CfnProcessingJob

class aws_cdk.aws_sagemaker.CfnProcessingJob(scope, id, *, app_specification, processing_resources, role_arn, environment=None, experiment_config=None, network_config=None, processing_inputs=None, processing_job_name=None, processing_output_config=None, stopping_condition=None, tags=None)

Bases: CfnResource

An Amazon SageMaker processing job that is used to analyze data and evaluate models.

For more information, see Process Data and Evaluate Models .

Also, note the following details specific to processing jobs created using CloudFormation stacks:

When you delete a CloudFormation stack with a processing job resource, the processing job is stopped using the StopProcessingJob API but not deleted. Any tags associated with the processing job are deleted using the DeleteTags API.
If any part of your CloudFormation stack deployment fails and a rollback initiates, processing jobs with a specified ProcessingJobName value might cause the stack to become stuck in a failed state. This occurs because during a rollback, CloudFormation attempts to recreate the stack resources. Processing job names must be unique, so when CloudFormation attempts to recreate a processing job using the already defined name, this results in an AlreadyExists error. To prevent this, we recommend that you don’t specify the optional ProcessingJobName property, thereby allowing SageMaker to auto-generate a unique name for your processing job. This ensures successful stack rollbacks when necessary. If you must use custom job names, you have to manually modify the ProcessingJobName and redeploy the stack to recover from a failed rollback.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-processingjob.html
CloudformationResource:: AWS::SageMaker::ProcessingJob
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_processing_job = sagemaker.CfnProcessingJob(self, "MyCfnProcessingJob",
    app_specification=sagemaker.CfnProcessingJob.AppSpecificationProperty(
        image_uri="imageUri",

        # the properties below are optional
        container_arguments=["containerArguments"],
        container_entrypoint=["containerEntrypoint"]
    ),
    processing_resources=sagemaker.CfnProcessingJob.ProcessingResourcesProperty(
        cluster_config=sagemaker.CfnProcessingJob.ClusterConfigProperty(
            instance_count=123,
            instance_type="instanceType",
            volume_size_in_gb=123,

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

    # the properties below are optional
    environment={
        "environment_key": "environment"
    },
    experiment_config=sagemaker.CfnProcessingJob.ExperimentConfigProperty(
        experiment_name="experimentName",
        run_name="runName",
        trial_component_display_name="trialComponentDisplayName",
        trial_name="trialName"
    ),
    network_config=sagemaker.CfnProcessingJob.NetworkConfigProperty(
        enable_inter_container_traffic_encryption=False,
        enable_network_isolation=False,
        vpc_config=sagemaker.CfnProcessingJob.VpcConfigProperty(
            security_group_ids=["securityGroupIds"],
            subnets=["subnets"]
        )
    ),
    processing_inputs=[sagemaker.CfnProcessingJob.ProcessingInputsObjectProperty(
        input_name="inputName",

        # the properties below are optional
        app_managed=False,
        dataset_definition=sagemaker.CfnProcessingJob.DatasetDefinitionProperty(
            athena_dataset_definition=sagemaker.CfnProcessingJob.AthenaDatasetDefinitionProperty(
                catalog="catalog",
                database="database",
                output_format="outputFormat",
                output_s3_uri="outputS3Uri",
                query_string="queryString",

                # the properties below are optional
                kms_key_id="kmsKeyId",
                output_compression="outputCompression",
                work_group="workGroup"
            ),
            data_distribution_type="dataDistributionType",
            input_mode="inputMode",
            local_path="localPath",
            redshift_dataset_definition=sagemaker.CfnProcessingJob.RedshiftDatasetDefinitionProperty(
                cluster_id="clusterId",
                cluster_role_arn="clusterRoleArn",
                database="database",
                db_user="dbUser",
                output_format="outputFormat",
                output_s3_uri="outputS3Uri",
                query_string="queryString",

                # the properties below are optional
                kms_key_id="kmsKeyId",
                output_compression="outputCompression"
            )
        ),
        s3_input=sagemaker.CfnProcessingJob.S3InputProperty(
            s3_data_type="s3DataType",
            s3_uri="s3Uri",

            # the properties below are optional
            local_path="localPath",
            s3_compression_type="s3CompressionType",
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode"
        )
    )],
    processing_job_name="processingJobName",
    processing_output_config=sagemaker.CfnProcessingJob.ProcessingOutputConfigProperty(
        outputs=[sagemaker.CfnProcessingJob.ProcessingOutputsObjectProperty(
            output_name="outputName",

