The authentication protocol to use to access the Git repository for an Amplify app.\n For a GitHub repository, specify TOKEN. For an Amazon Web Services CodeCommit repository,\n specify SIGV4. For GitLab and Bitbucket repositories, specify\n SSH.
This is for internal use.
\nThe Amplify service uses this parameter to specify the authentication protocol to use to access\n the Git repository for an Amplify app. Amplify specifies TOKEN for a GitHub\n repository, SIGV4 for an Amazon Web Services CodeCommit repository, and\n SSH for GitLab and Bitbucket repositories.
The OAuth token for a third-party source control system for an Amplify app. The OAuth\n token is used to create a webhook and a read-only deploy key. The OAuth token is not\n stored.
" + "smithy.api#documentation": "The OAuth token for a third-party source control system for an Amplify app. The OAuth\n token is used to create a webhook and a read-only deploy key using SSH cloning. The\n OAuth token is not stored.
\nUse oauthToken for repository providers other than GitHub, such as\n Bitbucket or CodeCommit. To authorize access to GitHub as your repository provider, use\n accessToken.
You must specify either oauthToken or accessToken when you\n create a new app.
Existing Amplify apps deployed from a GitHub repository using OAuth continue to work\n with CI/CD. However, we strongly recommend that you migrate these apps to use the GitHub\n App. For more information, see Migrating an existing OAuth app to the Amplify GitHub App in the\n Amplify User Guide .
" } }, "accessToken": { "target": "com.amazonaws.amplify#AccessToken", "traits": { - "smithy.api#documentation": "The personal access token for a third-party source control system for an Amplify app.\n The personal access token is used to create a webhook and a read-only deploy key. The\n token is not stored.
" + "smithy.api#documentation": "The personal access token for a GitHub repository for an Amplify app. The personal\n access token is used to authorize access to a GitHub repository using the Amplify GitHub\n App. The token is not stored.
\nUse accessToken for GitHub repositories only. To authorize access to a\n repository provider such as Bitbucket or CodeCommit, use oauthToken.
You must specify either accessToken or oauthToken when you\n create a new app.
Existing Amplify apps deployed from a GitHub repository using OAuth continue to work\n with CI/CD. However, we strongly recommend that you migrate these apps to use the GitHub\n App. For more information, see Migrating an existing OAuth app to the Amplify GitHub App in the\n Amplify User Guide .
" } }, "environmentVariables": { @@ -5008,13 +5008,13 @@ "oauthToken": { "target": "com.amazonaws.amplify#OauthToken", "traits": { - "smithy.api#documentation": "The OAuth token for a third-party source control system for an Amplify app. The token\n is used to create a webhook and a read-only deploy key. The OAuth token is not stored.\n
" + "smithy.api#documentation": "The OAuth token for a third-party source control system for an Amplify app. The OAuth\n token is used to create a webhook and a read-only deploy key using SSH cloning. The\n OAuth token is not stored.
\nUse oauthToken for repository providers other than GitHub, such as\n Bitbucket or CodeCommit.
To authorize access to GitHub as your repository provider, use\n accessToken.
You must specify either oauthToken or accessToken when you\n update an app.
Existing Amplify apps deployed from a GitHub repository using OAuth continue to work\n with CI/CD. However, we strongly recommend that you migrate these apps to use the GitHub\n App. For more information, see Migrating an existing OAuth app to the Amplify GitHub App in the\n Amplify User Guide .
" } }, "accessToken": { "target": "com.amazonaws.amplify#AccessToken", "traits": { - "smithy.api#documentation": "The personal access token for a third-party source control system for an Amplify app.\n The token is used to create webhook and a read-only deploy key. The token is not stored.\n
" + "smithy.api#documentation": "The personal access token for a GitHub repository for an Amplify app. The personal\n access token is used to authorize access to a GitHub repository using the Amplify GitHub\n App. The token is not stored.
\nUse accessToken for GitHub repositories only. To authorize access to a\n repository provider such as Bitbucket or CodeCommit, use oauthToken.
You must specify either accessToken or oauthToken when you\n update an app.
Existing Amplify apps deployed from a GitHub repository using OAuth continue to work\n with CI/CD. However, we strongly recommend that you migrate these apps to use the GitHub\n App. For more information, see Migrating an existing OAuth app to the Amplify GitHub App in the\n Amplify User Guide .
" } } }, diff --git a/codegen/sdk-codegen/aws-models/chime-sdk-media-pipelines.json b/codegen/sdk-codegen/aws-models/chime-sdk-media-pipelines.json new file mode 100644 index 00000000000..4d2650ca15c --- /dev/null +++ b/codegen/sdk-codegen/aws-models/chime-sdk-media-pipelines.json @@ -0,0 +1,1276 @@ +{ + "smithy": "1.0", + "metadata": { + "suppressions": [ + { + "id": "HttpMethodSemantics", + "namespace": "*" + }, + { + "id": "HttpResponseCodeSemantics", + "namespace": "*" + }, + { + "id": "PaginatedTrait", + "namespace": "*" + }, + { + "id": "HttpHeaderTrait", + "namespace": "*" + }, + { + "id": "HttpUriConflict", + "namespace": "*" + }, + { + "id": "Service", + "namespace": "*" + } + ] + }, + "shapes": { + "com.amazonaws.chimesdkmediapipelines#AmazonResourceName": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 1, + "max": 1011 + }, + "smithy.api#pattern": "^arn[\\/\\:\\-\\_\\.a-zA-Z0-9]+$" + } + }, + "com.amazonaws.chimesdkmediapipelines#Arn": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 1, + "max": 1024 + }, + "smithy.api#pattern": "^arn[\\/\\:\\-\\_\\.a-zA-Z0-9]+$", + "smithy.api#sensitive": {} + } + }, + "com.amazonaws.chimesdkmediapipelines#ArtifactsConfiguration": { + "type": "structure", + "members": { + "Audio": { + "target": "com.amazonaws.chimesdkmediapipelines#AudioArtifactsConfiguration", + "traits": { + "smithy.api#documentation": "The configuration for the audio artifacts.
", + "smithy.api#required": {} + } + }, + "Video": { + "target": "com.amazonaws.chimesdkmediapipelines#VideoArtifactsConfiguration", + "traits": { + "smithy.api#documentation": "The configuration for the video artifacts.
", + "smithy.api#required": {} + } + }, + "Content": { + "target": "com.amazonaws.chimesdkmediapipelines#ContentArtifactsConfiguration", + "traits": { + "smithy.api#documentation": "The configuration for the content artifacts.
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "The configuration for the artifacts.
" + } + }, + "com.amazonaws.chimesdkmediapipelines#ArtifactsState": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "Enabled", + "name": "Enabled" + }, + { + "value": "Disabled", + "name": "Disabled" + } + ] + } + }, + "com.amazonaws.chimesdkmediapipelines#AttendeeIdList": { + "type": "list", + "member": { + "target": "com.amazonaws.chimesdkmediapipelines#GuidString" + }, + "traits": { + "smithy.api#length": { + "min": 1 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#AudioArtifactsConfiguration": { + "type": "structure", + "members": { + "MuxType": { + "target": "com.amazonaws.chimesdkmediapipelines#AudioMuxType", + "traits": { + "smithy.api#documentation": "The MUX type of the audio artifact configuration object.
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "The audio artifact configuration object.
" + } + }, + "com.amazonaws.chimesdkmediapipelines#AudioMuxType": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "AudioOnly", + "name": "AudioOnly" + }, + { + "value": "AudioWithActiveSpeakerVideo", + "name": "AudioWithActiveSpeakerVideo" + } + ] + } + }, + "com.amazonaws.chimesdkmediapipelines#BadRequestException": { + "type": "structure", + "members": { + "Code": { + "target": "com.amazonaws.chimesdkmediapipelines#ErrorCode" + }, + "Message": { + "target": "com.amazonaws.chimesdkmediapipelines#String" + }, + "RequestId": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The request id associated with the call responsible for the exception.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The input parameters don't match the service's restrictions.
