CfnAlgorithm

class aws_cdk.aws_sagemaker.CfnAlgorithm(scope, id, *, algorithm_name, training_specification, algorithm_description=None, certify_for_marketplace=None, inference_specification=None, tags=None)

Bases: CfnResource

Resource Type definition for AWS::SageMaker::Algorithm.

See:

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

CloudformationResource:

AWS::SageMaker::Algorithm

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 = sagemaker.CfnAlgorithm(self, "MyCfnAlgorithm",
    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"
    )]
)

Create a new AWS::SageMaker::Algorithm.

Parameters:

  • scope (Construct) – Scope in which this resource is defined.

  • id (str) – Construct identifier for this resource (unique in its scope).

  • 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.

Methods

add_deletion_override(path)

Syntactic sugar for addOverride(path, undefined).

Parameters:

path (str) – The path of the value to delete.

Return type:

None

add_dependency(target)

(deprecated) Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This method has been renamed to addResourceDependency to more clearly set it apart from construct.node.addDependency. See the documentation of that function for more details.

Parameters:

target (CfnResource)

Deprecated:

Use addResourceDependency instead.

Stability:

deprecated

Return type:

None

add_depends_on(target)

(deprecated) Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.

This method has been renamed to addResourceDependency, which makes it more clear that this method operates at a different level from the construct-level construct.node.addDependency() mechanism.

Parameters:

target (CfnResource)

Deprecated:

Use addResourceDependency instead.

Stability:

deprecated

Return type:

None

add_metadata(key, value)

Add a value to the CloudFormation Resource Metadata.

Parameters:

  • key (str)

  • value (Any)

See:

Return type:

None

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

add_override(path, value)

Adds an override to the synthesized CloudFormation resource.

To add a property override, either use addPropertyOverride or prefix path with “Properties.” (i.e. Properties.TopicName).

If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.

To include a literal . in the property name, prefix with a \. In most programming languages you will need to write this as "\\." because the \ itself will need to be escaped.

For example:

cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"])
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")

would add the overrides Example:

"Properties": {
  "GlobalSecondaryIndexes": [
    {
      "Projection": {
        "NonKeyAttributes": [ "myattribute" ]
        ...
      }
      ...
    },
    {
      "ProjectionType": "INCLUDE"
      ...
    },
  ]
  ...
}

The value argument to addOverride will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.

Parameters:

  • path (str) –

    • The path of the property, you can use dot notation to override values in complex types. Any intermediate keys will be created as needed.

  • value (Any) –

    • The value. Could be primitive or complex.

Return type:

None

add_property_deletion_override(property_path)

Adds an override that deletes the value of a property from the resource definition.

Parameters:

property_path (str) – The path to the property.

Return type:

None

add_property_override(property_path, value)

Adds an override to a resource property.

Syntactic sugar for addOverride("Properties.<...>", value).

Parameters:

  • property_path (str) – The path of the property.

  • value (Any) – The value.

Return type:

None

add_resource_dependency(target, reason=None)

Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.

This method only adds dependencies between L1 resources. If you are looking for a generic construct-to-construct dependency mechanism that works for all constructs including L2s, use construct.node.addDependency instead.

Parameters:
Return type:

None

apply_cross_stack_reference_strength(strength)

Sets the cross-stack reference strength for this resource.

When set, any cross-stack reference to this resource will use the specified strength instead of the global default from the consuming stack’s context.

Parameters:

strength (ReferenceStrength) –

  • The reference strength to use for this resource.

Return type:

None

apply_removal_policy(policy=None, *, apply_to_update_replace_policy=None, default=None)

Sets the deletion policy of the resource based on the removal policy specified.

The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.

The resource can be deleted (RemovalPolicy.DESTROY), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN). In some cases, a snapshot can be taken of the resource prior to deletion (RemovalPolicy.SNAPSHOT). A list of resources that support this policy can be found in the following link:

Parameters:

  • policy (Optional[RemovalPolicy])

  • apply_to_update_replace_policy (Optional[bool]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: true

  • default (Optional[RemovalPolicy]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resource, please consult that specific resource’s documentation.

See:

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-deletionpolicy.html#aws-attribute-deletionpolicy-options

Return type:

None

cfn_property_name(cdk_property_name)
Parameters:

cdk_property_name (str)

Return type:

Optional[str]

get_att(attribute_name, type_hint=None)

Returns a token for an runtime attribute of this resource.

