Amazon SageMaker Sagemaker Model Quality Job Definition API
The Sagemaker Model Quality Job Definition API from Amazon SageMaker — 4 operation(s) for sagemaker model quality job definition.
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OpenAPI Specification
openapi: 3.1.0
info:
title: Amazon SageMaker Service Model Quality Job Definition API
version: '2017-07-24'
description: Generated from AWS's published Smithy model for Amazon SageMaker Service. 4 operation(s).
contact:
name: Amazon Web Services
url: https://aws.amazon.com/contact-us/
license:
name: Apache 2.0
identifier: Apache-2.0
url: https://github.com/aws/api-models-aws/blob/main/LICENSE
servers:
- url: https://api.sagemaker.{region}.amazonaws.com
variables:
region:
default: us-east-1
security:
- sigv4: []
paths:
/#CreateModelQualityJobDefinition:
post:
operationId: CreateModelQualityJobDefinition
summary: Create Model Quality Job Definition
description: Creates a definition for a job that monitors model quality and drift. For information about model monitor,
see Amazon SageMaker AI Model Monitor.
responses:
'200':
description: Success
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/CreateModelQualityJobDefinitionResponse'
'400':
description: ResourceInUse; ResourceLimitExceeded
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/ResourceInUse'
parameters:
- name: X-Amz-Target
in: header
required: true
schema:
type: string
enum:
- SageMaker.CreateModelQualityJobDefinition
requestBody:
required: true
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/CreateModelQualityJobDefinitionRequest'
tags:
- Model Quality Job Definition
/#DeleteModelQualityJobDefinition:
post:
operationId: DeleteModelQualityJobDefinition
summary: Delete Model Quality Job Definition
description: Deletes the secified model quality monitoring job definition.
responses:
'200':
description: Success
content:
application/x-amz-json-1.1:
schema:
type: string
'400':
description: ResourceNotFound
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/ResourceNotFound'
parameters:
- name: X-Amz-Target
in: header
required: true
schema:
type: string
enum:
- SageMaker.DeleteModelQualityJobDefinition
requestBody:
required: true
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/DeleteModelQualityJobDefinitionRequest'
tags:
- Model Quality Job Definition
/#DescribeModelQualityJobDefinition:
post:
operationId: DescribeModelQualityJobDefinition
summary: Describe Model Quality Job Definition
description: Returns a description of a model quality job definition.
responses:
'200':
description: Success
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/DescribeModelQualityJobDefinitionResponse'
'400':
description: ResourceNotFound
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/ResourceNotFound'
parameters:
- name: X-Amz-Target
in: header
required: true
schema:
type: string
enum:
- SageMaker.DescribeModelQualityJobDefinition
requestBody:
required: true
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/DescribeModelQualityJobDefinitionRequest'
tags:
- Model Quality Job Definition
/#ListModelQualityJobDefinitions:
post:
operationId: ListModelQualityJobDefinitions
summary: List Model Quality Job Definitions
description: Gets a list of model quality monitoring job definitions in your account.
responses:
'200':
description: Success
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/ListModelQualityJobDefinitionsResponse'
parameters:
- name: X-Amz-Target
in: header
required: true
schema:
type: string
enum:
- SageMaker.ListModelQualityJobDefinitions
requestBody:
required: true
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/ListModelQualityJobDefinitionsRequest'
tags:
- Model Quality Job Definition
components:
securitySchemes:
sigv4:
type: apiKey
in: header
name: Authorization
description: AWS Signature Version 4. The Authorization header carries an AWS4-HMAC-SHA256 signature over the canonical
request, with X-Amz-Date (and X-Amz-Security-Token for temporary credentials). Not a bearer token.
x-aws-sigv4:
signingName: sagemaker
schemas:
BatchTransformInput:
type: object
properties:
DataCapturedDestinationS3Uri:
type: string
description: The Amazon S3 location being used to capture the data.