", + "smithy.api#error": "client", + "smithy.api#httpError": 400 + } + }, + "com.amazonaws.chimesdkmediapipelines#ChimeSDKMediaPipelinesService": { + "type": "service", + "traits": { + "aws.api#service": { + "sdkId": "Chime SDK Media Pipelines", + "arnNamespace": "chime", + "cloudFormationName": "ChimeSDKMediaPipelines", + "cloudTrailEventSource": "chimesdkmediapipelines.amazonaws.com", + "endpointPrefix": "media-pipelines-chime" + }, + "aws.auth#sigv4": { + "name": "chime" + }, + "aws.protocols#restJson1": {}, + "smithy.api#documentation": "The Amazon Chime SDK media pipeline APIs in this section allow software developers to create Amazon Chime SDK media pipelines \n and capture audio, video, events, and data messages from Amazon Chime SDK meetings. For more information about media pipleines, see \n Amzon Chime SDK media pipelines.\n
", + "smithy.api#title": "Amazon Chime SDK Media Pipelines" + }, + "version": "2021-07-15", + "operations": [ + { + "target": "com.amazonaws.chimesdkmediapipelines#CreateMediaCapturePipeline" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#DeleteMediaCapturePipeline" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#GetMediaCapturePipeline" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ListMediaCapturePipelines" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ListTagsForResource" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#TagResource" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#UntagResource" + } + ] + }, + "com.amazonaws.chimesdkmediapipelines#ChimeSdkMeetingConfiguration": { + "type": "structure", + "members": { + "SourceConfiguration": { + "target": "com.amazonaws.chimesdkmediapipelines#SourceConfiguration", + "traits": { + "smithy.api#documentation": "The source configuration for a specified media capture pipline.
" + } + }, + "ArtifactsConfiguration": { + "target": "com.amazonaws.chimesdkmediapipelines#ArtifactsConfiguration", + "traits": { + "smithy.api#documentation": "The configuration for the artifacts in an Amazon Chime SDK meeting.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The configuration object of the Amazon Chime SDK meeting for a specified media capture pipeline. SourceType must be ChimeSdkMeeting.
Indicates whether the content artifact is enabled or disabled.
", + "smithy.api#required": {} + } + }, + "MuxType": { + "target": "com.amazonaws.chimesdkmediapipelines#ContentMuxType", + "traits": { + "smithy.api#documentation": "The MUX type of the artifact configuration.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The content artifact object.
" + } + }, + "com.amazonaws.chimesdkmediapipelines#ContentMuxType": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "ContentOnly", + "name": "ContentOnly" + } + ] + } + }, + "com.amazonaws.chimesdkmediapipelines#CreateMediaCapturePipeline": { + "type": "operation", + "input": { + "target": "com.amazonaws.chimesdkmediapipelines#CreateMediaCapturePipelineRequest" + }, + "output": { + "target": "com.amazonaws.chimesdkmediapipelines#CreateMediaCapturePipelineResponse" + }, + "errors": [ + { + "target": "com.amazonaws.chimesdkmediapipelines#BadRequestException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ForbiddenException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ResourceLimitExceededException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceFailureException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceUnavailableException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ThrottledClientException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#UnauthorizedClientException" + } + ], + "traits": { + "smithy.api#documentation": "Creates a media capture pipeline.
", + "smithy.api#http": { + "method": "POST", + "uri": "/sdk-media-capture-pipelines", + "code": 201 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#CreateMediaCapturePipelineRequest": { + "type": "structure", + "members": { + "SourceType": { + "target": "com.amazonaws.chimesdkmediapipelines#MediaPipelineSourceType", + "traits": { + "smithy.api#documentation": "Source type from which the media artifacts are captured. A Chime SDK Meeting \n is the only supported source.
", + "smithy.api#required": {} + } + }, + "SourceArn": { + "target": "com.amazonaws.chimesdkmediapipelines#Arn", + "traits": { + "smithy.api#documentation": "ARN of the source from which the media artifacts are captured.
", + "smithy.api#required": {} + } + }, + "SinkType": { + "target": "com.amazonaws.chimesdkmediapipelines#MediaPipelineSinkType", + "traits": { + "smithy.api#documentation": "Destination type to which the media artifacts are saved. You must use an S3 bucket.
", + "smithy.api#required": {} + } + }, + "SinkArn": { + "target": "com.amazonaws.chimesdkmediapipelines#Arn", + "traits": { + "smithy.api#documentation": "The ARN of the sink type.
", + "smithy.api#required": {} + } + }, + "ClientRequestToken": { + "target": "com.amazonaws.chimesdkmediapipelines#ClientRequestToken", + "traits": { + "smithy.api#documentation": "The token assigned to the client making the pipeline request.
", + "smithy.api#idempotencyToken": {} + } + }, + "ChimeSdkMeetingConfiguration": { + "target": "com.amazonaws.chimesdkmediapipelines#ChimeSdkMeetingConfiguration", + "traits": { + "smithy.api#documentation": "The configuration for a specified media capture pipeline. SourceType must be ChimeSdkMeeting.
The list of tags.
" + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#CreateMediaCapturePipelineResponse": { + "type": "structure", + "members": { + "MediaCapturePipeline": { + "target": "com.amazonaws.chimesdkmediapipelines#MediaCapturePipeline", + "traits": { + "smithy.api#documentation": "A media capture pipeline object, the ID, source type, source ARN, sink type, and sink ARN of a media capture pipeline object.
" + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#DeleteMediaCapturePipeline": { + "type": "operation", + "input": { + "target": "com.amazonaws.chimesdkmediapipelines#DeleteMediaCapturePipelineRequest" + }, + "output": { + "target": "smithy.api#Unit" + }, + "errors": [ + { + "target": "com.amazonaws.chimesdkmediapipelines#BadRequestException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ForbiddenException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#NotFoundException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceFailureException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceUnavailableException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ThrottledClientException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#UnauthorizedClientException" + } + ], + "traits": { + "smithy.api#documentation": "Deletes the media capture pipeline.
", + "smithy.api#http": { + "method": "DELETE", + "uri": "/sdk-media-capture-pipelines/{MediaPipelineId}", + "code": 204 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#DeleteMediaCapturePipelineRequest": { + "type": "structure", + "members": { + "MediaPipelineId": { + "target": "com.amazonaws.chimesdkmediapipelines#GuidString", + "traits": { + "smithy.api#documentation": "The ID of the media capture pipeline being deleted.
", + "smithy.api#httpLabel": {}, + "smithy.api#required": {} + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#ErrorCode": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "BadRequest", + "name": "BadRequest" + }, + { + "value": "Forbidden", + "name": "Forbidden" + }, + { + "value": "NotFound", + "name": "NotFound" + }, + { + "value": "ResourceLimitExceeded", + "name": "ResourceLimitExceeded" + }, + { + "value": "ServiceFailure", + "name": "ServiceFailure" + }, + { + "value": "ServiceUnavailable", + "name": "ServiceUnavailable" + }, + { + "value": "Throttling", + "name": "Throttling" + } + ] + } + }, + "com.amazonaws.chimesdkmediapipelines#ExternalUserIdList": { + "type": "list", + "member": { + "target": "com.amazonaws.chimesdkmediapipelines#ExternalUserIdType" + }, + "traits": { + "smithy.api#length": { + "min": 1 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#ExternalUserIdType": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 2, + "max": 64 + }, + "smithy.api#sensitive": {} + } + }, + "com.amazonaws.chimesdkmediapipelines#ForbiddenException": { + "type": "structure", + "members": { + "Code": { + "target": "com.amazonaws.chimesdkmediapipelines#ErrorCode" + }, + "Message": { + "target": "com.amazonaws.chimesdkmediapipelines#String" + }, + "RequestId": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The request id associated with the call responsible for the exception.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The client is permanently forbidden from making the request.
", + "smithy.api#error": "client", + "smithy.api#httpError": 403 + } + }, + "com.amazonaws.chimesdkmediapipelines#GetMediaCapturePipeline": { + "type": "operation", + "input": { + "target": "com.amazonaws.chimesdkmediapipelines#GetMediaCapturePipelineRequest" + }, + "output": { + "target": "com.amazonaws.chimesdkmediapipelines#GetMediaCapturePipelineResponse" + }, + "errors": [ + { + "target": "com.amazonaws.chimesdkmediapipelines#BadRequestException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ForbiddenException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#NotFoundException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceFailureException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceUnavailableException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ThrottledClientException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#UnauthorizedClientException" + } + ], + "traits": { + "smithy.api#documentation": "Gets an existing media capture pipeline.
