CfnMonitoringSchedulePropsMixin

class aws_cdk.mixins_preview.aws_sagemaker.mixins.CfnMonitoringSchedulePropsMixin(props, *, strategy=None)

Bases: Mixin

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.

See:

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

CloudformationResource:

AWS::SageMaker::MonitoringSchedule

Mixin:

true

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.mixins_preview import mixins
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

cfn_monitoring_schedule_props_mixin = sagemaker_mixins.CfnMonitoringSchedulePropsMixin(sagemaker_mixins.CfnMonitoringScheduleMixinProps(
    endpoint_name="endpointName",
    failure_reason="failureReason",
    last_monitoring_execution_summary=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringExecutionSummaryProperty(
        creation_time="creationTime",
        endpoint_name="endpointName",
        failure_reason="failureReason",
        last_modified_time="lastModifiedTime",
        monitoring_execution_status="monitoringExecutionStatus",
        monitoring_schedule_name="monitoringScheduleName",
        processing_job_arn="processingJobArn",
        scheduled_time="scheduledTime"
    ),
    monitoring_schedule_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringScheduleConfigProperty(
        monitoring_job_definition=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringJobDefinitionProperty(
            baseline_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.BaselineConfigProperty(
                constraints_resource=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ConstraintsResourceProperty(
                    s3_uri="s3Uri"
                ),
                statistics_resource=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.StatisticsResourceProperty(
                    s3_uri="s3Uri"
                )
            ),
            environment={
                "environment_key": "environment"
            },
            monitoring_app_specification=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringAppSpecificationProperty(
                container_arguments=["containerArguments"],
                container_entrypoint=["containerEntrypoint"],
                image_uri="imageUri",
                post_analytics_processor_source_uri="postAnalyticsProcessorSourceUri",
                record_preprocessor_source_uri="recordPreprocessorSourceUri"
            ),
            monitoring_inputs=[sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringInputProperty(
                batch_transform_input=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.BatchTransformInputProperty(
                    data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
                    dataset_format=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.DatasetFormatProperty(
                        csv=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.CsvProperty(
                            header=False
                        ),
                        json=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.JsonProperty(
                            line=False
                        ),
                        parquet=False
                    ),
                    exclude_features_attribute="excludeFeaturesAttribute",
                    local_path="localPath",
                    s3_data_distribution_type="s3DataDistributionType",
                    s3_input_mode="s3InputMode"
                ),
                endpoint_input=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.EndpointInputProperty(
                    endpoint_name="endpointName",
                    exclude_features_attribute="excludeFeaturesAttribute",
                    local_path="localPath",
                    s3_data_distribution_type="s3DataDistributionType",
                    s3_input_mode="s3InputMode"
                )
            )],
            monitoring_output_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringOutputConfigProperty(
                kms_key_id="kmsKeyId",
                monitoring_outputs=[sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringOutputProperty(
                    s3_output=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.S3OutputProperty(
                        local_path="localPath",
                        s3_upload_mode="s3UploadMode",
                        s3_uri="s3Uri"
                    )
                )]
            ),
            monitoring_resources=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringResourcesProperty(
                cluster_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ClusterConfigProperty(
                    instance_count=123,
                    instance_type="instanceType",
                    volume_kms_key_id="volumeKmsKeyId",
                    volume_size_in_gb=123
                )
            ),
            network_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.NetworkConfigProperty(
                enable_inter_container_traffic_encryption=False,
                enable_network_isolation=False,
                vpc_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.VpcConfigProperty(
                    security_group_ids=["securityGroupIds"],
                    subnets=["subnets"]
                )
            ),
            role_arn="roleArn",
            stopping_condition=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.StoppingConditionProperty(
                max_runtime_in_seconds=123
            )
        ),
        monitoring_job_definition_name="monitoringJobDefinitionName",
        monitoring_type="monitoringType",
        schedule_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ScheduleConfigProperty(
            data_analysis_end_time="dataAnalysisEndTime",
            data_analysis_start_time="dataAnalysisStartTime",
            schedule_expression="scheduleExpression"
        )
    ),
    monitoring_schedule_name="monitoringScheduleName",
    monitoring_schedule_status="monitoringScheduleStatus",
    tags=[CfnTag(
        key="key",
        value="value"
    )]
),
    strategy=mixins.PropertyMergeStrategy.OVERRIDE
)

Create a mixin to apply properties to AWS::SageMaker::MonitoringSchedule.