DatasetFormat:
$ref: '#/components/schemas/MonitoringDatasetFormat'
LocalPath:
type: string
description: Path to the filesystem where the batch transform data is available to the container.
S3InputMode:
type: string
enum:
- Pipe
- File
description: 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.
S3DataDistributionType:
type: string
enum:
- FullyReplicated
- ShardedByS3Key
description: Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
FullyReplicated
FeaturesAttribute:
type: string
description: The attributes of the input data that are the input features.
InferenceAttribute:
type: string
description: The attribute of the input data that represents the ground truth label.
ProbabilityAttribute:
type: string
description: In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute:
type: number
description: The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset:
type: string
description: If specified, monitoring jobs substract this time from the start time. For information about using
offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset:
type: string
description: If specified, monitoring jobs subtract this time from the end time. For information about using offsets
for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute:
type: string
description: The attributes of the input data to exclude from the analysis.
required:
- DataCapturedDestinationS3Uri
- DatasetFormat
- LocalPath
CreateModelQualityJobDefinitionRequest:
type: object
properties:
JobDefinitionName:
type: string
description: The name of the monitoring job definition.
ModelQualityBaselineConfig:
$ref: '#/components/schemas/ModelQualityBaselineConfig'
ModelQualityAppSpecification:
$ref: '#/components/schemas/ModelQualityAppSpecification'
ModelQualityJobInput:
$ref: '#/components/schemas/ModelQualityJobInput'
ModelQualityJobOutputConfig:
$ref: '#/components/schemas/MonitoringOutputConfig'
JobResources:
$ref: '#/components/schemas/MonitoringResources'
NetworkConfig:
$ref: '#/components/schemas/MonitoringNetworkConfig'
RoleArn:
type: string
description: The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks
on your behalf.
StoppingCondition:
$ref: '#/components/schemas/MonitoringStoppingCondition'
Tags:
type: array
items:
$ref: '#/components/schemas/Tag'
description: (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the
Amazon Web Services Billing and Cost Management User Guide.
required:
- JobDefinitionName
- ModelQualityAppSpecification
- ModelQualityJobInput
- ModelQualityJobOutputConfig
- JobResources
- RoleArn
CreateModelQualityJobDefinitionResponse:
type: object
properties:
JobDefinitionArn:
type: string
description: The Amazon Resource Name (ARN) of the model quality monitoring job.
required:
- JobDefinitionArn
DeleteModelQualityJobDefinitionRequest:
type: object
properties:
JobDefinitionName:
type: string
description: The name of the model quality monitoring job definition to delete.
required:
- JobDefinitionName
DescribeModelQualityJobDefinitionRequest:
type: object
properties:
JobDefinitionName:
type: string
description: The name of the model quality job. The name must be unique within an Amazon Web Services Region in
the Amazon Web Services account.
required:
- JobDefinitionName
DescribeModelQualityJobDefinitionResponse:
type: object
properties:
JobDefinitionArn:
type: string
description: The Amazon Resource Name (ARN) of the model quality job.
JobDefinitionName:
type: string
description: The name of the quality job definition. The name must be unique within an Amazon Web Services Region
in the Amazon Web Services account.
CreationTime:
type: string
format: date-time
description: The time at which the model quality job was created.
ModelQualityBaselineConfig:
$ref: '#/components/schemas/ModelQualityBaselineConfig'
ModelQualityAppSpecification:
$ref: '#/components/schemas/ModelQualityAppSpecification'
ModelQualityJobInput:
$ref: '#/components/schemas/ModelQualityJobInput'
ModelQualityJobOutputConfig:
$ref: '#/components/schemas/MonitoringOutputConfig'
JobResources:
$ref: '#/components/schemas/MonitoringResources'
NetworkConfig:
$ref: '#/components/schemas/MonitoringNetworkConfig'
RoleArn:
type: string
description: The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks
on your behalf.
