CfnMLTransform

class aws_cdk.aws_glue.CfnMLTransform(scope, id, *, input_record_tables, role, transform_parameters, description=None, glue_version=None, max_capacity=None, max_retries=None, name=None, number_of_workers=None, tags=None, timeout=None, transform_encryption=None, worker_type=None)

Bases: CfnResource

A CloudFormation AWS::Glue::MLTransform.

The AWS::Glue::MLTransform is an AWS Glue resource type that manages machine learning transforms.

CloudformationResource:: AWS::Glue::MLTransform
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.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_glue as glue

# tags: Any

cfn_mLTransform = glue.CfnMLTransform(self, "MyCfnMLTransform",
    input_record_tables=glue.CfnMLTransform.InputRecordTablesProperty(
        glue_tables=[glue.CfnMLTransform.GlueTablesProperty(
            database_name="databaseName",
            table_name="tableName",

            # the properties below are optional
            catalog_id="catalogId",
            connection_name="connectionName"
        )]
    ),
    role="role",
    transform_parameters=glue.CfnMLTransform.TransformParametersProperty(
        transform_type="transformType",

        # the properties below are optional
        find_matches_parameters=glue.CfnMLTransform.FindMatchesParametersProperty(
            primary_key_column_name="primaryKeyColumnName",

            # the properties below are optional
            accuracy_cost_tradeoff=123,
            enforce_provided_labels=False,
            precision_recall_tradeoff=123
        )
    ),

    # the properties below are optional
    description="description",
    glue_version="glueVersion",
    max_capacity=123,
    max_retries=123,
    name="name",
    number_of_workers=123,
    tags=tags,
    timeout=123,
    transform_encryption=glue.CfnMLTransform.TransformEncryptionProperty(
        ml_user_data_encryption=glue.CfnMLTransform.MLUserDataEncryptionProperty(
            ml_user_data_encryption_mode="mlUserDataEncryptionMode",

            # the properties below are optional
            kms_key_id="kmsKeyId"
        ),
        task_run_security_configuration_name="taskRunSecurityConfigurationName"
    ),
    worker_type="workerType"
)

Create a new AWS::Glue::MLTransform.

Parameters:

scope (Construct) –
- scope in which this resource is defined.
id (str) –
- scoped id of the resource.
input_record_tables (Union[IResolvable, InputRecordTablesProperty, Dict[str, Any]]) – A list of AWS Glue table definitions used by the transform.
role (str) – The name or Amazon Resource Name (ARN) of the IAM role with the required permissions. The required permissions include both AWS Glue service role permissions to AWS Glue resources, and Amazon S3 permissions required by the transform. - This role needs AWS Glue service role permissions to allow access to resources in AWS Glue . See Attach a Policy to IAM Users That Access AWS Glue . - This role needs permission to your Amazon Simple Storage Service (Amazon S3) sources, targets, temporary directory, scripts, and any libraries used by the task run for this transform.
transform_parameters (Union[IResolvable, TransformParametersProperty, Dict[str, Any]]) – The algorithm-specific parameters that are associated with the machine learning transform.
description (Optional[str]) – A user-defined, long-form description text for the machine learning transform.
glue_version (Optional[str]) – This value determines which version of AWS Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see AWS Glue Versions in the developer guide.
max_capacity (Union[int, float, None]) – The number of AWS Glue data processing units (DPUs) that are allocated to task runs for this transform. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType . - If either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set. - If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set. - If WorkerType is set, then NumberOfWorkers is required (and vice versa). - MaxCapacity and NumberOfWorkers must both be at least 1. When the WorkerType field is set to a value other than Standard , the MaxCapacity field is set automatically and becomes read-only.
max_retries (Union[int, float, None]) – The maximum number of times to retry after an MLTaskRun of the machine learning transform fails.
name (Optional[str]) – A user-defined name for the machine learning transform. Names are required to be unique. Name is optional:. - If you supply Name , the stack cannot be repeatedly created. - If Name is not provided, a randomly generated name will be used instead.
number_of_workers (Union[int, float, None]) – The number of workers of a defined workerType that are allocated when a task of the transform runs. If WorkerType is set, then NumberOfWorkers is required (and vice versa).
tags (Optional[Any]) – The tags to use with this machine learning transform. You may use tags to limit access to the machine learning transform. For more information about tags in AWS Glue , see AWS Tags in AWS Glue in the developer guide.
timeout (Union[int, float, None]) – The timeout in minutes of the machine learning transform.
transform_encryption (Union[IResolvable, TransformEncryptionProperty, Dict[str, Any], None]) – The encryption-at-rest settings of the transform that apply to accessing user data. Machine learning transforms can access user data encrypted in Amazon S3 using KMS. Additionally, imported labels and trained transforms can now be encrypted using a customer provided KMS key.
worker_type (Optional[str]) – The type of predefined worker that is allocated when a task of this transform runs. Accepts a value of Standard, G.1X, or G.2X. - For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. - For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. - For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType . - If either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set. - If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set. - If WorkerType is set, then NumberOfWorkers is required (and vice versa). - MaxCapacity and NumberOfWorkers must both be at least 1.