            # the properties below are optional
            app_managed=False,
            feature_store_output=sagemaker.CfnProcessingJob.FeatureStoreOutputProperty(
                feature_group_name="featureGroupName"
            ),
            s3_output=sagemaker.CfnProcessingJob.S3OutputProperty(
                s3_upload_mode="s3UploadMode",
                s3_uri="s3Uri",

                # the properties below are optional
                local_path="localPath"
            )
        )],

        # the properties below are optional
        kms_key_id="kmsKeyId"
    ),
    stopping_condition=sagemaker.CfnProcessingJob.StoppingConditionProperty(
        max_runtime_in_seconds=123
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)

Parameters:

scope (Construct) – Scope in which this resource is defined.
id (str) – Construct identifier for this resource (unique in its scope).
app_specification (Union[IResolvable, AppSpecificationProperty, Dict[str, Any]]) – Configuration to run a processing job in a specified container image.
processing_resources (Union[IResolvable, ProcessingResourcesProperty, Dict[str, Any]]) – Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
role_arn (str) – The ARN of the role used to create the processing job.
environment (Union[Mapping[str, str], IResolvable, None]) – Sets the environment variables 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. Specified when you call the CreateProcessingJob API.
network_config (Union[IResolvable, NetworkConfigProperty, Dict[str, Any], None]) – Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
processing_inputs (Union[IResolvable, Sequence[Union[IResolvable, ProcessingInputsObjectProperty, Dict[str, Any]]], None]) – List of input configurations for the processing job.
processing_job_name (Optional[str]) – The name of the processing job. If you don’t provide a job name, then a unique name is automatically created for the job.
processing_output_config (Union[IResolvable, ProcessingOutputConfigProperty, Dict[str, Any], None]) – Contains information about the output location for the compiled model and the target device that the model runs on. TargetDevice and TargetPlatform are mutually exclusive, so you need to choose one between the two to specify your target device or platform. If you cannot find your device you want to use from the TargetDevice list, use TargetPlatform to describe the platform of your edge device and CompilerOptions if there are specific settings that are required or recommended to use for particular TargetPlatform.
stopping_condition (Union[IResolvable, StoppingConditionProperty, Dict[str, Any], None]) – Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .

Methods

add_deletion_override(path)

Syntactic sugar for addOverride(path, undefined).

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

add_dependency(target)

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

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

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

add_depends_on(target)

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

Parameters:: target (CfnResource)
Deprecated:: use addDependency
Stability:: deprecated
Return type:: None

add_metadata(key, value)

Add a value to the CloudFormation Resource Metadata.

Parameters:

key (str)
value (Any)

See:

Return type:

None

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

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

add_override(path, value)

Adds an override to the synthesized CloudFormation resource.

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

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

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

For example:

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

would add the overrides Example:

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

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

Parameters:

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

Return type:

None

add_property_deletion_override(property_path)

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

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

add_property_override(property_path, value)

Adds an override to a resource property.

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

Parameters:

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

Return type:

None

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

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

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

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

Parameters:

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

See:

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

Return type:

None

get_att(attribute_name, type_hint=None)

Returns a token for an runtime attribute of this resource.

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

Parameters:

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

Return type:

Reference

get_metadata(key)

Retrieve a value value from the CloudFormation Resource Metadata.

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

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

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

inspect(inspector)

Examines the CloudFormation resource and discloses attributes.

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

obtain_dependencies()

Retrieves an array of resources this resource depends on.

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

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

obtain_resource_dependencies()

Get a shallow copy of dependencies between this resource and other resources in the same stack.

Return type:: List[CfnResource]

override_logical_id(new_logical_id)

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

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

remove_dependency(target)

Indicates that this resource no longer depends on another resource.

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

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

replace_dependency(target, new_target)

Replaces one dependency with another.

Parameters:

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

Return type:

None

to_string()

Returns a string representation of this construct.

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

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::SageMaker::ProcessingJob'

app_specification: Configuration to run a processing job in a specified container image.

attr_auto_ml_job_arn

The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.

CloudformationAttribute:: AutoMLJobArn

attr_creation_time

The time the processing job was created.

CloudformationAttribute:: CreationTime

attr_exit_message

A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

CloudformationAttribute:: ExitMessage

attr_failure_reason

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

CloudformationAttribute:: FailureReason

attr_last_modified_time

The time the processing job was last modified.

CloudformationAttribute:: LastModifiedTime

attr_monitoring_schedule_arn

The ARN of a monitoring schedule for an endpoint associated with this processing job.

CloudformationAttribute:: MonitoringScheduleArn

attr_processing_end_time

The time that the processing job ended.

CloudformationAttribute:: ProcessingEndTime

attr_processing_job_arn

The ARN of the processing job.