", + "smithy.api#http": { + "method": "GET", + "uri": "/sdk-media-capture-pipelines/{MediaPipelineId}", + "code": 200 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#GetMediaCapturePipelineRequest": { + "type": "structure", + "members": { + "MediaPipelineId": { + "target": "com.amazonaws.chimesdkmediapipelines#GuidString", + "traits": { + "smithy.api#documentation": "The ID of the pipeline that you want to get.
", + "smithy.api#httpLabel": {}, + "smithy.api#required": {} + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#GetMediaCapturePipelineResponse": { + "type": "structure", + "members": { + "MediaCapturePipeline": { + "target": "com.amazonaws.chimesdkmediapipelines#MediaCapturePipeline", + "traits": { + "smithy.api#documentation": "The media capture pipeline object.
" + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#GuidString": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 36, + "max": 36 + }, + "smithy.api#pattern": "^[a-fA-F0-9]{8}(?:-[a-fA-F0-9]{4}){3}-[a-fA-F0-9]{12}$" + } + }, + "com.amazonaws.chimesdkmediapipelines#Iso8601Timestamp": { + "type": "timestamp", + "traits": { + "smithy.api#timestampFormat": "date-time" + } + }, + "com.amazonaws.chimesdkmediapipelines#ListMediaCapturePipelines": { + "type": "operation", + "input": { + "target": "com.amazonaws.chimesdkmediapipelines#ListMediaCapturePipelinesRequest" + }, + "output": { + "target": "com.amazonaws.chimesdkmediapipelines#ListMediaCapturePipelinesResponse" + }, + "errors": [ + { + "target": "com.amazonaws.chimesdkmediapipelines#BadRequestException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ForbiddenException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ResourceLimitExceededException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceFailureException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceUnavailableException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ThrottledClientException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#UnauthorizedClientException" + } + ], + "traits": { + "smithy.api#documentation": "Returns a list of media capture pipelines.
", + "smithy.api#http": { + "method": "GET", + "uri": "/sdk-media-capture-pipelines", + "code": 200 + }, + "smithy.api#paginated": { + "inputToken": "NextToken", + "outputToken": "NextToken", + "pageSize": "MaxResults" + } + } + }, + "com.amazonaws.chimesdkmediapipelines#ListMediaCapturePipelinesRequest": { + "type": "structure", + "members": { + "NextToken": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The token used to retrieve the next page of results.
", + "smithy.api#httpQuery": "next-token" + } + }, + "MaxResults": { + "target": "com.amazonaws.chimesdkmediapipelines#ResultMax", + "traits": { + "smithy.api#documentation": "The maximum number of results to return in a single call. Valid Range: 1 - 99.
", + "smithy.api#httpQuery": "max-results" + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#ListMediaCapturePipelinesResponse": { + "type": "structure", + "members": { + "MediaCapturePipelines": { + "target": "com.amazonaws.chimesdkmediapipelines#MediaCapturePipelineSummaryList", + "traits": { + "smithy.api#documentation": "The media capture pipeline objects in the list.
" + } + }, + "NextToken": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The token used to retrieve the next page of results.
" + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#ListTagsForResource": { + "type": "operation", + "input": { + "target": "com.amazonaws.chimesdkmediapipelines#ListTagsForResourceRequest" + }, + "output": { + "target": "com.amazonaws.chimesdkmediapipelines#ListTagsForResourceResponse" + }, + "errors": [ + { + "target": "com.amazonaws.chimesdkmediapipelines#BadRequestException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ForbiddenException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#NotFoundException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceFailureException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceUnavailableException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ThrottledClientException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#UnauthorizedClientException" + } + ], + "traits": { + "smithy.api#documentation": "Lists the tags applied to an Amazon Chime SDK media capture pipeline.
", + "smithy.api#http": { + "method": "GET", + "uri": "/tags", + "code": 200 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#ListTagsForResourceRequest": { + "type": "structure", + "members": { + "ResourceARN": { + "target": "com.amazonaws.chimesdkmediapipelines#AmazonResourceName", + "traits": { + "smithy.api#documentation": "The resource ARN.
", + "smithy.api#httpQuery": "arn", + "smithy.api#required": {} + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#ListTagsForResourceResponse": { + "type": "structure", + "members": { + "Tags": { + "target": "com.amazonaws.chimesdkmediapipelines#TagList", + "traits": { + "smithy.api#documentation": "The tag key-value pairs.
" + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#MediaCapturePipeline": { + "type": "structure", + "members": { + "MediaPipelineId": { + "target": "com.amazonaws.chimesdkmediapipelines#GuidString", + "traits": { + "smithy.api#documentation": "The ID of a media capture pipeline.
" + } + }, + "MediaPipelineArn": { + "target": "com.amazonaws.chimesdkmediapipelines#AmazonResourceName", + "traits": { + "smithy.api#documentation": "The ARN of a media capture pipeline.
" + } + }, + "SourceType": { + "target": "com.amazonaws.chimesdkmediapipelines#MediaPipelineSourceType", + "traits": { + "smithy.api#documentation": "Source type from which media artifacts are saved. You must use ChimeMeeting.
ARN of the source from which the media artifacts are saved.
" + } + }, + "Status": { + "target": "com.amazonaws.chimesdkmediapipelines#MediaPipelineStatus", + "traits": { + "smithy.api#documentation": "The status of the media capture pipeline.
" + } + }, + "SinkType": { + "target": "com.amazonaws.chimesdkmediapipelines#MediaPipelineSinkType", + "traits": { + "smithy.api#documentation": "Destination type to which the media artifacts are saved. You must use an S3 Bucket.
" + } + }, + "SinkArn": { + "target": "com.amazonaws.chimesdkmediapipelines#Arn", + "traits": { + "smithy.api#documentation": "ARN of the destination to which the media artifacts are saved.
" + } + }, + "CreatedTimestamp": { + "target": "com.amazonaws.chimesdkmediapipelines#Iso8601Timestamp", + "traits": { + "smithy.api#documentation": "The time at which the capture pipeline was created, in ISO 8601 format.
" + } + }, + "UpdatedTimestamp": { + "target": "com.amazonaws.chimesdkmediapipelines#Iso8601Timestamp", + "traits": { + "smithy.api#documentation": "The time at which the capture pipeline was updated, in ISO 8601 format.
" + } + }, + "ChimeSdkMeetingConfiguration": { + "target": "com.amazonaws.chimesdkmediapipelines#ChimeSdkMeetingConfiguration", + "traits": { + "smithy.api#documentation": "The configuration for a specified media capture pipeline. SourceType must be ChimeSdkMeeting.
A media capture pipeline object consisting of an ID, source type, source ARN, a sink type, a sink ARN, and a configuration object.
" + } + }, + "com.amazonaws.chimesdkmediapipelines#MediaCapturePipelineSummary": { + "type": "structure", + "members": { + "MediaPipelineId": { + "target": "com.amazonaws.chimesdkmediapipelines#GuidString", + "traits": { + "smithy.api#documentation": "The ID of a media capture pipeline.
" + } + }, + "MediaPipelineArn": { + "target": "com.amazonaws.chimesdkmediapipelines#AmazonResourceName", + "traits": { + "smithy.api#documentation": "The ARN of a media capture pipeline.
" + } + } + }, + "traits": { + "smithy.api#documentation": "A summary of a media capture pipeline.
