CfnTransformJob

class aws_cdk.aws_sagemaker.CfnTransformJob(scope, id, *, model_name, transform_input, transform_output, transform_resources, batch_strategy=None, data_capture_config=None, data_processing=None, environment=None, experiment_config=None, max_concurrent_transforms=None, max_payload_in_mb=None, model_client_config=None, tags=None)

Bases: CfnResource

Resource type definition for AWS::SageMaker::TransformJob.

A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-transformjob.html
CloudformationResource:: AWS::SageMaker::TransformJob
ExampleMetadata:: fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

cfn_transform_job = sagemaker.CfnTransformJob(self, "MyCfnTransformJob",
    model_name="modelName",
    transform_input=sagemaker.CfnTransformJob.TransformInputProperty(
        data_source=sagemaker.CfnTransformJob.DataSourceProperty(
            s3_data_source=sagemaker.CfnTransformJob.S3DataSourceProperty(
                s3_data_type="s3DataType",
                s3_uri="s3Uri"
            )
        ),

        # the properties below are optional
        compression_type="compressionType",
        content_type="contentType",
        split_type="splitType"
    ),
    transform_output=sagemaker.CfnTransformJob.TransformOutputProperty(
        s3_output_path="s3OutputPath",

        # the properties below are optional
        accept="accept",
        assemble_with="assembleWith",
        kms_key_id="kmsKeyId"
    ),
    transform_resources=sagemaker.CfnTransformJob.TransformResourcesProperty(
        instance_count=123,
        instance_type="instanceType",

        # the properties below are optional
        volume_kms_key_id="volumeKmsKeyId"
    ),

    # the properties below are optional
    batch_strategy="batchStrategy",
    data_capture_config=sagemaker.CfnTransformJob.DataCaptureConfigProperty(
        destination_s3_uri="destinationS3Uri",

        # the properties below are optional
        generate_inference_id=False,
        kms_key_id="kmsKeyId"
    ),
    data_processing=sagemaker.CfnTransformJob.DataProcessingProperty(
        input_filter="inputFilter",
        join_source="joinSource",
        output_filter="outputFilter"
    ),
    environment={
        "environment_key": "environment"
    },
    experiment_config=sagemaker.CfnTransformJob.ExperimentConfigProperty(
        experiment_name="experimentName",
        trial_component_display_name="trialComponentDisplayName",
        trial_name="trialName"
    ),
    max_concurrent_transforms=123,
    max_payload_in_mb=123,
    model_client_config=sagemaker.CfnTransformJob.ModelClientConfigProperty(
        invocations_max_retries=123,
        invocations_timeout_in_seconds=123
    ),
    tags=[sagemaker.CfnTransformJob.TagsItemsProperty(
        key="key",
        value="value"
    )]
)

Create a new AWS::SageMaker::TransformJob.

Parameters:

scope (Construct) – Scope in which this resource is defined.
id (str) – Construct identifier for this resource (unique in its scope).
model_name (str) – The name of the model that you want to use for the transform job.
transform_input (Union[IResolvable, TransformInputProperty, Dict[str, Any]]) – Describes the input source and the way the transform job consumes it.
transform_output (Union[IResolvable, TransformOutputProperty, Dict[str, Any]]) – Describes the results of the transform job.
transform_resources (Union[IResolvable, TransformResourcesProperty, Dict[str, Any]]) – Describes the resources, including ML instance types and ML instance count, to use for the transform job.
batch_strategy (Optional[str]) – Specifies the number of records to include in a mini-batch for an HTTP inference request.
data_capture_config (Union[IResolvable, DataCaptureConfigProperty, Dict[str, Any], None]) – Configuration to control how SageMaker captures inference data.
data_processing (Union[IResolvable, DataProcessingProperty, Dict[str, Any], None]) – The data structure used to specify the data to be used for inference in a batch transform job.
environment (Union[IResolvable, Mapping[str, str], None]) – The environment variables to set in the Docker container.
experiment_config (Union[IResolvable, ExperimentConfigProperty, Dict[str, Any], None]) – Associates a SageMaker job as a trial component with an experiment and trial.
max_concurrent_transforms (Union[int, float, None]) – The maximum number of parallel requests that can be sent to each instance in a transform job.
max_payload_in_mb (Union[int, float, None]) – The maximum allowed size of the payload, in MB.
model_client_config (Union[IResolvable, ModelClientConfigProperty, Dict[str, Any], None]) – Configures the timeout and maximum number of retries for processing a transform job invocation.
tags (Optional[Sequence[Union[TagsItemsProperty, Dict[str, Any]]]]) – An array of key-value pairs.