CloudformationAttribute:: ProcessingJobArn

attr_processing_job_status

The status of the processing job.

CloudformationAttribute:: ProcessingJobStatus

attr_processing_start_time

The time that the processing job started.

CloudformationAttribute:: ProcessingStartTime

attr_training_job_arn

The ARN of the training job associated with this processing job.

CloudformationAttribute:: TrainingJobArn

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.

environment: Sets the environment variables 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.

network_config: Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.

node: The tree node.

processing_inputs: List of input configurations for the processing job.

processing_job_name: The name of the processing job.

processing_job_ref: A reference to a ProcessingJob resource.

processing_output_config: Contains information about the output location for the compiled model and the target device that the model runs on.

processing_resources: Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job.

ref

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

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

role_arn: The ARN of the role used to create the processing job.

stack

The stack in which this element is defined.

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

stopping_condition: Configures conditions under which the processing job should be stopped, such as how long the processing job has been running.

tags: An array of key-value pairs.

Static Methods

classmethod is_cfn_element(x)

Returns true if a construct is a stack element (i.e. part of the synthesized cloudformation template).

Uses duck-typing instead of instanceof to allow stack elements from different versions of this library to be included in the same stack.

Parameters:: x (Any)
Return type:: bool
Returns:: The construct as a stack element or undefined if it is not a stack element.

classmethod is_cfn_resource(x)

Check whether the given object is a CfnResource.

Parameters:: x (Any)
Return type:: bool

classmethod is_construct(x)

Checks if x is a construct.

Use this method instead of instanceof to properly detect Construct instances, even when the construct library is symlinked.

Explanation: in JavaScript, multiple copies of the constructs library on disk are seen as independent, completely different libraries. As a consequence, the class Construct in each copy of the constructs library is seen as a different class, and an instance of one class will not test as instanceof the other class. npm install will not create installations like this, but users may manually symlink construct libraries together or use a monorepo tool: in those cases, multiple copies of the constructs library can be accidentally installed, and instanceof will behave unpredictably. It is safest to avoid using instanceof, and using this type-testing method instead.

Parameters:: x (Any) – Any object.
Return type:: bool
Returns:: true if x is an object created from a class which extends Construct.

AppSpecificationProperty

class CfnProcessingJob.AppSpecificationProperty(*, image_uri, container_arguments=None, container_entrypoint=None)

Bases: object

Configuration to run a processing job in a specified container image.

Parameters:

image_uri (str) – The container image to be run by the processing job.
container_arguments (Optional[Sequence[str]]) – The arguments for a container used to run a processing job.
container_entrypoint (Optional[Sequence[str]]) – The entrypoint for a container used to run a processing job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-appspecification.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

app_specification_property = sagemaker.CfnProcessingJob.AppSpecificationProperty(
    image_uri="imageUri",

    # the properties below are optional
    container_arguments=["containerArguments"],
    container_entrypoint=["containerEntrypoint"]
)

Attributes

container_arguments

The arguments for a container used to run a processing job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-appspecification.html#cfn-sagemaker-processingjob-appspecification-containerarguments

container_entrypoint

The entrypoint for a container used to run a processing job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-appspecification.html#cfn-sagemaker-processingjob-appspecification-containerentrypoint

image_uri

The container image to be run by the processing job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-appspecification.html#cfn-sagemaker-processingjob-appspecification-imageuri

AthenaDatasetDefinitionProperty

class CfnProcessingJob.AthenaDatasetDefinitionProperty(*, catalog, database, output_format, output_s3_uri, query_string, kms_key_id=None, output_compression=None, work_group=None)

Bases: object

Configuration for Athena Dataset Definition input.

Parameters:

catalog (str) – The name of the data catalog used in Athena query execution.
database (str) – The name of the database used in the Athena query execution.
output_format (str) – The data storage format for Athena query results.
output_s3_uri (str) – The location in Amazon S3 where Athena query results are stored.
query_string (str) – The SQL query statements, to be executed.
kms_key_id (Optional[str]) – The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
output_compression (Optional[str]) – The compression used for Athena query results.
work_group (Optional[str]) – The name of the workgroup in which the Athena query is being started.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-athenadatasetdefinition.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

athena_dataset_definition_property = sagemaker.CfnProcessingJob.AthenaDatasetDefinitionProperty(
    catalog="catalog",
    database="database",
    output_format="outputFormat",
    output_s3_uri="outputS3Uri",
    query_string="queryString",

    # the properties below are optional
    kms_key_id="kmsKeyId",
    output_compression="outputCompression",
    work_group="workGroup"
)

Attributes

catalog

The name of the data catalog used in Athena query execution.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-athenadatasetdefinition.html#cfn-sagemaker-processingjob-athenadatasetdefinition-catalog

database

The name of the database used in the Athena query execution.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-athenadatasetdefinition.html#cfn-sagemaker-processingjob-athenadatasetdefinition-database

kms_key_id

The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.

