CfnMonitoringSchedule

class aws_cdk.aws_sagemaker.CfnMonitoringSchedule(scope, id, *, monitoring_schedule_config, monitoring_schedule_name, endpoint_name=None, failure_reason=None, last_monitoring_execution_summary=None, monitoring_schedule_status=None, tags=None)

Bases: CfnResource

A CloudFormation AWS::SageMaker::MonitoringSchedule.

The AWS::SageMaker::MonitoringSchedule resource is an Amazon SageMaker resource type that regularly starts SageMaker processing Jobs to monitor the data captured for a SageMaker endpoint.

CloudformationResource:: AWS::SageMaker::MonitoringSchedule
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-monitoringschedule.html
ExampleMetadata:: fixture=_generated

Example:

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

# json: Any

cfn_monitoring_schedule = sagemaker.CfnMonitoringSchedule(self, "MyCfnMonitoringSchedule",
    monitoring_schedule_config=sagemaker.CfnMonitoringSchedule.MonitoringScheduleConfigProperty(
        monitoring_job_definition=sagemaker.CfnMonitoringSchedule.MonitoringJobDefinitionProperty(
            monitoring_app_specification=sagemaker.CfnMonitoringSchedule.MonitoringAppSpecificationProperty(
                image_uri="imageUri",

                # the properties below are optional
                container_arguments=["containerArguments"],
                container_entrypoint=["containerEntrypoint"],
                post_analytics_processor_source_uri="postAnalyticsProcessorSourceUri",
                record_preprocessor_source_uri="recordPreprocessorSourceUri"
            ),
            monitoring_inputs=[sagemaker.CfnMonitoringSchedule.MonitoringInputProperty(
                batch_transform_input=sagemaker.CfnMonitoringSchedule.BatchTransformInputProperty(
                    data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
                    dataset_format=sagemaker.CfnMonitoringSchedule.DatasetFormatProperty(
                        csv=sagemaker.CfnMonitoringSchedule.CsvProperty(
                            header=False
                        ),
                        json=json,
                        parquet=False
                    ),
                    local_path="localPath",

                    # the properties below are optional
                    s3_data_distribution_type="s3DataDistributionType",
                    s3_input_mode="s3InputMode"
                ),
                endpoint_input=sagemaker.CfnMonitoringSchedule.EndpointInputProperty(
                    endpoint_name="endpointName",
                    local_path="localPath",

                    # the properties below are optional
                    s3_data_distribution_type="s3DataDistributionType",
                    s3_input_mode="s3InputMode"
                )
            )],
            monitoring_output_config=sagemaker.CfnMonitoringSchedule.MonitoringOutputConfigProperty(
                monitoring_outputs=[sagemaker.CfnMonitoringSchedule.MonitoringOutputProperty(
                    s3_output=sagemaker.CfnMonitoringSchedule.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"
            ),
            monitoring_resources=sagemaker.CfnMonitoringSchedule.MonitoringResourcesProperty(
                cluster_config=sagemaker.CfnMonitoringSchedule.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
            baseline_config=sagemaker.CfnMonitoringSchedule.BaselineConfigProperty(
                constraints_resource=sagemaker.CfnMonitoringSchedule.ConstraintsResourceProperty(
                    s3_uri="s3Uri"
                ),
                statistics_resource=sagemaker.CfnMonitoringSchedule.StatisticsResourceProperty(
                    s3_uri="s3Uri"
                )
            ),
            environment={
                "environment_key": "environment"
            },
            network_config=sagemaker.CfnMonitoringSchedule.NetworkConfigProperty(
                enable_inter_container_traffic_encryption=False,
                enable_network_isolation=False,
                vpc_config=sagemaker.CfnMonitoringSchedule.VpcConfigProperty(
                    security_group_ids=["securityGroupIds"],
                    subnets=["subnets"]
                )
            ),
            stopping_condition=sagemaker.CfnMonitoringSchedule.StoppingConditionProperty(
                max_runtime_in_seconds=123
            )
        ),
        monitoring_job_definition_name="monitoringJobDefinitionName",
        monitoring_type="monitoringType",
        schedule_config=sagemaker.CfnMonitoringSchedule.ScheduleConfigProperty(
            schedule_expression="scheduleExpression"
        )
    ),
    monitoring_schedule_name="monitoringScheduleName",

    # the properties below are optional
    endpoint_name="endpointName",
    failure_reason="failureReason",
    last_monitoring_execution_summary=sagemaker.CfnMonitoringSchedule.MonitoringExecutionSummaryProperty(
        creation_time="creationTime",
        last_modified_time="lastModifiedTime",
        monitoring_execution_status="monitoringExecutionStatus",
        monitoring_schedule_name="monitoringScheduleName",
        scheduled_time="scheduledTime",

        # the properties below are optional
        endpoint_name="endpointName",
        failure_reason="failureReason",
        processing_job_arn="processingJobArn"
    ),
    monitoring_schedule_status="monitoringScheduleStatus",
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)

Create a new AWS::SageMaker::MonitoringSchedule.

