CfnModelBiasJobDefinition

class aws_cdk.aws_sagemaker.CfnModelBiasJobDefinition(scope, id, *, job_resources, model_bias_app_specification, model_bias_job_input, model_bias_job_output_config, role_arn, endpoint_name=None, job_definition_name=None, model_bias_baseline_config=None, network_config=None, stopping_condition=None, tags=None)

Bases: CfnResource

Creates the definition for a model bias job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelbiasjobdefinition.html

CloudformationResource:

AWS::SageMaker::ModelBiasJobDefinition

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_model_bias_job_definition = sagemaker.CfnModelBiasJobDefinition(self, "MyCfnModelBiasJobDefinition",
    job_resources=sagemaker.CfnModelBiasJobDefinition.MonitoringResourcesProperty(
        cluster_config=sagemaker.CfnModelBiasJobDefinition.ClusterConfigProperty(
            instance_count=123,
            instance_type="instanceType",
            volume_size_in_gb=123,

            # the properties below are optional
            volume_kms_key_id="volumeKmsKeyId"
        )
    ),
    model_bias_app_specification=sagemaker.CfnModelBiasJobDefinition.ModelBiasAppSpecificationProperty(
        config_uri="configUri",
        image_uri="imageUri",

        # the properties below are optional
        environment={
            "environment_key": "environment"
        }
    ),
    model_bias_job_input=sagemaker.CfnModelBiasJobDefinition.ModelBiasJobInputProperty(
        ground_truth_s3_input=sagemaker.CfnModelBiasJobDefinition.MonitoringGroundTruthS3InputProperty(
            s3_uri="s3Uri"
        ),

        # the properties below are optional
        batch_transform_input=sagemaker.CfnModelBiasJobDefinition.BatchTransformInputProperty(
            data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
            dataset_format=sagemaker.CfnModelBiasJobDefinition.DatasetFormatProperty(
                csv=sagemaker.CfnModelBiasJobDefinition.CsvProperty(
                    header=False
                ),
                json=sagemaker.CfnModelBiasJobDefinition.JsonProperty(
                    line=False
                ),
                parquet=False
            ),
            local_path="localPath",

            # the properties below are optional
            end_time_offset="endTimeOffset",
            features_attribute="featuresAttribute",
            inference_attribute="inferenceAttribute",
            probability_attribute="probabilityAttribute",
            probability_threshold_attribute=123,
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode",
            start_time_offset="startTimeOffset"
        ),
        endpoint_input=sagemaker.CfnModelBiasJobDefinition.EndpointInputProperty(
            endpoint_name="endpointName",
            local_path="localPath",

            # the properties below are optional
            end_time_offset="endTimeOffset",
            features_attribute="featuresAttribute",
            inference_attribute="inferenceAttribute",
            probability_attribute="probabilityAttribute",
            probability_threshold_attribute=123,
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode",
            start_time_offset="startTimeOffset"
        )
    ),
    model_bias_job_output_config=sagemaker.CfnModelBiasJobDefinition.MonitoringOutputConfigProperty(
        monitoring_outputs=[sagemaker.CfnModelBiasJobDefinition.MonitoringOutputProperty(
            s3_output=sagemaker.CfnModelBiasJobDefinition.S3OutputProperty(
                local_path="localPath",
                s3_uri="s3Uri",

                # the properties below are optional
                s3_upload_mode="s3UploadMode"
            )
        )],

        # the properties below are optional
        kms_key_id="kmsKeyId"
    ),
    role_arn="roleArn",

    # the properties below are optional
    endpoint_name="endpointName",
    job_definition_name="jobDefinitionName",
    model_bias_baseline_config=sagemaker.CfnModelBiasJobDefinition.ModelBiasBaselineConfigProperty(
        baselining_job_name="baseliningJobName",
        constraints_resource=sagemaker.CfnModelBiasJobDefinition.ConstraintsResourceProperty(
            s3_uri="s3Uri"
        )
    ),
    network_config=sagemaker.CfnModelBiasJobDefinition.NetworkConfigProperty(
        enable_inter_container_traffic_encryption=False,
        enable_network_isolation=False,
        vpc_config=sagemaker.CfnModelBiasJobDefinition.VpcConfigProperty(
            security_group_ids=["securityGroupIds"],
            subnets=["subnets"]
        )
    ),
    stopping_condition=sagemaker.CfnModelBiasJobDefinition.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).

