Correct:
* Create a tuning job that runs multiple trials with different parameter values To best automate hyperparameter tuning in Azure Machine Learning, you should create a Sweep Job (in SDK v2) or a HyperDrive experiment (in SDK v1).
This creates a tuning job that automatically launches multiple training trials (child runs) using your single base training script without requiring manual code modifications for each run.
Key Components to Automate the Job
To set up this job successfully using the Azure Machine Learning Python SDK v2, you will define:
Parameterized Training Script: Write your code to accept hyperparameters as command-line arguments (e.g., using Python's argparse), allowing the tuning job to pass different values to each trial.
Search Space: Define the range or specific discrete/continuous choices for the values you want to test (e.g., learning rates, batch sizes).Sampling Algorithm: Choose how Azure ML should navigate your search space. Options include Random sampling, Grid sampling (testing every possible combination), or Bayesian sampling (using previous trial results to pick the next best values).Primary Metric: Specify the performance metric your script logs (like accuracy or loss) so Azure ML knows which target to optimize.
Early Termination Policy: Optional policy (like a Bandit Policy) to automatically cancel poorly performing trials early, saving you compute time and cost.
Incorrect:
* Adjust hyperparameters after model deployment.
* Duplicate the training script for each parameter combination.
* Manually change hyperparameter values between training runs.
* Run a single training job with fixed hyperparameters.
* Select hyperparameters based only on default model settings.
Reference:
https://learn.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive