Learn how to configure the instance_type parameter in Azure Machine Learning to select different types of compute nodes for your Kubernetes compute target using the Azure ML Python SDK v2.
Table of Contents
Question
You manage an Azure Machine Learning workspace. The workspace includes an Azure Machine Learning Kubernetes compute target configured as an Azure Kubernetes Service (AKS) cluster named AKS1. AKS1 is configured to enable the targeting of different nodes to train workloads.
You must run a command job on AKS1 by using the Azure ML Python SDK v2. The command job must select different types of compute nodes. The compute node types must be specified by using a command parameter.
You need to configure the command parameter.
Which parameter should you use?
A. environment
B. compute
C. limits
D. instance_type
Answer
D. instance_type
Explanation
To configure the command parameter for selecting different types of compute nodes in an Azure Machine Learning Kubernetes compute target using the Azure ML Python SDK v2, you should use the “instance_type” parameter. This parameter allows you to specify the type of compute nodes to target, such as “GPU” or “TPU”.
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