Ideally, use generated attribute accessors (e.g. resource.arn), but this can be used for future compatibility in case there is no generated attribute.

Parameters:

  • attribute_name (str) – The name of the attribute.

  • type_hint (Optional[ResolutionTypeHint])

Return type:

Reference

get_metadata(key)

Retrieve a value value from the CloudFormation Resource Metadata.

Parameters:

key (str)

See:

Return type:

Any

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

inspect(inspector)

Examines the CloudFormation resource and discloses attributes.

Parameters:

inspector (TreeInspector) – tree inspector to collect and process attributes.

Return type:

None

obtain_dependencies()

Retrieves an array of resources and stacks this resource depends on.

For resources depended on directly, returns the CfnResource object. For dependencies on other stacks, returns the Stack object. The order of the array is not guaranteed.

Return type:

List[Union[Stack, CfnResource]]

override_logical_id(new_logical_id)

Overrides the auto-generated logical ID with a specific ID.

Parameters:

new_logical_id (str) – The new logical ID to use for this stack element.

Return type:

None

remove_dependency(target)

(deprecated) Indicates that this resource no longer depends on another resource.

This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.

Parameters:

target (CfnResource)

Deprecated:

Use removeResourceDependency instead

Stability:

deprecated

Return type:

None

remove_resource_dependency(target)

Indicates that this resource no longer depends on another resource.

This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.

Parameters:

target (CfnResource)

Return type:

None

replace_dependency(target, new_target)

Replaces one dependency with another.

Parameters:
Return type:

None

to_string()

Returns a string representation of this construct.

Return type:

str

Returns:

a string representation of this resource

with_(*mixins)

Applies one or more mixins to this construct.

Mixins are applied in order. The list of constructs is captured at the start of the call, so constructs added by a mixin will not be visited. Use multiple with() calls if subsequent mixins should apply to added constructs.

Parameters:

mixins (IMixin)

Return type:

IConstruct

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::SageMaker::Algorithm'
algorithm_description

A description of the algorithm.

algorithm_name

The name of the algorithm.

algorithm_ref

A reference to a Algorithm resource.

attr_algorithm_arn

The Amazon Resource Name (ARN) of the algorithm.

CloudformationAttribute:

AlgorithmArn

attr_creation_time

A timestamp specifying when the algorithm was created.

CloudformationAttribute:

CreationTime

cdk_tag_manager

Tag Manager which manages the tags for this resource.

certify_for_marketplace

Whether to certify the algorithm so that it can be listed in AWS Marketplace.

cfn_options

Options for this resource, such as condition, update policy etc.

cfn_resource_type

AWS resource type.

creation_stack

return:

the stack trace of the point where this Resource was created from, sourced from the +metadata+ entry typed +aws:cdk:logicalId+, and with the bottom-most node +internal+ entries filtered.

env
inference_specification
logical_id

The logical ID for this CloudFormation stack element.

The logical ID of the element is calculated from the path of the resource node in the construct tree.

To override this value, use overrideLogicalId(newLogicalId).

Returns:

the logical ID as a stringified token. This value will only get resolved during synthesis.

node

The tree node.

ref

Return a string that will be resolved to a CloudFormation { Ref } for this element.

If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through Lazy.any({ produce: resource.ref }).

stack

The stack in which this element is defined.

CfnElements must be defined within a stack scope (directly or indirectly).

tags

An array of key-value pairs to apply to this resource.

training_specification

Static Methods

classmethod arn_for_algorithm(resource)
Parameters:

resource (IAlgorithmRef)

Return type:

str

classmethod is_cfn_algorithm(x)

Checks whether the given object is a CfnAlgorithm.

Parameters:

x (Any)

Return type:

bool

classmethod is_cfn_element(x)

Returns true if a construct is a stack element (i.e. part of the synthesized cloudformation template).

Uses duck-typing instead of instanceof to allow stack elements from different versions of this library to be included in the same stack.

Parameters:

x (Any)

Return type:

bool

Returns:

The construct as a stack element or undefined if it is not a stack element.

classmethod is_cfn_resource(x)

Check whether the given object is a CfnResource.

Parameters:

x (Any)

Return type:

bool

classmethod is_construct(x)

Checks if x is a construct.

