You can view the details of a SageMaker AI pipeline to understand its parameters, the dependencies of its steps, or monitor its progress and status. This can help you troubleshoot or optimize your workflow. You can access the details of a given pipeline using the Amazon SageMaker Studio console and explore its execution history, definition, parameters, and metadata.
Alternatively, if your pipeline is associated with a SageMaker AI Project, you can access the pipeline details from the project's details page. For more information, see View Project Resources.
To view the details of a SageMaker AI pipeline, complete the following steps based on whether you use Studio or Studio Classic.
Note
Model repacking happens when the pipeline needs to include a custom script in the compressed model file (model.tar.gz) to be uploaded to Amazon S3 and used to deploy a model to a SageMaker AI endpoint. When SageMaker AI pipeline trains a model and registers it to the model registry, it introduces a repack step if the trained model output from the training job needs to include a custom inference script. The repack step uncompresses the model, adds a new script, and recompresses the model. Running the pipeline adds the repack step as a training job.
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Open the SageMaker Studio console by following the instructions in Launch Amazon SageMaker Studio.
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In the left navigation pane, select Pipelines.
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(Optional) To filter the list of pipelines by name, enter a full or partial pipeline name in the search field.
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Select a pipeline name to view details about the pipeline.
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Choose one of the following tabs to view pipeline details:
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Executions – Details about the executions.
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Graph – The pipeline graph, including all steps.
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Parameters – The run parameters and metrics related to the pipeline.
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Information – The metadata associated with the pipeline, such as tags, the pipeline Amazon Resource Name (ARN), and role ARN. You can also edit the pipeline description from this page.
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