Methods

add_deletion_override(path)

Syntactic sugar for addOverride(path, undefined).

Parameters:: path (str) – The path of the value to delete.
Return type:: None

add_dependency(target)

(deprecated) Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This method has been renamed to addResourceDependency to more clearly set it apart from construct.node.addDependency. See the documentation of that function for more details.

Parameters:: target (CfnResource)
Deprecated:: Use addResourceDependency instead.
Stability:: deprecated
Return type:: None

add_depends_on(target)

(deprecated) Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This method has been renamed to addResourceDependency, which makes it more clear that this method operates at a different level from the construct-level construct.node.addDependency() mechanism.

Parameters:: target (CfnResource)
Deprecated:: Use addResourceDependency instead.
Stability:: deprecated
Return type:: None

add_metadata(key, value)

Add a value to the CloudFormation Resource Metadata.

Parameters:

key (str)
value (Any)

See:

Return type:

None

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

add_override(path, value)

Adds an override to the synthesized CloudFormation resource.

To add a property override, either use addPropertyOverride or prefix path with “Properties.” (i.e. Properties.TopicName).

If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.

To include a literal . in the property name, prefix with a \. In most programming languages you will need to write this as "\\." because the \ itself will need to be escaped.

For example:

cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"])
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")

would add the overrides Example:

"Properties": {
  "GlobalSecondaryIndexes": [
    {
      "Projection": {
        "NonKeyAttributes": [ "myattribute" ]
        ...
      }
      ...
    },
    {
      "ProjectionType": "INCLUDE"
      ...
    },
  ]
  ...
}

The value argument to addOverride will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.

Parameters:

path (str) –
- The path of the property, you can use dot notation to override values in complex types. Any intermediate keys will be created as needed.
value (Any) –
- The value. Could be primitive or complex.

Return type:

None

add_property_deletion_override(property_path)

Adds an override that deletes the value of a property from the resource definition.

Parameters:: property_path (str) – The path to the property.
Return type:: None

add_property_override(property_path, value)

Adds an override to a resource property.

Syntactic sugar for addOverride("Properties.<...>", value).

Parameters:

property_path (str) – The path of the property.
value (Any) – The value.

Return type:

None

add_resource_dependency(target, reason=None)

Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.

This method only adds dependencies between L1 resources. If you are looking for a generic construct-to-construct dependency mechanism that works for all constructs including L2s, use construct.node.addDependency instead.

Parameters:

target (CfnResource)
reason (Optional[str])

Return type:

None

apply_cross_stack_reference_strength(strength)

Sets the cross-stack reference strength for this resource.

When set, any cross-stack reference to this resource will use the specified strength instead of the global default from the consuming stack’s context.

Parameters:

strength (ReferenceStrength) –

The reference strength to use for this resource.

Return type:

None

apply_removal_policy(policy=None, *, apply_to_update_replace_policy=None, default=None)

Sets the deletion policy of the resource based on the removal policy specified.

The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.

The resource can be deleted (RemovalPolicy.DESTROY), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN). In some cases, a snapshot can be taken of the resource prior to deletion (RemovalPolicy.SNAPSHOT). A list of resources that support this policy can be found in the following link:

Parameters:

policy (Optional[RemovalPolicy])
apply_to_update_replace_policy (Optional[bool]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: true
default (Optional[RemovalPolicy]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resource, please consult that specific resource’s documentation.

See:

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-deletionpolicy.html#aws-attribute-deletionpolicy-options

Return type:

None

cfn_property_name(cdk_property_name)

Parameters:: cdk_property_name (str)
Return type:: Optional[str]

get_att(attribute_name, type_hint=None)

Returns a token for an runtime attribute of this resource.

Ideally, use generated attribute accessors (e.g. resource.arn), but this can be used for future compatibility in case there is no generated attribute.

Parameters:

attribute_name (str) – The name of the attribute.
type_hint (Optional[ResolutionTypeHint])

Return type:

Reference

get_metadata(key)

Retrieve a value value from the CloudFormation Resource Metadata.

Parameters:: key (str)
See:
Return type:: Any

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

inspect(inspector)

Examines the CloudFormation resource and discloses attributes.

Parameters:: inspector (TreeInspector) – tree inspector to collect and process attributes.
Return type:: None

obtain_dependencies()

Retrieves an array of resources this resource depends on.

This assembles dependencies on resources across stacks (including nested stacks) automatically.

Return type:: List[Union[CfnResource, Stack]]

override_logical_id(new_logical_id)

Overrides the auto-generated logical ID with a specific ID.