Parameters:

Methods

apply_to(construct)

Apply the mixin properties to the construct.

Parameters:

construct (IConstruct)

Return type:

IConstruct

supports(construct)

Check if this mixin supports the given construct.

Parameters:

construct (IConstruct)

Return type:

bool

Attributes

CFN_PROPERTY_KEYS = ['endpointName', 'failureReason', 'lastMonitoringExecutionSummary', 'monitoringScheduleConfig', 'monitoringScheduleName', 'monitoringScheduleStatus', 'tags']

Static Methods

classmethod is_mixin(x)

(experimental) Checks if x is a Mixin.

Parameters:

x (Any) – Any object.

Return type:

bool

Returns:

true if x is an object created from a class which extends Mixin.

Stability:

experimental

BaselineConfigProperty

class CfnMonitoringSchedulePropsMixin.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:
See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

baseline_config_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.BaselineConfigProperty(
    constraints_resource=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ConstraintsResourceProperty(
        s3_uri="s3Uri"
    ),
    statistics_resource=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.StatisticsResourceProperty(
        s3_uri="s3Uri"
    )
)

Attributes

constraints_resource

The Amazon S3 URI for the constraints resource.

See:

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.

See:

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

BatchTransformInputProperty

class CfnMonitoringSchedulePropsMixin.BatchTransformInputProperty(*, data_captured_destination_s3_uri=None, dataset_format=None, exclude_features_attribute=None, local_path=None, s3_data_distribution_type=None, s3_input_mode=None)

Bases: object

Input object for the batch transform job.

Parameters:

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

  • dataset_format (Union[IResolvable, DatasetFormatProperty, Dict[str, Any], None]) – The dataset format for your batch transform job.

  • exclude_features_attribute (Optional[str]) – The attributes of the input data to exclude from the analysis.

  • local_path (Optional[str]) – Path to the filesystem where the batch transform 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 .

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

batch_transform_input_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.BatchTransformInputProperty(
    data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
    dataset_format=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.DatasetFormatProperty(
        csv=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.CsvProperty(
            header=False
        ),
        json=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.JsonProperty(
            line=False
        ),
        parquet=False
    ),
    exclude_features_attribute="excludeFeaturesAttribute",
    local_path="localPath",
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode"
)

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-monitoringschedule-batchtransforminput.html#cfn-sagemaker-monitoringschedule-batchtransforminput-datacaptureddestinations3uri

dataset_format

The dataset format for your batch transform job.

See:

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

exclude_features_attribute

The attributes of the input data to exclude from the analysis.

See:

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

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-monitoringschedule-batchtransforminput.html#cfn-sagemaker-monitoringschedule-batchtransforminput-localpath

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-monitoringschedule-batchtransforminput.html#cfn-sagemaker-monitoringschedule-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-monitoringschedule-batchtransforminput.html#cfn-sagemaker-monitoringschedule-batchtransforminput-s3inputmode

ClusterConfigProperty

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

Bases: object

Configuration for the cluster used to run model monitoring jobs.

Parameters:

  • instance_count (Union[int, float, None]) – 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 (Optional[str]) – The ML compute instance type for the processing job.

  • 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.

  • volume_size_in_gb (Union[int, float, None]) – 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-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

cluster_config_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ClusterConfigProperty(
    instance_count=123,
    instance_type="instanceType",
    volume_kms_key_id="volumeKmsKeyId",
    volume_size_in_gb=123
)

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-monitoringschedule-clusterconfig.html#cfn-sagemaker-monitoringschedule-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-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.

See:

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.

See:

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

ConstraintsResourceProperty

class CfnMonitoringSchedulePropsMixin.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.

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

constraints_resource_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.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-monitoringschedule-constraintsresource.html#cfn-sagemaker-monitoringschedule-constraintsresource-s3uri

CsvProperty

class CfnMonitoringSchedulePropsMixin.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-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

csv_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.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-monitoringschedule-csv.html#cfn-sagemaker-monitoringschedule-csv-header

DatasetFormatProperty

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

Bases: object

The dataset format of the data to monitor.

