CfnAIWorkloadConfig

class aws_cdk.aws_sagemaker.CfnAIWorkloadConfig(scope, id, *, ai_workload_config_name, ai_workload_configs=None, dataset_config=None, tags=None)

Bases: CfnResource

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

A reusable AI workload configuration that defines datasets, data sources and benchmark tool settings for consistent performance testing of generative AI inference deployments on Amazon SageMaker AI.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-aiworkloadconfig.html
CloudformationResource:: AWS::SageMaker::AIWorkloadConfig
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_ai_workload_config = sagemaker.CfnAIWorkloadConfig(self, "MyCfnAIWorkloadConfig",
    ai_workload_config_name="aiWorkloadConfigName",

    # the properties below are optional
    ai_workload_configs=sagemaker.CfnAIWorkloadConfig.AIWorkloadConfigsProperty(
        workload_spec=sagemaker.CfnAIWorkloadConfig.WorkloadSpecProperty(
            inline="inline"
        )
    ),
    dataset_config=sagemaker.CfnAIWorkloadConfig.AIDatasetConfigProperty(
        input_data_config=[sagemaker.CfnAIWorkloadConfig.AIWorkloadInputDataConfigProperty(
            channel_name="channelName",
            data_source=sagemaker.CfnAIWorkloadConfig.AIWorkloadDataSourceProperty(
                s3_data_source=sagemaker.CfnAIWorkloadConfig.AIWorkloadS3DataSourceProperty(
                    s3_uri="s3Uri"
                )
            )
        )]
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)

Create a new AWS::SageMaker::AIWorkloadConfig.

Parameters:

scope (Construct) – Scope in which this resource is defined.
id (str) – Construct identifier for this resource (unique in its scope).
ai_workload_config_name (str) – The name of the AI workload configuration. The name must be unique within your AWS account in the current AWS Region. Only lowercase letters and digits are accepted: DeleteAIWorkloadConfig lowercases the name before looking it up, so a name containing an uppercase letter produces a configuration that can be created and read but never deleted.
ai_workload_configs (Union[IResolvable, AIWorkloadConfigsProperty, Dict[str, Any], None]) – The benchmark tool configuration for an AI workload.
dataset_config (Union[IResolvable, AIDatasetConfigProperty, Dict[str, Any], None]) – The dataset configuration for an AI workload.
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – The metadata that you apply to the AI workload configuration to help you categorize and organize it.

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:

target (CfnResource)
reason (Optional[str])

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:

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

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::AIWorkloadConfig'

ai_workload_config_name: The name of the AI workload configuration.

ai_workload_config_ref: A reference to a AIWorkloadConfig resource.

ai_workload_configs: The benchmark tool configuration for an AI workload.

attr_ai_workload_config_arn

The Amazon Resource Name (ARN) of the AI workload configuration.

The name segment is restricted to lowercase for the same reason as AIWorkloadConfigName: the engine derives that property from this ARN, so a permissive ARN would yield a derived name the schema itself rejects.

CloudformationAttribute:: AIWorkloadConfigArn

attr_creation_time

A timestamp that indicates when the AI workload configuration was created.

CloudformationAttribute:: CreationTime

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

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.

dataset_config: The dataset configuration for an AI workload.

env

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: The metadata that you apply to the AI workload configuration to help you categorize and organize it.

Static Methods

classmethod arn_for_ai_workload_config(resource)

Parameters:: resource (IAIWorkloadConfigRef)
Return type:: str

classmethod is_cfn_ai_workload_config(x)

Checks whether the given object is a CfnAIWorkloadConfig.

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.

AIDatasetConfigProperty

class CfnAIWorkloadConfig.AIDatasetConfigProperty(*, input_data_config)

Bases: object

The dataset configuration for an AI workload.

Parameters:: input_data_config (Union[IResolvable, Sequence[Union[IResolvable, AIWorkloadInputDataConfigProperty, Dict[str, Any]]]]) – An array of input data channel configurations for the workload.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aidatasetconfig.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

a_i_dataset_config_property = sagemaker.CfnAIWorkloadConfig.AIDatasetConfigProperty(
    input_data_config=[sagemaker.CfnAIWorkloadConfig.AIWorkloadInputDataConfigProperty(
        channel_name="channelName",
        data_source=sagemaker.CfnAIWorkloadConfig.AIWorkloadDataSourceProperty(
            s3_data_source=sagemaker.CfnAIWorkloadConfig.AIWorkloadS3DataSourceProperty(
                s3_uri="s3Uri"
            )
        )
    )]
)

Attributes

input_data_config

An array of input data channel configurations for the workload.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aidatasetconfig.html#cfn-sagemaker-aiworkloadconfig-aidatasetconfig-inputdataconfig

AIWorkloadConfigsProperty

class CfnAIWorkloadConfig.AIWorkloadConfigsProperty(*, workload_spec)

Bases: object

The benchmark tool configuration for an AI workload.