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

output_compression

The compression used for Athena query results.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-athenadatasetdefinition.html#cfn-sagemaker-processingjob-athenadatasetdefinition-outputcompression

output_format

The data storage format for Athena query results.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-athenadatasetdefinition.html#cfn-sagemaker-processingjob-athenadatasetdefinition-outputformat

output_s3_uri

The location in Amazon S3 where Athena query results are stored.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-athenadatasetdefinition.html#cfn-sagemaker-processingjob-athenadatasetdefinition-outputs3uri

query_string

The SQL query statements, to be executed.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-athenadatasetdefinition.html#cfn-sagemaker-processingjob-athenadatasetdefinition-querystring

work_group

The name of the workgroup in which the Athena query is being started.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-athenadatasetdefinition.html#cfn-sagemaker-processingjob-athenadatasetdefinition-workgroup

ClusterConfigProperty

class CfnProcessingJob.ClusterConfigProperty(*, instance_count, instance_type, volume_size_in_gb, volume_kms_key_id=None)

Bases: object

Configuration for the cluster used to run a processing job.

Parameters:

instance_count (Union[int, float]) – The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
instance_type (str) – The ML compute instance type for the processing job.
volume_size_in_gb (Union[int, float]) – The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario. .. epigraph:: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can’t request a VolumeSizeInGB greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
volume_kms_key_id (Optional[str]) –
The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job. .. epigraph:: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can’t request a VolumeKmsKeyId when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes . For more information about local instance storage encryption, see SSD Instance Store Volumes .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-clusterconfig.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

cluster_config_property = sagemaker.CfnProcessingJob.ClusterConfigProperty(
    instance_count=123,
    instance_type="instanceType",
    volume_size_in_gb=123,

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

Attributes

instance_count

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

For distributed processing jobs, specify a value greater than 1. The default value is 1.

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

instance_type

The ML compute instance type for the processing job.

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

volume_kms_key_id

The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.

Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can’t request a VolumeKmsKeyId when using an instance type with local storage.

For a list of instance types that support local instance storage, see Instance Store Volumes .

For more information about local instance storage encryption, see SSD Instance Store Volumes .

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

volume_size_in_gb

The size of the ML storage volume in gigabytes that you want to provision.

You must specify sufficient ML storage for your scenario. .. epigraph:

Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a ``VolumeSizeInGB`` greater than the total size of the local instance storage.

For a list of instance types that support local instance storage, including the total size per instance type, see `Instance Store Volumes <https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes>`_ .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-clusterconfig.html#cfn-sagemaker-processingjob-clusterconfig-volumesizeingb

DatasetDefinitionProperty

class CfnProcessingJob.DatasetDefinitionProperty(*, athena_dataset_definition=None, data_distribution_type=None, input_mode=None, local_path=None, redshift_dataset_definition=None)

Bases: object

Configuration for Dataset Definition inputs.

The Dataset Definition input must specify exactly one of either AthenaDatasetDefinition or RedshiftDatasetDefinition types.

Parameters:

athena_dataset_definition (Union[IResolvable, AthenaDatasetDefinitionProperty, Dict[str, Any], None]) – Configuration for Athena Dataset Definition input.
data_distribution_type (Optional[str]) – Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
input_mode (Optional[str]) – Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
local_path (Optional[str]) – The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
redshift_dataset_definition (Union[IResolvable, RedshiftDatasetDefinitionProperty, Dict[str, Any], None]) – Configuration for Redshift Dataset Definition input.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-datasetdefinition.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

dataset_definition_property = sagemaker.CfnProcessingJob.DatasetDefinitionProperty(
    athena_dataset_definition=sagemaker.CfnProcessingJob.AthenaDatasetDefinitionProperty(
        catalog="catalog",
        database="database",
        output_format="outputFormat",
        output_s3_uri="outputS3Uri",
        query_string="queryString",

        # the properties below are optional
        kms_key_id="kmsKeyId",
        output_compression="outputCompression",
        work_group="workGroup"
    ),
    data_distribution_type="dataDistributionType",
    input_mode="inputMode",
    local_path="localPath",
    redshift_dataset_definition=sagemaker.CfnProcessingJob.RedshiftDatasetDefinitionProperty(
        cluster_id="clusterId",
        cluster_role_arn="clusterRoleArn",
        database="database",
        db_user="dbUser",
        output_format="outputFormat",
        output_s3_uri="outputS3Uri",
        query_string="queryString",

        # the properties below are optional
        kms_key_id="kmsKeyId",
        output_compression="outputCompression"
    )
)

Attributes

athena_dataset_definition

Configuration for Athena Dataset Definition input.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-datasetdefinition.html#cfn-sagemaker-processingjob-datasetdefinition-athenadatasetdefinition

data_distribution_type

Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-datasetdefinition.html#cfn-sagemaker-processingjob-datasetdefinition-datadistributiontype

input_mode

Whether to use File or Pipe input mode.