Parameters:

scope (Construct) –
- scope in which this resource is defined.
id (str) –
- scoped id of the resource.
monitoring_schedule_config (Union[IResolvable, MonitoringScheduleConfigProperty, Dict[str, Any]]) – The configuration object that specifies the monitoring schedule and defines the monitoring job.
monitoring_schedule_name (str) – The name of the monitoring schedule.
endpoint_name (Optional[str]) – The name of the endpoint using the monitoring schedule.
failure_reason (Optional[str]) – Contains the reason a monitoring job failed, if it failed.
last_monitoring_execution_summary (Union[IResolvable, MonitoringExecutionSummaryProperty, Dict[str, Any], None]) – Describes metadata on the last execution to run, if there was one.
monitoring_schedule_status (Optional[str]) – The status of the monitoring schedule.
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_depends_on(target)

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

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

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

add_metadata(key, value)

Add a value to the CloudFormation Resource Metadata.

Parameters:

key (str)
value (Any)

See:

Return type:

None

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

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

add_override(path, value)

Adds an override to the synthesized CloudFormation resource.

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

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

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

For example:

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

would add the overrides Example:

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

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

Parameters:

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

Return type:

None

add_property_deletion_override(property_path)

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

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

add_property_override(property_path, value)

Adds an override to a resource property.

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

Parameters:

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

Return type:

None

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

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

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

The resource can be deleted (RemovalPolicy.DESTROY), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN).

Parameters:

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

Return type:

None

get_att(attribute_name)

Returns a token for an runtime attribute of this resource.

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

Parameters:: attribute_name (str) – The name of the attribute.
Return type:: Reference

get_metadata(key)

Retrieve a value value from the CloudFormation Resource Metadata.

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

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

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

inspect(inspector)

Examines the CloudFormation resource and discloses attributes.

Parameters:

inspector (TreeInspector) –

tree inspector to collect and process attributes.

Return type:

None

override_logical_id(new_logical_id)

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

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

to_string()

Returns a string representation of this construct.

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

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::SageMaker::MonitoringSchedule'

attr_creation_time

The time when the monitoring schedule was created.

CloudformationAttribute:: CreationTime

attr_last_modified_time

The last time that the monitoring schedule was modified.

CloudformationAttribute:: LastModifiedTime

attr_monitoring_schedule_arn

The Amazon Resource Name (ARN) of the monitoring schedule.

CloudformationAttribute:: MonitoringScheduleArn

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 using the monitoring schedule.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-monitoringschedule.html#cfn-sagemaker-monitoringschedule-endpointname

failure_reason

Contains the reason a monitoring job failed, if it failed.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-monitoringschedule.html#cfn-sagemaker-monitoringschedule-failurereason

last_monitoring_execution_summary

Describes metadata on the last execution to run, if there was one.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-monitoringschedule.html#cfn-sagemaker-monitoringschedule-lastmonitoringexecutionsummary

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.

monitoring_schedule_config

The configuration object that specifies the monitoring schedule and defines the monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-monitoringschedule.html#cfn-sagemaker-monitoringschedule-monitoringscheduleconfig

monitoring_schedule_name

The name of the monitoring schedule.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-monitoringschedule.html#cfn-sagemaker-monitoringschedule-monitoringschedulename

monitoring_schedule_status

The status of the monitoring schedule.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-monitoringschedule.html#cfn-sagemaker-monitoringschedule-monitoringschedulestatus

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

ref

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

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

stack

The stack in which this element is defined.

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

tags

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

For more information, see Tag .

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

Static Methods

classmethod is_cfn_element(x)

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

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

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

classmethod is_cfn_resource(construct)

Check whether the given construct is a CfnResource.

Parameters:: construct (IConstruct)
Return type:: bool

classmethod is_construct(x)

Return whether the given object is a Construct.

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

BaselineConfigProperty

class CfnMonitoringSchedule.BaselineConfigProperty(*, constraints_resource=None, statistics_resource=None)

Bases: object

Baseline configuration used to validate that the data conforms to the specified constraints and statistics.