Parameters:: workload_spec (Union[IResolvable, WorkloadSpecProperty, Dict[str, Any]]) – The workload specification for benchmark tool configuration.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aiworkloadconfigs.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

a_i_workload_configs_property = sagemaker.CfnAIWorkloadConfig.AIWorkloadConfigsProperty(
    workload_spec=sagemaker.CfnAIWorkloadConfig.WorkloadSpecProperty(
        inline="inline"
    )
)

Attributes

workload_spec

The workload specification for benchmark tool configuration.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aiworkloadconfigs.html#cfn-sagemaker-aiworkloadconfig-aiworkloadconfigs-workloadspec

AIWorkloadDataSourceProperty

class CfnAIWorkloadConfig.AIWorkloadDataSourceProperty(*, s3_data_source=None)

Bases: object

The data source for an AI workload input data channel.

Parameters:: s3_data_source (Union[IResolvable, AIWorkloadS3DataSourceProperty, Dict[str, Any], None]) – The Amazon S3 data source for an AI workload.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aiworkloaddatasource.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

a_i_workload_data_source_property = sagemaker.CfnAIWorkloadConfig.AIWorkloadDataSourceProperty(
    s3_data_source=sagemaker.CfnAIWorkloadConfig.AIWorkloadS3DataSourceProperty(
        s3_uri="s3Uri"
    )
)

Attributes

s3_data_source

The Amazon S3 data source for an AI workload.

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

AIWorkloadInputDataConfigProperty

class CfnAIWorkloadConfig.AIWorkloadInputDataConfigProperty(*, channel_name, data_source)

Bases: object

A channel of input data for an AI workload configuration.

Parameters:

channel_name (str) – The logical name for the data channel.
data_source (Union[IResolvable, AIWorkloadDataSourceProperty, Dict[str, Any]]) – The data source for an AI workload input data channel.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aiworkloadinputdataconfig.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

a_i_workload_input_data_config_property = sagemaker.CfnAIWorkloadConfig.AIWorkloadInputDataConfigProperty(
    channel_name="channelName",
    data_source=sagemaker.CfnAIWorkloadConfig.AIWorkloadDataSourceProperty(
        s3_data_source=sagemaker.CfnAIWorkloadConfig.AIWorkloadS3DataSourceProperty(
            s3_uri="s3Uri"
        )
    )
)

Attributes

channel_name

The logical name for the data channel.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aiworkloadinputdataconfig.html#cfn-sagemaker-aiworkloadconfig-aiworkloadinputdataconfig-channelname

data_source

The data source for an AI workload input data channel.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aiworkloadinputdataconfig.html#cfn-sagemaker-aiworkloadconfig-aiworkloadinputdataconfig-datasource

AIWorkloadS3DataSourceProperty

class CfnAIWorkloadConfig.AIWorkloadS3DataSourceProperty(*, s3_uri)

Bases: object

The Amazon S3 data source for an AI workload.

Parameters:: s3_uri (str) – The Amazon S3 URI of the data.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aiworkloads3datasource.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

a_i_workload_s3_data_source_property = sagemaker.CfnAIWorkloadConfig.AIWorkloadS3DataSourceProperty(
    s3_uri="s3Uri"
)

Attributes

s3_uri

The Amazon S3 URI of the data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-aiworkloads3datasource.html#cfn-sagemaker-aiworkloadconfig-aiworkloads3datasource-s3uri

WorkloadSpecProperty

class CfnAIWorkloadConfig.WorkloadSpecProperty(*, inline)

Bases: object

The workload specification for benchmark tool configuration.

Parameters:: inline (str) – An inline YAML or JSON string that defines benchmark parameters. The service validates the document against its own benchmark schema: it must declare a benchmark object whose type member matches the pattern ^(aiperf)$.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-workloadspec.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

workload_spec_property = sagemaker.CfnAIWorkloadConfig.WorkloadSpecProperty(
    inline="inline"
)

Attributes

inline

An inline YAML or JSON string that defines benchmark parameters.

The service validates the document against its own benchmark schema: it must declare a benchmark object whose type member matches the pattern ^(aiperf)$.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-aiworkloadconfig-workloadspec.html#cfn-sagemaker-aiworkloadconfig-workloadspec-inline