Parameters:
See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

dataset_format_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.DatasetFormatProperty(
    csv=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.CsvProperty(
        header=False
    ),
    json=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.JsonProperty(
        line=False
    ),
    parquet=False
)

Attributes

csv

The CSV format.

See:

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

json

The Json format.

See:

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

parquet

A flag indicating if the dataset format is Parquet.

See:

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

EndpointInputProperty

class CfnMonitoringSchedulePropsMixin.EndpointInputProperty(*, endpoint_name=None, exclude_features_attribute=None, local_path=None, s3_data_distribution_type=None, s3_input_mode=None)

Bases: object

Input object for the endpoint.

Parameters:

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

  • exclude_features_attribute (Optional[str]) – The attributes of the input data to exclude from the analysis.

  • local_path (Optional[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 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 .

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

endpoint_input_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.EndpointInputProperty(
    endpoint_name="endpointName",
    exclude_features_attribute="excludeFeaturesAttribute",
    local_path="localPath",
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode"
)

Attributes

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-monitoringschedule-endpointinput.html#cfn-sagemaker-monitoringschedule-endpointinput-endpointname

exclude_features_attribute

The attributes of the input data to exclude from the analysis.

See:

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

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-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 Amazon S3 key.

Defaults to FullyReplicated

See:

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 .

See:

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

JsonProperty

class CfnMonitoringSchedulePropsMixin.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-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

json_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.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-monitoringschedule-json.html#cfn-sagemaker-monitoringschedule-json-line

MonitoringAppSpecificationProperty

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

Bases: object

Container image configuration object for the monitoring job.

Parameters:

  • 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.

  • image_uri (Optional[str]) – The container image to be run by 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 flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_app_specification_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringAppSpecificationProperty(
    container_arguments=["containerArguments"],
    container_entrypoint=["containerEntrypoint"],
    image_uri="imageUri",
    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.

See:

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.

See:

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.

See:

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.

See:

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 flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

See:

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

MonitoringExecutionSummaryProperty

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

Bases: object

Summary of information about the last monitoring job to run.

Parameters:

  • creation_time (Optional[str]) – The time at which the monitoring job was created.

  • 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.

  • last_modified_time (Optional[str]) – A timestamp that indicates the last time the monitoring job was modified.

  • monitoring_execution_status (Optional[str]) – The status of the monitoring job.

  • monitoring_schedule_name (Optional[str]) – The name of the monitoring schedule.

  • processing_job_arn (Optional[str]) – The Amazon Resource Name (ARN) of the monitoring job.

  • scheduled_time (Optional[str]) – The time the monitoring job was scheduled.

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_execution_summary_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringExecutionSummaryProperty(
    creation_time="creationTime",
    endpoint_name="endpointName",
    failure_reason="failureReason",
    last_modified_time="lastModifiedTime",
    monitoring_execution_status="monitoringExecutionStatus",
    monitoring_schedule_name="monitoringScheduleName",
    processing_job_arn="processingJobArn",
    scheduled_time="scheduledTime"
)

Attributes

creation_time

The time at which the monitoring job was created.

See:

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.

See:

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.

See:

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.

See:

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.

See:

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.

See:

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.

See:

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.

See:

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

MonitoringInputProperty

class CfnMonitoringSchedulePropsMixin.MonitoringInputProperty(*, batch_transform_input=None, endpoint_input=None)

Bases: object

The inputs for a monitoring job.

Parameters:
See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_input_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringInputProperty(
    batch_transform_input=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.BatchTransformInputProperty(
        data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
        dataset_format=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.DatasetFormatProperty(
            csv=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.CsvProperty(
                header=False
            ),
            json=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.JsonProperty(
                line=False
            ),
            parquet=False
        ),
        exclude_features_attribute="excludeFeaturesAttribute",
        local_path="localPath",
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode"
    ),
    endpoint_input=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.EndpointInputProperty(
        endpoint_name="endpointName",
        exclude_features_attribute="excludeFeaturesAttribute",
        local_path="localPath",
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode"
    )
)

Attributes

batch_transform_input

Input object for the batch transform job.

