https://community.databricks.com/t5/support-faqs/i-am-running-mlflow-with-a-service-principal-and-getting-a-quot/ta-p/55588 <DIV class="lia-message-template-content-zone"> <P><SPAN>If you view a stack trace and it looks similar to the following:&nbsp;</SPAN></P> <PRE><SPAN>RestException Traceback (most recent call last)</SPAN><BR /><SPAN>File &lt;command-XXXXXXXXXXXX&gt;:7</SPAN><BR /><SPAN>2 mlflow.sklearn.autolog()</SPAN><BR /><SPAN>...</SPAN><BR /><BR /><SPAN>File /databricks/python/lib/python3.9/site-packages/mlflow/tracking/fluent.py:349, in start_run(run_id, experiment_id, run_name, nested, tags, description)</SPAN><BR /><SPAN>345 user_specified_tags[MLFLOW_RUN_NAME] = run_name</SPAN><BR /><SPAN>347 resolved_tags = context_registry.resolve_tags(user_specified_tags)</SPAN><BR /><SPAN>--&gt; 349 active_run_obj = client.create_run(</SPAN><BR /><SPAN>350 experiment_id=exp_id_for_run, tags=resolved_tags, run_name=run_name</SPAN><BR /><SPAN>351 )</SPAN><BR /><SPAN>353 _active_run_stack.append(ActiveRun(active_run_obj))</SPAN><BR /><SPAN>354 return _active_run_stack[-1]</SPAN></PRE> <P><SPAN>You should validate that the service principal has the necessary edit permissions.</SPAN></P> <P><SPAN>By granting edit permissions to the service principal, the job using the <FONT face="courier new,courier">client.create_run</FONT> method (the client is an instance of <FONT face="courier new,courier">MlflowClient</FONT>) can run successfully.</SPAN></P> <P><SPAN>This edit permission ensures the necessary resources, such as the node with the specified ID, are accessible and available for the MLflow operation.</SPAN></P> </DIV> Thu, 11 Jan 2024 01:00:00 GMT Adam_Pavlacka 2024-01-11T01:00:00Z https://community.databricks.com/t5/support-faqs/i-am-running-mlflow-with-a-service-principal-and-getting-a-quot/ta-p/55588 <DIV class="lia-message-template-content-zone"> <P><SPAN>If you view a stack trace and it looks similar to the following:&nbsp;</SPAN></P> <PRE><SPAN>RestException Traceback (most recent call last)</SPAN><BR /><SPAN>File &lt;command-XXXXXXXXXXXX&gt;:7</SPAN><BR /><SPAN>2 mlflow.sklearn.autolog()</SPAN><BR /><SPAN>...</SPAN><BR /><BR /><SPAN>File /databricks/python/lib/python3.9/site-packages/mlflow/tracking/fluent.py:349, in start_run(run_id, experiment_id, run_name, nested, tags, description)</SPAN><BR /><SPAN>345 user_specified_tags[MLFLOW_RUN_NAME] = run_name</SPAN><BR /><SPAN>347 resolved_tags = context_registry.resolve_tags(user_specified_tags)</SPAN><BR /><SPAN>--&gt; 349 active_run_obj = client.create_run(</SPAN><BR /><SPAN>350 experiment_id=exp_id_for_run, tags=resolved_tags, run_name=run_name</SPAN><BR /><SPAN>351 )</SPAN><BR /><SPAN>353 _active_run_stack.append(ActiveRun(active_run_obj))</SPAN><BR /><SPAN>354 return _active_run_stack[-1]</SPAN></PRE> <P><SPAN>You should validate that the service principal has the necessary edit permissions.</SPAN></P> <P><SPAN>By granting edit permissions to the service principal, the job using the <FONT face="courier new,courier">client.create_run</FONT> method (the client is an instance of <FONT face="courier new,courier">MlflowClient</FONT>) can run successfully.</SPAN></P> <P><SPAN>This edit permission ensures the necessary resources, such as the node with the specified ID, are accessible and available for the MLflow operation.</SPAN></P> </DIV> Thu, 11 Jan 2024 01:00:00 GMT https://community.databricks.com/t5/support-faqs/i-am-running-mlflow-with-a-service-principal-and-getting-a-quot/ta-p/55588 Adam_Pavlacka 2024-01-11T01:00:00Z