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Lab 8A: Tuning Hyperparameters

Hyperparameters are variables that affect how a model is trained, but which can't be derived from the training data. Choosing the optimal hyperparameter values for model training can be difficult, and usually involved a great deal of trial and error.

In this lab, you'll use Azure Machine Learning to tune hyperparameters by performing multiple training runs in parallel.

Before You Start

Before you start this lab, ensure that you have completed Lab 1A and Lab 1B, which include tasks to create the Azure Machine Learning workspace and other resources used in this lab.

Task 1: Tune Hyperparameters

In this task, you'll run an experiment to optimize hyperparameters for model training.

1. In Azure Machine Learning studio, view the Compute page for your workspace; and on the Compute Instances tab, ensure your compute instance is running. If not, start it.
2. When the compute instance is running, click the JupyterLab link to open JupyterLab in a new browser tab.
3. In the JupyterLab file browser, open the MachineLearningCourse/materialy/azure-ml/en folder you cloned in Lab 1B, and then open the 08A - Tuning Hyperparameters.ipynb notebook. Then read the notes in the notebook, running each code cell in turn.

Note: If you intend to continue straight to the next exercise, leave your compute instance running. If you're taking a break, you might want to close all Jupyter tabs and Stop your compute instance to avoid incurring unnecessary costs.