In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-datasetdefinition.html#cfn-sagemaker-processingjob-datasetdefinition-inputmode

local_path

The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job.

LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-datasetdefinition.html#cfn-sagemaker-processingjob-datasetdefinition-localpath

redshift_dataset_definition

Configuration for Redshift Dataset Definition input.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-datasetdefinition.html#cfn-sagemaker-processingjob-datasetdefinition-redshiftdatasetdefinition

ExperimentConfigProperty

class CfnProcessingJob.ExperimentConfigProperty(*, experiment_name=None, run_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.

Specified when you call the CreateProcessingJob API.

Parameters:

experiment_name (Optional[str]) – The name of an existing experiment to associate with the trial component.
run_name (Optional[str]) – The name of the experiment run to associate with the trial component.
trial_component_display_name (Optional[str]) – The display name for the trial component. If this key isn’t specified, the display name is the trial component name.
trial_name (Optional[str]) – The name of an existing trial to associate the trial component with. If not specified, a new trial is created.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-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.CfnProcessingJob.ExperimentConfigProperty(
    experiment_name="experimentName",
    run_name="runName",
    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-processingjob-experimentconfig.html#cfn-sagemaker-processingjob-experimentconfig-experimentname

run_name

The name of the experiment run to associate with the trial component.

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

trial_component_display_name

The display name for the trial component.

If this key isn’t specified, the display name is the trial component name.

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

trial_name

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

If not specified, a new trial is created.

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

FeatureStoreOutputProperty

class CfnProcessingJob.FeatureStoreOutputProperty(*, feature_group_name)

Bases: object

Configuration for processing job outputs in Amazon SageMaker Feature Store.

Parameters:: feature_group_name (str) – The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-featurestoreoutput.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

feature_store_output_property = sagemaker.CfnProcessingJob.FeatureStoreOutputProperty(
    feature_group_name="featureGroupName"
)

Attributes

feature_group_name

The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output.

Note that your processing script is responsible for putting records into your Feature Store.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-featurestoreoutput.html#cfn-sagemaker-processingjob-featurestoreoutput-featuregroupname

NetworkConfigProperty

class CfnProcessingJob.NetworkConfigProperty(*, enable_inter_container_traffic_encryption=None, enable_network_isolation=None, vpc_config=None)

Bases: object

Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.

Parameters:

enable_inter_container_traffic_encryption (Union[bool, IResolvable, None]) – Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
enable_network_isolation (Union[bool, IResolvable, None]) – Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
vpc_config (Union[IResolvable, VpcConfigProperty, Dict[str, Any], None]) – Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-networkconfig.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

network_config_property = sagemaker.CfnProcessingJob.NetworkConfigProperty(
    enable_inter_container_traffic_encryption=False,
    enable_network_isolation=False,
    vpc_config=sagemaker.CfnProcessingJob.VpcConfigProperty(
        security_group_ids=["securityGroupIds"],
        subnets=["subnets"]
    )
)

Attributes

enable_inter_container_traffic_encryption

Whether to encrypt all communications between distributed processing jobs.

Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-networkconfig.html#cfn-sagemaker-processingjob-networkconfig-enableintercontainertrafficencryption

enable_network_isolation

Whether to allow inbound and outbound network calls to and from the containers used for the processing job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-networkconfig.html#cfn-sagemaker-processingjob-networkconfig-enablenetworkisolation

vpc_config

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to.

You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-networkconfig.html#cfn-sagemaker-processingjob-networkconfig-vpcconfig

ProcessingInputsObjectProperty

class CfnProcessingJob.ProcessingInputsObjectProperty(*, input_name, app_managed=None, dataset_definition=None, s3_input=None)

Bases: object

The inputs for a processing job.

The processing input must specify exactly one of either S3Input or DatasetDefinition types.