See:

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.

See:

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

MonitoringJobDefinitionProperty

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

Bases: object

Defines the monitoring job.

Parameters:

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

  • monitoring_app_specification (Union[IResolvable, MonitoringAppSpecificationProperty, Dict[str, Any], None]) – Configures the monitoring job to run a specified Docker container image.

  • monitoring_inputs (Union[IResolvable, Sequence[Union[IResolvable, MonitoringInputProperty, Dict[str, Any]]], None]) – The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker AI Endpoint.

  • monitoring_output_config (Union[IResolvable, MonitoringOutputConfigProperty, Dict[str, Any], None]) – The array of outputs from the monitoring job to be uploaded to Amazon S3.

  • monitoring_resources (Union[IResolvable, MonitoringResourcesProperty, Dict[str, Any], None]) – 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.

  • network_config (Union[IResolvable, NetworkConfigProperty, Dict[str, Any], None]) – Specifies networking options for an monitoring job.

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

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

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_job_definition_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringJobDefinitionProperty(
    baseline_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.BaselineConfigProperty(
        constraints_resource=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ConstraintsResourceProperty(
            s3_uri="s3Uri"
        ),
        statistics_resource=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.StatisticsResourceProperty(
            s3_uri="s3Uri"
        )
    ),
    environment={
        "environment_key": "environment"
    },
    monitoring_app_specification=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringAppSpecificationProperty(
        container_arguments=["containerArguments"],
        container_entrypoint=["containerEntrypoint"],
        image_uri="imageUri",
        post_analytics_processor_source_uri="postAnalyticsProcessorSourceUri",
        record_preprocessor_source_uri="recordPreprocessorSourceUri"
    ),
    monitoring_inputs=[sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringInputProperty(
        batch_transform_input=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.BatchTransformInputProperty(
            data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
            dataset_format=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.DatasetFormatProperty(
                csv=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.CsvProperty(
                    header=False
                ),
                json=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.JsonProperty(
                    line=False
                ),
                parquet=False
            ),
            exclude_features_attribute="excludeFeaturesAttribute",
            local_path="localPath",
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode"
        ),
        endpoint_input=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.EndpointInputProperty(
            endpoint_name="endpointName",
            exclude_features_attribute="excludeFeaturesAttribute",
            local_path="localPath",
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode"
        )
    )],
    monitoring_output_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringOutputConfigProperty(
        kms_key_id="kmsKeyId",
        monitoring_outputs=[sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringOutputProperty(
            s3_output=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.S3OutputProperty(
                local_path="localPath",
                s3_upload_mode="s3UploadMode",
                s3_uri="s3Uri"
            )
        )]
    ),
    monitoring_resources=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringResourcesProperty(
        cluster_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ClusterConfigProperty(
            instance_count=123,
            instance_type="instanceType",
            volume_kms_key_id="volumeKmsKeyId",
            volume_size_in_gb=123
        )
    ),
    network_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.NetworkConfigProperty(
        enable_inter_container_traffic_encryption=False,
        enable_network_isolation=False,
        vpc_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.VpcConfigProperty(
            security_group_ids=["securityGroupIds"],
            subnets=["subnets"]
        )
    ),
    role_arn="roleArn",
    stopping_condition=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.StoppingConditionProperty(
        max_runtime_in_seconds=123
    )
)

Attributes

baseline_config

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

See:

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.

See:

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.

See:

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 AI Endpoint.

See:

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 S3.

See:

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.

See:

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.

See:

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 AI can assume to perform tasks on your behalf.

See:

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.

See:

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

MonitoringOutputConfigProperty

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

Bases: object

The output configuration for monitoring jobs.