Use this method instead of instanceof to properly detect Construct instances, even when the construct library is symlinked.

Explanation: in JavaScript, multiple copies of the constructs library on disk are seen as independent, completely different libraries. As a consequence, the class Construct in each copy of the constructs library is seen as a different class, and an instance of one class will not test as instanceof the other class. npm install will not create installations like this, but users may manually symlink construct libraries together or use a monorepo tool: in those cases, multiple copies of the constructs library can be accidentally installed, and instanceof will behave unpredictably. It is safest to avoid using instanceof, and using this type-testing method instead.

Parameters:

x (Any) – Any object.

Return type:

bool

Returns:

true if x is an object created from a class which extends Construct.

CategoricalParameterRangeSpecificationProperty

class CfnAlgorithm.CategoricalParameterRangeSpecificationProperty(*, values)

Bases: object

Parameters:

values (Sequence[str])

See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

categorical_parameter_range_specification_property = sagemaker.CfnAlgorithm.CategoricalParameterRangeSpecificationProperty(
    values=["values"]
)

Attributes

values

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-categoricalparameterrangespecification.html#cfn-sagemaker-algorithm-categoricalparameterrangespecification-values

Type:

see

ChannelSpecificationProperty

class CfnAlgorithm.ChannelSpecificationProperty(*, name, supported_content_types, supported_input_modes, description=None, is_required=None, supported_compression_types=None)

Bases: object

Parameters:

  • name (str)

  • supported_content_types (Sequence[str])

  • supported_input_modes (Sequence[str])

  • description (Optional[str])

  • is_required (Union[bool, IResolvable, None])

  • supported_compression_types (Optional[Sequence[str]])

See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

channel_specification_property = 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"]
)

Attributes

description

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-channelspecification.html#cfn-sagemaker-algorithm-channelspecification-description

Type:

see

is_required

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-channelspecification.html#cfn-sagemaker-algorithm-channelspecification-isrequired

Type:

see

name

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-channelspecification.html#cfn-sagemaker-algorithm-channelspecification-name

Type:

see

supported_compression_types

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-channelspecification.html#cfn-sagemaker-algorithm-channelspecification-supportedcompressiontypes

Type:

see

supported_content_types

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-channelspecification.html#cfn-sagemaker-algorithm-channelspecification-supportedcontenttypes

Type:

see

supported_input_modes

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-channelspecification.html#cfn-sagemaker-algorithm-channelspecification-supportedinputmodes

Type:

see

ContinuousParameterRangeSpecificationProperty

class CfnAlgorithm.ContinuousParameterRangeSpecificationProperty(*, max_value, min_value)

Bases: object

Parameters:

  • max_value (str)

  • min_value (str)

See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

continuous_parameter_range_specification_property = sagemaker.CfnAlgorithm.ContinuousParameterRangeSpecificationProperty(
    max_value="maxValue",
    min_value="minValue"
)

Attributes

max_value

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-continuousparameterrangespecification.html#cfn-sagemaker-algorithm-continuousparameterrangespecification-maxvalue

Type:

see

min_value

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-continuousparameterrangespecification.html#cfn-sagemaker-algorithm-continuousparameterrangespecification-minvalue

Type:

see

HyperParameterSpecificationProperty

class CfnAlgorithm.HyperParameterSpecificationProperty(*, name, type, default_value=None, description=None, is_required=None, is_tunable=None, range=None)

Bases: object

Parameters:
See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

hyper_parameter_specification_property = 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"
        )
    )
)

Attributes

default_value

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-hyperparameterspecification.html#cfn-sagemaker-algorithm-hyperparameterspecification-defaultvalue

Type:

see

description

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-hyperparameterspecification.html#cfn-sagemaker-algorithm-hyperparameterspecification-description

Type:

see

is_required

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-hyperparameterspecification.html#cfn-sagemaker-algorithm-hyperparameterspecification-isrequired

Type:

see

is_tunable

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-hyperparameterspecification.html#cfn-sagemaker-algorithm-hyperparameterspecification-istunable

Type:

see

name

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-hyperparameterspecification.html#cfn-sagemaker-algorithm-hyperparameterspecification-name

Type:

see

range

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-hyperparameterspecification.html#cfn-sagemaker-algorithm-hyperparameterspecification-range

