Hey Guys,
I'm trying to make automated process to run ML training sessions using mlflow and databricks jobs.
While developing the model on my local machine using IDE, When finished I have a template notebook that get as parameters the mlflow project path and params.
While trying to run a job that will run this mlflow project i faced some issues and hope you will be able to help me.
Inside the training code ( e.g. main entry point ), I'm using set_experiment and start run with specific names for those run / experiment.
When trying to run this code as mlflow project using run api call, When not specified exp_name / run_name in the run api call im getting an error that i can't create an experiment from a job.
On the other hand When exp_name and run_name are specified within the run api call mlflow ignore set_experiment and start_run with the run name i wanted, Do you know if there is an option to enable creation of a experiments from a job? or way to overcome the need to specify exp_name and run name inside the run call?
After some tries i saw that mlflow create an experiment before the training code actually run, this is little problematic because if i need to specify the run name and the experiment name manually this process not gonna be to much automated 😅
Code example:
import mlflow
# This line throw an error, screen shot is attached.
mlflow.run ( dbutils.widgets.get('Project path), parameters=params)
# This line ignore any set_experiment / start_run(run_name='something') specifed in the code.
mlflow.run ( dbutils.widgets.get('Project path), parameters=params, experiment_name=dbutils.widgets.get('experiment_name'), run_name='test')
