Explanation:
MLflow supports hierarchical run organization, allowing parent and child runs to be logically grouped. For hyperparameter tuning, specifying nested=True when starting each child run ensures it is correctly nested under the parent run. This method provides a clear structure for reviewing and comparing different hyperparameter combinations.
nested=True parameter, which is essential for creating a parent-child relationship.nested=True should be specified for child runs, not the parent run.Ultimate access to all questions.
No comments yet.
A data scientist is using MLflow to track a machine learning experiment, including hyperparameter tuning. They aim to organize the experiment with one parent run for the tuning process and child runs for each unique hyperparameter combination, all initiated manually with mlflow.start_run(). What is the best method to achieve this organization?
A
Enable Databricks Autologging to automatically organize the runs.
B
Start each child run within the indented code block of the parent run using mlflow.start_run() without any additional parameters.
C
Specify nested=True when initiating the child run for each unique combination of hyperparameter values.
D
Use the same experiment ID for both parent and child runs to group them together.
E
Specify nested=True when initiating the parent run for the tuning process.