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:
aws_cdk.core.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
]) – 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
]) – 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 withNumberOfWorkers
andWorkerType
. - If eitherNumberOfWorkers
orWorkerType
is set, thenMaxCapacity
cannot be set. - IfMaxCapacity
is set then neitherNumberOfWorkers
orWorkerType
can be set. - IfWorkerType
is set, thenNumberOfWorkers
is required (and vice versa). -MaxCapacity
andNumberOfWorkers
must both be at least 1. When theWorkerType
field is set to a value other thanStandard
, theMaxCapacity
field is set automatically and becomes read-only.max_retries (
Union
[int
,float
,None
]) – The maximum number of times to retry after anMLTaskRun
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 supplyName
, the stack cannot be repeatedly created. - IfName
is not provided, a randomly generated name will be used instead.number_of_workers (
Union
[int
,float
,None
]) – The number of workers of a definedworkerType
that are allocated when a task of the transform runs. IfWorkerType
is set, thenNumberOfWorkers
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
,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 theStandard
worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. - For theG.1X
worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. - For theG.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 withNumberOfWorkers
andWorkerType
. - If eitherNumberOfWorkers
orWorkerType
is set, thenMaxCapacity
cannot be set. - IfMaxCapacity
is set then neitherNumberOfWorkers
orWorkerType
can be set. - IfWorkerType
is set, thenNumberOfWorkers
is required (and vice versa). -MaxCapacity
andNumberOfWorkers
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
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.
- Return type
None
-
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
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.
- Return type
Any
-
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.
- Return type
-
cfn_resource_type
¶ AWS resource type.
- Return type
str
-
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.
- Return type
List
[str
]
-
description
¶ A user-defined, long-form description text for the machine learning transform.
-
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.
-
input_record_tables
¶ A list of AWS Glue table definitions used by the transform.
-
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)
.- Return type
str
- 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 withNumberOfWorkers
andWorkerType
.If either
NumberOfWorkers
orWorkerType
is set, thenMaxCapacity
cannot be set.If
MaxCapacity
is set then neitherNumberOfWorkers
orWorkerType
can be set.If
WorkerType
is set, thenNumberOfWorkers
is required (and vice versa).MaxCapacity
andNumberOfWorkers
must both be at least 1.
When the
WorkerType
field is set to a value other thanStandard
, theMaxCapacity
field is set automatically and becomes read-only.- Link
- Return type
Union
[int
,float
,None
]
-
max_retries
¶ The maximum number of times to retry after an
MLTaskRun
of the machine learning transform fails.- Link
- Return type
Union
[int
,float
,None
]
-
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
- Type
A user-defined name for the machine learning transform. Names are required to be unique.
Name
is optional- Return type
Optional
[str
]
-
node
¶ The construct tree node associated with this construct.
- Return type
-
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, thenNumberOfWorkers
is required (and vice versa).- Link
- Return type
Union
[int
,float
,None
]
-
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 })
.- Return type
str
-
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.
-
stack
¶ The stack in which this element is defined.
CfnElements must be defined within a stack scope (directly or indirectly).
- Return type
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
¶ The timeout in minutes of the machine learning transform.
- Link
- Return type
Union
[int
,float
,None
]
-
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.
-
transform_parameters
¶ The algorithm-specific parameters that are associated with the machine learning transform.
-
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 withNumberOfWorkers
andWorkerType
.If either
NumberOfWorkers
orWorkerType
is set, thenMaxCapacity
cannot be set.If
MaxCapacity
is set then neitherNumberOfWorkers
orWorkerType
can be set.If
WorkerType
is set, thenNumberOfWorkers
is required (and vice versa).MaxCapacity
andNumberOfWorkers
must both be at least 1.
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 accurateFindMatches
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 isTrue
, thefind matches
transform forces the output to match the provided labels. The results override the normal conflation results. If the value isFalse
, thefind 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
- 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.
-
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
, thefind matches
transform forces the output to match the provided labels. The results override the normal conflation results. If the value isFalse
, thefind 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
¶ 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.
-
primary_key_column_name
¶ The name of a column that uniquely identifies rows in the source table.
Used to help identify matching records.
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
- 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 .
-
connection_name
¶ The name of the connection to the AWS Glue Data Catalog .
-
database_name
¶ A database name in the AWS Glue Data Catalog .
-
table_name
¶ A table name in the AWS Glue Data Catalog .
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
]],None
]) – The database and table in the AWS Glue Data Catalog that is used for input or output data.- 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_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.
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
- 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.
-
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
- Type
The encryption mode applied to user data. Valid values are
- Return type
str
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
,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
- 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.
-
task_run_security_configuration_name
¶ The name of the security configuration.
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
,None
]) – The parameters for the find matches algorithm.
- 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_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.
-
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 .