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

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 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: 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 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:: 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

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::Glue::MLTransform'

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

A user-defined, long-form description text for the machine learning transform.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-description

glue_version

This value determines which version of AWS Glue this machine learning transform is compatible with.

Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see AWS Glue Versions in the developer guide.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-glueversion

input_record_tables

A list of AWS Glue table definitions used by the transform.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-inputrecordtables

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.

max_capacity

The number of AWS Glue data processing units (DPUs) that are allocated to task runs for this transform.

You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page .

MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType .

If either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set.
If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set.
If WorkerType is set, then NumberOfWorkers is required (and vice versa).
MaxCapacity and NumberOfWorkers must both be at least 1.

When the WorkerType field is set to a value other than Standard , the MaxCapacity field is set automatically and becomes read-only.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-maxcapacity

max_retries

The maximum number of times to retry after an MLTaskRun of the machine learning transform fails.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-maxretries

name

.

If you supply Name , the stack cannot be repeatedly created.
If Name is not provided, a randomly generated name will be used instead.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-name
Type:: A user-defined name for the machine learning transform. Names are required to be unique. Name is optional

node: The construct tree node associated with this construct.

number_of_workers

The number of workers of a defined workerType that are allocated when a task of the transform runs.

If WorkerType is set, then NumberOfWorkers is required (and vice versa).

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-numberofworkers

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

The name or Amazon Resource Name (ARN) of the IAM role with the required permissions.

The required permissions include both AWS Glue service role permissions to AWS Glue resources, and Amazon S3 permissions required by the transform.

This role needs AWS Glue service role permissions to allow access to resources in AWS Glue . See Attach a Policy to IAM Users That Access AWS Glue .
This role needs permission to your Amazon Simple Storage Service (Amazon S3) sources, targets, temporary directory, scripts, and any libraries used by the task run for this transform.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-role

stack

The stack in which this element is defined.

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

tags

The tags to use with this machine learning transform.

You may use tags to limit access to the machine learning transform. For more information about tags in AWS Glue , see AWS Tags in AWS Glue in the developer guide.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-tags

timeout

The timeout in minutes of the machine learning transform.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-timeout

transform_encryption

The encryption-at-rest settings of the transform that apply to accessing user data.

Machine learning transforms can access user data encrypted in Amazon S3 using KMS.

Additionally, imported labels and trained transforms can now be encrypted using a customer provided KMS key.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-transformencryption

transform_parameters

The algorithm-specific parameters that are associated with the machine learning transform.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-transformparameters

worker_type

The type of predefined worker that is allocated when a task of this transform runs.

Accepts a value of Standard, G.1X, or G.2X.

For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.
For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker.
For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker.

MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType .

If either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set.
If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set.
If WorkerType is set, then NumberOfWorkers is required (and vice versa).
MaxCapacity and NumberOfWorkers must both be at least 1.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html#cfn-glue-mltransform-workertype

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

FindMatchesParametersProperty

class CfnMLTransform.FindMatchesParametersProperty(*, primary_key_column_name, accuracy_cost_tradeoff=None, enforce_provided_labels=None, precision_recall_tradeoff=None)

Bases: object

The parameters to configure the find matches transform.

