Question

A data scientist is running a hyperparameter tuning job that trains 20 different model configurations within a single Python function. They want each configuration's parameters and metrics tracked separately in MLflow, but also want all 20 trials grouped under one logical parent run in the experiment UI for easy comparison. Which MLflow approach accomplishes this?

Accepted Answer

Start a parent run with mlflow.start_run(), then start each trial with mlflow.start_run(nested=True) inside the parent's context

Answer

Call mlflow.set_experiment() 20 times, once per trial, so each configuration gets its own experiment folder

Answer

Log all 20 configurations into a single run using mlflow.log_params() with a dictionary containing all parameter combinations

Answer

Use mlflow.autolog() alone, which automatically creates a parent-child hierarchy for any tuning loop