CfnFlywheel
- class aws_cdk.aws_comprehend.CfnFlywheel(scope, id, *, data_access_role_arn, data_lake_s3_uri, flywheel_name, active_model_arn=None, data_security_config=None, model_type=None, tags=None, task_config=None)
Bases:
CfnResource
A CloudFormation
AWS::Comprehend::Flywheel
.A flywheel is an AWS resource that orchestrates the ongoing training of a model for custom classification or custom entity recognition. You can create a flywheel to start with an existing trained model, or Comprehend can create and train a new model.
When you create the flywheel, Comprehend creates a data lake in your account. The data lake holds the training data and test data for all versions of the model.
To use a flywheel with an existing trained model, you specify the active model version. Comprehend copies the model’s training data and test data into the flywheel’s data lake.
To use the flywheel with a new model, you need to provide a dataset for training data (and optional test data) when you create the flywheel.
For more information about flywheels, see Flywheel overview in the Amazon Comprehend Developer Guide .
- CloudformationResource:
AWS::Comprehend::Flywheel
- Link:
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-comprehend-flywheel.html
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_comprehend as comprehend cfn_flywheel = comprehend.CfnFlywheel(self, "MyCfnFlywheel", data_access_role_arn="dataAccessRoleArn", data_lake_s3_uri="dataLakeS3Uri", flywheel_name="flywheelName", # the properties below are optional active_model_arn="activeModelArn", data_security_config=comprehend.CfnFlywheel.DataSecurityConfigProperty( data_lake_kms_key_id="dataLakeKmsKeyId", model_kms_key_id="modelKmsKeyId", volume_kms_key_id="volumeKmsKeyId", vpc_config=comprehend.CfnFlywheel.VpcConfigProperty( security_group_ids=["securityGroupIds"], subnets=["subnets"] ) ), model_type="modelType", tags=[CfnTag( key="key", value="value" )], task_config=comprehend.CfnFlywheel.TaskConfigProperty( language_code="languageCode", # the properties below are optional document_classification_config=comprehend.CfnFlywheel.DocumentClassificationConfigProperty( mode="mode", # the properties below are optional labels=["labels"] ), entity_recognition_config=comprehend.CfnFlywheel.EntityRecognitionConfigProperty( entity_types=[comprehend.CfnFlywheel.EntityTypesListItemProperty( type="type" )] ) ) )
Create a new
AWS::Comprehend::Flywheel
.- Parameters:
scope (
Construct
) –scope in which this resource is defined.
id (
str
) –scoped id of the resource.
data_access_role_arn (
str
) – The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend permission to access the flywheel data.data_lake_s3_uri (
str
) – Amazon S3 URI of the data lake location.flywheel_name (
str
) – Name for the flywheel.active_model_arn (
Optional
[str
]) – The Amazon Resource Number (ARN) of the active model version.data_security_config (
Union
[DataSecurityConfigProperty
,Dict
[str
,Any
],IResolvable
,None
]) – Data security configuration.model_type (
Optional
[str
]) – Model type of the flywheel’s model.tags (
Optional
[Sequence
[Union
[CfnTag
,Dict
[str
,Any
]]]]) – Tags associated with the endpoint being created. A tag is a key-value pair that adds metadata to the endpoint. For example, a tag with “Sales” as the key might be added to an endpoint to indicate its use by the sales department.task_config (
Union
[IResolvable
,TaskConfigProperty
,Dict
[str
,Any
],None
]) – Configuration about the model associated with a flywheel.
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_depends_on(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_metadata(key, value)
Add a value to the CloudFormation Resource Metadata.
- Parameters:
key (
str
)value (
Any
)
- See:
- Return type:
None
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 prefixpath
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 toaddOverride
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 intermdediate 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
).- Parameters:
policy (
Optional
[RemovalPolicy
])apply_to_update_replace_policy (
Optional
[bool
]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: truedefault (
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 resoure, please consult that specific resource’s documentation.
- Return type:
None
- get_att(attribute_name)
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.- Return type:
- get_metadata(key)
Retrieve a value value from the CloudFormation Resource Metadata.
- Parameters:
key (
str
)- See:
- Return type:
Any
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
- 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
- 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::Comprehend::Flywheel'
- active_model_arn
The Amazon Resource Number (ARN) of the active model version.
