Upgrade pipeline endpoints to SDK v2

Once you have a pipeline up and running, you can publish a pipeline so that it runs with different inputs. This was known as Published Pipelines.

What has changed?

Batch Endpoint proposes a similar yet more powerful way to handle multiple assets running under a durable API, which is why the Published pipelines functionality was moved to Pipeline component deployments in batch endpoints.

Batch endpoints decouples the interface (endpoint) from the actual implementation (deployment) and allow the user to decide which deployment serves the default implementation of the endpoint. Pipeline component deployments in batch endpoints allow users to deploy pipeline components instead of pipelines, which make a better use of reusable assets for those organizations looking to streamline their MLOps practice.

The following table shows a comparison of each of the concepts:

Concept SDK v1 SDK v2
Pipeline's REST endpoint for invocation Pipeline endpoint Batch endpoint
Pipeline's specific version under the endpoint Published pipeline Pipeline component deployment
Pipeline's arguments on invocation Pipeline parameter Job inputs
Job generated from a published pipeline Pipeline job Batch job

To learn how to create your first pipeline component deployment see How to deploy pipelines in Batch Endpoints.

Moving to batch endpoints

Use the following guidelines to learn how to move from SDK v1 to SDK v2 using the concepts in Batch Endpoints.

Publish a pipeline

Compare how publishing a pipeline has changed from v1 to v2:

  1. First, we need to get the pipeline we want to publish:

    pipeline1 = Pipeline(workspace=ws, steps=[step1, step2])
  2. We can publish the pipeline as follows:

    from azureml.pipeline.core import PipelineEndpoint
    endpoint_name = "PipelineEndpointTest"
    pipeline_endpoint = PipelineEndpoint.publish(
        description="A hello world endpoint for component deployments"

Submit a job to a pipeline endpoint

To call the default version of the pipeline, you can use:

pipeline_endpoint = PipelineEndpoint.get(workspace=ws, name="PipelineEndpointTest")
run_id = pipeline_endpoint.submit("PipelineEndpointExperiment")

You can also submit a job to a specific version:

run_id = pipeline_endpoint.submit(endpoint_name, pipeline_version="0")

Get all pipelines deployed

all_pipelines = PublishedPipeline.get_all(ws)

Using the REST API

You can create jobs from the endpoints by using the REST API of the invocation URL. See the following examples to see how invocation has changed from v1 to v2.

pipeline_endpoint = PipelineEndpoint.get(workspace=ws, name=endpoint_name)
rest_endpoint = pipeline_endpoint.endpoint

response = requests.post(
        "ExperimentName": "PipelineEndpointExperiment",
        "RunSource": "API",
        "ParameterAssignments": {"argument1": "united", "argument2":45}

Next steps