CfnModel

class aws_cdk.aws_sagemaker.CfnModel(scope, id, *, containers=None, enable_network_isolation=None, execution_role_arn=None, inference_execution_config=None, model_name=None, primary_container=None, tags=None, vpc_config=None)

Bases: CfnResource

The AWS::SageMaker::Model resource to create a model to host at an Amazon SageMaker endpoint.

For more information, see Deploying a Model on Amazon SageMaker Hosting Services in the Amazon SageMaker Developer Guide .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html
CloudformationResource:: AWS::SageMaker::Model
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

# environment: Any

cfn_model = sagemaker.CfnModel(self, "MyCfnModel",
    containers=[sagemaker.CfnModel.ContainerDefinitionProperty(
        container_hostname="containerHostname",
        environment=environment,
        image="image",
        image_config=sagemaker.CfnModel.ImageConfigProperty(
            repository_access_mode="repositoryAccessMode",

            # the properties below are optional
            repository_auth_config=sagemaker.CfnModel.RepositoryAuthConfigProperty(
                repository_credentials_provider_arn="repositoryCredentialsProviderArn"
            )
        ),
        inference_specification_name="inferenceSpecificationName",
        mode="mode",
        model_data_source=sagemaker.CfnModel.ModelDataSourceProperty(
            s3_data_source=sagemaker.CfnModel.S3DataSourceProperty(
                compression_type="compressionType",
                s3_data_type="s3DataType",
                s3_uri="s3Uri",

                # the properties below are optional
                hub_access_config=sagemaker.CfnModel.HubAccessConfigProperty(
                    hub_content_arn="hubContentArn"
                ),
                model_access_config=sagemaker.CfnModel.ModelAccessConfigProperty(
                    accept_eula=False
                )
            )
        ),
        model_data_url="modelDataUrl",
        model_package_name="modelPackageName",
        multi_model_config=sagemaker.CfnModel.MultiModelConfigProperty(
            model_cache_setting="modelCacheSetting"
        )
    )],
    enable_network_isolation=False,
    execution_role_arn="executionRoleArn",
    inference_execution_config=sagemaker.CfnModel.InferenceExecutionConfigProperty(
        mode="mode"
    ),
    model_name="modelName",
    primary_container=sagemaker.CfnModel.ContainerDefinitionProperty(
        container_hostname="containerHostname",
        environment=environment,
        image="image",
        image_config=sagemaker.CfnModel.ImageConfigProperty(
            repository_access_mode="repositoryAccessMode",

            # the properties below are optional
            repository_auth_config=sagemaker.CfnModel.RepositoryAuthConfigProperty(
                repository_credentials_provider_arn="repositoryCredentialsProviderArn"
            )
        ),
        inference_specification_name="inferenceSpecificationName",
        mode="mode",
        model_data_source=sagemaker.CfnModel.ModelDataSourceProperty(
            s3_data_source=sagemaker.CfnModel.S3DataSourceProperty(
                compression_type="compressionType",
                s3_data_type="s3DataType",
                s3_uri="s3Uri",

                # the properties below are optional
                hub_access_config=sagemaker.CfnModel.HubAccessConfigProperty(
                    hub_content_arn="hubContentArn"
                ),
                model_access_config=sagemaker.CfnModel.ModelAccessConfigProperty(
                    accept_eula=False
                )
            )
        ),
        model_data_url="modelDataUrl",
        model_package_name="modelPackageName",
        multi_model_config=sagemaker.CfnModel.MultiModelConfigProperty(
            model_cache_setting="modelCacheSetting"
        )
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )],
    vpc_config=sagemaker.CfnModel.VpcConfigProperty(
        security_group_ids=["securityGroupIds"],
        subnets=["subnets"]
    )
)

Parameters:

scope (Construct) – Scope in which this resource is defined.
id (str) – Construct identifier for this resource (unique in its scope).
containers (Union[IResolvable, Sequence[Union[IResolvable, ContainerDefinitionProperty, Dict[str, Any]]], None]) – Specifies the containers in the inference pipeline.
enable_network_isolation (Union[bool, IResolvable, None]) – Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
execution_role_arn (Optional[str]) – The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles . .. epigraph:: To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.
inference_execution_config (Union[IResolvable, InferenceExecutionConfigProperty, Dict[str, Any], None]) – Specifies details of how containers in a multi-container endpoint are called.
model_name (Optional[str]) – The name of the new model.
primary_container (Union[IResolvable, ContainerDefinitionProperty, Dict[str, Any], None]) – The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – A list of key-value pairs to apply to this resource. For more information, see Resource Tag and Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .
vpc_config (Union[IResolvable, VpcConfigProperty, Dict[str, Any], None]) – A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud .

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)

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.

Parameters:

target (CfnResource) –

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.

