def delete(model_name: str): print(f"Deleting the registered model named {model_name}...") client = MlflowClient(tracking_uri="databricks") for version in client.search_model_versions("name = '%s'" % model_name): if version.current_stage != "None": print(f"Transition {model_name}:{version.version} from {version.current_stage} to None...") client.transition_model_version_stage(model_name, version.version, "None") client.delete_registered_model(model_name)
print(f"description: {rm.description}") mlflow.set_tracking_uri("sqlite:///mlruns.db") client = MlflowClient() # Register a couple of models with respctive names, tags and descs for name, tags, desc in [("name1", {"t1":"t1"}, "description1"), ("name2", {"t2":"t2"}, "description2")]: client.create_registered_model(name, tags, desc) # Fetch all registered models print_registered_models_info(client.list_registered_models()) # Delete one registered model and fetch again client.delete_registered_model("name1") print_registered_models_info(client.list_registered_models())
print("=" * 80) [ print(pprint.pprint(dict(rm), indent=4)) for rm in client.list_registered_models() ] # Get a list of specific versions of the named models print(f"List of Model = {model_name} and Versions") print("=" * 80) [ pprint.pprint(dict(mv), indent=4) for mv in client.search_model_versions("name='sk-learn-random-forest-reg-model'") ] client.delete_model_version(name="sk-learn-random-forest-reg-model", version=1) print("=" * 80) [ pprint.pprint(dict(mv), indent=4) for mv in client.search_model_versions("name='sk-learn-random-forest-reg-model'") ] client.delete_registered_model(model_name) # # check if all are removed from the registry # print("=" * 80) [ print(pprint.pprint(dict(rm), indent=4)) for rm in client.list_registered_models() ]
import mlflow from mlflow.tracking import MlflowClient import warnings if __name__ == "__main__": warnings.filterwarnings("ignore") print(mlflow.__version__) # set the tracking server to be localhost with sqlite as tracking store local_registry = "sqlite:///mlruns.db" mlflow.set_tracking_uri(local_registry) print(f"Running local model registry={local_registry}") model_name = "WeatherForecastModel" client = MlflowClient() # Get all versions of this model and transition them to Archive for deletion model_versions = client.search_model_versions( "name='WeatherForecastModel'") if model_versions: for mv in model_versions: client.transition_model_version_stage(name="WeatherForecastModel", version=mv.version, stage="archived") # Delete this registered model client.delete_registered_model("WeatherForecastModel")