" + } + }, + "com.amazonaws.chimesdkmediapipelines#MediaCapturePipelineSummaryList": { + "type": "list", + "member": { + "target": "com.amazonaws.chimesdkmediapipelines#MediaCapturePipelineSummary" + } + }, + "com.amazonaws.chimesdkmediapipelines#MediaPipelineSinkType": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "S3Bucket", + "name": "S3Bucket" + } + ] + } + }, + "com.amazonaws.chimesdkmediapipelines#MediaPipelineSourceType": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "ChimeSdkMeeting", + "name": "ChimeSdkMeeting" + } + ] + } + }, + "com.amazonaws.chimesdkmediapipelines#MediaPipelineStatus": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "Initializing", + "name": "Initializing" + }, + { + "value": "InProgress", + "name": "InProgress" + }, + { + "value": "Failed", + "name": "Failed" + }, + { + "value": "Stopping", + "name": "Stopping" + }, + { + "value": "Stopped", + "name": "Stopped" + } + ] + } + }, + "com.amazonaws.chimesdkmediapipelines#NotFoundException": { + "type": "structure", + "members": { + "Code": { + "target": "com.amazonaws.chimesdkmediapipelines#ErrorCode" + }, + "Message": { + "target": "com.amazonaws.chimesdkmediapipelines#String" + }, + "RequestId": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The request id associated with the call responsible for the exception.
" + } + } + }, + "traits": { + "smithy.api#documentation": "One or more of the resources in the request does not exist in the system.
", + "smithy.api#error": "client", + "smithy.api#httpError": 404 + } + }, + "com.amazonaws.chimesdkmediapipelines#ResourceLimitExceededException": { + "type": "structure", + "members": { + "Code": { + "target": "com.amazonaws.chimesdkmediapipelines#ErrorCode" + }, + "Message": { + "target": "com.amazonaws.chimesdkmediapipelines#String" + }, + "RequestId": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The request id associated with the call responsible for the exception.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The request exceeds the resource limit.
", + "smithy.api#error": "client", + "smithy.api#httpError": 400 + } + }, + "com.amazonaws.chimesdkmediapipelines#ResultMax": { + "type": "integer", + "traits": { + "smithy.api#box": {}, + "smithy.api#range": { + "min": 1, + "max": 100 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#SelectedVideoStreams": { + "type": "structure", + "members": { + "AttendeeIds": { + "target": "com.amazonaws.chimesdkmediapipelines#AttendeeIdList", + "traits": { + "smithy.api#documentation": "The attendee IDs of the streams selected for a media capture pipeline.
" + } + }, + "ExternalUserIds": { + "target": "com.amazonaws.chimesdkmediapipelines#ExternalUserIdList", + "traits": { + "smithy.api#documentation": "The external user IDs of the streams selected for a media capture pipeline.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The video streams to capture for a specified media capture pipeline. The total number of video streams can't exceed 25.
" + } + }, + "com.amazonaws.chimesdkmediapipelines#ServiceFailureException": { + "type": "structure", + "members": { + "Code": { + "target": "com.amazonaws.chimesdkmediapipelines#ErrorCode" + }, + "Message": { + "target": "com.amazonaws.chimesdkmediapipelines#String" + }, + "RequestId": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The request id associated with the call responsible for the exception.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The service encountered an unexpected error.
", + "smithy.api#error": "server", + "smithy.api#httpError": 500 + } + }, + "com.amazonaws.chimesdkmediapipelines#ServiceUnavailableException": { + "type": "structure", + "members": { + "Code": { + "target": "com.amazonaws.chimesdkmediapipelines#ErrorCode" + }, + "Message": { + "target": "com.amazonaws.chimesdkmediapipelines#String" + }, + "RequestId": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The request id associated with the call responsible for the exception.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The service is currently unavailable.
", + "smithy.api#error": "server", + "smithy.api#httpError": 503 + } + }, + "com.amazonaws.chimesdkmediapipelines#SourceConfiguration": { + "type": "structure", + "members": { + "SelectedVideoStreams": { + "target": "com.amazonaws.chimesdkmediapipelines#SelectedVideoStreams", + "traits": { + "smithy.api#documentation": "The selected video streams to capture for a specified media capture pipeline. The number of video streams can't exceed 25.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Source configuration for a specified media capture pipeline.
" + } + }, + "com.amazonaws.chimesdkmediapipelines#String": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 0, + "max": 4096 + }, + "smithy.api#pattern": ".*" + } + }, + "com.amazonaws.chimesdkmediapipelines#Tag": { + "type": "structure", + "members": { + "Key": { + "target": "com.amazonaws.chimesdkmediapipelines#TagKey", + "traits": { + "smithy.api#documentation": "The key of the tag.
", + "smithy.api#required": {} + } + }, + "Value": { + "target": "com.amazonaws.chimesdkmediapipelines#TagValue", + "traits": { + "smithy.api#documentation": "The value of the tag.
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "Describes a tag applied to a resource.
" + } + }, + "com.amazonaws.chimesdkmediapipelines#TagKey": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 1, + "max": 128 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#TagKeyList": { + "type": "list", + "member": { + "target": "com.amazonaws.chimesdkmediapipelines#TagKey" + }, + "traits": { + "smithy.api#length": { + "min": 1, + "max": 50 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#TagList": { + "type": "list", + "member": { + "target": "com.amazonaws.chimesdkmediapipelines#Tag" + }, + "traits": { + "smithy.api#length": { + "min": 1, + "max": 50 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#TagResource": { + "type": "operation", + "input": { + "target": "com.amazonaws.chimesdkmediapipelines#TagResourceRequest" + }, + "output": { + "target": "com.amazonaws.chimesdkmediapipelines#TagResourceResponse" + }, + "errors": [ + { + "target": "com.amazonaws.chimesdkmediapipelines#BadRequestException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ForbiddenException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#NotFoundException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceFailureException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceUnavailableException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ThrottledClientException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#UnauthorizedClientException" + } + ], + "traits": { + "smithy.api#documentation": "Applies the specified tags to the specified Amazon Chime SDK media capture pipeline.
", + "smithy.api#http": { + "method": "POST", + "uri": "/tags?operation=tag-resource", + "code": 204 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#TagResourceRequest": { + "type": "structure", + "members": { + "ResourceARN": { + "target": "com.amazonaws.chimesdkmediapipelines#AmazonResourceName", + "traits": { + "smithy.api#documentation": "The resource ARN.
", + "smithy.api#required": {} + } + }, + "Tags": { + "target": "com.amazonaws.chimesdkmediapipelines#TagList", + "traits": { + "smithy.api#documentation": "The tag key-value pairs.
", + "smithy.api#required": {} + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#TagResourceResponse": { + "type": "structure", + "members": {} + }, + "com.amazonaws.chimesdkmediapipelines#TagValue": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 0, + "max": 256 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#ThrottledClientException": { + "type": "structure", + "members": { + "Code": { + "target": "com.amazonaws.chimesdkmediapipelines#ErrorCode" + }, + "Message": { + "target": "com.amazonaws.chimesdkmediapipelines#String" + }, + "RequestId": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The request id associated with the call responsible for the exception.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The client exceeded its request rate limit.
", + "smithy.api#error": "client", + "smithy.api#httpError": 429 + } + }, + "com.amazonaws.chimesdkmediapipelines#UnauthorizedClientException": { + "type": "structure", + "members": { + "Code": { + "target": "com.amazonaws.chimesdkmediapipelines#ErrorCode" + }, + "Message": { + "target": "com.amazonaws.chimesdkmediapipelines#String" + }, + "RequestId": { + "target": "com.amazonaws.chimesdkmediapipelines#String", + "traits": { + "smithy.api#documentation": "The request id associated with the call responsible for the exception.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The client is not currently authorized to make the request.
", + "smithy.api#error": "client", + "smithy.api#httpError": 401 + } + }, + "com.amazonaws.chimesdkmediapipelines#UntagResource": { + "type": "operation", + "input": { + "target": "com.amazonaws.chimesdkmediapipelines#UntagResourceRequest" + }, + "output": { + "target": "com.amazonaws.chimesdkmediapipelines#UntagResourceResponse" + }, + "errors": [ + { + "target": "com.amazonaws.chimesdkmediapipelines#BadRequestException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ForbiddenException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#NotFoundException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceFailureException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ServiceUnavailableException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#ThrottledClientException" + }, + { + "target": "com.amazonaws.chimesdkmediapipelines#UnauthorizedClientException" + } + ], + "traits": { + "smithy.api#documentation": "Removes the specified tags from the specified Amazon Chime SDK media capture pipeline.
