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")
