AlgorithmSpecification ¶

class AlgorithmSpecification(TypedDict, closed=True):
    training_image: NotRequired["capo_sagemaker.types.algorithm_image.AlgorithmImage"]
    """<p>The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html">Docker Registry Paths and Example Code</a> in the <i>Amazon SageMaker developer guide</i>. SageMaker supports both <code>registry/repository[:tag]</code> and <code>registry/repository[@digest]</code> image path formats. For more information about using your custom training container, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html">Using Your Own Algorithms with Amazon SageMaker</a>.</p> <note> <p>You must specify either the algorithm name to the <code>AlgorithmName</code> parameter or the image URI of the algorithm container to the <code>TrainingImage</code> parameter.</p> <p>For more information, see the note in the <code>AlgorithmName</code> parameter description.</p> </note>"""
    algorithm_name: NotRequired["capo_sagemaker.types.arn_or_name.ArnOrName"]
    """<p>The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.</p> <note> <p>You must specify either the algorithm name to the <code>AlgorithmName</code> parameter or the image URI of the algorithm container to the <code>TrainingImage</code> parameter.</p> <p>Note that the <code>AlgorithmName</code> parameter is mutually exclusive with the <code>TrainingImage</code> parameter. If you specify a value for the <code>AlgorithmName</code> parameter, you can't specify a value for <code>TrainingImage</code>, and vice versa.</p> <p>If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a <code>null</code> error.</p> </note>"""
    training_input_mode: NotRequired[
        "capo_sagemaker.types.training_input_mode.TrainingInputMode"
    ]
    metric_definitions: NotRequired[
        "capo_sagemaker.types.metric_definition_list.MetricDefinitionList"
    ]
    """<p>A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.</p>"""
    enable_sage_maker_metrics_time_series: NotRequired[
        "capo_sagemaker.types.boolean.Boolean"
    ]
    """<p>To generate and save time-series metrics during training, set to <code>true</code>. The default is <code>false</code> and time-series metrics aren't generated except in the following cases:</p> <ul> <li> <p>You use one of the SageMaker built-in algorithms</p> </li> <li> <p>You use one of the following <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/pre-built-containers-frameworks-deep-learning.html">Prebuilt SageMaker Docker Images</a>:</p> <ul> <li> <p>Tensorflow (version &gt;= 1.15)</p> </li> <li> <p>MXNet (version &gt;= 1.6)</p> </li> <li> <p>PyTorch (version &gt;= 1.3)</p> </li> </ul> </li> <li> <p>You specify at least one <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_MetricDefinition.html">MetricDefinition</a> </p> </li> </ul>"""
    container_entrypoint: NotRequired[
        "capo_sagemaker.types.training_container_entrypoint.TrainingContainerEntrypoint"
    ]
    """<p>The <a href="https://docs.docker.com/engine/reference/builder/">entrypoint script for a Docker container</a> used to run a training job. This script takes precedence over the default train processing instructions. See <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo-dockerfile.html">How Amazon SageMaker Runs Your Training Image</a> for more information.</p>"""
    container_arguments: NotRequired[
        "capo_sagemaker.types.training_container_arguments.TrainingContainerArguments"
    ]
    """<p>The arguments for a container used to run a training job. See <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo-dockerfile.html">How Amazon SageMaker Runs Your Training Image</a> for additional information.</p>"""
    training_image_config: NotRequired[
        "capo_sagemaker.types.training_image_config.TrainingImageConfig"
    ]
    """<p>The configuration to use an image from a private Docker registry for a training job.</p>"""