def _get_run_link(self, tracking_uri, run_id): # if using the default Databricks tracking URI and in a notebook, we can automatically # figure out the run-link. if is_databricks_default_tracking_uri(tracking_uri) and ( is_in_databricks_notebook() or is_in_databricks_job()): # use DBUtils to determine workspace information. workspace_host, workspace_id = get_workspace_info_from_dbutils() else: # in this scenario, we're not able to automatically extract the workspace ID # to proceed, and users will need to pass in a databricks profile with the scheme: # databricks://scope:prefix and store the host and workspace-ID as a secret in the # Databricks Secret Manager with scope=<scope> and key=<prefix>-workspaceid. workspace_host, workspace_id = get_workspace_info_from_databricks_secrets( tracking_uri) if not workspace_id: print( "No workspace ID specified; if your Databricks workspaces share the same" " host URL, you may want to specify the workspace ID (along with the host" " information in the secret manager) for run lineage tracking. For more" " details on how to specify this information in the secret manager," " please refer to the model registry documentation.") # retrieve experiment ID of the run for the URL experiment_id = self.get_run(run_id).info.experiment_id if workspace_host and run_id and experiment_id: return construct_run_url(workspace_host, experiment_id, run_id, workspace_id)
def create_model_version(self, name, source, run_id, tags=None, run_link=None): """ Create a new model version from given source or run ID. :param name: Name ID for containing registered model. :param source: Source path where the MLflow model is stored. :param run_id: Run ID from MLflow tracking server that generated the model :param tags: A dictionary of key-value pairs that are converted into :py:class:`mlflow.entities.model_registry.ModelVersionTag` objects. :param run_link: Link to the run from an MLflow tracking server that generated this model. :return: Single :py:class:`mlflow.entities.model_registry.ModelVersion` object created by backend. """ tracking_uri = self._tracking_client.tracking_uri # for Databricks backends, we support automatically populating the run link field if is_databricks_uri( tracking_uri ) and tracking_uri != self._registry_uri and not run_link: # if using the default Databricks tracking URI and in a notebook, we can automatically # figure out the run-link. if is_databricks_default_tracking_uri( tracking_uri) and is_in_databricks_notebook(): # use DBUtils to determine workspace information. workspace_host, workspace_id = get_workspace_info_from_dbutils( ) else: # in this scenario, we're not able to automatically extract the workspace ID # to proceed, and users will need to pass in a databricks profile with the scheme: # databricks://scope/prefix and store the host and workspace-ID as a secret in the # Databricks Secret Manager with scope=<scope> and key=<prefix>-workspaceid. workspace_host, workspace_id = \ get_workspace_info_from_databricks_secrets(tracking_uri) if not workspace_id: print( "No workspace ID specified; if your Databricks workspaces share the same" " host URL, you may want to specify the workspace ID (along with the host" " information in the secret manager) for run lineage tracking. For more" " details on how to specify this information in the secret manager," " please refer to the model registry documentation.") # retrieve experiment ID of the run for the URL experiment_id = self.get_run(run_id).info.experiment_id if workspace_host and run_id and experiment_id: run_link = construct_run_url(workspace_host, experiment_id, run_id, workspace_id) return self._get_registry_client().create_model_version( name=name, source=source, run_id=run_id, tags=tags, run_link=run_link)
def test_is_databricks_default_tracking_uri(tracking_uri, result): assert is_databricks_default_tracking_uri(tracking_uri) == result