CfnAlgorithmProps

class aws_cdk.aws_sagemaker.CfnAlgorithmProps(*, algorithm_name, training_specification, algorithm_description=None, certify_for_marketplace=None, inference_specification=None, tags=None)

Bases: object

Properties for defining a CfnAlgorithm.

Parameters:

algorithm_name (str) – The name of the algorithm.
training_specification (Union[IResolvable, TrainingSpecificationProperty, Dict[str, Any]])
algorithm_description (Optional[str]) – A description of the algorithm.
certify_for_marketplace (Union[bool, IResolvable, None]) – Whether to certify the algorithm so that it can be listed in AWS Marketplace.
inference_specification (Union[IResolvable, InferenceSpecificationProperty, Dict[str, Any], None])
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – An array of key-value pairs to apply to this resource.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-algorithm.html

ExampleMetadata:

fixture=_generated

Example:

from aws_cdk import CfnTag
# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

cfn_algorithm_props = sagemaker.CfnAlgorithmProps(
    algorithm_name="algorithmName",
    training_specification=sagemaker.CfnAlgorithm.TrainingSpecificationProperty(
        supported_training_instance_types=["supportedTrainingInstanceTypes"],
        training_channels=[sagemaker.CfnAlgorithm.ChannelSpecificationProperty(
            name="name",
            supported_content_types=["supportedContentTypes"],
            supported_input_modes=["supportedInputModes"],

            # the properties below are optional
            description="description",
            is_required=False,
            supported_compression_types=["supportedCompressionTypes"]
        )],
        training_image="trainingImage",

        # the properties below are optional
        metric_definitions=[sagemaker.CfnAlgorithm.MetricDefinitionProperty(
            name="name",
            regex="regex"
        )],
        supported_hyper_parameters=[sagemaker.CfnAlgorithm.HyperParameterSpecificationProperty(
            name="name",
            type="type",

            # the properties below are optional
            default_value="defaultValue",
            description="description",
            is_required=False,
            is_tunable=False,
            range=sagemaker.CfnAlgorithm.ParameterRangeProperty(
                categorical_parameter_range_specification=sagemaker.CfnAlgorithm.CategoricalParameterRangeSpecificationProperty(
                    values=["values"]
                ),
                continuous_parameter_range_specification=sagemaker.CfnAlgorithm.ContinuousParameterRangeSpecificationProperty(
                    max_value="maxValue",
                    min_value="minValue"
                ),
                integer_parameter_range_specification=sagemaker.CfnAlgorithm.IntegerParameterRangeSpecificationProperty(
                    max_value="maxValue",
                    min_value="minValue"
                )
            )
        )],
        supported_tuning_job_objective_metrics=[sagemaker.CfnAlgorithm.HyperParameterTuningJobObjectiveProperty(
            metric_name="metricName",
            type="type"
        )],
        supports_distributed_training=False,
        training_image_digest="trainingImageDigest"
    ),

    # the properties below are optional
    algorithm_description="algorithmDescription",
    certify_for_marketplace=False,
    inference_specification=sagemaker.CfnAlgorithm.InferenceSpecificationProperty(
        containers=[sagemaker.CfnAlgorithm.ModelPackageContainerDefinitionProperty(
            image="image",

            # the properties below are optional
            container_hostname="containerHostname",
            environment={
                "environment_key": "environment"
            },
            framework="framework",
            framework_version="frameworkVersion",
            image_digest="imageDigest",
            is_checkpoint=False,
            model_input=sagemaker.CfnAlgorithm.ModelInputProperty(
                data_input_config="dataInputConfig"
            ),
            nearest_model_name="nearestModelName"
        )],

        # the properties below are optional
        supported_content_types=["supportedContentTypes"],
        supported_realtime_inference_instance_types=["supportedRealtimeInferenceInstanceTypes"],
        supported_response_mime_types=["supportedResponseMimeTypes"],
        supported_transform_instance_types=["supportedTransformInstanceTypes"]
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)