Parameters:

constraints_resource (Union[IResolvable, ConstraintsResourceProperty, Dict[str, Any], None]) – The Amazon S3 URI for the constraints resource.
statistics_resource (Union[IResolvable, StatisticsResourceProperty, Dict[str, Any], None]) – The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-baselineconfig.html

ExampleMetadata:

fixture=_generated

Example:

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

baseline_config_property = sagemaker.CfnMonitoringSchedule.BaselineConfigProperty(
    constraints_resource=sagemaker.CfnMonitoringSchedule.ConstraintsResourceProperty(
        s3_uri="s3Uri"
    ),
    statistics_resource=sagemaker.CfnMonitoringSchedule.StatisticsResourceProperty(
        s3_uri="s3Uri"
    )
)

Attributes

constraints_resource

The Amazon S3 URI for the constraints resource.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-baselineconfig.html#cfn-sagemaker-monitoringschedule-baselineconfig-constraintsresource

statistics_resource

The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-baselineconfig.html#cfn-sagemaker-monitoringschedule-baselineconfig-statisticsresource

BatchTransformInputProperty

class CfnMonitoringSchedule.BatchTransformInputProperty(*, data_captured_destination_s3_uri, dataset_format, local_path, s3_data_distribution_type=None, s3_input_mode=None)

Bases: object

Parameters:

data_captured_destination_s3_uri (str) – CfnMonitoringSchedule.BatchTransformInputProperty.DataCapturedDestinationS3Uri.
dataset_format (Union[IResolvable, DatasetFormatProperty, Dict[str, Any]]) – CfnMonitoringSchedule.BatchTransformInputProperty.DatasetFormat.
local_path (str) – CfnMonitoringSchedule.BatchTransformInputProperty.LocalPath.
s3_data_distribution_type (Optional[str]) – CfnMonitoringSchedule.BatchTransformInputProperty.S3DataDistributionType.
s3_input_mode (Optional[str]) – CfnMonitoringSchedule.BatchTransformInputProperty.S3InputMode.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

# json: Any

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

    # the properties below are optional
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode"
)

Attributes

data_captured_destination_s3_uri

CfnMonitoringSchedule.BatchTransformInputProperty.DataCapturedDestinationS3Uri.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-batchtransforminput.html#cfn-sagemaker-monitoringschedule-batchtransforminput-datacaptureddestinations3uri

dataset_format

CfnMonitoringSchedule.BatchTransformInputProperty.DatasetFormat.

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

local_path

CfnMonitoringSchedule.BatchTransformInputProperty.LocalPath.

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

s3_data_distribution_type

CfnMonitoringSchedule.BatchTransformInputProperty.S3DataDistributionType.

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

s3_input_mode

CfnMonitoringSchedule.BatchTransformInputProperty.S3InputMode.

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

ClusterConfigProperty

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

Bases: object

Configuration for the cluster used to run model monitoring jobs.

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.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

cluster_config_property = sagemaker.CfnMonitoringSchedule.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.

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

instance_type

The ML compute instance type for the processing job.

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

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

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

ConstraintsResourceProperty

class CfnMonitoringSchedule.ConstraintsResourceProperty(*, s3_uri=None)

Bases: object

The Amazon S3 URI for the constraints resource.

Parameters:: s3_uri (Optional[str]) – The Amazon S3 URI for the constraints resource.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

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

Attributes

s3_uri

The Amazon S3 URI for the constraints resource.

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

CsvProperty

class CfnMonitoringSchedule.CsvProperty(*, header=None)

Bases: object

Parameters:: header (Union[bool, IResolvable, None]) – CfnMonitoringSchedule.CsvProperty.Header.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

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

Attributes

header

CfnMonitoringSchedule.CsvProperty.Header.

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

DatasetFormatProperty

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

Bases: object

Parameters:

csv (Union[IResolvable, CsvProperty, Dict[str, Any], None]) – CfnMonitoringSchedule.DatasetFormatProperty.Csv.
json (Optional[Any]) – CfnMonitoringSchedule.DatasetFormatProperty.Json.
parquet (Union[bool, IResolvable, None]) – CfnMonitoringSchedule.DatasetFormatProperty.Parquet.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

# json: Any

dataset_format_property = sagemaker.CfnMonitoringSchedule.DatasetFormatProperty(
    csv=sagemaker.CfnMonitoringSchedule.CsvProperty(
        header=False
    ),
    json=json,
    parquet=False
)

Attributes

csv

CfnMonitoringSchedule.DatasetFormatProperty.Csv.

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

json

CfnMonitoringSchedule.DatasetFormatProperty.Json.

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

parquet

CfnMonitoringSchedule.DatasetFormatProperty.Parquet.

