DescribeTransformJobResponse ¶

class DescribeTransformJobResponse(TypedDict, closed=True):
    transform_job_name: NotRequired[
        "capo_sagemaker.types.transform_job_name.TransformJobName"
    ]
    """<p>The name of the transform job.</p>"""
    transform_job_arn: NotRequired[
        "capo_sagemaker.types.transform_job_arn.TransformJobArn"
    ]
    """<p>The Amazon Resource Name (ARN) of the transform job.</p>"""
    transform_job_status: NotRequired[
        "capo_sagemaker.types.transform_job_status.TransformJobStatus"
    ]
    """<p>The status of the transform job. If the transform job failed, the reason is returned in the <code>FailureReason</code> field.</p>"""
    failure_reason: NotRequired["capo_sagemaker.types.failure_reason.FailureReason"]
    """<p>If the transform job failed, <code>FailureReason</code> describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/logging-cloudwatch.html">Log Amazon SageMaker Events with Amazon CloudWatch</a>.</p>"""
    model_name: NotRequired["capo_sagemaker.types.model_name.ModelName"]
    """<p>The name of the model used in the transform job.</p>"""
    max_concurrent_transforms: NotRequired[
        "capo_sagemaker.types.max_concurrent_transforms.MaxConcurrentTransforms"
    ]
    """<p>The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.</p>"""
    model_client_config: NotRequired[
        "capo_sagemaker.types.model_client_config.ModelClientConfig"
    ]
    """<p>The timeout and maximum number of retries for processing a transform job invocation.</p>"""
    max_payload_in_mb: NotRequired[
        "capo_sagemaker.types.max_payload_in_mb.MaxPayloadInMB"
    ]
    """<p>The maximum payload size, in MB, used in the transform job.</p>"""
    batch_strategy: NotRequired["capo_sagemaker.types.batch_strategy.BatchStrategy"]
    """<p>Specifies the number of records to include in a mini-batch for an HTTP inference request. A <i>record</i> <i/> is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. </p> <p>To enable the batch strategy, you must set <code>SplitType</code> to <code>Line</code>, <code>RecordIO</code>, or <code>TFRecord</code>.</p>"""
    environment: NotRequired[
        "capo_sagemaker.types.transform_environment_map.TransformEnvironmentMap"
    ]
    """<p>The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.</p>"""
    transform_input: NotRequired["capo_sagemaker.types.transform_input.TransformInput"]
    """<p>Describes the dataset to be transformed and the Amazon S3 location where it is stored.</p>"""
    transform_output: NotRequired[
        "capo_sagemaker.types.transform_output.TransformOutput"
    ]
    """<p>Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.</p>"""
    data_capture_config: NotRequired[
        "capo_sagemaker.types.batch_data_capture_config.BatchDataCaptureConfig"
    ]
    """<p>Configuration to control how SageMaker captures inference data.</p>"""
    transform_resources: NotRequired[
        "capo_sagemaker.types.transform_resources.TransformResources"
    ]
    """<p>Describes the resources, including ML instance types and ML instance count, to use for the transform job.</p>"""
    creation_time: NotRequired["capo_sagemaker.types.timestamp.Timestamp"]
    """<p>A timestamp that shows when the transform Job was created.</p>"""
    transform_start_time: NotRequired["capo_sagemaker.types.timestamp.Timestamp"]
    """<p>Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of <code>TransformEndTime</code>.</p>"""
    transform_end_time: NotRequired["capo_sagemaker.types.timestamp.Timestamp"]
    """<p>Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of <code>TransformStartTime</code>.</p>"""
    labeling_job_arn: NotRequired[
        "capo_sagemaker.types.labeling_job_arn.LabelingJobArn"
    ]
    """<p>The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.</p>"""
    auto_ml_job_arn: NotRequired["capo_sagemaker.types.auto_ml_job_arn.AutoMLJobArn"]
    """<p>The Amazon Resource Name (ARN) of the AutoML transform job.</p>"""
    data_processing: NotRequired["capo_sagemaker.types.data_processing.DataProcessing"]
    experiment_config: NotRequired[
        "capo_sagemaker.types.experiment_config.ExperimentConfig"
    ]