Getting errors in DLT Pipeline while using ML Model

vittal
New Contributor

‎01-24-2023 10:35 PM

I am getting the following error when I try to run ML Models in Delta live Table Pipeline

  File "/local_disk0/.ephemeral_nfs/repl_tmp_data/ReplId-55c61-9b898-2c4b6-d/mlflow/envs/virtualenv_envs/mlflow-888f8c9b966409e6bddca3894244b4df9d1f94c1/lib/python3.9/site-packages/databricks_cli/configure/provider.py", line 134, in get_config
    raise InvalidConfigurationError.for_profile(None)
databricks_cli.utils.InvalidConfigurationError: You haven't configured the CLI yet! Please configure by entering `/local_disk0/.ephemeral_nfs/repl_tmp_data/ReplId-55c61-9b898-2c4b6-d/mlflow/envs/virtualenv_envs/mlflow-888f8c9b966409e6bddca3894244b4df9d1f94c1/bin/gunicorn configure`

Any pointers to solve this will be helpful, thanks.

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shan_chandra
Databricks Employee
Databricks Employee

‎04-27-2023 09:17 AM

@Vittal Pai​  - In general, please follow the below steps for the mlflow CLI error,

Step 1: set up API token and create secrets as mentioned in the below document

https://docs.databricks.com/machine-learning/manage-model-lifecycle/multiple-workspaces.html#set-up-...

Step 2: embed the below piece of code at the top of your notebook

import os
os.environ["DATABRICKS_TOKEN"] = access_token
os.environ["DATABRICKS_HOST"] = "https://<workspace-url>;"
access_token = dbutils.secrets.get(scope="{<secret-name>}", key="mlflow-access-token")
from databricks_cli.configure import provider
config_provider = provider.EnvironmentVariableConfigProvider()
provider.set_config_provider(config_provider)
 

In case you face an issue, Please ensure the token is refreshed, as the token has a shell life. 

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