StoppingCondition:
$ref: '#/components/schemas/MonitoringStoppingCondition'
required:
- JobDefinitionArn
- JobDefinitionName
- CreationTime
- ModelQualityAppSpecification
- ModelQualityJobInput
- ModelQualityJobOutputConfig
- JobResources
- RoleArn
EndpointInput:
type: object
properties:
EndpointName:
type: string
description: An endpoint in customer's account which has enabled DataCaptureConfig enabled.
LocalPath:
type: string
description: Path to the filesystem where the endpoint data is available to the container.
S3InputMode:
type: string
enum:
- Pipe
- File
description: 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.
S3DataDistributionType:
type: string
enum:
- FullyReplicated
- ShardedByS3Key
description: Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults
to FullyReplicated
FeaturesAttribute:
type: string
description: The attributes of the input data that are the input features.
InferenceAttribute:
type: string
description: The attribute of the input data that represents the ground truth label.
ProbabilityAttribute:
type: string
description: In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute:
type: number
description: The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset:
type: string
description: If specified, monitoring jobs substract this time from the start time. For information about using
offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset:
type: string
description: If specified, monitoring jobs substract this time from the end time. For information about using offsets
for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute:
type: string
description: The attributes of the input data to exclude from the analysis.
required:
- EndpointName
- LocalPath
ListModelQualityJobDefinitionsRequest:
type: object
properties:
EndpointName:
type: string
description: A filter that returns only model quality monitoring job definitions that are associated with the specified
endpoint.
SortBy:
type: string
enum:
- Name
- CreationTime
description: The field to sort results by. The default is CreationTime.
SortOrder:
type: string
enum:
- Ascending
- Descending
description: Whether to sort the results in Ascending or Descending order. The default is Descending.
NextToken:
type: string
description: If the result of the previous ListModelQualityJobDefinitions request was truncated, the response includes
a NextToken. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.
MaxResults:
type: integer
description: The maximum number of results to return in a call to ListModelQualityJobDefinitions.
NameContains:
type: string
description: A string in the transform job name. This filter returns only model quality monitoring job definitions
whose name contains the specified string.
CreationTimeBefore:
type: string
format: date-time
description: A filter that returns only model quality monitoring job definitions created before the specified time.
CreationTimeAfter:
type: string
format: date-time
description: A filter that returns only model quality monitoring job definitions created after the specified time.
ListModelQualityJobDefinitionsResponse:
type: object
properties:
JobDefinitionSummaries:
type: array
items:
$ref: '#/components/schemas/MonitoringJobDefinitionSummary'
description: A list of summaries of model quality monitoring job definitions.
NextToken:
type: string
description: If the response is truncated, Amazon SageMaker AI returns this token. To retrieve the next set of model
quality monitoring job definitions, use it in the next request.
required:
- JobDefinitionSummaries
ModelQualityAppSpecification:
type: object
properties:
ImageUri:
type: string
description: The address of the container image that the monitoring job runs.
ContainerEntrypoint:
type: array
items:
type: string
description: Specifies the entrypoint for a container that the monitoring job runs.
ContainerArguments:
type: array
items:
type: string
description: An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri:
type: string
description: 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.
PostAnalyticsProcessorSourceUri:
type: string
description: An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for
the built-in (first party) containers.
ProblemType:
type: string
enum:
- BinaryClassification
- MulticlassClassification
- Regression
description: The machine learning problem type of the model that the monitoring job monitors.
Environment:
type: object
additionalProperties:
type: string
description: Sets the environment variables in the container that the monitoring job runs.
required:
- ImageUri
ModelQualityBaselineConfig:
type: object
properties:
BaseliningJobName:
type: string
description: The name of the job that performs baselining for the monitoring job.
ConstraintsResource:
$ref: '#/components/schemas/MonitoringConstraintsResource'
ModelQualityJobInput:
type: object
properties:
EndpointInput:
$ref: '#/components/schemas/EndpointInput'
BatchTransformInput:
$ref: '#/components/schemas/BatchTransformInput'
GroundTruthS3Input:
$ref: '#/components/schemas/MonitoringGroundTruthS3Input'
required:
- GroundTruthS3Input
MonitoringClusterConfig:
type: object
properties:
InstanceCount:
type: integer
description: 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.