- attr_arn
The Amazon Resource Name (ARN) of the flywheel.
- CloudformationAttribute:
Arn
- 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.
- data_access_role_arn
The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend permission to access the flywheel data.
- data_lake_s3_uri
Amazon S3 URI of the data lake location.
- data_security_config
Data security configuration.
- flywheel_name
Name for the flywheel.
- 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_type
Model type of the flywheel’s model.
- node
The construct tree node associated with this construct.
- 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
Tags associated with the endpoint being created.
A tag is a key-value pair that adds metadata to the endpoint. For example, a tag with “Sales” as the key might be added to an endpoint to indicate its use by the sales department.
- task_config
Configuration about the model associated with a flywheel.
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(construct)
Check whether the given construct is a CfnResource.
- Parameters:
construct (
IConstruct
)- Return type:
bool
- classmethod is_construct(x)
Return whether the given object is a Construct.
- Parameters:
x (
Any
)- Return type:
bool
DataSecurityConfigProperty
- class CfnFlywheel.DataSecurityConfigProperty(*, data_lake_kms_key_id=None, model_kms_key_id=None, volume_kms_key_id=None, vpc_config=None)
Bases:
object
Data security configuration.
- Parameters:
data_lake_kms_key_id (
Optional
[str
]) – ID for the AWS KMS key that Amazon Comprehend uses to encrypt the data in the data lake.model_kms_key_id (
Optional
[str
]) – ID for the AWS KMS key that Amazon Comprehend uses to encrypt trained custom models. The ModelKmsKeyId can be either of the following formats: - KMS Key ID:"1234abcd-12ab-34cd-56ef-1234567890ab"
- Amazon Resource Name (ARN) of a KMS Key:"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
volume_kms_key_id (
Optional
[str
]) – ID for the AWS KMS key that Amazon Comprehend uses to encrypt the volume.vpc_config (
Union
[IResolvable
,VpcConfigProperty
,Dict
[str
,Any
],None
]) – Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for the job. For more information, see Amazon VPC .
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_comprehend as comprehend data_security_config_property = comprehend.CfnFlywheel.DataSecurityConfigProperty( data_lake_kms_key_id="dataLakeKmsKeyId", model_kms_key_id="modelKmsKeyId", volume_kms_key_id="volumeKmsKeyId", vpc_config=comprehend.CfnFlywheel.VpcConfigProperty( security_group_ids=["securityGroupIds"], subnets=["subnets"] ) )
Attributes
- data_lake_kms_key_id
ID for the AWS KMS key that Amazon Comprehend uses to encrypt the data in the data lake.
- model_kms_key_id
ID for the AWS KMS key that Amazon Comprehend uses to encrypt trained custom models.
The ModelKmsKeyId can be either of the following formats:
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS Key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
- volume_kms_key_id
ID for the AWS KMS key that Amazon Comprehend uses to encrypt the volume.
- vpc_config
Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for the job.
For more information, see Amazon VPC .
DocumentClassificationConfigProperty
- class CfnFlywheel.DocumentClassificationConfigProperty(*, mode, labels=None)
Bases:
object
Configuration required for a document classification model.
- Parameters:
mode (
str
) – Classification mode indicates whether the documents areMULTI_CLASS
orMULTI_LABEL
.labels (
Optional
[Sequence
[str
]]) – One or more labels to associate with the custom classifier.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_comprehend as comprehend document_classification_config_property = comprehend.CfnFlywheel.DocumentClassificationConfigProperty( mode="mode", # the properties below are optional labels=["labels"] )
Attributes
- labels
One or more labels to associate with the custom classifier.
- mode
Classification mode indicates whether the documents are
MULTI_CLASS
orMULTI_LABEL
.
EntityRecognitionConfigProperty
- class CfnFlywheel.EntityRecognitionConfigProperty(*, entity_types=None)
Bases:
object
Configuration required for an entity recognition model.
- Parameters:
entity_types (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,EntityTypesListItemProperty
,Dict
[str
,Any
]]],None
]) – Up to 25 entity types that the model is trained to recognize.- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_comprehend as comprehend entity_recognition_config_property = comprehend.CfnFlywheel.EntityRecognitionConfigProperty( entity_types=[comprehend.CfnFlywheel.EntityTypesListItemProperty( type="type" )] )
Attributes
- entity_types
Up to 25 entity types that the model is trained to recognize.
