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Machine learning in Azure: How do I publish a pipeline to the workspace once I've already built it in Python using the SDK?

Stack Overflow Asked by testgauss321 on December 3, 2021

I don’t know where else to ask this question so would appreciate any help or feedback. I’ve been reading the SDK documentation for azure machine learning service (in particular azureml.core). There’s a class called Pipeline that has methdods validate() and publish(). Here are the docs for this:

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py

When I call validate(), everything validates and I call publish but it seems to only create an API endpoint in the workspace, it doesn’t register my pipeline under Pipelines and there’s obviously nothing in the designer.

My question: I want to publish my pipeline so I just have to launch from the workspace with one click. I’ve built it already using the SDK (Python code). I don’t want to work with an API. Is there any way to do this or would I have to rebuild the entire pipeline using the designer (drag and drop)?

One Answer

Totally empathize with your confusion. Our team has been working with Azure ML pipelines for quite some time but PublishedPipelines still confused me initially because:

  • what the SDK calls a PublishedPipeline is called as a Pipeline Endpoint in the Studio UI, and
  • it is semi-related to Dataset and Model's .register() method, but fundamentally different.

TL;DR: all Pipeline.publish() does is create an endpoint that you can use to:

You can see PublishedPipelines in the Studio UI in two places:

  • Pipelines page :: Pipeline Endpoints tab
  • Endpoints page :: Pipeline Endpoints tab

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Answered by Anders Swanson on December 3, 2021

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