InstanceType:
type: string
enum:
- ml.t3.medium
- ml.t3.large
- ml.t3.xlarge
- ml.t3.2xlarge
- ml.m4.xlarge
- ml.m4.2xlarge
- ml.m4.4xlarge
- ml.m4.10xlarge
- ml.m4.16xlarge
- ml.c4.xlarge
- ml.c4.2xlarge
- ml.c4.4xlarge
- ml.c4.8xlarge
- ml.p2.xlarge
- ml.p2.8xlarge
- ml.p2.16xlarge
- ml.p3.2xlarge
- ml.p3.8xlarge
- ml.p3.16xlarge
- ml.c5.xlarge
- ml.c5.2xlarge
- ml.c5.4xlarge
- ml.c5.9xlarge
- ml.c5.18xlarge
- ml.m5.large
- ml.m5.xlarge
- ml.m5.2xlarge
- ml.m5.4xlarge
- ml.m5.12xlarge
- ml.m5.24xlarge
- ml.r5.large
- ml.r5.xlarge
- ml.r5.2xlarge
- ml.r5.4xlarge
- ml.r5.8xlarge
- ml.r5.12xlarge
- ml.r5.16xlarge
- ml.r5.24xlarge
- ml.g4dn.xlarge
- ml.g4dn.2xlarge
- ml.g4dn.4xlarge
- ml.g4dn.8xlarge
- ml.g4dn.12xlarge
- ml.g4dn.16xlarge
- ml.g5.xlarge
- ml.g5.2xlarge
- ml.g5.4xlarge
- ml.g5.8xlarge
- ml.g5.16xlarge
- ml.g5.12xlarge
- ml.g5.24xlarge
- ml.g5.48xlarge
- ml.r5d.large
- ml.r5d.xlarge
- ml.r5d.2xlarge
- ml.r5d.4xlarge
- ml.r5d.8xlarge
- ml.r5d.12xlarge
- ml.r5d.16xlarge
- ml.r5d.24xlarge
- ml.g6.xlarge
- ml.g6.2xlarge
- ml.g6.4xlarge
- ml.g6.8xlarge
- ml.g6.12xlarge
- ml.g6.16xlarge
- ml.g6.24xlarge
- ml.g6.48xlarge
- ml.g6e.xlarge
- ml.g6e.2xlarge
- ml.g6e.4xlarge
- ml.g6e.8xlarge
- ml.g6e.12xlarge
- ml.g6e.16xlarge
- ml.g6e.24xlarge
- ml.g6e.48xlarge
- ml.m6i.large
- ml.m6i.xlarge
- ml.m6i.2xlarge
- ml.m6i.4xlarge
- ml.m6i.8xlarge
- ml.m6i.12xlarge
- ml.m6i.16xlarge
- ml.m6i.24xlarge
- ml.m6i.32xlarge
- ml.c6i.xlarge
- ml.c6i.2xlarge
- ml.c6i.4xlarge
- ml.c6i.8xlarge
- ml.c6i.12xlarge
- ml.c6i.16xlarge
- ml.c6i.24xlarge
- ml.c6i.32xlarge
- ml.m7i.large
- ml.m7i.xlarge
- ml.m7i.2xlarge
- ml.m7i.4xlarge
- ml.m7i.8xlarge
- ml.m7i.12xlarge
- ml.m7i.16xlarge
- ml.m7i.24xlarge
- ml.m7i.48xlarge
- ml.c7i.large
- ml.c7i.xlarge
- ml.c7i.2xlarge
- ml.c7i.4xlarge
- ml.c7i.8xlarge
- ml.c7i.12xlarge
- ml.c7i.16xlarge
- ml.c7i.24xlarge
- ml.c7i.48xlarge
- ml.r7i.large
- ml.r7i.xlarge
- ml.r7i.2xlarge
- ml.r7i.4xlarge
- ml.r7i.8xlarge
- ml.r7i.12xlarge
- ml.r7i.16xlarge
- ml.r7i.24xlarge
- ml.r7i.48xlarge
- ml.p5.4xlarge
- ml.g7e.2xlarge
- ml.g7e.4xlarge
- ml.g7e.8xlarge
- ml.g7e.12xlarge
- ml.g7e.24xlarge
