If you want to create a notebook job, you have multiple options. The following provides the SageMaker AI options for you to create a notebook job.
You can create a job in your JupyterLab notebook in the Studio UI, or you can programmatically create a job with the SageMaker Python SDK:
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If you create your notebook job in the Studio UI, you supply details about the image and kernel, security configurations, and any custom variables or scripts, and your job is scheduled. For details about how to schedule your job using SageMaker Notebook Jobs, see Create a notebook job in Studio.
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To create a notebook job with the SageMaker Python SDK, you create a pipeline with a Notebook Job step and initiate an on-demand run or optionally use the pipeline scheduling feature to schedule future runs. The SageMaker SDK gives you the flexibility to customize your pipeline—you can expand your pipeline to a workflow with multiple notebook job steps. Since you create both a SageMaker Notebook Job step and a pipeline, you can track your pipeline execution status in the SageMaker Notebook Jobs job dashboard and also view your pipeline graph in Studio. For details about how to schedule your job with the SageMaker Python SDK and links to example notebooks, see Create notebook job with SageMaker AI Python SDK example.