Parameters:

input_name (str) – The name for the processing job input.
app_managed (Union[bool, IResolvable, None]) – When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
dataset_definition (Union[IResolvable, DatasetDefinitionProperty, Dict[str, Any], None]) – Configuration for Dataset Definition inputs. The Dataset Definition input must specify exactly one of either AthenaDatasetDefinition or RedshiftDatasetDefinition types.
s3_input (Union[IResolvable, S3InputProperty, Dict[str, Any], None]) – Configuration for downloading input data from Amazon S3 into the processing container.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processinginputsobject.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

processing_inputs_object_property = sagemaker.CfnProcessingJob.ProcessingInputsObjectProperty(
    input_name="inputName",

    # the properties below are optional
    app_managed=False,
    dataset_definition=sagemaker.CfnProcessingJob.DatasetDefinitionProperty(
        athena_dataset_definition=sagemaker.CfnProcessingJob.AthenaDatasetDefinitionProperty(
            catalog="catalog",
            database="database",
            output_format="outputFormat",
            output_s3_uri="outputS3Uri",
            query_string="queryString",

            # the properties below are optional
            kms_key_id="kmsKeyId",
            output_compression="outputCompression",
            work_group="workGroup"
        ),
        data_distribution_type="dataDistributionType",
        input_mode="inputMode",
        local_path="localPath",
        redshift_dataset_definition=sagemaker.CfnProcessingJob.RedshiftDatasetDefinitionProperty(
            cluster_id="clusterId",
            cluster_role_arn="clusterRoleArn",
            database="database",
            db_user="dbUser",
            output_format="outputFormat",
            output_s3_uri="outputS3Uri",
            query_string="queryString",

            # the properties below are optional
            kms_key_id="kmsKeyId",
            output_compression="outputCompression"
        )
    ),
    s3_input=sagemaker.CfnProcessingJob.S3InputProperty(
        s3_data_type="s3DataType",
        s3_uri="s3Uri",

        # the properties below are optional
        local_path="localPath",
        s3_compression_type="s3CompressionType",
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode"
    )
)

Attributes

app_managed

When True , input operations such as data download are managed natively by the processing job application.

When False (default), input operations are managed by Amazon SageMaker.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processinginputsobject.html#cfn-sagemaker-processingjob-processinginputsobject-appmanaged

dataset_definition

Configuration for Dataset Definition inputs.

The Dataset Definition input must specify exactly one of either AthenaDatasetDefinition or RedshiftDatasetDefinition types.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processinginputsobject.html#cfn-sagemaker-processingjob-processinginputsobject-datasetdefinition

input_name

The name for the processing job input.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processinginputsobject.html#cfn-sagemaker-processingjob-processinginputsobject-inputname

s3_input

Configuration for downloading input data from Amazon S3 into the processing container.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processinginputsobject.html#cfn-sagemaker-processingjob-processinginputsobject-s3input

ProcessingOutputConfigProperty

class CfnProcessingJob.ProcessingOutputConfigProperty(*, outputs, kms_key_id=None)

Bases: object

Configuration for uploading output from the processing container.

Parameters:

outputs (Union[IResolvable, Sequence[Union[IResolvable, ProcessingOutputsObjectProperty, Dict[str, Any]]]]) – An array of outputs configuring the data to upload from the processing container.
kms_key_id (Optional[str]) – The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processingoutputconfig.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

processing_output_config_property = sagemaker.CfnProcessingJob.ProcessingOutputConfigProperty(
    outputs=[sagemaker.CfnProcessingJob.ProcessingOutputsObjectProperty(
        output_name="outputName",

        # the properties below are optional
        app_managed=False,
        feature_store_output=sagemaker.CfnProcessingJob.FeatureStoreOutputProperty(
            feature_group_name="featureGroupName"
        ),
        s3_output=sagemaker.CfnProcessingJob.S3OutputProperty(
            s3_upload_mode="s3UploadMode",
            s3_uri="s3Uri",

            # the properties below are optional
            local_path="localPath"
        )
    )],

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

Attributes

kms_key_id

The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output.

KmsKeyId can be an ID of a KMS key, ARN of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.

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

outputs

An array of outputs configuring the data to upload from the processing container.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processingoutputconfig.html#cfn-sagemaker-processingjob-processingoutputconfig-outputs

ProcessingOutputsObjectProperty

class CfnProcessingJob.ProcessingOutputsObjectProperty(*, output_name, app_managed=None, feature_store_output=None, s3_output=None)

Bases: object

Describes the results of a processing job.

The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.