- ml.g7e.48xlarge
- ml.g7.2xlarge
- ml.g7.4xlarge
- ml.g7.8xlarge
- ml.g7.12xlarge
- ml.g7.24xlarge
- ml.g7.48xlarge
- ml.r6i.large
- ml.r6i.xlarge
- ml.r6i.2xlarge
- ml.r6i.4xlarge
- ml.r6i.8xlarge
- ml.r6i.12xlarge
- ml.r6i.16xlarge
- ml.r6i.24xlarge
- ml.r6i.32xlarge
- ml.m8i.large
- ml.m8i.xlarge
- ml.m8i.2xlarge
- ml.m8i.4xlarge
- ml.m8i.8xlarge
- ml.m8i.12xlarge
- ml.m8i.16xlarge
- ml.m8i.24xlarge
- ml.m8i.32xlarge
- ml.m8i.48xlarge
- ml.m8i.96xlarge
- ml.c8i.xlarge
- ml.c8i.2xlarge
- ml.c8i.4xlarge
- ml.c8i.8xlarge
- ml.c8i.12xlarge
- ml.c8i.16xlarge
- ml.c8i.24xlarge
- ml.c8i.32xlarge
- ml.c8i.48xlarge
- ml.c8i.96xlarge
- ml.r8i.large
- ml.r8i.xlarge
- ml.r8i.2xlarge
- ml.r8i.4xlarge
- ml.r8i.8xlarge
- ml.r8i.12xlarge
- ml.r8i.16xlarge
- ml.r8i.24xlarge
- ml.r8i.32xlarge
- ml.r8i.48xlarge
- ml.r8i.96xlarge
- ml.m7i-flex.large
- ml.m7i-flex.xlarge
- ml.m7i-flex.2xlarge
- ml.m7i-flex.4xlarge
- ml.m7i-flex.8xlarge
- ml.m7i-flex.12xlarge
- ml.m7i-flex.16xlarge
- ml.c7i-flex.xlarge
- ml.c7i-flex.2xlarge
- ml.c7i-flex.4xlarge
- ml.c7i-flex.8xlarge
- ml.c7i-flex.12xlarge
- ml.c7i-flex.16xlarge
- ml.m8i-flex.large
- ml.m8i-flex.xlarge
- ml.m8i-flex.2xlarge
- ml.m8i-flex.4xlarge
- ml.m8i-flex.8xlarge
- ml.m8i-flex.12xlarge
- ml.m8i-flex.16xlarge
- ml.c8i-flex.xlarge
- ml.c8i-flex.2xlarge
- ml.c8i-flex.4xlarge
- ml.c8i-flex.8xlarge
- ml.c8i-flex.12xlarge
- ml.c8i-flex.16xlarge
- ml.r8i-flex.large
- ml.r8i-flex.xlarge
- ml.r8i-flex.2xlarge
- ml.r8i-flex.4xlarge
- ml.r8i-flex.8xlarge
- ml.r8i-flex.12xlarge
- ml.r8i-flex.16xlarge
description: The ML compute instance type for the processing job.
VolumeSizeInGB:
type: integer
description: The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient
ML storage for your scenario.
VolumeKmsKeyId:
type: string
description: The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt data on the storage volume
attached to the ML compute instance(s) that run the model monitoring job.
required:
- InstanceCount
- InstanceType
- VolumeSizeInGB
MonitoringConstraintsResource:
type: object
properties:
S3Uri:
type: string
description: The Amazon S3 URI for the constraints resource.
