CfnTrainingDataset

class aws_cdk.aws_cleanroomsml.CfnTrainingDataset(scope, id, *, name, role_arn, training_data, description=None, tags=None)

Bases: CfnResource

Defines the information necessary to create a training dataset.

In Clean Rooms ML, the TrainingDataset is metadata that points to a Glue table, which is read only during AudienceModel creation.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-cleanroomsml-trainingdataset.html
CloudformationResource:: AWS::CleanRoomsML::TrainingDataset
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_cleanroomsml as cleanroomsml

cfn_training_dataset = cleanroomsml.CfnTrainingDataset(self, "MyCfnTrainingDataset",
    name="name",
    role_arn="roleArn",
    training_data=[cleanroomsml.CfnTrainingDataset.DatasetProperty(
        input_config=cleanroomsml.CfnTrainingDataset.DatasetInputConfigProperty(
            data_source=cleanroomsml.CfnTrainingDataset.DataSourceProperty(
                glue_data_source=cleanroomsml.CfnTrainingDataset.GlueDataSourceProperty(
                    database_name="databaseName",
                    table_name="tableName",

                    # the properties below are optional
                    catalog_id="catalogId"
                )
            ),
            schema=[cleanroomsml.CfnTrainingDataset.ColumnSchemaProperty(
                column_name="columnName",
                column_types=["columnTypes"]
            )]
        ),
        type="type"
    )],

    # the properties below are optional
    description="description",
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)

Parameters:

scope (Construct) – Scope in which this resource is defined.
id (str) – Construct identifier for this resource (unique in its scope).
name (str) – The name of the training dataset.
role_arn (str) – The ARN of the IAM role that Clean Rooms ML can assume to read the data referred to in the dataSource field of each dataset. Passing a role across accounts is not allowed. If you pass a role that isn’t in your account, you get an AccessDeniedException error.
training_data (Union[IResolvable, Sequence[Union[IResolvable, DatasetProperty, Dict[str, Any]]]]) – An array of information that lists the Dataset objects, which specifies the dataset type and details on its location and schema. You must provide a role that has read access to these tables.
description (Optional[str]) – The description of the training dataset.
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define. The following basic restrictions apply to tags: - Maximum number of tags per resource - 50. - For each resource, each tag key must be unique, and each tag key can have only one value. - Maximum key length - 128 Unicode characters in UTF-8. - Maximum value length - 256 Unicode characters in UTF-8. - If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : /

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::CleanRoomsML::TrainingDataset'

attr_status

The status of the training dataset.

CloudformationAttribute:: Status

attr_training_dataset_arn

The Amazon Resource Name (ARN) of the training dataset.

CloudformationAttribute:: TrainingDatasetArn

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.

description: The description of the training dataset.

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.

name: The name of the training dataset.

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 }).

role_arn: The ARN of the IAM role that Clean Rooms ML can assume to read the data referred to in the dataSource field of each dataset.

stack

The stack in which this element is defined.

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

tags: The optional metadata that you apply to the resource to help you categorize and organize them.

training_data: An array of information that lists the Dataset objects, which specifies the dataset type and details on its location and schema.

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.

ColumnSchemaProperty

class CfnTrainingDataset.ColumnSchemaProperty(*, column_name, column_types)

Bases: object

Metadata for a column.

Parameters:

column_name (str) – The name of a column.
column_types (Sequence[str]) – The data type of column.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-columnschema.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_cleanroomsml as cleanroomsml

column_schema_property = cleanroomsml.CfnTrainingDataset.ColumnSchemaProperty(
    column_name="columnName",
    column_types=["columnTypes"]
)

Attributes

column_name

The name of a column.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-columnschema.html#cfn-cleanroomsml-trainingdataset-columnschema-columnname

column_types

The data type of column.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-columnschema.html#cfn-cleanroomsml-trainingdataset-columnschema-columntypes

DataSourceProperty

class CfnTrainingDataset.DataSourceProperty(*, glue_data_source)

Bases: object

Defines information about the Glue data source that contains the training data.