Parameters:

primary_key_column_name (str) – The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.
accuracy_cost_tradeoff (Union[int, float, None]) – The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy. Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall. Cost measures how many compute resources, and thus money, are consumed to run the transform.
enforce_provided_labels (Union[bool, IResolvable, None]) – The value to switch on or off to force the output to match the provided labels from users. If the value is True , the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False , the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model. Note that setting this value to true may increase the conflation execution time.
precision_recall_tradeoff (Union[int, float, None]) – The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision. The precision metric indicates how often your model is correct when it predicts a match. The recall metric indicates that for an actual match, how often your model predicts the match.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters-findmatchesparameters.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_glue as glue

find_matches_parameters_property = glue.CfnMLTransform.FindMatchesParametersProperty(
    primary_key_column_name="primaryKeyColumnName",

    # the properties below are optional
    accuracy_cost_tradeoff=123,
    enforce_provided_labels=False,
    precision_recall_tradeoff=123
)

Attributes

accuracy_cost_tradeoff

The value that is selected when tuning your transform for a balance between accuracy and cost.

A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.

Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.

Cost measures how many compute resources, and thus money, are consumed to run the transform.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters-findmatchesparameters.html#cfn-glue-mltransform-transformparameters-findmatchesparameters-accuracycosttradeoff

enforce_provided_labels

The value to switch on or off to force the output to match the provided labels from users.

If the value is True , the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False , the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model.

Note that setting this value to true may increase the conflation execution time.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters-findmatchesparameters.html#cfn-glue-mltransform-transformparameters-findmatchesparameters-enforceprovidedlabels

precision_recall_tradeoff

The value selected when tuning your transform for a balance between precision and recall.

A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.

The precision metric indicates how often your model is correct when it predicts a match.

The recall metric indicates that for an actual match, how often your model predicts the match.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters-findmatchesparameters.html#cfn-glue-mltransform-transformparameters-findmatchesparameters-precisionrecalltradeoff

primary_key_column_name

The name of a column that uniquely identifies rows in the source table.

Used to help identify matching records.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters-findmatchesparameters.html#cfn-glue-mltransform-transformparameters-findmatchesparameters-primarykeycolumnname

GlueTablesProperty

class CfnMLTransform.GlueTablesProperty(*, database_name, table_name, catalog_id=None, connection_name=None)

Bases: object

The database and table in the AWS Glue Data Catalog that is used for input or output data.

Parameters:

database_name (str) – A database name in the AWS Glue Data Catalog .
table_name (str) – A table name in the AWS Glue Data Catalog .
catalog_id (Optional[str]) – A unique identifier for the AWS Glue Data Catalog .
connection_name (Optional[str]) – The name of the connection to the AWS Glue Data Catalog .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-inputrecordtables-gluetables.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_glue as glue

glue_tables_property = glue.CfnMLTransform.GlueTablesProperty(
    database_name="databaseName",
    table_name="tableName",

    # the properties below are optional
    catalog_id="catalogId",
    connection_name="connectionName"
)

Attributes

catalog_id

A unique identifier for the AWS Glue Data Catalog .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-inputrecordtables-gluetables.html#cfn-glue-mltransform-inputrecordtables-gluetables-catalogid

connection_name

The name of the connection to the AWS Glue Data Catalog .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-inputrecordtables-gluetables.html#cfn-glue-mltransform-inputrecordtables-gluetables-connectionname

database_name

A database name in the AWS Glue Data Catalog .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-inputrecordtables-gluetables.html#cfn-glue-mltransform-inputrecordtables-gluetables-databasename

table_name

A table name in the AWS Glue Data Catalog .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-inputrecordtables-gluetables.html#cfn-glue-mltransform-inputrecordtables-gluetables-tablename

InputRecordTablesProperty

class CfnMLTransform.InputRecordTablesProperty(*, glue_tables=None)

Bases: object

A list of AWS Glue table definitions used by the transform.

Parameters:: glue_tables (Union[IResolvable, Sequence[Union[IResolvable, GlueTablesProperty, Dict[str, Any]]], None]) – The database and table in the AWS Glue Data Catalog that is used for input or output data.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-inputrecordtables.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_glue as glue

input_record_tables_property = glue.CfnMLTransform.InputRecordTablesProperty(
    glue_tables=[glue.CfnMLTransform.GlueTablesProperty(
        database_name="databaseName",
        table_name="tableName",

        # the properties below are optional
        catalog_id="catalogId",
        connection_name="connectionName"
    )]
)

Attributes

glue_tables

The database and table in the AWS Glue Data Catalog that is used for input or output data.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-inputrecordtables.html#cfn-glue-mltransform-inputrecordtables-gluetables

MLUserDataEncryptionProperty

class CfnMLTransform.MLUserDataEncryptionProperty(*, ml_user_data_encryption_mode, kms_key_id=None)

Bases: object

The encryption-at-rest settings of the transform that apply to accessing user data.