MonitoringCsvDatasetFormat:
type: object
properties:
Header:
type: boolean
description: Indicates if the CSV data has a header.
MonitoringDatasetFormat:
type: object
properties:
Csv:
$ref: '#/components/schemas/MonitoringCsvDatasetFormat'
Json:
$ref: '#/components/schemas/MonitoringJsonDatasetFormat'
Parquet:
$ref: '#/components/schemas/MonitoringParquetDatasetFormat'
MonitoringGroundTruthS3Input:
type: object
properties:
S3Uri:
type: string
description: The address of the Amazon S3 location of the ground truth labels.
MonitoringJobDefinitionSummary:
type: object
properties:
MonitoringJobDefinitionName:
type: string
description: The name of the monitoring job.
MonitoringJobDefinitionArn:
type: string
description: The Amazon Resource Name (ARN) of the monitoring job.
CreationTime:
type: string
format: date-time
description: The time that the monitoring job was created.
EndpointName:
type: string
description: The name of the endpoint that the job monitors.
required:
- MonitoringJobDefinitionName
- MonitoringJobDefinitionArn
- CreationTime
- EndpointName
MonitoringJsonDatasetFormat:
type: object
properties:
Line:
type: boolean
description: Indicates if the file should be read as a JSON object per line.
MonitoringNetworkConfig:
type: object
properties:
EnableInterContainerTrafficEncryption:
type: boolean
description: Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might
take longer.
EnableNetworkIsolation:
type: boolean
description: Whether to allow inbound and outbound network calls to and from the containers used for the monitoring
job.
VpcConfig:
$ref: '#/components/schemas/VpcConfig'
MonitoringOutput:
type: object
properties:
S3Output:
$ref: '#/components/schemas/MonitoringS3Output'
required:
- S3Output
MonitoringOutputConfig:
type: object
properties:
MonitoringOutputs:
type: array
items:
$ref: '#/components/schemas/MonitoringOutput'
description: Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is
uploaded.
KmsKeyId:
type: string
description: The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt the model artifacts at
rest using Amazon S3 server-side encryption.
required:
- MonitoringOutputs
MonitoringParquetDatasetFormat:
type: object
properties: {}
MonitoringResources:
type: object
properties:
ClusterConfig:
$ref: '#/components/schemas/MonitoringClusterConfig'
required:
- ClusterConfig
MonitoringS3Output:
type: object
properties:
S3Uri:
type: string
description: A URI that identifies the Amazon S3 storage location where Amazon SageMaker AI saves the results of
a monitoring job.
LocalPath:
type: string
description: The local path to the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring
job. LocalPath is an absolute path for the output data.
S3UploadMode:
type: string
enum:
- Continuous
- EndOfJob
description: Whether to upload the results of the monitoring job continuously or after the job completes.
required:
- S3Uri
- LocalPath
MonitoringStoppingCondition:
type: object
properties:
MaxRuntimeInSeconds:
type: integer
description: The maximum runtime allowed in seconds. The MaxRuntimeInSeconds cannot exceed the frequency of the
job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model
bias and model quality hourly schedules, this can be up to 1800 seconds.
required:
- MaxRuntimeInSeconds
ResourceInUse:
type: object
properties:
Message:
type: string
ResourceNotFound:
type: object
properties:
Message:
type: string
Tag:
type: object
properties:
Key:
type: string
description: The tag key. Tag keys must be unique per resource.
Value:
type: string
description: The tag value.
required:
- Key
- Value
VpcConfig:
type: object
properties:
SecurityGroupIds:
type: array
items:
type: string
description: 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:
type: array
items:
type: string
description: 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.
required:
- SecurityGroupIds
- Subnets
x-provenance:
method: harvested
publisher: Amazon Web Services
source: https://raw.githubusercontent.com/aws/api-models-aws/582e55931c45b944739742a2c1620b6026dd2d13/models/sagemaker/service/2017-07-24/sagemaker-2017-07-24.json
note: converted by smithy-to-openapi.py from AWS's Smithy model (roadmap#1001)