You manage an Azure Machine Learning workspace. You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2. Which parameter should you use?

The correct answer is C. build. The main difference between using the build and image parameters when defining an environment using the Azure Machine Learning Python SDK v2 is whether you want to define the environment from a Dockerfile or a Docker image. If you want to define an environment from a Dockerfile,

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Question

You manage an Azure Machine Learning workspace. You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2. Which parameter should you use?

Options

Aproperties
Bimage
Cbuild
Dconda_file

How the community answered

(41 responses)

A
2% (1)
B
7% (3)
C
88% (36)
D
2% (1)

Explanation

The main difference between using the build and image parameters when defining an environment using the Azure Machine Learning Python SDK v2 is whether you want to define the environment from a Dockerfile or a Docker image. If you want to define an environment from a Dockerfile, you should use the build parameter and set it to a dictionary that specifies the contents of the Dockerfile using the dockerfile key. If you want to define an environment from a Docker image, you should use the image parameter and set it to the name of the Docker image you want to use.

Topics

#Azure Machine Learning#Python SDK v2#Environments#Docker

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