Type:

see

type

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-hyperparameterspecification.html#cfn-sagemaker-algorithm-hyperparameterspecification-type

Type:

see

HyperParameterTuningJobObjectiveProperty

class CfnAlgorithm.HyperParameterTuningJobObjectiveProperty(*, metric_name, type)

Bases: object

Parameters:

  • metric_name (str)

  • type (str)

See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

hyper_parameter_tuning_job_objective_property = sagemaker.CfnAlgorithm.HyperParameterTuningJobObjectiveProperty(
    metric_name="metricName",
    type="type"
)

Attributes

metric_name

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-hyperparametertuningjobobjective.html#cfn-sagemaker-algorithm-hyperparametertuningjobobjective-metricname

Type:

see

type

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-hyperparametertuningjobobjective.html#cfn-sagemaker-algorithm-hyperparametertuningjobobjective-type

Type:

see

InferenceSpecificationProperty

class CfnAlgorithm.InferenceSpecificationProperty(*, containers, supported_content_types=None, supported_realtime_inference_instance_types=None, supported_response_mime_types=None, supported_transform_instance_types=None)

Bases: object

Parameters:

  • containers (Union[IResolvable, Sequence[Union[IResolvable, ModelPackageContainerDefinitionProperty, Dict[str, Any]]]])

  • supported_content_types (Optional[Sequence[str]])

  • supported_realtime_inference_instance_types (Optional[Sequence[str]])

  • supported_response_mime_types (Optional[Sequence[str]])

  • supported_transform_instance_types (Optional[Sequence[str]])

See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

inference_specification_property = 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"]
)

Attributes

containers

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-inferencespecification.html#cfn-sagemaker-algorithm-inferencespecification-containers

Type:

see

supported_content_types

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-inferencespecification.html#cfn-sagemaker-algorithm-inferencespecification-supportedcontenttypes

Type:

see

supported_realtime_inference_instance_types

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-inferencespecification.html#cfn-sagemaker-algorithm-inferencespecification-supportedrealtimeinferenceinstancetypes

Type:

see

supported_response_mime_types

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-inferencespecification.html#cfn-sagemaker-algorithm-inferencespecification-supportedresponsemimetypes

Type:

see

supported_transform_instance_types

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-inferencespecification.html#cfn-sagemaker-algorithm-inferencespecification-supportedtransforminstancetypes

Type:

see

IntegerParameterRangeSpecificationProperty

class CfnAlgorithm.IntegerParameterRangeSpecificationProperty(*, max_value, min_value)

Bases: object

Parameters:

  • max_value (str)

  • min_value (str)

See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

integer_parameter_range_specification_property = sagemaker.CfnAlgorithm.IntegerParameterRangeSpecificationProperty(
    max_value="maxValue",
    min_value="minValue"
)

Attributes

max_value

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-integerparameterrangespecification.html#cfn-sagemaker-algorithm-integerparameterrangespecification-maxvalue

Type:

see

min_value

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-integerparameterrangespecification.html#cfn-sagemaker-algorithm-integerparameterrangespecification-minvalue

Type:

see

MetricDefinitionProperty

class CfnAlgorithm.MetricDefinitionProperty(*, name, regex)

Bases: object

Parameters:

  • name (str)

  • regex (str)

See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

metric_definition_property = sagemaker.CfnAlgorithm.MetricDefinitionProperty(
    name="name",
    regex="regex"
)

Attributes

name

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-metricdefinition.html#cfn-sagemaker-algorithm-metricdefinition-name

Type:

see

regex

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-metricdefinition.html#cfn-sagemaker-algorithm-metricdefinition-regex

Type:

see

ModelInputProperty

class CfnAlgorithm.ModelInputProperty(*, data_input_config)

Bases: object

Parameters:

data_input_config (str)

See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

model_input_property = sagemaker.CfnAlgorithm.ModelInputProperty(
    data_input_config="dataInputConfig"
)

Attributes

data_input_config

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelinput.html#cfn-sagemaker-algorithm-modelinput-datainputconfig

Type:

see

ModelPackageContainerDefinitionProperty

class CfnAlgorithm.ModelPackageContainerDefinitionProperty(*, image, container_hostname=None, environment=None, framework=None, framework_version=None, image_digest=None, is_checkpoint=None, model_input=None, nearest_model_name=None)

