Creating a Custom Environment in Azure ML using my compute-cluster instead of Serverless

[anaarmas-sys]

I need to know how to create my custom environment forcing it to use my compute-cluster that already has "No public IP" on true.

I have tried create the custom environment by code:

my_env = Environment(name=env_name,
image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest",
conda_file=path,
description="I'm using azure sdk version 2.0")
my_env = ml_client.environments.create_or_update(my_env)

I have trying forcing it through running the code with a pipeline using

@dsl.pipeline(
compute = my_cluster
)
and didn´t work because the default job name "prepare image" is using the Serverless which in this moment has the parameter ServerlessComputeNoPublicIP=False (by default), my Azure ML is under a networking, then using my compute-cluster i thing won't be problems when creating the custom environment (what do you think?).

I have trying change ServerlessComputeNoPublicIP to True but i got the error:

Another option for my case, is if i have to use serverless as it is to "prepare image" job, how to avoid job download the snapshot (set it in disabled, something like that) into the Storge Account because i have the next error:

There are blocks from virtual networks, etc.

Another question: ServerlessComputeNoPublicIP parameter could be handle from the AML template when is deploy it?

Thanks for your help in advanced!

[github-actions]

Thanks for the feedback! We are routing this to the appropriate team for follow-up. cc @Azure/azure-ml-sdk @azureml-github.

[anaarmas-sys]

Hello @eniac871, Do you have any idea/feedback for me? Thanks in advanced!