Parameters:

ml_user_data_encryption_mode (str) – The encryption mode applied to user data. Valid values are:. - DISABLED: encryption is disabled. - SSEKMS: use of server-side encryption with AWS Key Management Service (SSE-KMS) for user data stored in Amazon S3.
kms_key_id (Optional[str]) – The ID for the customer-provided KMS key.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformencryption-mluserdataencryption.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_glue as glue

m_lUser_data_encryption_property = glue.CfnMLTransform.MLUserDataEncryptionProperty(
    ml_user_data_encryption_mode="mlUserDataEncryptionMode",

    # the properties below are optional
    kms_key_id="kmsKeyId"
)

Attributes

kms_key_id

The ID for the customer-provided KMS key.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformencryption-mluserdataencryption.html#cfn-glue-mltransform-transformencryption-mluserdataencryption-kmskeyid

ml_user_data_encryption_mode

.

DISABLED: encryption is disabled.
SSEKMS: use of server-side encryption with AWS Key Management Service (SSE-KMS) for user data stored in Amazon S3.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformencryption-mluserdataencryption.html#cfn-glue-mltransform-transformencryption-mluserdataencryption-mluserdataencryptionmode
Type:: The encryption mode applied to user data. Valid values are

TransformEncryptionProperty

class CfnMLTransform.TransformEncryptionProperty(*, ml_user_data_encryption=None, task_run_security_configuration_name=None)

Bases: object

The encryption-at-rest settings of the transform that apply to accessing user data.

Machine learning transforms can access user data encrypted in Amazon S3 using KMS.

Additionally, imported labels and trained transforms can now be encrypted using a customer provided KMS key.

Parameters:

ml_user_data_encryption (Union[IResolvable, MLUserDataEncryptionProperty, Dict[str, Any], None]) – The encryption-at-rest settings of the transform that apply to accessing user data.
task_run_security_configuration_name (Optional[str]) – The name of the security configuration.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformencryption.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_glue as glue

transform_encryption_property = glue.CfnMLTransform.TransformEncryptionProperty(
    ml_user_data_encryption=glue.CfnMLTransform.MLUserDataEncryptionProperty(
        ml_user_data_encryption_mode="mlUserDataEncryptionMode",

        # the properties below are optional
        kms_key_id="kmsKeyId"
    ),
    task_run_security_configuration_name="taskRunSecurityConfigurationName"
)

Attributes

ml_user_data_encryption

The encryption-at-rest settings of the transform that apply to accessing user data.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformencryption.html#cfn-glue-mltransform-transformencryption-mluserdataencryption

task_run_security_configuration_name

The name of the security configuration.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformencryption.html#cfn-glue-mltransform-transformencryption-taskrunsecurityconfigurationname

TransformParametersProperty

class CfnMLTransform.TransformParametersProperty(*, transform_type, find_matches_parameters=None)

Bases: object

The algorithm-specific parameters that are associated with the machine learning transform.

Parameters:

transform_type (str) – The type of machine learning transform. FIND_MATCHES is the only option. For information about the types of machine learning transforms, see Creating Machine Learning Transforms .
find_matches_parameters (Union[IResolvable, FindMatchesParametersProperty, Dict[str, Any], None]) – The parameters for the find matches algorithm.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters.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_glue as glue

transform_parameters_property = glue.CfnMLTransform.TransformParametersProperty(
    transform_type="transformType",

    # the properties below are optional
    find_matches_parameters=glue.CfnMLTransform.FindMatchesParametersProperty(
        primary_key_column_name="primaryKeyColumnName",

        # the properties below are optional
        accuracy_cost_tradeoff=123,
        enforce_provided_labels=False,
        precision_recall_tradeoff=123
    )
)

Attributes

find_matches_parameters

The parameters for the find matches algorithm.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters.html#cfn-glue-mltransform-transformparameters-findmatchesparameters

transform_type

The type of machine learning transform. FIND_MATCHES is the only option.

For information about the types of machine learning transforms, see Creating Machine Learning Transforms .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters.html#cfn-glue-mltransform-transformparameters-transformtype