Bases: object

Parameters:

  • image (str)

  • container_hostname (Optional[str])

  • environment (Union[IResolvable, Mapping[str, str], None])

  • framework (Optional[str])

  • framework_version (Optional[str])

  • image_digest (Optional[str])

  • is_checkpoint (Union[bool, IResolvable, None])

  • model_input (Union[IResolvable, ModelInputProperty, Dict[str, Any], None])

  • nearest_model_name (Optional[str])

See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

model_package_container_definition_property = 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"
)

Attributes

container_hostname

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelpackagecontainerdefinition.html#cfn-sagemaker-algorithm-modelpackagecontainerdefinition-containerhostname

Type:

see

environment

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelpackagecontainerdefinition.html#cfn-sagemaker-algorithm-modelpackagecontainerdefinition-environment

Type:

see

framework

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelpackagecontainerdefinition.html#cfn-sagemaker-algorithm-modelpackagecontainerdefinition-framework

Type:

see

framework_version

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelpackagecontainerdefinition.html#cfn-sagemaker-algorithm-modelpackagecontainerdefinition-frameworkversion

Type:

see

image

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelpackagecontainerdefinition.html#cfn-sagemaker-algorithm-modelpackagecontainerdefinition-image

Type:

see

image_digest

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelpackagecontainerdefinition.html#cfn-sagemaker-algorithm-modelpackagecontainerdefinition-imagedigest

Type:

see

is_checkpoint

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelpackagecontainerdefinition.html#cfn-sagemaker-algorithm-modelpackagecontainerdefinition-ischeckpoint

Type:

see

model_input

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelpackagecontainerdefinition.html#cfn-sagemaker-algorithm-modelpackagecontainerdefinition-modelinput

Type:

see

nearest_model_name

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-modelpackagecontainerdefinition.html#cfn-sagemaker-algorithm-modelpackagecontainerdefinition-nearestmodelname

Type:

see

ParameterRangeProperty

class CfnAlgorithm.ParameterRangeProperty(*, categorical_parameter_range_specification=None, continuous_parameter_range_specification=None, integer_parameter_range_specification=None)

Bases: object

Parameters:
See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

parameter_range_property = 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"
    )
)

Attributes

categorical_parameter_range_specification

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-parameterrange.html#cfn-sagemaker-algorithm-parameterrange-categoricalparameterrangespecification

Type:

see

continuous_parameter_range_specification

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-parameterrange.html#cfn-sagemaker-algorithm-parameterrange-continuousparameterrangespecification

Type:

see

integer_parameter_range_specification

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-parameterrange.html#cfn-sagemaker-algorithm-parameterrange-integerparameterrangespecification

Type:

see

TrainingSpecificationProperty

class CfnAlgorithm.TrainingSpecificationProperty(*, supported_training_instance_types, training_channels, training_image, metric_definitions=None, supported_hyper_parameters=None, supported_tuning_job_objective_metrics=None, supports_distributed_training=None, training_image_digest=None)

Bases: object

Parameters:
See:

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

ExampleMetadata:

fixture=_generated

Example:

# 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

training_specification_property = 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"
)

Attributes

metric_definitions

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-trainingspecification.html#cfn-sagemaker-algorithm-trainingspecification-metricdefinitions

Type:

see

supported_hyper_parameters

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-trainingspecification.html#cfn-sagemaker-algorithm-trainingspecification-supportedhyperparameters

Type:

see

supported_training_instance_types

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-trainingspecification.html#cfn-sagemaker-algorithm-trainingspecification-supportedtraininginstancetypes

Type:

see

supported_tuning_job_objective_metrics

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-trainingspecification.html#cfn-sagemaker-algorithm-trainingspecification-supportedtuningjobobjectivemetrics

Type:

see

supports_distributed_training

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-trainingspecification.html#cfn-sagemaker-algorithm-trainingspecification-supportsdistributedtraining

Type:

see

training_channels

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-trainingspecification.html#cfn-sagemaker-algorithm-trainingspecification-trainingchannels

Type:

see

training_image

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-trainingspecification.html#cfn-sagemaker-algorithm-trainingspecification-trainingimage

Type:

see

training_image_digest

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-algorithm-trainingspecification.html#cfn-sagemaker-algorithm-trainingspecification-trainingimagedigest

Type:

see