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

EndpointInputProperty

class CfnMonitoringSchedule.EndpointInputProperty(*, endpoint_name, local_path, s3_data_distribution_type=None, s3_input_mode=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.
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 .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

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

    # the properties below are optional
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode"
)

Attributes

endpoint_name

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

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

local_path

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

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

s3_data_distribution_type

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

Defaults to FullyReplicated

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

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

JsonProperty

class CfnMonitoringSchedule.JsonProperty(*, line=None)

Bases: object

Parameters:: line (Union[bool, IResolvable, None]) – CfnMonitoringSchedule.JsonProperty.Line.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

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

Attributes

line

CfnMonitoringSchedule.JsonProperty.Line.

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

MonitoringAppSpecificationProperty

class CfnMonitoringSchedule.MonitoringAppSpecificationProperty(*, image_uri, container_arguments=None, container_entrypoint=None, post_analytics_processor_source_uri=None, record_preprocessor_source_uri=None)

Bases: object

Container image configuration object for the monitoring job.

Parameters:

image_uri (str) – The container image to be run by the monitoring job.
container_arguments (Optional[Sequence[str]]) – An array of arguments for the container used to run the monitoring job.
container_entrypoint (Optional[Sequence[str]]) – Specifies the entrypoint for a container used to run the monitoring job.
post_analytics_processor_source_uri (Optional[str]) – An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
record_preprocessor_source_uri (Optional[str]) – An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringappspecification.html

ExampleMetadata:

fixture=_generated

Example:

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

monitoring_app_specification_property = sagemaker.CfnMonitoringSchedule.MonitoringAppSpecificationProperty(
    image_uri="imageUri",

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

Attributes

container_arguments

An array of arguments for the container used to run the monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringappspecification.html#cfn-sagemaker-monitoringschedule-monitoringappspecification-containerarguments

container_entrypoint

Specifies the entrypoint for a container used to run the monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringappspecification.html#cfn-sagemaker-monitoringschedule-monitoringappspecification-containerentrypoint

image_uri

The container image to be run by the monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringappspecification.html#cfn-sagemaker-monitoringschedule-monitoringappspecification-imageuri

post_analytics_processor_source_uri

An Amazon S3 URI to a script that is called after analysis has been performed.

Applicable only for the built-in (first party) containers.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringappspecification.html#cfn-sagemaker-monitoringschedule-monitoringappspecification-postanalyticsprocessorsourceuri

record_preprocessor_source_uri

An Amazon S3 URI to a script that is called per row prior to running analysis.

It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringappspecification.html#cfn-sagemaker-monitoringschedule-monitoringappspecification-recordpreprocessorsourceuri

MonitoringExecutionSummaryProperty

class CfnMonitoringSchedule.MonitoringExecutionSummaryProperty(*, creation_time, last_modified_time, monitoring_execution_status, monitoring_schedule_name, scheduled_time, endpoint_name=None, failure_reason=None, processing_job_arn=None)

Bases: object

Summary of information about the last monitoring job to run.

Parameters:

creation_time (str) – The time at which the monitoring job was created.
last_modified_time (str) – A timestamp that indicates the last time the monitoring job was modified.
monitoring_execution_status (str) – The status of the monitoring job.
monitoring_schedule_name (str) – The name of the monitoring schedule.
scheduled_time (str) – The time the monitoring job was scheduled.
endpoint_name (Optional[str]) – The name of the endpoint used to run the monitoring job.
failure_reason (Optional[str]) – Contains the reason a monitoring job failed, if it failed.
processing_job_arn (Optional[str]) – The Amazon Resource Name (ARN) of the monitoring job.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringexecutionsummary.html

ExampleMetadata:

fixture=_generated

Example:

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

monitoring_execution_summary_property = sagemaker.CfnMonitoringSchedule.MonitoringExecutionSummaryProperty(
    creation_time="creationTime",
    last_modified_time="lastModifiedTime",
    monitoring_execution_status="monitoringExecutionStatus",
    monitoring_schedule_name="monitoringScheduleName",
    scheduled_time="scheduledTime",

    # the properties below are optional
    endpoint_name="endpointName",
    failure_reason="failureReason",
    processing_job_arn="processingJobArn"
)