Parameters:: new_logical_id (str) – The new logical ID to use for this stack element.
Return type:: None

remove_dependency(target)

(deprecated) Indicates that this resource no longer depends on another resource.

This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.

Parameters:: target (CfnResource)
Deprecated:: Use removeResourceDependency instead
Stability:: deprecated
Return type:: None

remove_resource_dependency(target)

Indicates that this resource no longer depends on another resource.

This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.

Parameters:: target (CfnResource)
Return type:: None

replace_dependency(target, new_target)

Replaces one dependency with another.

Parameters:

target (CfnResource) – The dependency to replace.
new_target (CfnResource) – The new dependency to add.

Return type:

None

to_string()

Returns a string representation of this construct.

Return type:: str
Returns:: a string representation of this resource

with_(*mixins)

Applies one or more mixins to this construct.

Mixins are applied in order. The list of constructs is captured at the start of the call, so constructs added by a mixin will not be visited. Use multiple with() calls if subsequent mixins should apply to added constructs.

Parameters:: mixins (IMixin)
Return type:: IConstruct

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::SageMaker::TransformJob'

attr_creation_time

A timestamp that shows when the transform job was created.

CloudformationAttribute:: CreationTime

attr_transform_end_time

Indicates when the transform job has been completed, or has stopped or failed.

CloudformationAttribute:: TransformEndTime

attr_transform_job_arn

The Amazon Resource Name (ARN) of the transform job.

CloudformationAttribute:: TransformJobArn

attr_transform_job_name

The name of the transform job.

The name must be unique within an AWS Region in an AWS account.

CloudformationAttribute:: TransformJobName

attr_transform_job_status

The status of the transform job.

CloudformationAttribute:: TransformJobStatus

attr_transform_start_time

Indicates when the transform job starts on ML instances.

CloudformationAttribute:: TransformStartTime

batch_strategy: Specifies the number of records to include in a mini-batch for an HTTP inference request.

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.

data_capture_config: Configuration to control how SageMaker captures inference data.

data_processing: The data structure used to specify the data to be used for inference in a batch transform job.

env

environment: The environment variables to set in the Docker container.

experiment_config: Associates a SageMaker job as a trial component with an experiment and trial.

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_concurrent_transforms: The maximum number of parallel requests that can be sent to each instance in a transform job.

max_payload_in_mb: The maximum allowed size of the payload, in MB.

model_client_config: Configures the timeout and maximum number of retries for processing a transform job invocation.

model_name: The name of the model that you want to use for the transform job.

node: The tree node.

ref

Return a string that will be resolved to a CloudFormation { Ref } for this element.

If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through Lazy.any({ produce: resource.ref }).

stack

The stack in which this element is defined.

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

tags: An array of key-value pairs.

transform_input: Describes the input source and the way the transform job consumes it.

transform_job_ref: A reference to a TransformJob resource.

transform_output: Describes the results of the transform job.

transform_resources: Describes the resources, including ML instance types and ML instance count, to use for the transform job.

Static Methods

classmethod arn_for_transform_job(resource)

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

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_cfn_transform_job(x)

Checks whether the given object is a CfnTransformJob.

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.

DataCaptureConfigProperty

class CfnTransformJob.DataCaptureConfigProperty(*, destination_s3_uri, generate_inference_id=None, kms_key_id=None)

Bases: object

Configuration to control how SageMaker captures inference data.

Parameters:

destination_s3_uri (str) – The Amazon S3 location being used to capture the data.
generate_inference_id (Union[bool, IResolvable, None]) – Flag that indicates whether to append inference id to the output.
kms_key_id (Optional[str]) – The ARN of a KMS key that SageMaker uses to encrypt data on the storage volume.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-datacaptureconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

data_capture_config_property = sagemaker.CfnTransformJob.DataCaptureConfigProperty(
    destination_s3_uri="destinationS3Uri",

    # the properties below are optional
    generate_inference_id=False,
    kms_key_id="kmsKeyId"
)

Attributes

destination_s3_uri

The Amazon S3 location being used to capture the data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-datacaptureconfig.html#cfn-sagemaker-transformjob-datacaptureconfig-destinations3uri

generate_inference_id

Flag that indicates whether to append inference id to the output.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-datacaptureconfig.html#cfn-sagemaker-transformjob-datacaptureconfig-generateinferenceid

kms_key_id

The ARN of a KMS key that SageMaker uses to encrypt data on the storage volume.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-datacaptureconfig.html#cfn-sagemaker-transformjob-datacaptureconfig-kmskeyid

DataProcessingProperty

class CfnTransformJob.DataProcessingProperty(*, input_filter=None, join_source=None, output_filter=None)

Bases: object

The data structure used to specify the data to be used for inference in a batch transform job.