• job_resources (Union[IResolvable, MonitoringResourcesProperty, Dict[str, Any]]) – Identifies the resources to deploy for a monitoring job.

• model_bias_app_specification (Union[IResolvable, ModelBiasAppSpecificationProperty, Dict[str, Any]]) – Configures the model bias job to run a specified Docker container image.

• model_bias_job_input (Union[IResolvable, ModelBiasJobInputProperty, Dict[str, Any]]) – Inputs for the model bias job.

• model_bias_job_output_config (Union[IResolvable, MonitoringOutputConfigProperty, Dict[str, Any]]) – The output configuration for monitoring jobs.

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

• endpoint_name (Optional[str]) – The name of the endpoint used to run the monitoring job.

• job_definition_name (Optional[str]) – The name of the bias job definition. The name must be unique within an AWS Region in the AWS account.

• model_bias_baseline_config (Union[IResolvable, ModelBiasBaselineConfigProperty, Dict[str, Any], None]) – The baseline configuration for a model bias job.

• network_config (Union[IResolvable, NetworkConfigProperty, Dict[str, Any], None]) – Networking options for a model bias job.

• stopping_condition (Union[IResolvable, StoppingConditionProperty, Dict[str, Any], None]) – A time limit for how long the monitoring job is allowed to run before stopping.

• tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – An array of key-value pairs to apply to this resource. For more information, see Tag .

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::ModelBiasJobDefinition'

attr_creation_time

The time when the job definition was created.

CloudformationAttribute:

CreationTime

attr_job_definition_arn

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

CloudformationAttribute:

JobDefinitionArn

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.

endpoint_name

The name of the endpoint used to run the monitoring job.

job_definition_name

The name of the bias job definition.

job_resources

Identifies the resources to deploy for a monitoring job.

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.

model_bias_app_specification

Configures the model bias job to run a specified Docker container image.

model_bias_baseline_config

The baseline configuration for a model bias job.

model_bias_job_input

Inputs for the model bias job.

model_bias_job_output_config

The output configuration for monitoring jobs.

network_config

Networking options for a model bias 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 }).

role_arn

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

stack

The stack in which this element is defined.

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

stopping_condition

A time limit for how long the monitoring job is allowed to run before stopping.

tags

Tag Manager which manages the tags for this resource.

tags_raw

An array of key-value pairs to apply to this resource.

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.

BatchTransformInputProperty

class CfnModelBiasJobDefinition.BatchTransformInputProperty(*, data_captured_destination_s3_uri, dataset_format, local_path, end_time_offset=None, features_attribute=None, inference_attribute=None, probability_attribute=None, probability_threshold_attribute=None, s3_data_distribution_type=None, s3_input_mode=None, start_time_offset=None)

Bases: object

Input object for the batch transform job.

Parameters:

• data_captured_destination_s3_uri (str) – The Amazon S3 location being used to capture the data.

• dataset_format (Union[IResolvable, DatasetFormatProperty, Dict[str, Any]]) – The dataset format for your batch transform job.

• local_path (str) – Path to the filesystem where the batch transform data is available to the container.

• end_time_offset (Optional[str]) – If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .

• features_attribute (Optional[str]) – The attributes of the input data that are the input features.

• inference_attribute (Optional[str]) – The attribute of the input data that represents the ground truth label.

• probability_attribute (Optional[str]) – In a classification problem, the attribute that represents the class probability.

• probability_threshold_attribute (Union[int, float, None]) – The threshold for the class probability to be evaluated as a positive result.