Parameters:

target (CfnResource) –

Deprecated:

use addDependency

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

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

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 this resource depends on.

This assembles dependencies on resources across stacks (including nested stacks) automatically.

Return type:: List[Union[Stack, CfnResource]]

obtain_resource_dependencies()

Get a shallow copy of dependencies between this resource and other resources in the same stack.

Return type:: List[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)

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:

target (CfnResource) – The dependency to replace.
new_target (CfnResource) – The new dependency to add.

Return type:

None

to_string()

Returns a string representation of this construct.

Return type:: str
Returns:: a string representation of this resource

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::SageMaker::Model'

attr_id

Id

Type:: cloudformationAttribute

attr_model_name

The name of the model, such as MyModel .

CloudformationAttribute:: ModelName

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

cfn_resource_type: AWS resource type.

containers: Specifies the containers in the inference pipeline.

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.

enable_network_isolation: Isolates the model container.

execution_role_arn: The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs.

inference_execution_config: Specifies details of how containers in a multi-container endpoint are called.

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.

model_name: The name of the new model.

node: The tree node.

primary_container: The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

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: Tag Manager which manages the tags for this resource.

tags_raw: A list of key-value pairs to apply to this resource.

vpc_config

//docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html>`_ and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud .

Type:: A `VpcConfig <https
Type:: //docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html>`_ object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see `Protect Endpoints by Using an Amazon Virtual Private Cloud <https

Static Methods

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.

ContainerDefinitionProperty

class CfnModel.ContainerDefinitionProperty(*, container_hostname=None, environment=None, image=None, image_config=None, inference_specification_name=None, mode=None, model_data_source=None, model_data_url=None, model_package_name=None, multi_model_config=None)

Bases: object

Describes the container, as part of model definition.

Parameters:

container_hostname (Optional[str]) – This parameter is ignored for models that contain only a PrimaryContainer . When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don’t specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
environment (Any) – The environment variables to set in the Docker container. Don’t include any sensitive data in your environment variables. The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.
image (Optional[str]) – The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker . .. epigraph:: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
image_config (Union[IResolvable, ImageConfigProperty, Dict[str, Any], None]) – Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers . .. epigraph:: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
inference_specification_name (Optional[str]) – The inference specification name in the model package version.
mode (Optional[str]) – Whether the container hosts a single model or multiple models.
model_data_source (Union[IResolvable, ModelDataSourceProperty, Dict[str, Any], None]) – Specifies the location of ML model data to deploy. .. epigraph:: Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
model_data_url (Optional[str]) – The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters . .. epigraph:: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating. If you provide a value for this parameter, SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your AWS account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide . .. epigraph:: If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
model_package_name (Optional[str]) – The name or Amazon Resource Name (ARN) of the model package to use to create the model.
multi_model_config (Union[IResolvable, MultiModelConfigProperty, Dict[str, Any], None]) – Specifies additional configuration for multi-model endpoints.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.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

# environment: Any

container_definition_property = sagemaker.CfnModel.ContainerDefinitionProperty(
    container_hostname="containerHostname",
    environment=environment,
    image="image",
    image_config=sagemaker.CfnModel.ImageConfigProperty(
        repository_access_mode="repositoryAccessMode",

        # the properties below are optional
        repository_auth_config=sagemaker.CfnModel.RepositoryAuthConfigProperty(
            repository_credentials_provider_arn="repositoryCredentialsProviderArn"
        )
    ),
    inference_specification_name="inferenceSpecificationName",
    mode="mode",
    model_data_source=sagemaker.CfnModel.ModelDataSourceProperty(
        s3_data_source=sagemaker.CfnModel.S3DataSourceProperty(
            compression_type="compressionType",
            s3_data_type="s3DataType",
            s3_uri="s3Uri",

            # the properties below are optional
            hub_access_config=sagemaker.CfnModel.HubAccessConfigProperty(
                hub_content_arn="hubContentArn"
            ),
            model_access_config=sagemaker.CfnModel.ModelAccessConfigProperty(
                accept_eula=False
            )
        )
    ),
    model_data_url="modelDataUrl",
    model_package_name="modelPackageName",
    multi_model_config=sagemaker.CfnModel.MultiModelConfigProperty(
        model_cache_setting="modelCacheSetting"
    )
)

Attributes

container_hostname

This parameter is ignored for models that contain only a PrimaryContainer .

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don’t specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-containerhostname

environment

The environment variables to set in the Docker container. Don’t include any sensitive data in your environment variables.

The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-environment

image

The path where inference code is stored.

This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker . .. epigraph:

The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-image

image_config

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).

For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers . .. epigraph:

The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-imageconfig

inference_specification_name

The inference specification name in the model package version.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-inferencespecificationname

mode

Whether the container hosts a single model or multiple models.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-mode

model_data_source

Specifies the location of ML model data to deploy.

Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-modeldatasource

model_data_url

The S3 path where the model artifacts, which result from model training, are stored.

This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters . .. epigraph:

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your AWS account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide . .. epigraph:

If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ``ModelDataUrl`` .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-modeldataurl

model_package_name

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-modelpackagename

multi_model_config

Specifies additional configuration for multi-model endpoints.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-multimodelconfig

HubAccessConfigProperty

class CfnModel.HubAccessConfigProperty(*, hub_content_arn)

Bases: object

Parameters:: hub_content_arn (str) –
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-hubaccessconfig.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

hub_access_config_property = sagemaker.CfnModel.HubAccessConfigProperty(
    hub_content_arn="hubContentArn"
)

Attributes

hub_content_arn

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-hubaccessconfig.html#cfn-sagemaker-model-hubaccessconfig-hubcontentarn

Type:: see

ImageConfigProperty

class CfnModel.ImageConfigProperty(*, repository_access_mode, repository_auth_config=None)

Bases: object

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).

Parameters:

repository_access_mode (str) – Set this to one of the following values:. - Platform - The model image is hosted in Amazon ECR. - Vpc - The model image is hosted in a private Docker registry in your VPC.
repository_auth_config (Union[IResolvable, RepositoryAuthConfigProperty, Dict[str, Any], None]) – (Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-imageconfig.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

image_config_property = sagemaker.CfnModel.ImageConfigProperty(
    repository_access_mode="repositoryAccessMode",

    # the properties below are optional
    repository_auth_config=sagemaker.CfnModel.RepositoryAuthConfigProperty(
        repository_credentials_provider_arn="repositoryCredentialsProviderArn"
    )
)

Attributes

repository_access_mode

.

Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-imageconfig.html#cfn-sagemaker-model-imageconfig-repositoryaccessmode
Type:: Set this to one of the following values

repository_auth_config

(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted.

Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-imageconfig.html#cfn-sagemaker-model-imageconfig-repositoryauthconfig

InferenceExecutionConfigProperty

class CfnModel.InferenceExecutionConfigProperty(*, mode)

Bases: object

Specifies details about how containers in a multi-container endpoint are run.

Parameters:: mode (str) – How containers in a multi-container are run. The following values are valid. - Serial - Containers run as a serial pipeline. - Direct - Only the individual container that you specify is run.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-inferenceexecutionconfig.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_execution_config_property = sagemaker.CfnModel.InferenceExecutionConfigProperty(
    mode="mode"
)

Attributes

mode

How containers in a multi-container are run. The following values are valid.

Serial - Containers run as a serial pipeline.
Direct - Only the individual container that you specify is run.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-inferenceexecutionconfig.html#cfn-sagemaker-model-inferenceexecutionconfig-mode

ModelAccessConfigProperty

class CfnModel.ModelAccessConfigProperty(*, accept_eula)

Bases: object

The access configuration file to control access to the ML model.

You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig .

If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.
If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API .

Parameters:: accept_eula (Union[bool, IResolvable]) – Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-modelaccessconfig.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_access_config_property = sagemaker.CfnModel.ModelAccessConfigProperty(
    accept_eula=False
)

Attributes

accept_eula

Specifies agreement to the model end-user license agreement (EULA).

The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-modelaccessconfig.html#cfn-sagemaker-model-modelaccessconfig-accepteula

ModelDataSourceProperty

class CfnModel.ModelDataSourceProperty(*, s3_data_source)

Bases: object

Specifies the location of ML model data to deploy.

If specified, you must specify one and only one of the available data sources.

Parameters:: s3_data_source (Union[IResolvable, S3DataSourceProperty, Dict[str, Any]]) – Specifies the S3 location of ML model data to deploy.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-modeldatasource.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_data_source_property = sagemaker.CfnModel.ModelDataSourceProperty(
    s3_data_source=sagemaker.CfnModel.S3DataSourceProperty(
        compression_type="compressionType",
        s3_data_type="s3DataType",
        s3_uri="s3Uri",

        # the properties below are optional
        hub_access_config=sagemaker.CfnModel.HubAccessConfigProperty(
            hub_content_arn="hubContentArn"
        ),
        model_access_config=sagemaker.CfnModel.ModelAccessConfigProperty(
            accept_eula=False
        )
    )
)

Attributes

s3_data_source

Specifies the S3 location of ML model data to deploy.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-modeldatasource.html#cfn-sagemaker-model-modeldatasource-s3datasource

MultiModelConfigProperty

class CfnModel.MultiModelConfigProperty(*, model_cache_setting=None)

Bases: object

Specifies additional configuration for hosting multi-model endpoints.

Parameters:: model_cache_setting (Optional[str]) – Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-multimodelconfig.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

multi_model_config_property = sagemaker.CfnModel.MultiModelConfigProperty(
    model_cache_setting="modelCacheSetting"
)

Attributes

model_cache_setting

Whether to cache models for a multi-model endpoint.

