def _get_model_info(uri: str, client: MlflowClient) -> str:
"""Transform the rikai model uri to something that mlflow understands"""
parsed = urlparse(uri)
try:
client.get_registered_model(parsed.hostname)
return _parse_model_ref(parsed, client)
except MlflowException:
return _parse_runid_ref(parsed, client)
def test_client_registry_operations_raise_exception_with_unsupported_registry_store():
"""
This test case ensures that Model Registry operations invoked on the `MlflowClient`
fail with an informative error message when the registry store URI refers to a
store that does not support Model Registry features (e.g., FileStore).
"""
with TempDir() as tmp:
client = MlflowClient(registry_uri=tmp.path())
expected_failure_functions = [
client._get_registry_client,
lambda: client.create_registered_model("test"),
lambda: client.get_registered_model("test"),
lambda: client.create_model_version("test", "source", "run_id"),
lambda: client.get_model_version("test", 1),
]
for func in expected_failure_functions:
with pytest.raises(MlflowException) as exc:
func()
assert exc.value.error_code == ErrorCode.Name(FEATURE_DISABLED)
# MAGIC %md
# MAGIC ## Load parameters form MLFlow Registry
# COMMAND ----------
import mlflow
from mlflow.tracking import MlflowClient
# set model & registry params
model_name = 'Airbnb_Model'
cmr_uri = 'databricks://mlops_webinar:CMR'
dev_uri = 'databricks://mlops_webinar:dev'
# fetch model from registry
client = MlflowClient(tracking_uri=dev_uri, registry_uri=cmr_uri)
reg_mdl = client.get_registered_model(model_name)
# COMMAND ----------
# find latest production model
prod_mdl = None
for mdl in reg_mdl.latest_versions:
print(
f"{mdl.name} (v. {mdl.version}) in {mdl.current_stage} is {mdl.status}"
)
if mdl.current_stage == "Production":
prod_mdl = mdl
if prod_mdl is None:
raise RuntimeError("No Production Model Found")
import mlflow
from mlflow.tracking import MlflowClient
if __name__ == "__main__":
def print_registered_model_info(rm):
print("name: {}".format(rm.name))
print("tags: {}".format(rm.tags))
print("description: {}".format(rm.description))
name = "SocialTextAnalyzer"
tags = {"nlp.framework": "Spark NLP"}
desc = "This sentiment analysis model classifies the tone-happy, sad, angry."
mlflow.set_tracking_uri("sqlite:///mlruns.db")
client = MlflowClient()
client.create_registered_model(name, tags, desc)
print_registered_model_info(client.get_registered_model(name))
print("--")
# rename the model
new_name = "SocialMediaTextAnalyzer"
client.rename_registered_model(name, new_name)
print_registered_model_info(client.get_registered_model(new_name))
import mlflow
from mlflow.tracking import MlflowClient
if __name__ == "__main__":
def print_model_info(rm):
print("--")
print("name: {}".format(rm.name))
print("tags: {}".format(rm.tags))
name = "SocialMediaTextAnalyzer"
tags = {"nlp.framework1": "Spark NLP"}
mlflow.set_tracking_uri("sqlite:///mlruns.db")
client = MlflowClient()
# Create registered model, set an additional tag, and fetch
# update model info
client.create_registered_model(name, tags)
model = client.get_registered_model(name)
print_model_info(model)
client.set_registered_model_tag(name, "nlp.framework2", "VADER")
model = client.get_registered_model(name)
print_model_info(model)