Parameters:

output_name (str) – The name for the processing job output.
app_managed (Union[bool, IResolvable, None]) – When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
feature_store_output (Union[IResolvable, FeatureStoreOutputProperty, Dict[str, Any], None]) – Configuration for processing job outputs in Amazon SageMaker Feature Store.
s3_output (Union[IResolvable, S3OutputProperty, Dict[str, Any], None]) – Configuration for uploading output data to Amazon S3 from the processing container.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processingoutputsobject.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

processing_outputs_object_property = sagemaker.CfnProcessingJob.ProcessingOutputsObjectProperty(
    output_name="outputName",

    # the properties below are optional
    app_managed=False,
    feature_store_output=sagemaker.CfnProcessingJob.FeatureStoreOutputProperty(
        feature_group_name="featureGroupName"
    ),
    s3_output=sagemaker.CfnProcessingJob.S3OutputProperty(
        s3_upload_mode="s3UploadMode",
        s3_uri="s3Uri",

        # the properties below are optional
        local_path="localPath"
    )
)

Attributes

app_managed

When True , output operations such as data upload are managed natively by the processing job application.

When False (default), output operations are managed by Amazon SageMaker.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processingoutputsobject.html#cfn-sagemaker-processingjob-processingoutputsobject-appmanaged

feature_store_output

Configuration for processing job outputs in Amazon SageMaker Feature Store.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processingoutputsobject.html#cfn-sagemaker-processingjob-processingoutputsobject-featurestoreoutput

output_name

The name for the processing job output.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processingoutputsobject.html#cfn-sagemaker-processingjob-processingoutputsobject-outputname

s3_output

Configuration for uploading output data to Amazon S3 from the processing container.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processingoutputsobject.html#cfn-sagemaker-processingjob-processingoutputsobject-s3output

ProcessingResourcesProperty

class CfnProcessingJob.ProcessingResourcesProperty(*, cluster_config)

Bases: object

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job.

In distributed training, you specify more than one instance.

Parameters:: cluster_config (Union[IResolvable, ClusterConfigProperty, Dict[str, Any]]) – The configuration for the resources in a cluster used to run the processing job.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processingresources.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

processing_resources_property = sagemaker.CfnProcessingJob.ProcessingResourcesProperty(
    cluster_config=sagemaker.CfnProcessingJob.ClusterConfigProperty(
        instance_count=123,
        instance_type="instanceType",
        volume_size_in_gb=123,

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

Attributes

cluster_config

The configuration for the resources in a cluster used to run the processing job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-processingresources.html#cfn-sagemaker-processingjob-processingresources-clusterconfig

RedshiftDatasetDefinitionProperty

class CfnProcessingJob.RedshiftDatasetDefinitionProperty(*, cluster_id, cluster_role_arn, database, db_user, output_format, output_s3_uri, query_string, kms_key_id=None, output_compression=None)

Bases: object

Configuration for Redshift Dataset Definition input.

Parameters:

cluster_id (str) – The Redshift cluster Identifier.
cluster_role_arn (str) – The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
database (str) – The name of the Redshift database used in Redshift query execution.
db_user (str) – The database user name used in Redshift query execution.
output_format (str) – The data storage format for Redshift query results.
output_s3_uri (str) – The location in Amazon S3 where the Redshift query results are stored.
query_string (str) – The SQL query statements to be executed.
kms_key_id (Optional[str]) – The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
output_compression (Optional[str]) – The compression used for Redshift query results.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-redshiftdatasetdefinition.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

redshift_dataset_definition_property = sagemaker.CfnProcessingJob.RedshiftDatasetDefinitionProperty(
    cluster_id="clusterId",
    cluster_role_arn="clusterRoleArn",
    database="database",
    db_user="dbUser",
    output_format="outputFormat",
    output_s3_uri="outputS3Uri",
    query_string="queryString",

    # the properties below are optional
    kms_key_id="kmsKeyId",
    output_compression="outputCompression"
)

Attributes

cluster_id

The Redshift cluster Identifier.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-redshiftdatasetdefinition.html#cfn-sagemaker-processingjob-redshiftdatasetdefinition-clusterid

cluster_role_arn

The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-redshiftdatasetdefinition.html#cfn-sagemaker-processingjob-redshiftdatasetdefinition-clusterrolearn

database

The name of the Redshift database used in Redshift query execution.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-redshiftdatasetdefinition.html#cfn-sagemaker-processingjob-redshiftdatasetdefinition-database

db_user

The database user name used in Redshift query execution.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-redshiftdatasetdefinition.html#cfn-sagemaker-processingjob-redshiftdatasetdefinition-dbuser

kms_key_id

The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.