By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-multimodelconfig.html#cfn-sagemaker-model-multimodelconfig-modelcachesetting

RepositoryAuthConfigProperty

class CfnModel.RepositoryAuthConfigProperty(*, repository_credentials_provider_arn)

Bases: object

Specifies an authentication configuration for the private docker registry where your model image is hosted.

Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field of the ImageConfig object that you passed to a call to CreateModel and the private Docker registry where the model image is hosted requires authentication.

Parameters:: repository_credentials_provider_arn (str) – The Amazon Resource Name (ARN) of an AWS Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an AWS Lambda function, see Create a Lambda function with the console in the AWS Lambda Developer Guide .
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-repositoryauthconfig.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

repository_auth_config_property = sagemaker.CfnModel.RepositoryAuthConfigProperty(
    repository_credentials_provider_arn="repositoryCredentialsProviderArn"
)

Attributes

repository_credentials_provider_arn

The Amazon Resource Name (ARN) of an AWS Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted.

For information about how to create an AWS Lambda function, see Create a Lambda function with the console in the AWS Lambda Developer Guide .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-repositoryauthconfig.html#cfn-sagemaker-model-repositoryauthconfig-repositorycredentialsproviderarn

S3DataSourceProperty

class CfnModel.S3DataSourceProperty(*, compression_type, s3_data_type, s3_uri, hub_access_config=None, model_access_config=None)

Bases: object

Describes the S3 data source.

Your input bucket must be in the same AWS region as your training job.

Parameters:

compression_type (str) –
s3_data_type (str) – If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel’s input mode is Pipe .
s3_uri (str) – Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example: - A key name prefix might look like this: s3://bucketname/exampleprefix/ - A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf. Your input bucket must be located in same AWS region as your training job.
hub_access_config (Union[IResolvable, HubAccessConfigProperty, Dict[str, Any], None]) –
model_access_config (Union[IResolvable, ModelAccessConfigProperty, Dict[str, Any], None]) –

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-s3datasource.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

s3_data_source_property = sagemaker.CfnModel.S3DataSourceProperty(
    compression_type="compressionType",
    s3_data_type="s3DataType",
    s3_uri="s3Uri",

    # the properties below are optional
    hub_access_config=sagemaker.CfnModel.HubAccessConfigProperty(
        hub_content_arn="hubContentArn"
    ),
    model_access_config=sagemaker.CfnModel.ModelAccessConfigProperty(
        accept_eula=False
    )
)

Attributes

compression_type

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-s3datasource.html#cfn-sagemaker-model-s3datasource-compressiontype

Type:: see

hub_access_config

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-s3datasource.html#cfn-sagemaker-model-s3datasource-hubaccessconfig

Type:: see

model_access_config

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-s3datasource.html#cfn-sagemaker-model-s3datasource-modelaccessconfig

Type:: see

s3_data_type

If you choose S3Prefix , S3Uri identifies a key name prefix.

SageMaker uses all objects that match the specified key name prefix for model training.

If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel’s input mode is Pipe .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-s3datasource.html#cfn-sagemaker-model-s3datasource-s3datatype

s3_uri

Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest.

For example:

A key name prefix might look like this: s3://bucketname/exampleprefix/
A manifest might look like this: s3://bucketname/example.manifest

A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets.

The following code example shows a valid manifest format:

[ {"prefix": "s3://customer_bucket/some/prefix/"},

"relative/path/to/custdata-1",

"relative/path/custdata-2",

...

"relative/path/custdata-N"

]

This JSON is equivalent to the following S3Uri list:

s3://customer_bucket/some/prefix/relative/path/to/custdata-1

s3://customer_bucket/some/prefix/relative/path/custdata-2

...

s3://customer_bucket/some/prefix/relative/path/custdata-N

The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.

Your input bucket must be located in same AWS region as your training job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-s3datasource.html#cfn-sagemaker-model-s3datasource-s3uri

VpcConfigProperty

class CfnModel.VpcConfigProperty(*, security_group_ids, subnets)

Bases: object

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to.

You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

Parameters:

security_group_ids (Sequence[str]) – The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
subnets (Sequence[str]) – The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-vpcconfig.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

vpc_config_property = sagemaker.CfnModel.VpcConfigProperty(
    security_group_ids=["securityGroupIds"],
    subnets=["subnets"]
)

Attributes

security_group_ids

The VPC security group IDs, in the form sg-xxxxxxxx .

Specify the security groups for the VPC that is specified in the Subnets field.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-vpcconfig.html#cfn-sagemaker-model-vpcconfig-securitygroupids

subnets

The ID of the subnets in the VPC to which you want to connect your training job or model.

For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-vpcconfig.html#cfn-sagemaker-model-vpcconfig-subnets