Parameters:

input_filter (Optional[str]) – A JSONPath expression used to select a portion of the input data to pass to the algorithm.
join_source (Optional[str]) – Specifies the source of the data to join with the transformed data.
output_filter (Optional[str]) – A JSONPath expression used to select a portion of the joined dataset to save in the output file.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-dataprocessing.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

data_processing_property = sagemaker.CfnTransformJob.DataProcessingProperty(
    input_filter="inputFilter",
    join_source="joinSource",
    output_filter="outputFilter"
)

Attributes

input_filter

A JSONPath expression used to select a portion of the input data to pass to the algorithm.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-dataprocessing.html#cfn-sagemaker-transformjob-dataprocessing-inputfilter

join_source

Specifies the source of the data to join with the transformed data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-dataprocessing.html#cfn-sagemaker-transformjob-dataprocessing-joinsource

output_filter

A JSONPath expression used to select a portion of the joined dataset to save in the output file.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-dataprocessing.html#cfn-sagemaker-transformjob-dataprocessing-outputfilter

DataSourceProperty

class CfnTransformJob.DataSourceProperty(*, s3_data_source)

Bases: object

Describes the location of the channel data.

Parameters:: s3_data_source (Union[IResolvable, S3DataSourceProperty, Dict[str, Any]]) – The S3 location of the data source.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-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_sagemaker as sagemaker

data_source_property = sagemaker.CfnTransformJob.DataSourceProperty(
    s3_data_source=sagemaker.CfnTransformJob.S3DataSourceProperty(
        s3_data_type="s3DataType",
        s3_uri="s3Uri"
    )
)

Attributes

s3_data_source

The S3 location of the data source.

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

ExperimentConfigProperty

class CfnTransformJob.ExperimentConfigProperty(*, experiment_name=None, trial_component_display_name=None, trial_name=None)

Bases: object

Associates a SageMaker job as a trial component with an experiment and trial.

Parameters:

experiment_name (Optional[str]) – The name of an existing experiment to associate with the trial component.
trial_component_display_name (Optional[str]) – The display name for the trial component.
trial_name (Optional[str]) – The name of an existing trial to associate the trial component with.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-experimentconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

experiment_config_property = sagemaker.CfnTransformJob.ExperimentConfigProperty(
    experiment_name="experimentName",
    trial_component_display_name="trialComponentDisplayName",
    trial_name="trialName"
)

Attributes

experiment_name

The name of an existing experiment to associate with the trial component.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-experimentconfig.html#cfn-sagemaker-transformjob-experimentconfig-experimentname

trial_component_display_name

The display name for the trial component.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-experimentconfig.html#cfn-sagemaker-transformjob-experimentconfig-trialcomponentdisplayname

trial_name

The name of an existing trial to associate the trial component with.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-experimentconfig.html#cfn-sagemaker-transformjob-experimentconfig-trialname

ModelClientConfigProperty

class CfnTransformJob.ModelClientConfigProperty(*, invocations_max_retries=None, invocations_timeout_in_seconds=None)

Bases: object

Configures the timeout and maximum number of retries for processing a transform job invocation.

Parameters:

invocations_max_retries (Union[int, float, None]) – The maximum number of retries when invocation requests are failing.
invocations_timeout_in_seconds (Union[int, float, None]) – The timeout value in seconds for an invocation request.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-modelclientconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

model_client_config_property = sagemaker.CfnTransformJob.ModelClientConfigProperty(
    invocations_max_retries=123,
    invocations_timeout_in_seconds=123
)

Attributes

invocations_max_retries

The maximum number of retries when invocation requests are failing.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-modelclientconfig.html#cfn-sagemaker-transformjob-modelclientconfig-invocationsmaxretries

invocations_timeout_in_seconds

The timeout value in seconds for an invocation request.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-modelclientconfig.html#cfn-sagemaker-transformjob-modelclientconfig-invocationstimeoutinseconds

S3DataSourceProperty

class CfnTransformJob.S3DataSourceProperty(*, s3_data_type, s3_uri)

Bases: object

The S3 location of the data source.