• s3_data_distribution_type (Optional[str]) – Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

• s3_input_mode (Optional[str]) – Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

• start_time_offset (Optional[str]) –

  If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.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

batch_transform_input_property = sagemaker.CfnModelBiasJobDefinition.BatchTransformInputProperty(
    data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
    dataset_format=sagemaker.CfnModelBiasJobDefinition.DatasetFormatProperty(
        csv=sagemaker.CfnModelBiasJobDefinition.CsvProperty(
            header=False
        ),
        json=sagemaker.CfnModelBiasJobDefinition.JsonProperty(
            line=False
        ),
        parquet=False
    ),
    local_path="localPath",

    # the properties below are optional
    end_time_offset="endTimeOffset",
    features_attribute="featuresAttribute",
    inference_attribute="inferenceAttribute",
    probability_attribute="probabilityAttribute",
    probability_threshold_attribute=123,
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode",
    start_time_offset="startTimeOffset"
)

Attributes

data_captured_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-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-datacaptureddestinations3uri

dataset_format

The dataset format for your batch transform job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-datasetformat

end_time_offset

If specified, monitoring jobs subtract this time from the end time.

For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-endtimeoffset

features_attribute

The attributes of the input data that are the input features.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-featuresattribute

inference_attribute

The attribute of the input data that represents the ground truth label.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-inferenceattribute

local_path

Path to the filesystem where the batch transform data is available to the container.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-localpath

probability_attribute

In a classification problem, the attribute that represents the class probability.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-probabilityattribute

probability_threshold_attribute

The threshold for the class probability to be evaluated as a positive result.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-probabilitythresholdattribute

s3_data_distribution_type

Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key.

Defaults to FullyReplicated

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-s3datadistributiontype

s3_input_mode

Whether the Pipe or File is used as the input mode for transferring data for the monitoring job.

Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-s3inputmode

start_time_offset

If specified, monitoring jobs substract this time from the start time.

For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-batchtransforminput.html#cfn-sagemaker-modelbiasjobdefinition-batchtransforminput-starttimeoffset

ClusterConfigProperty

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

Bases: object

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

Parameters:

• instance_count (Union[int, float]) – The number of ML compute instances to use in the model monitoring 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.

• 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 model monitoring job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-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.CfnModelBiasJobDefinition.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 model monitoring 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-modelbiasjobdefinition-clusterconfig.html#cfn-sagemaker-modelbiasjobdefinition-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-modelbiasjobdefinition-clusterconfig.html#cfn-sagemaker-modelbiasjobdefinition-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 model monitoring job.

See:

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

See:

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

ConstraintsResourceProperty

class CfnModelBiasJobDefinition.ConstraintsResourceProperty(*, s3_uri=None)

Bases: object

The constraints resource for a monitoring job.

Parameters:

s3_uri (Optional[str]) – The Amazon S3 URI for the constraints resource.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-constraintsresource.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

constraints_resource_property = sagemaker.CfnModelBiasJobDefinition.ConstraintsResourceProperty(
    s3_uri="s3Uri"
)

Attributes

s3_uri

The Amazon S3 URI for the constraints resource.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-constraintsresource.html#cfn-sagemaker-modelbiasjobdefinition-constraintsresource-s3uri

CsvProperty

class CfnModelBiasJobDefinition.CsvProperty(*, header=None)

Bases: object

The CSV format.

Parameters:

header (Union[bool, IResolvable, None]) – A boolean flag indicating if given CSV has header.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-csv.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

csv_property = sagemaker.CfnModelBiasJobDefinition.CsvProperty(
    header=False
)

Attributes

header

A boolean flag indicating if given CSV has header.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-csv.html#cfn-sagemaker-modelbiasjobdefinition-csv-header

DatasetFormatProperty

class CfnModelBiasJobDefinition.DatasetFormatProperty(*, csv=None, json=None, parquet=None)

Bases: object

The dataset format of the data to monitor.