", + "smithy.api#http": { + "method": "POST", + "uri": "/tags?operation=untag-resource", + "code": 204 + } + } + }, + "com.amazonaws.chimesdkmediapipelines#UntagResourceRequest": { + "type": "structure", + "members": { + "ResourceARN": { + "target": "com.amazonaws.chimesdkmediapipelines#AmazonResourceName", + "traits": { + "smithy.api#documentation": "The resource ARN.
", + "smithy.api#required": {} + } + }, + "TagKeys": { + "target": "com.amazonaws.chimesdkmediapipelines#TagKeyList", + "traits": { + "smithy.api#documentation": "The tag keys.
", + "smithy.api#required": {} + } + } + } + }, + "com.amazonaws.chimesdkmediapipelines#UntagResourceResponse": { + "type": "structure", + "members": {} + }, + "com.amazonaws.chimesdkmediapipelines#VideoArtifactsConfiguration": { + "type": "structure", + "members": { + "State": { + "target": "com.amazonaws.chimesdkmediapipelines#ArtifactsState", + "traits": { + "smithy.api#documentation": "Indicates whether the video artifact is enabled or disabled.
", + "smithy.api#required": {} + } + }, + "MuxType": { + "target": "com.amazonaws.chimesdkmediapipelines#VideoMuxType", + "traits": { + "smithy.api#documentation": "The MUX type of the video artifact configuration object.
" + } + } + }, + "traits": { + "smithy.api#documentation": "The video artifact configuration object.
" + } + }, + "com.amazonaws.chimesdkmediapipelines#VideoMuxType": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "VideoOnly", + "name": "VideoOnly" + } + ] + } + } + } +} diff --git a/codegen/sdk-codegen/aws-models/cloudtrail.json b/codegen/sdk-codegen/aws-models/cloudtrail.json index 99462381ff3..40f5fd13e22 100644 --- a/codegen/sdk-codegen/aws-models/cloudtrail.json +++ b/codegen/sdk-codegen/aws-models/cloudtrail.json @@ -76,7 +76,7 @@ } ], "traits": { - "smithy.api#documentation": "Adds one or more tags to a trail, up to a limit of 50. Overwrites an existing tag's value when a new value is specified for an existing tag key. \n Tag key names must be unique for a trail; you cannot have two keys with the same name but different values. \n If you specify a key without a value, the tag will be created with the specified key and a value of null. \n You can tag a trail that applies to all Amazon Web Services Regions only from the Region in which the trail was created (also known as its home region).
", + "smithy.api#documentation": "Adds one or more tags to a trail or event data store, up to a limit of 50. Overwrites an \n existing tag's value when a new value is specified for an existing tag key. \n Tag key names must be unique for a trail; you cannot have two keys with the same name but \n different values. \n If you specify a key without a value, the tag will be created with the specified key and a \n value of null. \n You can tag a trail or event data store that applies to all Amazon Web Services Regions \n only from the Region in which the trail or event data store was created (also known as its \n home region).
", "smithy.api#idempotent": {} } }, @@ -86,7 +86,7 @@ "ResourceId": { "target": "com.amazonaws.cloudtrail#String", "traits": { - "smithy.api#documentation": "Specifies the ARN of the trail to which one or more tags will be added. The format of a trail ARN is:
\n\n arn:aws:cloudtrail:us-east-2:123456789012:trail/MyTrail\n
Specifies the ARN of the trail or event data store to which one or more tags will be added. The format of a trail ARN is:
\n\n arn:aws:cloudtrail:us-east-2:123456789012:trail/MyTrail\n
Specifies the tags to add to a trail.
" + "smithy.api#documentation": "Specifies the tags to add to a trail or event data store.
" } }, "com.amazonaws.cloudtrail#AddTagsResponse": { @@ -493,7 +493,7 @@ "code": "ConflictException", "httpResponseCode": 409 }, - "smithy.api#documentation": "This exception is thrown when the specified resource is not ready for an operation. \n This can occur when you try to run an operation on a trail before CloudTrail has time to fully load the trail. \n If this exception occurs, wait a few minutes, and then try the operation again.
", + "smithy.api#documentation": "This exception is thrown when the specified resource is not ready for an operation. \n This can occur when you try to run an operation on a resource before CloudTrail has time to fully load the resource. \n If this exception occurs, wait a few minutes, and then try the operation again.
", "smithy.api#error": "client", "smithy.api#httpError": 409 } @@ -1328,49 +1328,73 @@ "TerminationProtectionEnabled": { "target": "com.amazonaws.cloudtrail#TerminationProtectionEnabled", "traits": { - "smithy.api#documentation": "Indicates whether the event data store is protected from termination.
" + "smithy.api#deprecated": { + "message": "TerminationProtectionEnabled is no longer returned by ListEventDataStores" + }, + "smithy.api#documentation": "This field is being deprecated. Indicates whether the event data store is protected from termination.
" } }, "Status": { "target": "com.amazonaws.cloudtrail#EventDataStoreStatus", "traits": { - "smithy.api#documentation": "The status of an event data store. Values are ENABLED and PENDING_DELETION.
This field is being deprecated. The status of an event data store. Values are ENABLED and PENDING_DELETION.
The advanced event selectors that were used to select events for the data store.
" + "smithy.api#deprecated": { + "message": "AdvancedEventSelectors is no longer returned by ListEventDataStores" + }, + "smithy.api#documentation": "This field is being deprecated. The advanced event selectors that were used to select events for the data store.
" } }, "MultiRegionEnabled": { "target": "com.amazonaws.cloudtrail#Boolean", "traits": { - "smithy.api#documentation": "Indicates whether the event data store includes events from all regions, or only from the region in which it was created.
" + "smithy.api#deprecated": { + "message": "MultiRegionEnabled is no longer returned by ListEventDataStores" + }, + "smithy.api#documentation": "This field is being deprecated. Indicates whether the event data store includes events from all regions, or only from the region in which it was created.
" } }, "OrganizationEnabled": { "target": "com.amazonaws.cloudtrail#Boolean", "traits": { - "smithy.api#documentation": "Indicates that an event data store is collecting logged events for an organization.
" + "smithy.api#deprecated": { + "message": "OrganizationEnabled is no longer returned by ListEventDataStores" + }, + "smithy.api#documentation": "This field is being deprecated. Indicates that an event data store is collecting logged events for an organization.
" } }, "RetentionPeriod": { "target": "com.amazonaws.cloudtrail#RetentionPeriod", "traits": { - "smithy.api#documentation": "The retention period, in days.
" + "smithy.api#deprecated": { + "message": "RetentionPeriod is no longer returned by ListEventDataStores" + }, + "smithy.api#documentation": "This field is being deprecated. The retention period, in days.
" } }, "CreatedTimestamp": { "target": "com.amazonaws.cloudtrail#Date", "traits": { - "smithy.api#documentation": "The timestamp of the event data store's creation.
" + "smithy.api#deprecated": { + "message": "CreatedTimestamp is no longer returned by ListEventDataStores" + }, + "smithy.api#documentation": "This field is being deprecated. The timestamp of the event data store's creation.
" } }, "UpdatedTimestamp": { "target": "com.amazonaws.cloudtrail#Date", "traits": { - "smithy.api#documentation": "The timestamp showing when an event data store was updated, if applicable. UpdatedTimestamp is always either the same or newer than the time shown in CreatedTimestamp.
This field is being deprecated. The timestamp showing when an event data store was updated, if applicable. UpdatedTimestamp is always either the same or newer than the time shown in CreatedTimestamp.
The event data store against which you ran your query is inactive.
", + "smithy.api#documentation": "The event data store is inactive.
", "smithy.api#error": "client", "smithy.api#httpError": 400 } @@ -2221,7 +2245,7 @@ "code": "InsufficientDependencyServiceAccessPermission", "httpResponseCode": 400 }, - "smithy.api#documentation": "This exception is thrown when the IAM user or role that is used to create the organization trail is lacking one or more required permissions for \n creating an organization trail in a required service. For more information, see \n Prepare For Creating a Trail For Your Organization.
", + "smithy.api#documentation": "This exception is thrown when the IAM user or role that is used to create \n the organization resource lacks one or more required permissions for \n creating an organization resource in a required service.