Parameters:: glue_data_source (Union[IResolvable, GlueDataSourceProperty, Dict[str, Any]]) – A GlueDataSource object that defines the catalog ID, database name, and table name for the training data.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-datasource.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_cleanroomsml as cleanroomsml

data_source_property = cleanroomsml.CfnTrainingDataset.DataSourceProperty(
    glue_data_source=cleanroomsml.CfnTrainingDataset.GlueDataSourceProperty(
        database_name="databaseName",
        table_name="tableName",

        # the properties below are optional
        catalog_id="catalogId"
    )
)

Attributes

glue_data_source

A GlueDataSource object that defines the catalog ID, database name, and table name for the training data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-datasource.html#cfn-cleanroomsml-trainingdataset-datasource-gluedatasource

DatasetInputConfigProperty

class CfnTrainingDataset.DatasetInputConfigProperty(*, data_source, schema)

Bases: object

Defines the Glue data source and schema mapping information.

Parameters:

data_source (Union[IResolvable, DataSourceProperty, Dict[str, Any]]) – A DataSource object that specifies the Glue data source for the training data.
schema (Union[IResolvable, Sequence[Union[IResolvable, ColumnSchemaProperty, Dict[str, Any]]]]) – The schema information for the training data.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-datasetinputconfig.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_cleanroomsml as cleanroomsml

dataset_input_config_property = cleanroomsml.CfnTrainingDataset.DatasetInputConfigProperty(
    data_source=cleanroomsml.CfnTrainingDataset.DataSourceProperty(
        glue_data_source=cleanroomsml.CfnTrainingDataset.GlueDataSourceProperty(
            database_name="databaseName",
            table_name="tableName",

            # the properties below are optional
            catalog_id="catalogId"
        )
    ),
    schema=[cleanroomsml.CfnTrainingDataset.ColumnSchemaProperty(
        column_name="columnName",
        column_types=["columnTypes"]
    )]
)

Attributes

data_source

A DataSource object that specifies the Glue data source for the training data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-datasetinputconfig.html#cfn-cleanroomsml-trainingdataset-datasetinputconfig-datasource

schema

The schema information for the training data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-datasetinputconfig.html#cfn-cleanroomsml-trainingdataset-datasetinputconfig-schema

DatasetProperty

class CfnTrainingDataset.DatasetProperty(*, input_config, type)

Bases: object

Defines where the training dataset is located, what type of data it contains, and how to access the data.

Parameters:

input_config (Union[IResolvable, DatasetInputConfigProperty, Dict[str, Any]]) – A DatasetInputConfig object that defines the data source and schema mapping.
type (str) – What type of information is found in the dataset.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-dataset.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_cleanroomsml as cleanroomsml

dataset_property = cleanroomsml.CfnTrainingDataset.DatasetProperty(
    input_config=cleanroomsml.CfnTrainingDataset.DatasetInputConfigProperty(
        data_source=cleanroomsml.CfnTrainingDataset.DataSourceProperty(
            glue_data_source=cleanroomsml.CfnTrainingDataset.GlueDataSourceProperty(
                database_name="databaseName",
                table_name="tableName",

                # the properties below are optional
                catalog_id="catalogId"
            )
        ),
        schema=[cleanroomsml.CfnTrainingDataset.ColumnSchemaProperty(
            column_name="columnName",
            column_types=["columnTypes"]
        )]
    ),
    type="type"
)

Attributes

input_config

A DatasetInputConfig object that defines the data source and schema mapping.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-dataset.html#cfn-cleanroomsml-trainingdataset-dataset-inputconfig

type

What type of information is found in the dataset.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-dataset.html#cfn-cleanroomsml-trainingdataset-dataset-type

GlueDataSourceProperty

class CfnTrainingDataset.GlueDataSourceProperty(*, database_name, table_name, catalog_id=None)

Bases: object

Defines the Glue data source that contains the training data.

Parameters:

database_name (str) – The Glue database that contains the training data.
table_name (str) – The Glue table that contains the training data.
catalog_id (Optional[str]) – The Glue catalog that contains the training data.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-gluedatasource.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_cleanroomsml as cleanroomsml

glue_data_source_property = cleanroomsml.CfnTrainingDataset.GlueDataSourceProperty(
    database_name="databaseName",
    table_name="tableName",

    # the properties below are optional
    catalog_id="catalogId"
)

Attributes

catalog_id

The Glue catalog that contains the training data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-gluedatasource.html#cfn-cleanroomsml-trainingdataset-gluedatasource-catalogid

database_name

The Glue database that contains the training data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-gluedatasource.html#cfn-cleanroomsml-trainingdataset-gluedatasource-databasename

table_name

The Glue table that contains the training data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-cleanroomsml-trainingdataset-gluedatasource.html#cfn-cleanroomsml-trainingdataset-gluedatasource-tablename