Attributes

creation_time

The time at which the monitoring job was created.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringexecutionsummary.html#cfn-sagemaker-monitoringschedule-monitoringexecutionsummary-creationtime

endpoint_name

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringexecutionsummary.html#cfn-sagemaker-monitoringschedule-monitoringexecutionsummary-endpointname

failure_reason

Contains the reason a monitoring job failed, if it failed.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringexecutionsummary.html#cfn-sagemaker-monitoringschedule-monitoringexecutionsummary-failurereason

last_modified_time

A timestamp that indicates the last time the monitoring job was modified.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringexecutionsummary.html#cfn-sagemaker-monitoringschedule-monitoringexecutionsummary-lastmodifiedtime

monitoring_execution_status

The status of the monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringexecutionsummary.html#cfn-sagemaker-monitoringschedule-monitoringexecutionsummary-monitoringexecutionstatus

monitoring_schedule_name

The name of the monitoring schedule.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringexecutionsummary.html#cfn-sagemaker-monitoringschedule-monitoringexecutionsummary-monitoringschedulename

processing_job_arn

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringexecutionsummary.html#cfn-sagemaker-monitoringschedule-monitoringexecutionsummary-processingjobarn

scheduled_time

The time the monitoring job was scheduled.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringexecutionsummary.html#cfn-sagemaker-monitoringschedule-monitoringexecutionsummary-scheduledtime

MonitoringInputProperty

class CfnMonitoringSchedule.MonitoringInputProperty(*, batch_transform_input=None, endpoint_input=None)

Bases: object

The inputs for a monitoring job.

Parameters:

batch_transform_input (Union[IResolvable, BatchTransformInputProperty, Dict[str, Any], None]) – CfnMonitoringSchedule.MonitoringInputProperty.BatchTransformInput.
endpoint_input (Union[IResolvable, EndpointInputProperty, Dict[str, Any], None]) – The endpoint for a monitoring job.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringinput.html

ExampleMetadata:

fixture=_generated

Example:

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

# json: Any

monitoring_input_property = sagemaker.CfnMonitoringSchedule.MonitoringInputProperty(
    batch_transform_input=sagemaker.CfnMonitoringSchedule.BatchTransformInputProperty(
        data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
        dataset_format=sagemaker.CfnMonitoringSchedule.DatasetFormatProperty(
            csv=sagemaker.CfnMonitoringSchedule.CsvProperty(
                header=False
            ),
            json=json,
            parquet=False
        ),
        local_path="localPath",

        # the properties below are optional
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode"
    ),
    endpoint_input=sagemaker.CfnMonitoringSchedule.EndpointInputProperty(
        endpoint_name="endpointName",
        local_path="localPath",

        # the properties below are optional
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode"
    )
)

Attributes

batch_transform_input

CfnMonitoringSchedule.MonitoringInputProperty.BatchTransformInput.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringinput.html#cfn-sagemaker-monitoringschedule-monitoringinput-batchtransforminput

endpoint_input

The endpoint for a monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringinput.html#cfn-sagemaker-monitoringschedule-monitoringinput-endpointinput

MonitoringJobDefinitionProperty

class CfnMonitoringSchedule.MonitoringJobDefinitionProperty(*, monitoring_app_specification, monitoring_inputs, monitoring_output_config, monitoring_resources, role_arn, baseline_config=None, environment=None, network_config=None, stopping_condition=None)

Bases: object

Defines the monitoring job.

Parameters:

monitoring_app_specification (Union[IResolvable, MonitoringAppSpecificationProperty, Dict[str, Any]]) – Configures the monitoring job to run a specified Docker container image.
monitoring_inputs (Union[IResolvable, Sequence[Union[IResolvable, MonitoringInputProperty, Dict[str, Any]]]]) – The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
monitoring_output_config (Union[IResolvable, MonitoringOutputConfigProperty, Dict[str, Any]]) – The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
monitoring_resources (Union[IResolvable, MonitoringResourcesProperty, Dict[str, Any]]) – Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
baseline_config (Union[IResolvable, BaselineConfigProperty, Dict[str, Any], None]) – Baseline configuration used to validate that the data conforms to the specified constraints and statistics.
environment (Union[IResolvable, Mapping[str, str], None]) – Sets the environment variables in the Docker container.
network_config (Union[IResolvable, NetworkConfigProperty, Dict[str, Any], None]) – Specifies networking options for an monitoring job.
stopping_condition (Union[IResolvable, StoppingConditionProperty, Dict[str, Any], None]) – Specifies a time limit for how long the monitoring job is allowed to run.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html

ExampleMetadata:

fixture=_generated

Example:

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

# json: Any

monitoring_job_definition_property = sagemaker.CfnMonitoringSchedule.MonitoringJobDefinitionProperty(
    monitoring_app_specification=sagemaker.CfnMonitoringSchedule.MonitoringAppSpecificationProperty(
        image_uri="imageUri",

