Getting all existing piplines from an Azure Machine Learning Workspace

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I use azure machine learning services (aml) to run a ml-model. When I go to the GUI of AML I can see all the exisiting piplines, but I can't see how they are scheduled. I need to get all puplished piplines and the belonging meta data.

How I can get information about an existing pipline with the python sdk?

azure-machine-learning-workbench azure-machine-learning-service python
2 Answers

From azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule.list(ws) to get information about an existing pipeline with the python sdk.

Azure documentation about Schedule Recurrence class and Schedule.list.

user_159698580

once published a pipeline and was able to see from notebook.

Every publish will create a REST endpoint as shown here.

published_pipeline1 = pipeline_run1.publish_pipeline(
     name="Published_Titanic_Pipeline_Notebook",
     description="Titanic_Pipeline_Notebook Published Pipeline Description",
     version="1.0")

Metadata from Azure ML: You can enable Web service schema (OpenAPI specification) in the service and consume it by querying the swagger URL of the service.

user_112974060
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