", "smithy.api#error": "client", "smithy.api#httpError": 400 } @@ -2347,7 +2371,7 @@ "code": "InvalidDateRange", "httpResponseCode": 400 }, - "smithy.api#documentation": "A date range for the query was specified that is not valid. For more information \n about writing a query, see Create \n or edit a query in the CloudTrail User Guide.
", + "smithy.api#documentation": "A date range for the query was specified that is not valid. Be sure that the start time is chronologically \n before the end time. For more information \n about writing a query, see Create \n or edit a query in the CloudTrail User Guide.
", "smithy.api#error": "client", "smithy.api#httpError": 400 } @@ -3124,7 +3148,7 @@ } ], "traits": { - "smithy.api#documentation": "Lists the tags for the trail in the current region.
", + "smithy.api#documentation": "Lists the tags for the trail or event data store in the current region.
", "smithy.api#idempotent": {}, "smithy.api#paginated": { "inputToken": "NextToken", @@ -3139,7 +3163,7 @@ "ResourceIdList": { "target": "com.amazonaws.cloudtrail#ResourceIdList", "traits": { - "smithy.api#documentation": "Specifies a list of trail ARNs whose tags will be listed. The list has a limit of 20 ARNs. The following is the format of \n a trail ARN.
\n\n arn:aws:cloudtrail:us-east-2:123456789012:trail/MyTrail\n
Specifies a list of trail and event data store ARNs whose tags will be listed. The list \n has a limit of 20 ARNs.
", "smithy.api#required": {} } }, @@ -3151,7 +3175,7 @@ } }, "traits": { - "smithy.api#documentation": "Specifies a list of trail tags to return.
" + "smithy.api#documentation": "Specifies a list of tags to return.
" } }, "com.amazonaws.cloudtrail#ListTagsResponse": { @@ -3485,7 +3509,7 @@ "code": "NotOrganizationMasterAccount", "httpResponseCode": 400 }, - "smithy.api#documentation": "This exception is thrown when the Amazon Web Services account making the request to create or update an organization trail is not the management account for an \n organization in Organizations. For more information, see \n Prepare For Creating a Trail For Your Organization.
", + "smithy.api#documentation": "This exception is thrown when the Amazon Web Services account making the request to create \n or update an organization trail or event data store is not the management account for an \n organization in Organizations. For more information, see \n Prepare For Creating a Trail For Your Organization or Create an event data store.
", "smithy.api#error": "client", "smithy.api#httpError": 400 } @@ -3546,7 +3570,7 @@ "code": "OrganizationNotInAllFeaturesMode", "httpResponseCode": 400 }, - "smithy.api#documentation": "This exception is thrown when Organizations is not configured to support all features. All features must be enabled in Organizations to support\n creating an organization trail. For more information, see \n Prepare For Creating a Trail For Your Organization.
", + "smithy.api#documentation": "This exception is thrown when Organizations is not configured to support all \n features. All features must be enabled in Organizations to support\n creating an organization trail or event data store.
", "smithy.api#error": "client", "smithy.api#httpError": 400 } @@ -4033,7 +4057,7 @@ } ], "traits": { - "smithy.api#documentation": "Removes the specified tags from a trail.
", + "smithy.api#documentation": "Removes the specified tags from a trail or event data store.
", "smithy.api#idempotent": {} } }, @@ -4043,7 +4067,7 @@ "ResourceId": { "target": "com.amazonaws.cloudtrail#String", "traits": { - "smithy.api#documentation": "Specifies the ARN of the trail from which tags should be removed. The format of a trail ARN is:
\n\n arn:aws:cloudtrail:us-east-2:123456789012:trail/MyTrail\n
Specifies the ARN of the trail or event data store from which tags should be removed.
\n\n Example trail ARN format: arn:aws:cloudtrail:us-east-2:123456789012:trail/MyTrail\n
Example event data store ARN format: arn:aws:cloudtrail:us-east-2:12345678910:eventdatastore/EXAMPLE-f852-4e8f-8bd1-bcf6cEXAMPLE\n
Specifies the tags to remove from a trail.
" + "smithy.api#documentation": "Specifies the tags to remove from a trail or event data store.
" } }, "com.amazonaws.cloudtrail#RemoveTagsResponse": { @@ -4300,7 +4324,7 @@ "smithy.api#box": {}, "smithy.api#range": { "min": 7, - "max": 2555 + "max": 2557 } } }, diff --git a/codegen/sdk-codegen/aws-models/iot-wireless.json b/codegen/sdk-codegen/aws-models/iot-wireless.json index d88e65bbb83..d56e7f7bbee 100644 --- a/codegen/sdk-codegen/aws-models/iot-wireless.json +++ b/codegen/sdk-codegen/aws-models/iot-wireless.json @@ -43,6 +43,12 @@ "traits": { "smithy.api#documentation": "Session keys for ABP v1.0.x
" } + }, + "FCntStart": { + "target": "com.amazonaws.iotwireless#FCntStart", + "traits": { + "smithy.api#documentation": "The FCnt init value.
" + } } }, "traits": { @@ -63,6 +69,12 @@ "traits": { "smithy.api#documentation": "Session keys for ABP v1.1
" } + }, + "FCntStart": { + "target": "com.amazonaws.iotwireless#FCntStart", + "traits": { + "smithy.api#documentation": "The FCnt init value.
" + } } }, "traits": { @@ -780,6 +792,40 @@ ] } }, + "com.amazonaws.iotwireless#ConnectionStatusEventConfiguration": { + "type": "structure", + "members": { + "LoRaWAN": { + "target": "com.amazonaws.iotwireless#LoRaWANConnectionStatusEventNotificationConfigurations", + "traits": { + "smithy.api#documentation": "Connection status event configuration object for enabling or disabling LoRaWAN related event topics.
" + } + }, + "WirelessGatewayIdEventTopic": { + "target": "com.amazonaws.iotwireless#EventNotificationTopicStatus", + "traits": { + "smithy.api#documentation": "Enum to denote whether the wireless gateway id connection status event topic is enabled or disabled\n .
" + } + } + }, + "traits": { + "smithy.api#documentation": "Connection status event configuration object for enabling or disabling topic.
" + } + }, + "com.amazonaws.iotwireless#ConnectionStatusResourceTypeEventConfiguration": { + "type": "structure", + "members": { + "LoRaWAN": { + "target": "com.amazonaws.iotwireless#LoRaWANConnectionStatusResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Connection status resource type event configuration object for enabling or disabling LoRaWAN related\n event topics.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Connection status resource type event configuration object for enabling or disabling topic.
" + } + }, "com.amazonaws.iotwireless#Crc": { "type": "long", "traits": { @@ -1138,6 +1184,95 @@ } } }, + "com.amazonaws.iotwireless#CreateNetworkAnalyzerConfiguration": { + "type": "operation", + "input": { + "target": "com.amazonaws.iotwireless#CreateNetworkAnalyzerConfigurationRequest" + }, + "output": { + "target": "com.amazonaws.iotwireless#CreateNetworkAnalyzerConfigurationResponse" + }, + "errors": [ + { + "target": "com.amazonaws.iotwireless#AccessDeniedException" + }, + { + "target": "com.amazonaws.iotwireless#ConflictException" + }, + { + "target": "com.amazonaws.iotwireless#InternalServerException" + }, + { + "target": "com.amazonaws.iotwireless#ResourceNotFoundException" + }, + { + "target": "com.amazonaws.iotwireless#ThrottlingException" + }, + { + "target": "com.amazonaws.iotwireless#ValidationException" + } + ], + "traits": { + "smithy.api#documentation": "Creates a new network analyzer configuration.
", + "smithy.api#http": { + "method": "POST", + "uri": "/network-analyzer-configurations", + "code": 201 + } + } + }, + "com.amazonaws.iotwireless#CreateNetworkAnalyzerConfigurationRequest": { + "type": "structure", + "members": { + "Name": { + "target": "com.amazonaws.iotwireless#NetworkAnalyzerConfigurationName", + "traits": { + "smithy.api#required": {} + } + }, + "TraceContent": { + "target": "com.amazonaws.iotwireless#TraceContent" + }, + "WirelessDevices": { + "target": "com.amazonaws.iotwireless#WirelessDeviceList", + "traits": { + "smithy.api#documentation": "Wireless device resources to add to the network analyzer configuration. Provide the WirelessDeviceId of the resource to add in the input array.