        # the properties below are optional
        container_arguments=["containerArguments"],
        container_entrypoint=["containerEntrypoint"],
        post_analytics_processor_source_uri="postAnalyticsProcessorSourceUri",
        record_preprocessor_source_uri="recordPreprocessorSourceUri"
    ),
    monitoring_inputs=[sagemaker.CfnMonitoringSchedule.MonitoringInputProperty(
        batch_transform_input=sagemaker.CfnMonitoringSchedule.BatchTransformInputProperty(
            data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
            dataset_format=sagemaker.CfnMonitoringSchedule.DatasetFormatProperty(
                csv=sagemaker.CfnMonitoringSchedule.CsvProperty(
                    header=False
                ),
                json=json,
                parquet=False
            ),
            local_path="localPath",

            # the properties below are optional
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode"
        ),
        endpoint_input=sagemaker.CfnMonitoringSchedule.EndpointInputProperty(
            endpoint_name="endpointName",
            local_path="localPath",

            # the properties below are optional
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode"
        )
    )],
    monitoring_output_config=sagemaker.CfnMonitoringSchedule.MonitoringOutputConfigProperty(
        monitoring_outputs=[sagemaker.CfnMonitoringSchedule.MonitoringOutputProperty(
            s3_output=sagemaker.CfnMonitoringSchedule.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"
    ),
    monitoring_resources=sagemaker.CfnMonitoringSchedule.MonitoringResourcesProperty(
        cluster_config=sagemaker.CfnMonitoringSchedule.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
    baseline_config=sagemaker.CfnMonitoringSchedule.BaselineConfigProperty(
        constraints_resource=sagemaker.CfnMonitoringSchedule.ConstraintsResourceProperty(
            s3_uri="s3Uri"
        ),
        statistics_resource=sagemaker.CfnMonitoringSchedule.StatisticsResourceProperty(
            s3_uri="s3Uri"
        )
    ),
    environment={
        "environment_key": "environment"
    },
    network_config=sagemaker.CfnMonitoringSchedule.NetworkConfigProperty(
        enable_inter_container_traffic_encryption=False,
        enable_network_isolation=False,
        vpc_config=sagemaker.CfnMonitoringSchedule.VpcConfigProperty(
            security_group_ids=["securityGroupIds"],
            subnets=["subnets"]
        )
    ),
    stopping_condition=sagemaker.CfnMonitoringSchedule.StoppingConditionProperty(
        max_runtime_in_seconds=123
    )
)

Attributes

baseline_config

Baseline configuration used to validate that the data conforms to the specified constraints and statistics.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html#cfn-sagemaker-monitoringschedule-monitoringjobdefinition-baselineconfig

environment

Sets the environment variables in the Docker container.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html#cfn-sagemaker-monitoringschedule-monitoringjobdefinition-environment

monitoring_app_specification

Configures the monitoring job to run a specified Docker container image.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html#cfn-sagemaker-monitoringschedule-monitoringjobdefinition-monitoringappspecification

monitoring_inputs

The array of inputs for the monitoring job.

Currently we support monitoring an Amazon SageMaker Endpoint.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html#cfn-sagemaker-monitoringschedule-monitoringjobdefinition-monitoringinputs

monitoring_output_config

The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html#cfn-sagemaker-monitoringschedule-monitoringjobdefinition-monitoringoutputconfig

monitoring_resources

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

In distributed processing, you specify more than one instance.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html#cfn-sagemaker-monitoringschedule-monitoringjobdefinition-monitoringresources

network_config

Specifies networking options for an monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html#cfn-sagemaker-monitoringschedule-monitoringjobdefinition-networkconfig

role_arn

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html#cfn-sagemaker-monitoringschedule-monitoringjobdefinition-rolearn

stopping_condition

Specifies a time limit for how long the monitoring job is allowed to run.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringjobdefinition.html#cfn-sagemaker-monitoringschedule-monitoringjobdefinition-stoppingcondition

MonitoringOutputConfigProperty

class CfnMonitoringSchedule.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.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

monitoring_output_config_property = sagemaker.CfnMonitoringSchedule.MonitoringOutputConfigProperty(
    monitoring_outputs=[sagemaker.CfnMonitoringSchedule.MonitoringOutputProperty(
        s3_output=sagemaker.CfnMonitoringSchedule.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.

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

monitoring_outputs

Monitoring outputs for monitoring jobs.

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

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

MonitoringOutputProperty

class CfnMonitoringSchedule.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.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

monitoring_output_property = sagemaker.CfnMonitoringSchedule.MonitoringOutputProperty(
    s3_output=sagemaker.CfnMonitoringSchedule.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.