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

output_compression

The compression used for Redshift query results.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-redshiftdatasetdefinition.html#cfn-sagemaker-processingjob-redshiftdatasetdefinition-outputcompression

output_format

The data storage format for Redshift query results.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-redshiftdatasetdefinition.html#cfn-sagemaker-processingjob-redshiftdatasetdefinition-outputformat

output_s3_uri

The location in Amazon S3 where the Redshift query results are stored.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-redshiftdatasetdefinition.html#cfn-sagemaker-processingjob-redshiftdatasetdefinition-outputs3uri

query_string

The SQL query statements to be executed.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-redshiftdatasetdefinition.html#cfn-sagemaker-processingjob-redshiftdatasetdefinition-querystring

S3InputProperty

class CfnProcessingJob.S3InputProperty(*, s3_data_type, s3_uri, local_path=None, s3_compression_type=None, s3_data_distribution_type=None, s3_input_mode=None)

Bases: object

Configuration for downloading input data from Amazon S3 into the processing container.

Parameters:

s3_data_type (str) – Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
s3_uri (str) – The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
local_path (Optional[str]) – The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False (default).
s3_compression_type (Optional[str]) – Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
s3_data_distribution_type (Optional[str]) – Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.
s3_input_mode (Optional[str]) – Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3input.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_input_property = sagemaker.CfnProcessingJob.S3InputProperty(
    s3_data_type="s3DataType",
    s3_uri="s3Uri",

    # the properties below are optional
    local_path="localPath",
    s3_compression_type="s3CompressionType",
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode"
)

Attributes

local_path

The local path in your container where you want Amazon SageMaker to write input data to.

LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False (default).

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3input.html#cfn-sagemaker-processingjob-s3input-localpath

s3_compression_type

Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container.

Gzip can only be used when Pipe mode is specified as the S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3input.html#cfn-sagemaker-processingjob-s3input-s3compressiontype

s3_data_distribution_type

Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3input.html#cfn-sagemaker-processingjob-s3input-s3datadistributiontype

s3_data_type

Whether you use an S3Prefix or a ManifestFile for the data type.

If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3input.html#cfn-sagemaker-processingjob-s3input-s3datatype

s3_input_mode

Whether to use File or Pipe input mode.

In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3input.html#cfn-sagemaker-processingjob-s3input-s3inputmode

s3_uri

The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3input.html#cfn-sagemaker-processingjob-s3input-s3uri

S3OutputProperty

class CfnProcessingJob.S3OutputProperty(*, s3_upload_mode, s3_uri, local_path=None)

Bases: object

Configuration for uploading output data to Amazon S3 from the processing container.

Parameters:

s3_upload_mode (str) – Whether to upload the results of the processing job continuously or after the job completes.
s3_uri (str) – The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
local_path (Optional[str]) – The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container’s entrypoint is invoked.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3output.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_output_property = sagemaker.CfnProcessingJob.S3OutputProperty(
    s3_upload_mode="s3UploadMode",
    s3_uri="s3Uri",

    # the properties below are optional
    local_path="localPath"
)

Attributes

local_path

The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3.

LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container’s entrypoint is invoked.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3output.html#cfn-sagemaker-processingjob-s3output-localpath

s3_upload_mode

Whether to upload the results of the processing job continuously or after the job completes.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3output.html#cfn-sagemaker-processingjob-s3output-s3uploadmode

s3_uri

The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-s3output.html#cfn-sagemaker-processingjob-s3output-s3uri

StoppingConditionProperty

class CfnProcessingJob.StoppingConditionProperty(*, max_runtime_in_seconds)

Bases: object

Configures conditions under which the processing job should be stopped, such as how long the processing job has been running.

After the condition is met, the processing job is stopped.

Parameters:: max_runtime_in_seconds (Union[int, float]) – The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model. For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days. The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-stoppingcondition.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

stopping_condition_property = sagemaker.CfnProcessingJob.StoppingConditionProperty(
    max_runtime_in_seconds=123
)

Attributes

max_runtime_in_seconds

The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-stoppingcondition.html#cfn-sagemaker-processingjob-stoppingcondition-maxruntimeinseconds

VpcConfigProperty

class CfnProcessingJob.VpcConfigProperty(*, security_group_ids, subnets)

Bases: object

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to.

You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

Parameters:

security_group_ids (Sequence[str]) – The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
subnets (Sequence[str]) – The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-vpcconfig.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

vpc_config_property = sagemaker.CfnProcessingJob.VpcConfigProperty(
    security_group_ids=["securityGroupIds"],
    subnets=["subnets"]
)

Attributes

security_group_ids

The VPC security group IDs, in the form sg-xxxxxxxx .

Specify the security groups for the VPC that is specified in the Subnets field.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-vpcconfig.html#cfn-sagemaker-processingjob-vpcconfig-securitygroupids

subnets

The ID of the subnets in the VPC to which you want to connect your training job or model.

For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-processingjob-vpcconfig.html#cfn-sagemaker-processingjob-vpcconfig-subnets