoint.metadata.uid {model_endpoint.metadata.uid}."
f"\nMake sure the supplied function_uri, and model are configured as intended"
)
# Since the endpoint records are created automatically, at point of serving function deployment, we need to use
# V3IO_ACCESS_KEY here
mlrun.api.crud.ModelEndpoints().create_or_patch(
db_session=db_session,
access_key=os.environ.get("V3IO_ACCESS_KEY"),
model_endpoint=model_endpoint,
auth_info=auth_info,
)
return Response(status_code=HTTPStatus.NO_CONTENT.value)
def verify_endpoint(project, endpoint_id):
endpoint_id_project, _ = endpoint_id.split(".")
if endpoint_id_project != project:
raise MLRunConflictError(
f"project: {project} and endpoint_id: {endpoint_id} missmatch."
)