Parameters:

  • kms_key_id (Optional[str]) – The AWS Key Management Service ( AWS ) key that Amazon SageMaker AI uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

  • monitoring_outputs (Union[IResolvable, Sequence[Union[IResolvable, MonitoringOutputProperty, Dict[str, Any]]], None]) – 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-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_output_config_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringOutputConfigProperty(
    kms_key_id="kmsKeyId",
    monitoring_outputs=[sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringOutputProperty(
        s3_output=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.S3OutputProperty(
            local_path="localPath",
            s3_upload_mode="s3UploadMode",
            s3_uri="s3Uri"
        )
    )]
)

Attributes

kms_key_id

The AWS Key Management Service ( AWS ) key that Amazon SageMaker AI 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-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.

See:

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

MonitoringOutputProperty

class CfnMonitoringSchedulePropsMixin.MonitoringOutputProperty(*, s3_output=None)

Bases: object

The output object for a monitoring job.

Parameters:

s3_output (Union[IResolvable, S3OutputProperty, Dict[str, Any], None]) – 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-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_output_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringOutputProperty(
    s3_output=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.S3OutputProperty(
        local_path="localPath",
        s3_upload_mode="s3UploadMode",
        s3_uri="s3Uri"
    )
)

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-monitoringschedule-monitoringoutput.html#cfn-sagemaker-monitoringschedule-monitoringoutput-s3output

MonitoringResourcesProperty

class CfnMonitoringSchedulePropsMixin.MonitoringResourcesProperty(*, cluster_config=None)

Bases: object

Identifies the resources to deploy for a monitoring job.

Parameters:

cluster_config (Union[IResolvable, ClusterConfigProperty, Dict[str, Any], None]) – The configuration for the cluster resources used to run the processing job.

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_resources_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringResourcesProperty(
    cluster_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ClusterConfigProperty(
        instance_count=123,
        instance_type="instanceType",
        volume_kms_key_id="volumeKmsKeyId",
        volume_size_in_gb=123
    )
)

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-monitoringschedule-monitoringresources.html#cfn-sagemaker-monitoringschedule-monitoringresources-clusterconfig

MonitoringScheduleConfigProperty

class CfnMonitoringSchedulePropsMixin.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.

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_schedule_config_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringScheduleConfigProperty(
    monitoring_job_definition=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringJobDefinitionProperty(
        baseline_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.BaselineConfigProperty(
            constraints_resource=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ConstraintsResourceProperty(
                s3_uri="s3Uri"
            ),
            statistics_resource=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.StatisticsResourceProperty(
                s3_uri="s3Uri"
            )
        ),
        environment={
            "environment_key": "environment"
        },
        monitoring_app_specification=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringAppSpecificationProperty(
            container_arguments=["containerArguments"],
            container_entrypoint=["containerEntrypoint"],
            image_uri="imageUri",
            post_analytics_processor_source_uri="postAnalyticsProcessorSourceUri",
            record_preprocessor_source_uri="recordPreprocessorSourceUri"
        ),
        monitoring_inputs=[sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringInputProperty(
            batch_transform_input=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.BatchTransformInputProperty(
                data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
                dataset_format=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.DatasetFormatProperty(
                    csv=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.CsvProperty(
                        header=False
                    ),
                    json=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.JsonProperty(
                        line=False
                    ),
                    parquet=False
                ),
                exclude_features_attribute="excludeFeaturesAttribute",
                local_path="localPath",
                s3_data_distribution_type="s3DataDistributionType",
                s3_input_mode="s3InputMode"
            ),
            endpoint_input=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.EndpointInputProperty(
                endpoint_name="endpointName",
                exclude_features_attribute="excludeFeaturesAttribute",
                local_path="localPath",
                s3_data_distribution_type="s3DataDistributionType",
                s3_input_mode="s3InputMode"
            )
        )],
        monitoring_output_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringOutputConfigProperty(
            kms_key_id="kmsKeyId",
            monitoring_outputs=[sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringOutputProperty(
                s3_output=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.S3OutputProperty(
                    local_path="localPath",
                    s3_upload_mode="s3UploadMode",
                    s3_uri="s3Uri"
                )
            )]
        ),
        monitoring_resources=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.MonitoringResourcesProperty(
            cluster_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ClusterConfigProperty(
                instance_count=123,
                instance_type="instanceType",
                volume_kms_key_id="volumeKmsKeyId",
                volume_size_in_gb=123
            )
        ),
        network_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.NetworkConfigProperty(
            enable_inter_container_traffic_encryption=False,
            enable_network_isolation=False,
            vpc_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.VpcConfigProperty(
                security_group_ids=["securityGroupIds"],
                subnets=["subnets"]
            )
        ),
        role_arn="roleArn",
        stopping_condition=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.StoppingConditionProperty(
            max_runtime_in_seconds=123
        )
    ),
    monitoring_job_definition_name="monitoringJobDefinitionName",
    monitoring_type="monitoringType",
    schedule_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ScheduleConfigProperty(
        data_analysis_end_time="dataAnalysisEndTime",
        data_analysis_start_time="dataAnalysisStartTime",
        schedule_expression="scheduleExpression"
    )
)