Parameters:

s3_data_type (str) – The data type.
s3_uri (str) – The S3 URI.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-s3datasource.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

s3_data_source_property = sagemaker.CfnTransformJob.S3DataSourceProperty(
    s3_data_type="s3DataType",
    s3_uri="s3Uri"
)

Attributes

s3_data_type

The data type.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-s3datasource.html#cfn-sagemaker-transformjob-s3datasource-s3datatype

s3_uri

The S3 URI.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-s3datasource.html#cfn-sagemaker-transformjob-s3datasource-s3uri

TagsItemsProperty

class CfnTransformJob.TagsItemsProperty(*, key, value)

Bases: object

Parameters:

key (str) – The tag key.
value (str) – The tag value.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-tagsitems.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

tags_items_property = sagemaker.CfnTransformJob.TagsItemsProperty(
    key="key",
    value="value"
)

Attributes

key

The tag key.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-tagsitems.html#cfn-sagemaker-transformjob-tagsitems-key

value

The tag value.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-tagsitems.html#cfn-sagemaker-transformjob-tagsitems-value

TransformInputProperty

class CfnTransformJob.TransformInputProperty(*, data_source, compression_type=None, content_type=None, split_type=None)

Bases: object

Describes the input source and the way the transform job consumes it.

Parameters:

data_source (Union[IResolvable, DataSourceProperty, Dict[str, Any]]) – Describes the location of the channel data.
compression_type (Optional[str]) – If your transform data is compressed, specify the compression type.
content_type (Optional[str]) – The multipurpose internet mail extension (MIME) type of the data.
split_type (Optional[str]) – The method to use to split the transform job’s data files into smaller batches.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transforminput.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

transform_input_property = sagemaker.CfnTransformJob.TransformInputProperty(
    data_source=sagemaker.CfnTransformJob.DataSourceProperty(
        s3_data_source=sagemaker.CfnTransformJob.S3DataSourceProperty(
            s3_data_type="s3DataType",
            s3_uri="s3Uri"
        )
    ),

    # the properties below are optional
    compression_type="compressionType",
    content_type="contentType",
    split_type="splitType"
)

Attributes

compression_type

If your transform data is compressed, specify the compression type.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transforminput.html#cfn-sagemaker-transformjob-transforminput-compressiontype

content_type

The multipurpose internet mail extension (MIME) type of the data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transforminput.html#cfn-sagemaker-transformjob-transforminput-contenttype

data_source

Describes the location of the channel data.

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

split_type

The method to use to split the transform job’s data files into smaller batches.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transforminput.html#cfn-sagemaker-transformjob-transforminput-splittype

TransformOutputProperty

class CfnTransformJob.TransformOutputProperty(*, s3_output_path, accept=None, assemble_with=None, kms_key_id=None)

Bases: object

Describes the results of the transform job.

Parameters:

s3_output_path (str) – The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job.
accept (Optional[str]) – The MIME type used to specify the output data.
assemble_with (Optional[str]) – Defines how to assemble the results of the transform job as a single S3 object.
kms_key_id (Optional[str]) – The AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transformoutput.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

transform_output_property = sagemaker.CfnTransformJob.TransformOutputProperty(
    s3_output_path="s3OutputPath",

    # the properties below are optional
    accept="accept",
    assemble_with="assembleWith",
    kms_key_id="kmsKeyId"
)

Attributes

accept

The MIME type used to specify the output data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transformoutput.html#cfn-sagemaker-transformjob-transformoutput-accept

assemble_with

Defines how to assemble the results of the transform job as a single S3 object.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transformoutput.html#cfn-sagemaker-transformjob-transformoutput-assemblewith

kms_key_id

The AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transformoutput.html#cfn-sagemaker-transformjob-transformoutput-kmskeyid

s3_output_path

The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transformoutput.html#cfn-sagemaker-transformjob-transformoutput-s3outputpath

TransformResourcesProperty

class CfnTransformJob.TransformResourcesProperty(*, instance_count, instance_type, volume_kms_key_id=None)

Bases: object

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

Parameters:

instance_count (Union[int, float]) – The number of ML compute instances to use in the transform job.
instance_type (str) – The ML compute instance type for the transform job.
volume_kms_key_id (Optional[str]) – The AWS KMS key that Amazon SageMaker uses to encrypt model data on the storage volume.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transformresources.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

transform_resources_property = sagemaker.CfnTransformJob.TransformResourcesProperty(
    instance_count=123,
    instance_type="instanceType",

    # the properties below are optional
    volume_kms_key_id="volumeKmsKeyId"
)

Attributes

instance_count

The number of ML compute instances to use in the transform job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transformresources.html#cfn-sagemaker-transformjob-transformresources-instancecount

instance_type

The ML compute instance type for the transform job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transformresources.html#cfn-sagemaker-transformjob-transformresources-instancetype

volume_kms_key_id

The AWS KMS key that Amazon SageMaker uses to encrypt model data on the storage volume.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-transformjob-transformresources.html#cfn-sagemaker-transformjob-transformresources-volumekmskeyid