Parameters:

• csv (Union[IResolvable, CsvProperty, Dict[str, Any], None]) – The CSV format.

• json (Union[IResolvable, JsonProperty, Dict[str, Any], None]) – The Json format.

• parquet (Union[bool, IResolvable, None]) – A flag indicate if the dataset format is Parquet.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-datasetformat.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_format_property = sagemaker.CfnModelBiasJobDefinition.DatasetFormatProperty(
    csv=sagemaker.CfnModelBiasJobDefinition.CsvProperty(
        header=False
    ),
    json=sagemaker.CfnModelBiasJobDefinition.JsonProperty(
        line=False
    ),
    parquet=False
)

Attributes

csv

The CSV format.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-datasetformat.html#cfn-sagemaker-modelbiasjobdefinition-datasetformat-csv

json

The Json format.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-datasetformat.html#cfn-sagemaker-modelbiasjobdefinition-datasetformat-json

parquet

A flag indicate if the dataset format is Parquet.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-datasetformat.html#cfn-sagemaker-modelbiasjobdefinition-datasetformat-parquet

EndpointInputProperty

class CfnModelBiasJobDefinition.EndpointInputProperty(*, endpoint_name, local_path, end_time_offset=None, features_attribute=None, inference_attribute=None, probability_attribute=None, probability_threshold_attribute=None, s3_data_distribution_type=None, s3_input_mode=None, start_time_offset=None)

Bases: object

Input object for the endpoint.

Parameters:

• endpoint_name (str) – An endpoint in customer’s account which has enabled DataCaptureConfig enabled.

• local_path (str) – Path to the filesystem where the endpoint data is available to the container.

• end_time_offset (Optional[str]) –

  If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .

• features_attribute (Optional[str]) – The attributes of the input data that are the input features.

• inference_attribute (Optional[str]) – The attribute of the input data that represents the ground truth label.

• probability_attribute (Optional[str]) – In a classification problem, the attribute that represents the class probability.

• probability_threshold_attribute (Union[int, float, None]) – The threshold for the class probability to be evaluated as a positive result.

• s3_data_distribution_type (Optional[str]) – Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to FullyReplicated

• s3_input_mode (Optional[str]) – Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

• start_time_offset (Optional[str]) –

  If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.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

endpoint_input_property = sagemaker.CfnModelBiasJobDefinition.EndpointInputProperty(
    endpoint_name="endpointName",
    local_path="localPath",

    # the properties below are optional
    end_time_offset="endTimeOffset",
    features_attribute="featuresAttribute",
    inference_attribute="inferenceAttribute",
    probability_attribute="probabilityAttribute",
    probability_threshold_attribute=123,
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode",
    start_time_offset="startTimeOffset"
)

Attributes

end_time_offset

If specified, monitoring jobs substract this time from the end time.

For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-endtimeoffset

endpoint_name

An endpoint in customer’s account which has enabled DataCaptureConfig enabled.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-endpointname

features_attribute

The attributes of the input data that are the input features.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-featuresattribute

inference_attribute

The attribute of the input data that represents the ground truth label.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-inferenceattribute

local_path

Path to the filesystem where the endpoint data is available to the container.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-localpath

probability_attribute

In a classification problem, the attribute that represents the class probability.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-probabilityattribute

probability_threshold_attribute

The threshold for the class probability to be evaluated as a positive result.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-probabilitythresholdattribute

s3_data_distribution_type

Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key.

Defaults to FullyReplicated

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-s3datadistributiontype

s3_input_mode

Whether the Pipe or File is used as the input mode for transferring data for the monitoring job.

Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-s3inputmode

start_time_offset

If specified, monitoring jobs substract this time from the start time.

For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-endpointinput.html#cfn-sagemaker-modelbiasjobdefinition-endpointinput-starttimeoffset

JsonProperty

class CfnModelBiasJobDefinition.JsonProperty(*, line=None)

Bases: object

The Json format.