Attributes

monitoring_job_definition

Defines the monitoring job.

See:

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.

See:

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.

See:

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.

See:

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

NetworkConfigProperty

class CfnMonitoringSchedulePropsMixin.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 .

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

network_config_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.NetworkConfigProperty(
    enable_inter_container_traffic_encryption=False,
    enable_network_isolation=False,
    vpc_config=sagemaker_mixins.CfnMonitoringSchedulePropsMixin.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-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.

See:

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 .

See:

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

S3OutputProperty

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

Bases: object

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

Parameters:

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

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

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

s3_output_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.S3OutputProperty(
    local_path="localPath",
    s3_upload_mode="s3UploadMode",
    s3_uri="s3Uri"
)

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.

See:

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.

See:

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.

See:

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

ScheduleConfigProperty

class CfnMonitoringSchedulePropsMixin.ScheduleConfigProperty(*, data_analysis_end_time=None, data_analysis_start_time=None, schedule_expression=None)

Bases: object

Configuration details about the monitoring schedule.

Parameters:

  • data_analysis_end_time (Optional[str]) – Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H" . The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter. If you set ScheduleExpression to NOW , this parameter is required.

  • data_analysis_start_time (Optional[str]) – Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H" . The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter. If you set ScheduleExpression to NOW , this parameter is required.

  • schedule_expression (Optional[str]) – A cron expression that describes details about the monitoring schedule. The supported cron expressions are: - If you want to set the job to start every hour, use the following: Hourly: cron(0 * ? * * *) - If you want to start the job daily: cron(0 [00-23] ? * * *) - If you want to run the job one time, immediately, use the following keyword: NOW 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 AI will pick a time for running every day. You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

schedule_config_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.ScheduleConfigProperty(
    data_analysis_end_time="dataAnalysisEndTime",
    data_analysis_start_time="dataAnalysisStartTime",
    schedule_expression="scheduleExpression"
)

Attributes

data_analysis_end_time

Sets the end time for a monitoring job window.

Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H" .

The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter.

If you set ScheduleExpression to NOW , this parameter is required.

See:

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

data_analysis_start_time

Sets the start time for a monitoring job window.

Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H" .

The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter.

If you set ScheduleExpression to NOW , this parameter is required.

See:

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

schedule_expression

A cron expression that describes details about the monitoring schedule.

The supported cron expressions are:

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

Hourly: cron(0 * ? * * *)

  • If you want to start the job daily:

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

  • If you want to run the job one time, immediately, use the following keyword:

NOW

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 AI will pick a time for running every day.

You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.

See:

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

StatisticsResourceProperty

class CfnMonitoringSchedulePropsMixin.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.

See:

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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

statistics_resource_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.StatisticsResourceProperty(
    s3_uri="s3Uri"
)

Attributes

s3_uri

The S3 URI for the statistics resource.

See:

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

StoppingConditionProperty

class CfnMonitoringSchedulePropsMixin.StoppingConditionProperty(*, max_runtime_in_seconds=None)

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, None]) – 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-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

stopping_condition_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.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-monitoringschedule-stoppingcondition.html#cfn-sagemaker-monitoringschedule-stoppingcondition-maxruntimeinseconds

VpcConfigProperty

class CfnMonitoringSchedulePropsMixin.VpcConfigProperty(*, security_group_ids=None, subnets=None)

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 (Optional[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 (Optional[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-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

vpc_config_property = sagemaker_mixins.CfnMonitoringSchedulePropsMixin.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-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 .

See:

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