notebooks/smproc-stopgap/try-smproc-stopgap.py [30:58]:
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        framework_version=framework_version,
        py_version=py_version,
        role=role,
        instance_count=1,
        instance_type="ml.m5.large",
        s3_prefix=str(s3_prefix),
    )
    logger.info("Container uri: {}", processor.image_uri)

    # Whether processor output follows SageMaker training's style where output
    # goes under s3://..../jobname/output/.
    if separate_output:
        job_name = None
        output_dst = f"{s3_prefix}/output_always_overriden"
    else:
        job_name = processor._generate_current_job_name()
        output_dst = f"{s3_prefix}/{job_name}/output"

    processor.run(
        entry_point="processing.py",
        source_dir="./sourcedir",
        dependencies=["./dummy_util"],
        inputs=None,
        outputs=[
            ProcessingOutput(source="/opt/ml/processing/output", destination=output_dst),
        ],
        arguments=None,
        job_name=job_name,
    )
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notebooks/smproc-stopgap/try-smproc-stopgap.py [71:99]:
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        framework_version=framework_version,
        py_version=py_version,
        role=role,
        instance_count=1,
        instance_type="ml.m5.large",
        s3_prefix=str(s3_prefix),
    )
    logger.info("Container uri: {}", processor.image_uri)

    # Whether processor output follows SageMaker training's style where output
    # goes under s3://..../jobname/output/.
    if separate_output:
        job_name = None
        output_dst = f"{s3_prefix}/output_always_overriden"
    else:
        job_name = processor._generate_current_job_name()
        output_dst = f"{s3_prefix}/{job_name}/output"

    processor.run(
        entry_point="processing.py",
        source_dir="./sourcedir",
        dependencies=["./dummy_util"],
        inputs=None,
        outputs=[
            ProcessingOutput(source="/opt/ml/processing/output", destination=output_dst),
        ],
        arguments=None,
        job_name=job_name,
    )
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