Parameters:

line (Union[bool, IResolvable, None]) – A boolean flag indicating if it is JSON line format.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-json.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

json_property = sagemaker.CfnModelBiasJobDefinition.JsonProperty(
    line=False
)

Attributes

line

A boolean flag indicating if it is JSON line format.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-json.html#cfn-sagemaker-modelbiasjobdefinition-json-line

ModelBiasAppSpecificationProperty

class CfnModelBiasJobDefinition.ModelBiasAppSpecificationProperty(*, config_uri, image_uri, environment=None)

Bases: object

Docker container image configuration object for the model bias job.

Parameters:

• config_uri (str) – JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters .

• image_uri (str) – The container image to be run by the model bias job.

• environment (Union[IResolvable, Mapping[str, str], None]) – Sets the environment variables in the Docker container.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasappspecification.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_bias_app_specification_property = sagemaker.CfnModelBiasJobDefinition.ModelBiasAppSpecificationProperty(
    config_uri="configUri",
    image_uri="imageUri",

    # the properties below are optional
    environment={
        "environment_key": "environment"
    }
)

Attributes

config_uri

JSON formatted S3 file that defines bias parameters.

For more information on this JSON configuration file, see Configure bias parameters .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasappspecification.html#cfn-sagemaker-modelbiasjobdefinition-modelbiasappspecification-configuri

environment

Sets the environment variables in the Docker container.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasappspecification.html#cfn-sagemaker-modelbiasjobdefinition-modelbiasappspecification-environment

image_uri

The container image to be run by the model bias job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasappspecification.html#cfn-sagemaker-modelbiasjobdefinition-modelbiasappspecification-imageuri

ModelBiasBaselineConfigProperty

class CfnModelBiasJobDefinition.ModelBiasBaselineConfigProperty(*, baselining_job_name=None, constraints_resource=None)

Bases: object

The configuration for a baseline model bias job.

Parameters:

• baselining_job_name (Optional[str]) – The name of the baseline model bias job.

• constraints_resource (Union[IResolvable, ConstraintsResourceProperty, Dict[str, Any], None]) – The constraints resource for a monitoring job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasbaselineconfig.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_bias_baseline_config_property = sagemaker.CfnModelBiasJobDefinition.ModelBiasBaselineConfigProperty(
    baselining_job_name="baseliningJobName",
    constraints_resource=sagemaker.CfnModelBiasJobDefinition.ConstraintsResourceProperty(
        s3_uri="s3Uri"
    )
)

Attributes

baselining_job_name

The name of the baseline model bias job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasbaselineconfig.html#cfn-sagemaker-modelbiasjobdefinition-modelbiasbaselineconfig-baseliningjobname

constraints_resource

The constraints resource for a monitoring job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasbaselineconfig.html#cfn-sagemaker-modelbiasjobdefinition-modelbiasbaselineconfig-constraintsresource

ModelBiasJobInputProperty

class CfnModelBiasJobDefinition.ModelBiasJobInputProperty(*, ground_truth_s3_input, batch_transform_input=None, endpoint_input=None)

Bases: object

Inputs for the model bias job.

Parameters:

• ground_truth_s3_input (Union[IResolvable, MonitoringGroundTruthS3InputProperty, Dict[str, Any]]) – Location of ground truth labels to use in model bias job.

• batch_transform_input (Union[IResolvable, BatchTransformInputProperty, Dict[str, Any], None]) – Input object for the batch transform job.

