MlflowException: Unsupported Databricks profile key prefix: ''. Key prefixes cannot be empty.

AnnamalaiVR — Mon, 24 Jul 2023 14:26:31 GMT

I am trying to fetch data from mlflow model registry in Databricks and to use it in my local notebook. But I don't find any resource in internet to do so. I want to configure my mlflow in such a way i can fetch model registry values from databricks workspace. Also, I am sharing the code for more clarification.

In the below code. I'm getting the error in client.search_model_versions() line.

databricks_host = "*********************" databricks_token = "**********" databricks_org_id = "******" # Set the Databricks tracking URI tracking_uri = f"databricks://{databricks_host}?org_id={databricks_org_id}" nw_dict = dict() for mv in client.search_model_versions("name='sk-learn-logistic-reg-model'"): dic = dict(mv) run_data_dict = client.get_run(dic['run_id']).data.to_dictionary() print(run_data_dict['metrics']['accuracy score']) nw_dict[dic['run_id']] = run_data_dict['metrics']['accuracy score']

Re: MlflowException: Unsupported Databricks profile key prefix: ''. Key prefixes cannot be empty.

Kumaran — Thu, 27 Jul 2023 20:41:46 GMT

Hi @AnnamalaiVR,

Thank you for posting the question in Databricks Community.

In your Python code, import the MLflow library and create a client object to access your Model Registry.

%python

import mlflow

# Set the Databricks tracking URI

databricks_host = "*********************"

databricks_token = "**********"

databricks_org_id = "******"

tracking_uri = f"databricks://{databricks_host}?org_id={databricks_org_id}"

mlflow.set_tracking_uri(tracking_uri)

# Configure the MLflow client

client = mlflow.tracking.MlflowClient()

Now you can query the Model Registry using the client object. Here's an example to fetch the registered model versions for a given model name:

%python

model_name = "my_model"

model_versions = client.search_model_versions(f"name='{model_name}'")

for model_version in model_versions:

# Fetch the run ID and metrics for the model version

run_id = model_version.run_id

metrics = client.get_run(run_id).data.metrics

# Add the metrics to a dictionary

accuracy_score = metrics["accuracy score"]

nw_dict[run_id] = accuracy_score

Make sure to replace the model_name variable with the name of your registered model.

topic Re: MlflowException: Unsupported Databricks profile key prefix: ''. Key prefixes cannot be empty. in Machine Learning

MlflowException: Unsupported Databricks profile key prefix: ''. Key prefixes cannot be empty.

Re: MlflowException: Unsupported Databricks profile key prefix: ''. Key prefixes cannot be empty.