EntityTypesListItemProperty
- class CfnFlywheel.EntityTypesListItemProperty(*, type)
Bases:
object
An entity type within a labeled training dataset that Amazon Comprehend uses to train a custom entity recognizer.
- Parameters:
type (
str
) – An entity type within a labeled training dataset that Amazon Comprehend uses to train a custom entity recognizer. Entity types must not contain the following invalid characters: n (line break), n (escaped line break, r (carriage return), r (escaped carriage return), t (tab), t (escaped tab), space, and , (comma).- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_comprehend as comprehend entity_types_list_item_property = comprehend.CfnFlywheel.EntityTypesListItemProperty( type="type" )
Attributes
- type
An entity type within a labeled training dataset that Amazon Comprehend uses to train a custom entity recognizer.
Entity types must not contain the following invalid characters: n (line break), n (escaped line break, r (carriage return), r (escaped carriage return), t (tab), t (escaped tab), space, and , (comma).
TaskConfigProperty
- class CfnFlywheel.TaskConfigProperty(*, language_code, document_classification_config=None, entity_recognition_config=None)
Bases:
object
Configuration about the model associated with a flywheel.
- Parameters:
language_code (
str
) – Language code for the language that the model supports.document_classification_config (
Union
[IResolvable
,DocumentClassificationConfigProperty
,Dict
[str
,Any
],None
]) – Configuration required for a document classification model.entity_recognition_config (
Union
[IResolvable
,EntityRecognitionConfigProperty
,Dict
[str
,Any
],None
]) – Configuration required for an entity recognition model.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_comprehend as comprehend task_config_property = comprehend.CfnFlywheel.TaskConfigProperty( language_code="languageCode", # the properties below are optional document_classification_config=comprehend.CfnFlywheel.DocumentClassificationConfigProperty( mode="mode", # the properties below are optional labels=["labels"] ), entity_recognition_config=comprehend.CfnFlywheel.EntityRecognitionConfigProperty( entity_types=[comprehend.CfnFlywheel.EntityTypesListItemProperty( type="type" )] ) )
Attributes
- document_classification_config
Configuration required for a document classification model.
- entity_recognition_config
Configuration required for an entity recognition model.
- language_code
Language code for the language that the model supports.
VpcConfigProperty
- class CfnFlywheel.VpcConfigProperty(*, security_group_ids, subnets)
Bases:
object
Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for the job.
For more information, see Amazon VPC .
- Parameters:
security_group_ids (
Sequence
[str
]) – The ID number for a security group on an instance of your private VPC. Security groups on your VPC function serve as a virtual firewall to control inbound and outbound traffic and provides security for the resources that you’ll be accessing on the VPC. This ID number is preceded by “sg-”, for instance: “sg-03b388029b0a285ea”. For more information, see Security Groups for your VPC .subnets (
Sequence
[str
]) – The ID for each subnet being used in your private VPC. This subnet is a subset of the a range of IPv4 addresses used by the VPC and is specific to a given availability zone in the VPC’s Region. This ID number is preceded by “subnet-”, for instance: “subnet-04ccf456919e69055”. For more information, see VPCs and Subnets .
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_comprehend as comprehend vpc_config_property = comprehend.CfnFlywheel.VpcConfigProperty( security_group_ids=["securityGroupIds"], subnets=["subnets"] )
Attributes
- security_group_ids
The ID number for a security group on an instance of your private VPC.
Security groups on your VPC function serve as a virtual firewall to control inbound and outbound traffic and provides security for the resources that you’ll be accessing on the VPC. This ID number is preceded by “sg-”, for instance: “sg-03b388029b0a285ea”. For more information, see Security Groups for your VPC .
- subnets
The ID for each subnet being used in your private VPC.
This subnet is a subset of the a range of IPv4 addresses used by the VPC and is specific to a given availability zone in the VPC’s Region. This ID number is preceded by “subnet-”, for instance: “subnet-04ccf456919e69055”. For more information, see VPCs and Subnets .