How to add on-premises computers to Azure Machine Learning Compute

Takayoshi Enomoto Greetings!

I believe you are referring to RemoteCompute Class.

Note that, You can use any of the following resources for a training compute target for most jobs. Not all resources can be used for automated machine learning, machine learning pipelines, or designer. see the below list for details.

When I tried using "RemoteCompute.attach" from the Azure Machine Learning SDK, I got the error "This method is DEPRECATED."

This could be due to v1 reference in notebook. See here

The explanation of the RemoteCompute class says "The remote resource can be an Azure VM, a remote server in your organization, or on-premises, as long as the resource is accessible to Azure Machine Learning." But I don't know how to do that.

As mentioned here, Azure Machine Learning doesn't manage an unmanaged compute target. You create this type of compute target outside Azure Machine Learning and then attach it to your workspace. Unmanaged compute resources can require extra steps for you to maintain or to improve performance for machine learning workloads.

Azure Machine Learning supports the following unmanaged compute types:

• Remote virtual machines
• Azure HDInsight
• Azure Databricks
• Azure Data Lake Analytics
• Kubernetes

For more information, see Manage compute resources.

Do let me know if that helps or have any further queries.