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

MonitoringResourcesProperty

class CfnMonitoringSchedule.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.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

monitoring_resources_property = sagemaker.CfnMonitoringSchedule.MonitoringResourcesProperty(
    cluster_config=sagemaker.CfnMonitoringSchedule.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.

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

MonitoringScheduleConfigProperty

class CfnMonitoringSchedule.MonitoringScheduleConfigProperty(*, monitoring_job_definition=None, monitoring_job_definition_name=None, monitoring_type=None, schedule_config=None)

Bases: object

Configures the monitoring schedule and defines the monitoring job.

Parameters:

monitoring_job_definition (Union[IResolvable, MonitoringJobDefinitionProperty, Dict[str, Any], None]) – Defines the monitoring job.
monitoring_job_definition_name (Optional[str]) – The name of the monitoring job definition to schedule.
monitoring_type (Optional[str]) – The type of the monitoring job definition to schedule.
schedule_config (Union[IResolvable, ScheduleConfigProperty, Dict[str, Any], None]) – Configures the monitoring schedule.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringscheduleconfig.html

ExampleMetadata:

fixture=_generated

Example:

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

# json: Any

monitoring_schedule_config_property = sagemaker.CfnMonitoringSchedule.MonitoringScheduleConfigProperty(
    monitoring_job_definition=sagemaker.CfnMonitoringSchedule.MonitoringJobDefinitionProperty(
        monitoring_app_specification=sagemaker.CfnMonitoringSchedule.MonitoringAppSpecificationProperty(
            image_uri="imageUri",

            # the properties below are optional
            container_arguments=["containerArguments"],
            container_entrypoint=["containerEntrypoint"],
            post_analytics_processor_source_uri="postAnalyticsProcessorSourceUri",
            record_preprocessor_source_uri="recordPreprocessorSourceUri"
        ),
        monitoring_inputs=[sagemaker.CfnMonitoringSchedule.MonitoringInputProperty(
            batch_transform_input=sagemaker.CfnMonitoringSchedule.BatchTransformInputProperty(
                data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
                dataset_format=sagemaker.CfnMonitoringSchedule.DatasetFormatProperty(
                    csv=sagemaker.CfnMonitoringSchedule.CsvProperty(
                        header=False
                    ),
                    json=json,
                    parquet=False
                ),
                local_path="localPath",

                # the properties below are optional
                s3_data_distribution_type="s3DataDistributionType",
                s3_input_mode="s3InputMode"
            ),
            endpoint_input=sagemaker.CfnMonitoringSchedule.EndpointInputProperty(
                endpoint_name="endpointName",
                local_path="localPath",

                # the properties below are optional
                s3_data_distribution_type="s3DataDistributionType",
                s3_input_mode="s3InputMode"
            )
        )],
        monitoring_output_config=sagemaker.CfnMonitoringSchedule.MonitoringOutputConfigProperty(
            monitoring_outputs=[sagemaker.CfnMonitoringSchedule.MonitoringOutputProperty(
                s3_output=sagemaker.CfnMonitoringSchedule.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"
        ),
        monitoring_resources=sagemaker.CfnMonitoringSchedule.MonitoringResourcesProperty(
            cluster_config=sagemaker.CfnMonitoringSchedule.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
        baseline_config=sagemaker.CfnMonitoringSchedule.BaselineConfigProperty(
            constraints_resource=sagemaker.CfnMonitoringSchedule.ConstraintsResourceProperty(
                s3_uri="s3Uri"
            ),
            statistics_resource=sagemaker.CfnMonitoringSchedule.StatisticsResourceProperty(
                s3_uri="s3Uri"
            )
        ),
        environment={
            "environment_key": "environment"
        },
        network_config=sagemaker.CfnMonitoringSchedule.NetworkConfigProperty(
            enable_inter_container_traffic_encryption=False,
            enable_network_isolation=False,
            vpc_config=sagemaker.CfnMonitoringSchedule.VpcConfigProperty(
                security_group_ids=["securityGroupIds"],
                subnets=["subnets"]
            )
        ),
        stopping_condition=sagemaker.CfnMonitoringSchedule.StoppingConditionProperty(
            max_runtime_in_seconds=123
        )
    ),
    monitoring_job_definition_name="monitoringJobDefinitionName",
    monitoring_type="monitoringType",
    schedule_config=sagemaker.CfnMonitoringSchedule.ScheduleConfigProperty(
        schedule_expression="scheduleExpression"
    )
)