Wireless gateway resources to add to the network analyzer configuration. Provide the WirelessGatewayId of the resource to add in the input array.
The Amazon Resource Name of the new resource.
" + } + }, + "Name": { + "target": "com.amazonaws.iotwireless#NetworkAnalyzerConfigurationName" + } + } + }, "com.amazonaws.iotwireless#CreateServiceProfile": { "type": "operation", "input": { @@ -1793,6 +1928,59 @@ "type": "structure", "members": {} }, + "com.amazonaws.iotwireless#DeleteNetworkAnalyzerConfiguration": { + "type": "operation", + "input": { + "target": "com.amazonaws.iotwireless#DeleteNetworkAnalyzerConfigurationRequest" + }, + "output": { + "target": "com.amazonaws.iotwireless#DeleteNetworkAnalyzerConfigurationResponse" + }, + "errors": [ + { + "target": "com.amazonaws.iotwireless#AccessDeniedException" + }, + { + "target": "com.amazonaws.iotwireless#ConflictException" + }, + { + "target": "com.amazonaws.iotwireless#InternalServerException" + }, + { + "target": "com.amazonaws.iotwireless#ResourceNotFoundException" + }, + { + "target": "com.amazonaws.iotwireless#ThrottlingException" + }, + { + "target": "com.amazonaws.iotwireless#ValidationException" + } + ], + "traits": { + "smithy.api#documentation": "Deletes a network analyzer configuration.
", + "smithy.api#http": { + "method": "DELETE", + "uri": "/network-analyzer-configurations/{ConfigurationName}", + "code": 204 + } + } + }, + "com.amazonaws.iotwireless#DeleteNetworkAnalyzerConfigurationRequest": { + "type": "structure", + "members": { + "ConfigurationName": { + "target": "com.amazonaws.iotwireless#NetworkAnalyzerConfigurationName", + "traits": { + "smithy.api#httpLabel": {}, + "smithy.api#required": {} + } + } + } + }, + "com.amazonaws.iotwireless#DeleteNetworkAnalyzerConfigurationResponse": { + "type": "structure", + "members": {} + }, "com.amazonaws.iotwireless#DeleteQueuedMessages": { "type": "operation", "input": { @@ -1819,7 +2007,7 @@ } ], "traits": { - "smithy.api#documentation": "The operation to delete queued messages.
", + "smithy.api#documentation": "Remove queued messages from the downlink queue.
", "smithy.api#http": { "method": "DELETE", "uri": "/wireless-devices/{Id}/data", @@ -1833,7 +2021,7 @@ "Id": { "target": "com.amazonaws.iotwireless#WirelessDeviceId", "traits": { - "smithy.api#documentation": "Id of a given wireless device which messages will be deleted
", + "smithy.api#documentation": "The ID of a given wireless device for which downlink messages will be deleted.
", "smithy.api#httpLabel": {}, "smithy.api#required": {} } @@ -1841,7 +2029,7 @@ "MessageId": { "target": "com.amazonaws.iotwireless#MessageId", "traits": { - "smithy.api#documentation": "if messageID==\"*\", the queue for a particular wireless deviceId will be purged, otherwise, the specific message with messageId will be deleted
", + "smithy.api#documentation": "If message ID is \"*\", it cleares the entire downlink queue for a given\n device, specified by the wireless device ID. Otherwise, the downlink message with the\n specified message ID will be deleted.
The wireless device type, it is either Sidewalk or LoRaWAN.
", + "smithy.api#documentation": "The wireless device type, which can be either Sidewalk or LoRaWAN.
", "smithy.api#httpQuery": "WirelessDeviceType" } } @@ -2282,12 +2470,32 @@ "traits": { "smithy.api#documentation": "Device registration state event configuration object for enabling or disabling Sidewalk related event\n topics.
" } + }, + "WirelessDeviceIdEventTopic": { + "target": "com.amazonaws.iotwireless#EventNotificationTopicStatus", + "traits": { + "smithy.api#documentation": "Enum to denote whether the wireless device id device registration state event topic is enabled or disabled.
" + } } }, "traits": { "smithy.api#documentation": "Device registration state event configuration object for enabling and disabling relevant topics.
" } }, + "com.amazonaws.iotwireless#DeviceRegistrationStateResourceTypeEventConfiguration": { + "type": "structure", + "members": { + "Sidewalk": { + "target": "com.amazonaws.iotwireless#SidewalkResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Device registration resource type state event configuration object for enabling or disabling Sidewalk\n related event topics.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Device registration state resource type event configuration object for enabling or disabling topic.
" + } + }, "com.amazonaws.iotwireless#DeviceState": { "type": "string", "traits": { @@ -2784,19 +2992,19 @@ "MessageId": { "target": "com.amazonaws.iotwireless#MessageId", "traits": { - "smithy.api#documentation": "The messageId allocated by IoT Wireless for tracing purpose
" + "smithy.api#documentation": "The message ID assigned by IoT Wireless to each downlink message, which helps identify the\n message.
" } }, "TransmitMode": { "target": "com.amazonaws.iotwireless#TransmitMode", "traits": { - "smithy.api#documentation": "The transmit mode to use to send data to the wireless device. Can be: 0 for UM (unacknowledge mode) or 1 for AM (acknowledge mode).
The transmit mode to use for sending data to the wireless device. This can be 0 for UM (unacknowledge mode)\n or 1 for AM (acknowledge mode).
The timestamp that Iot Wireless received the message.
" + "smithy.api#documentation": "The time at which Iot Wireless received the downlink message.
" } }, "LoRaWAN": { @@ -2804,7 +3012,7 @@ } }, "traits": { - "smithy.api#documentation": "The message in downlink queue.
" + "smithy.api#documentation": "The message in the downlink queue.
" } }, "com.amazonaws.iotwireless#DownlinkQueueMessagesList": { @@ -2868,6 +3076,73 @@ ] } }, + "com.amazonaws.iotwireless#EventConfigurationItem": { + "type": "structure", + "members": { + "Identifier": { + "target": "com.amazonaws.iotwireless#Identifier", + "traits": { + "smithy.api#documentation": "Resource identifier opted in for event messaging.
" + } + }, + "IdentifierType": { + "target": "com.amazonaws.iotwireless#IdentifierType", + "traits": { + "smithy.api#documentation": "Identifier type of the particular resource identifier for event configuration.
" + } + }, + "PartnerType": { + "target": "com.amazonaws.iotwireless#EventNotificationPartnerType", + "traits": { + "smithy.api#documentation": "Partner type of the resource if the identifier type is PartnerAccountId.
" + } + }, + "Events": { + "target": "com.amazonaws.iotwireless#EventNotificationItemConfigurations" + } + }, + "traits": { + "smithy.api#documentation": "Event configuration object for a single resource.
" + } + }, + "com.amazonaws.iotwireless#EventConfigurationsList": { + "type": "list", + "member": { + "target": "com.amazonaws.iotwireless#EventConfigurationItem" + } + }, + "com.amazonaws.iotwireless#EventNotificationItemConfigurations": { + "type": "structure", + "members": { + "DeviceRegistrationState": { + "target": "com.amazonaws.iotwireless#DeviceRegistrationStateEventConfiguration", + "traits": { + "smithy.api#documentation": "Device registration state event configuration for an event configuration item.
" + } + }, + "Proximity": { + "target": "com.amazonaws.iotwireless#ProximityEventConfiguration", + "traits": { + "smithy.api#documentation": "Proximity event configuration for an event configuration item.
" + } + }, + "Join": { + "target": "com.amazonaws.iotwireless#JoinEventConfiguration", + "traits": { + "smithy.api#documentation": "Join event configuration for an event configuration item.
" + } + }, + "ConnectionStatus": { + "target": "com.amazonaws.iotwireless#ConnectionStatusEventConfiguration", + "traits": { + "smithy.api#documentation": "Connection status event configuration for an event configuration item.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Object of all event configurations and the status of the event topics.
