sagemaker_run_notebook/lambda_function.py [40:99]:
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    if notebook == None:
        notebook = input_path

    base = os.path.basename(notebook)
    nb_name, nb_ext = os.path.splitext(base)
    timestamp = time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())

    job_name = (
        ("papermill-" + re.sub(r"[^-a-zA-Z0-9]", "-", nb_name))[: 62 - len(timestamp)]
        + "-"
        + timestamp
    )
    input_directory = "/opt/ml/processing/input/"
    local_input = input_directory + os.path.basename(input_path)
    result = "{}-{}{}".format(nb_name, timestamp, nb_ext)
    local_output = "/opt/ml/processing/output/"

    api_args = {
        "ProcessingInputs": [
            {
                "InputName": "notebook",
                "S3Input": {
                    "S3Uri": input_path,
                    "LocalPath": input_directory,
                    "S3DataType": "S3Prefix",
                    "S3InputMode": "File",
                    "S3DataDistributionType": "FullyReplicated",
                },
            },
        ],
        "ProcessingOutputConfig": {
            "Outputs": [
                {
                    "OutputName": "result",
                    "S3Output": {
                        "S3Uri": output_prefix,
                        "LocalPath": local_output,
                        "S3UploadMode": "EndOfJob",
                    },
                },
            ],
        },
        "ProcessingJobName": job_name,
        "ProcessingResources": {
            "ClusterConfig": {
                "InstanceCount": 1,
                "InstanceType": instance_type,
                "VolumeSizeInGB": 40,
            }
        },
        "StoppingCondition": {"MaxRuntimeInSeconds": 7200},
        "AppSpecification": {
            "ImageUri": image,
            "ContainerArguments": [
                "run_notebook",
            ],
        },
        "RoleArn": role,
        "Environment": {},
    }
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sagemaker_run_notebook/run_notebook.py [135:194]:
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    if notebook == None:
        notebook = input_path

    base = os.path.basename(notebook)
    nb_name, nb_ext = os.path.splitext(base)
    timestamp = time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())

    job_name = (
        ("papermill-" + re.sub(r"[^-a-zA-Z0-9]", "-", nb_name))[: 62 - len(timestamp)]
        + "-"
        + timestamp
    )
    input_directory = "/opt/ml/processing/input/"
    local_input = input_directory + os.path.basename(input_path)
    result = "{}-{}{}".format(nb_name, timestamp, nb_ext)
    local_output = "/opt/ml/processing/output/"

    api_args = {
        "ProcessingInputs": [
            {
                "InputName": "notebook",
                "S3Input": {
                    "S3Uri": input_path,
                    "LocalPath": input_directory,
                    "S3DataType": "S3Prefix",
                    "S3InputMode": "File",
                    "S3DataDistributionType": "FullyReplicated",
                },
            },
        ],
        "ProcessingOutputConfig": {
            "Outputs": [
                {
                    "OutputName": "result",
                    "S3Output": {
                        "S3Uri": output_prefix,
                        "LocalPath": local_output,
                        "S3UploadMode": "EndOfJob",
                    },
                },
            ],
        },
        "ProcessingJobName": job_name,
        "ProcessingResources": {
            "ClusterConfig": {
                "InstanceCount": 1,
                "InstanceType": instance_type,
                "VolumeSizeInGB": 40,
            }
        },
        "StoppingCondition": {"MaxRuntimeInSeconds": 7200},
        "AppSpecification": {
            "ImageUri": image,
            "ContainerArguments": [
                "run_notebook",
            ],
        },
        "RoleArn": role,
        "Environment": {},
    }
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