Attributes

monitoring_job_definition

Defines the monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringscheduleconfig.html#cfn-sagemaker-monitoringschedule-monitoringscheduleconfig-monitoringjobdefinition

monitoring_job_definition_name

The name of the monitoring job definition to schedule.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringscheduleconfig.html#cfn-sagemaker-monitoringschedule-monitoringscheduleconfig-monitoringjobdefinitionname

monitoring_type

The type of the monitoring job definition to schedule.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringscheduleconfig.html#cfn-sagemaker-monitoringschedule-monitoringscheduleconfig-monitoringtype

schedule_config

Configures the monitoring schedule.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-monitoringscheduleconfig.html#cfn-sagemaker-monitoringschedule-monitoringscheduleconfig-scheduleconfig

NetworkConfigProperty

class CfnMonitoringSchedule.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. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

network_config_property = sagemaker.CfnMonitoringSchedule.NetworkConfigProperty(
    enable_inter_container_traffic_encryption=False,
    enable_network_isolation=False,
    vpc_config=sagemaker.CfnMonitoringSchedule.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.

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

enable_network_isolation

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-networkconfig.html#cfn-sagemaker-monitoringschedule-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. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .

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

S3OutputProperty

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

Bases: object

Information about where and how you want to store the results of a monitoring job.

Parameters:

local_path (str) – The local path to the S3 storage location where 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 S3 storage location where 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.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

s3_output_property = sagemaker.CfnMonitoringSchedule.S3OutputProperty(
    local_path="localPath",
    s3_uri="s3Uri",

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

Attributes

local_path

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

LocalPath is an absolute path for the output data.

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

s3_upload_mode

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

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

s3_uri

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

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

ScheduleConfigProperty

class CfnMonitoringSchedule.ScheduleConfigProperty(*, schedule_expression)

Bases: object

Configuration details about the monitoring schedule.

Parameters:: schedule_expression (str) – A cron expression that describes details about the monitoring schedule. Currently the only supported cron expressions are: - If you want to set the job to start every hour, please use the following: Hourly: cron(0 * ? * * *) - If you want to start the job daily: cron(0 [00-23] ? * * *) For example, the following are valid cron expressions: - Daily at noon UTC: cron(0 12 ? * * *) - Daily at midnight UTC: cron(0 0 ? * * *) To support running every 6, 12 hours, the following are also supported: cron(0 [00-23]/[01-24] ? * * *) For example, the following are valid cron expressions: - Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *) - Every two hours starting at midnight: cron(0 0/2 ? * * *) .. epigraph:: - Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution. - We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-scheduleconfig.html
ExampleMetadata:: fixture=_generated

Example:

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

schedule_config_property = sagemaker.CfnMonitoringSchedule.ScheduleConfigProperty(
    schedule_expression="scheduleExpression"
)

Attributes

schedule_expression

A cron expression that describes details about the monitoring schedule.

Currently the only supported cron expressions are:

If you want to set the job to start every hour, please use the following:

Hourly: cron(0 * ? * * *)

If you want to start the job daily:

cron(0 [00-23] ? * * *)

For example, the following are valid cron expressions:

Daily at noon UTC: cron(0 12 ? * * *)
Daily at midnight UTC: cron(0 0 ? * * *)

To support running every 6, 12 hours, the following are also supported:

cron(0 [00-23]/[01-24] ? * * *)

For example, the following are valid cron expressions:

Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)
Every two hours starting at midnight: cron(0 0/2 ? * * *)

Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.

We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-scheduleconfig.html#cfn-sagemaker-monitoringschedule-scheduleconfig-scheduleexpression

StatisticsResourceProperty

class CfnMonitoringSchedule.StatisticsResourceProperty(*, s3_uri=None)

Bases: object

The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

Parameters:: s3_uri (Optional[str]) – The S3 URI for the statistics resource.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-statisticsresource.html
ExampleMetadata:: fixture=_generated

Example:

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

statistics_resource_property = sagemaker.CfnMonitoringSchedule.StatisticsResourceProperty(
    s3_uri="s3Uri"
)

Attributes

s3_uri

The S3 URI for the statistics resource.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-statisticsresource.html#cfn-sagemaker-monitoringschedule-statisticsresource-s3uri

StoppingConditionProperty

class CfnMonitoringSchedule.StoppingConditionProperty(*, max_runtime_in_seconds)

Bases: object

Specifies a limit to how long a model training job or model compilation job can run.

It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training or compilation job. Use this API to cap model training 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.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

stopping_condition_property = sagemaker.CfnMonitoringSchedule.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.

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

VpcConfigProperty

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

Bases: object

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. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .

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 .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-monitoringschedule-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.
import aws_cdk.aws_sagemaker as sagemaker

vpc_config_property = sagemaker.CfnMonitoringSchedule.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.

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

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