Notatnik do tego ćwiczenia

Lab 5B: Working with Compute Targets

While you can run experiments on your local compute, in many cases you'll want to leverage cloud compute for increased scalability.

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: Work with Compute Targets

In this task, you'll run a training job on a cloud-based compute target, using the Azure Machine Learning SDK v2.

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, select its JupyterLab link to open the JupyterLab interface in a new browser tab.
3. In JupyterLab, open the MachineLearningCourse/materialy/azure-ml/en folder (clone or pull the repository first if you haven't already - see Lab 1B) and open the 05B - Working with Compute Targets.ipynb notebook. Confirm that the notebook is using the Python 3.10 - SDK v2 kernel, then read the notes and run 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.