• endpoint_input (Union[IResolvable, EndpointInputProperty, Dict[str, Any], None]) – Input object for the endpoint.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasjobinput.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_bias_job_input_property = sagemaker.CfnModelBiasJobDefinition.ModelBiasJobInputProperty(
    ground_truth_s3_input=sagemaker.CfnModelBiasJobDefinition.MonitoringGroundTruthS3InputProperty(
        s3_uri="s3Uri"
    ),

    # the properties below are optional
    batch_transform_input=sagemaker.CfnModelBiasJobDefinition.BatchTransformInputProperty(
        data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
        dataset_format=sagemaker.CfnModelBiasJobDefinition.DatasetFormatProperty(
            csv=sagemaker.CfnModelBiasJobDefinition.CsvProperty(
                header=False
            ),
            json=sagemaker.CfnModelBiasJobDefinition.JsonProperty(
                line=False
            ),
            parquet=False
        ),
        local_path="localPath",

        # the properties below are optional
        end_time_offset="endTimeOffset",
        features_attribute="featuresAttribute",
        inference_attribute="inferenceAttribute",
        probability_attribute="probabilityAttribute",
        probability_threshold_attribute=123,
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode",
        start_time_offset="startTimeOffset"
    ),
    endpoint_input=sagemaker.CfnModelBiasJobDefinition.EndpointInputProperty(
        endpoint_name="endpointName",
        local_path="localPath",

        # the properties below are optional
        end_time_offset="endTimeOffset",
        features_attribute="featuresAttribute",
        inference_attribute="inferenceAttribute",
        probability_attribute="probabilityAttribute",
        probability_threshold_attribute=123,
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode",
        start_time_offset="startTimeOffset"
    )
)

Attributes

batch_transform_input

Input object for the batch transform job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasjobinput.html#cfn-sagemaker-modelbiasjobdefinition-modelbiasjobinput-batchtransforminput

endpoint_input

Input object for the endpoint.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasjobinput.html#cfn-sagemaker-modelbiasjobdefinition-modelbiasjobinput-endpointinput

ground_truth_s3_input

Location of ground truth labels to use in model bias job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-modelbiasjobinput.html#cfn-sagemaker-modelbiasjobdefinition-modelbiasjobinput-groundtruths3input

MonitoringGroundTruthS3InputProperty

class CfnModelBiasJobDefinition.MonitoringGroundTruthS3InputProperty(*, s3_uri)

Bases: object

The ground truth labels for the dataset used for the monitoring job.

Parameters:

s3_uri (str) – The address of the Amazon S3 location of the ground truth labels.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-monitoringgroundtruths3input.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

monitoring_ground_truth_s3_input_property = sagemaker.CfnModelBiasJobDefinition.MonitoringGroundTruthS3InputProperty(
    s3_uri="s3Uri"
)

Attributes

s3_uri

The address of the Amazon S3 location of the ground truth labels.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-monitoringgroundtruths3input.html#cfn-sagemaker-modelbiasjobdefinition-monitoringgroundtruths3input-s3uri

MonitoringOutputConfigProperty

class CfnModelBiasJobDefinition.MonitoringOutputConfigProperty(*, monitoring_outputs, kms_key_id=None)

Bases: object

The output configuration for monitoring jobs.

Parameters:

• monitoring_outputs (Union[IResolvable, Sequence[Union[IResolvable, MonitoringOutputProperty, Dict[str, Any]]]]) – Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

• kms_key_id (Optional[str]) – The AWS Key Management Service ( 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-modelbiasjobdefinition-monitoringoutputconfig.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

monitoring_output_config_property = sagemaker.CfnModelBiasJobDefinition.MonitoringOutputConfigProperty(
    monitoring_outputs=[sagemaker.CfnModelBiasJobDefinition.MonitoringOutputProperty(
        s3_output=sagemaker.CfnModelBiasJobDefinition.S3OutputProperty(
            local_path="localPath",
            s3_uri="s3Uri",

            # the properties below are optional
            s3_upload_mode="s3UploadMode"
        )
    )],

    # 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 model artifacts at rest using Amazon S3 server-side encryption.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-monitoringoutputconfig.html#cfn-sagemaker-modelbiasjobdefinition-monitoringoutputconfig-kmskeyid

monitoring_outputs

Monitoring outputs for monitoring jobs.

This is where the output of the periodic monitoring jobs is uploaded.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-monitoringoutputconfig.html#cfn-sagemaker-modelbiasjobdefinition-monitoringoutputconfig-monitoringoutputs

MonitoringOutputProperty

class CfnModelBiasJobDefinition.MonitoringOutputProperty(*, s3_output)

Bases: object

The output object for a monitoring job.

Parameters:

s3_output (Union[IResolvable, S3OutputProperty, Dict[str, Any]]) – The Amazon S3 storage location where the results of a monitoring job are saved.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-monitoringoutput.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

monitoring_output_property = sagemaker.CfnModelBiasJobDefinition.MonitoringOutputProperty(
    s3_output=sagemaker.CfnModelBiasJobDefinition.S3OutputProperty(
        local_path="localPath",
        s3_uri="s3Uri",

        # the properties below are optional
        s3_upload_mode="s3UploadMode"
    )
)

Attributes

s3_output

The Amazon S3 storage location where the results of a monitoring job are saved.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-monitoringoutput.html#cfn-sagemaker-modelbiasjobdefinition-monitoringoutput-s3output

MonitoringResourcesProperty

class CfnModelBiasJobDefinition.MonitoringResourcesProperty(*, cluster_config)

Bases: object

Identifies the resources to deploy for a monitoring job.

Parameters:

cluster_config (Union[IResolvable, ClusterConfigProperty, Dict[str, Any]]) – The configuration for the cluster resources used to run the processing job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-monitoringresources.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

monitoring_resources_property = sagemaker.CfnModelBiasJobDefinition.MonitoringResourcesProperty(
    cluster_config=sagemaker.CfnModelBiasJobDefinition.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 cluster resources used to run the processing job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-monitoringresources.html#cfn-sagemaker-modelbiasjobdefinition-monitoringresources-clusterconfig

NetworkConfigProperty

class CfnModelBiasJobDefinition.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 a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-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.CfnModelBiasJobDefinition.NetworkConfigProperty(
    enable_inter_container_traffic_encryption=False,
    enable_network_isolation=False,
    vpc_config=sagemaker.CfnModelBiasJobDefinition.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-modelbiasjobdefinition-networkconfig.html#cfn-sagemaker-modelbiasjobdefinition-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-modelbiasjobdefinition-networkconfig.html#cfn-sagemaker-modelbiasjobdefinition-networkconfig-enablenetworkisolation

vpc_config

Specifies a VPC that your training jobs and hosted models have access to.

Control access to and from your training and model containers by configuring the VPC.

See:

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

S3OutputProperty

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

Bases: object

The Amazon S3 storage location where the results of a monitoring job are saved.

Parameters:

• local_path (str) – The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

• s3_uri (str) – A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

• s3_upload_mode (Optional[str]) – Whether to upload the results of the monitoring job continuously or after the job completes.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelbiasjobdefinition-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.CfnModelBiasJobDefinition.S3OutputProperty(
    local_path="localPath",
    s3_uri="s3Uri",

    # the properties below are optional
    s3_upload_mode="s3UploadMode"
)

Attributes

local_path

The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

LocalPath is an absolute path for the output data.

See:

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

s3_upload_mode

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

See:

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

s3_uri

A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

See:

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

StoppingConditionProperty

class CfnModelBiasJobDefinition.StoppingConditionProperty(*, max_runtime_in_seconds)

Bases: object

Specifies a limit to how long a job can run.

When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.

To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel . .. epigraph:

The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

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-modelbiasjobdefinition-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.CfnModelBiasJobDefinition.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-modelbiasjobdefinition-stoppingcondition.html#cfn-sagemaker-modelbiasjobdefinition-stoppingcondition-maxruntimeinseconds

VpcConfigProperty

class CfnModelBiasJobDefinition.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-modelbiasjobdefinition-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.CfnModelBiasJobDefinition.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-modelbiasjobdefinition-vpcconfig.html#cfn-sagemaker-modelbiasjobdefinition-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-modelbiasjobdefinition-vpcconfig.html#cfn-sagemaker-modelbiasjobdefinition-vpcconfig-subnets