" + } + }, "com.amazonaws.iotwireless#EventNotificationPartnerType": { "type": "string", "traits": { @@ -2879,6 +3154,25 @@ ] } }, + "com.amazonaws.iotwireless#EventNotificationResourceType": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "SidewalkAccount", + "name": "SidewalkAccount" + }, + { + "value": "WirelessDevice", + "name": "WirelessDevice" + }, + { + "value": "WirelessGateway", + "name": "WirelessGateway" + } + ] + } + }, "com.amazonaws.iotwireless#EventNotificationTopicStatus": { "type": "string", "traits": { @@ -2918,6 +3212,17 @@ ] } }, + "com.amazonaws.iotwireless#FCntStart": { + "type": "integer", + "traits": { + "smithy.api#box": {}, + "smithy.api#documentation": "The FCnt init value.
", + "smithy.api#range": { + "min": 0, + "max": 65535 + } + } + }, "com.amazonaws.iotwireless#FNwkSIntKey": { "type": "string", "traits": { @@ -3307,6 +3612,67 @@ } } }, + "com.amazonaws.iotwireless#GetEventConfigurationByResourceTypes": { + "type": "operation", + "input": { + "target": "com.amazonaws.iotwireless#GetEventConfigurationByResourceTypesRequest" + }, + "output": { + "target": "com.amazonaws.iotwireless#GetEventConfigurationByResourceTypesResponse" + }, + "errors": [ + { + "target": "com.amazonaws.iotwireless#AccessDeniedException" + }, + { + "target": "com.amazonaws.iotwireless#InternalServerException" + }, + { + "target": "com.amazonaws.iotwireless#ThrottlingException" + } + ], + "traits": { + "smithy.api#documentation": "Get the event configuration by resource types.
", + "smithy.api#http": { + "method": "GET", + "uri": "/event-configurations-resource-types", + "code": 200 + } + } + }, + "com.amazonaws.iotwireless#GetEventConfigurationByResourceTypesRequest": { + "type": "structure", + "members": {} + }, + "com.amazonaws.iotwireless#GetEventConfigurationByResourceTypesResponse": { + "type": "structure", + "members": { + "DeviceRegistrationState": { + "target": "com.amazonaws.iotwireless#DeviceRegistrationStateResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Resource type event configuration for the device registration state event
" + } + }, + "Proximity": { + "target": "com.amazonaws.iotwireless#ProximityResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Resource type event configuration for the proximity event
" + } + }, + "Join": { + "target": "com.amazonaws.iotwireless#JoinResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Resource type event configuration for the join event
" + } + }, + "ConnectionStatus": { + "target": "com.amazonaws.iotwireless#ConnectionStatusResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Resource type event configuration for the connection status event
" + } + } + } + }, "com.amazonaws.iotwireless#GetFuotaTask": { "type": "operation", "input": { @@ -3589,7 +3955,7 @@ } ], "traits": { - "smithy.api#documentation": "Get NetworkAnalyzer configuration.
", + "smithy.api#documentation": "Get network analyzer configuration.
", "smithy.api#http": { "method": "GET", "uri": "/network-analyzer-configurations/{ConfigurationName}", @@ -3618,14 +3984,26 @@ "WirelessDevices": { "target": "com.amazonaws.iotwireless#WirelessDeviceList", "traits": { - "smithy.api#documentation": "List of WirelessDevices in the NetworkAnalyzerConfiguration.
" + "smithy.api#documentation": "List of wireless gateway resources that have been added to the network analyzer configuration.
" } }, "WirelessGateways": { "target": "com.amazonaws.iotwireless#WirelessGatewayList", "traits": { - "smithy.api#documentation": "List of WirelessGateways in the NetworkAnalyzerConfiguration.
" + "smithy.api#documentation": "List of wireless gateway resources that have been added to the network analyzer configuration.
" + } + }, + "Description": { + "target": "com.amazonaws.iotwireless#Description" + }, + "Arn": { + "target": "com.amazonaws.iotwireless#NetworkAnalyzerConfigurationArn", + "traits": { + "smithy.api#documentation": "The Amazon Resource Name of the new resource.
" } + }, + "Name": { + "target": "com.amazonaws.iotwireless#NetworkAnalyzerConfigurationName" } } }, @@ -3774,6 +4152,18 @@ "traits": { "smithy.api#documentation": "Event configuration for the Proximity event
" } + }, + "Join": { + "target": "com.amazonaws.iotwireless#JoinEventConfiguration", + "traits": { + "smithy.api#documentation": "Event configuration for the join event.
" + } + }, + "ConnectionStatus": { + "target": "com.amazonaws.iotwireless#ConnectionStatusEventConfiguration", + "traits": { + "smithy.api#documentation": "Event configuration for the connection status event.
" + } } } }, @@ -3876,7 +4266,7 @@ "ServiceType": { "target": "com.amazonaws.iotwireless#WirelessGatewayServiceType", "traits": { - "smithy.api#documentation": "The service type for which to get endpoint information about. Can be CUPS for the Configuration and Update Server endpoint, or LNS for the LoRaWAN Network Server endpoint.
The service type for which to get endpoint information about. Can be CUPS for the\n Configuration and Update Server endpoint, or LNS for the LoRaWAN Network Server endpoint or\n CLAIM for the global endpoint.
Join event configuration object for enabling or disabling LoRaWAN related event topics.
" + } + }, + "WirelessDeviceIdEventTopic": { + "target": "com.amazonaws.iotwireless#EventNotificationTopicStatus", + "traits": { + "smithy.api#documentation": "Enum to denote whether the wireless device id join event topic is enabled or disabled.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Join event configuration object for enabling or disabling topic.
" + } + }, + "com.amazonaws.iotwireless#JoinResourceTypeEventConfiguration": { + "type": "structure", + "members": { + "LoRaWAN": { + "target": "com.amazonaws.iotwireless#LoRaWANJoinResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Join resource type event configuration object for enabling or disabling LoRaWAN related\n event topics.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Join resource type event configuration object for enabling or disabling topic.
" + } + }, "com.amazonaws.iotwireless#ListDestinations": { "type": "operation", "input": { @@ -4845,19 +5285,93 @@ } } }, - "com.amazonaws.iotwireless#ListDeviceProfilesResponse": { + "com.amazonaws.iotwireless#ListDeviceProfilesResponse": { + "type": "structure", + "members": { + "NextToken": { + "target": "com.amazonaws.iotwireless#NextToken", + "traits": { + "smithy.api#documentation": "The token to use to get the next set of results, or null if there are no additional results.
" + } + }, + "DeviceProfileList": { + "target": "com.amazonaws.iotwireless#DeviceProfileList", + "traits": { + "smithy.api#documentation": "The list of device profiles.
" + } + } + } + }, + "com.amazonaws.iotwireless#ListEventConfigurations": { + "type": "operation", + "input": { + "target": "com.amazonaws.iotwireless#ListEventConfigurationsRequest" + }, + "output": { + "target": "com.amazonaws.iotwireless#ListEventConfigurationsResponse" + }, + "errors": [ + { + "target": "com.amazonaws.iotwireless#AccessDeniedException" + }, + { + "target": "com.amazonaws.iotwireless#InternalServerException" + }, + { + "target": "com.amazonaws.iotwireless#ThrottlingException" + }, + { + "target": "com.amazonaws.iotwireless#ValidationException" + } + ], + "traits": { + "smithy.api#documentation": "List event configurations where at least one event topic has been enabled.
", + "smithy.api#http": { + "method": "GET", + "uri": "/event-configurations", + "code": 200 + } + } + }, + "com.amazonaws.iotwireless#ListEventConfigurationsRequest": { + "type": "structure", + "members": { + "ResourceType": { + "target": "com.amazonaws.iotwireless#EventNotificationResourceType", + "traits": { + "smithy.api#documentation": "Resource type to filter event configurations.
", + "smithy.api#httpQuery": "resourceType", + "smithy.api#required": {} + } + }, + "MaxResults": { + "target": "com.amazonaws.iotwireless#MaxResults", + "traits": { + "smithy.api#httpQuery": "maxResults" + } + }, + "NextToken": { + "target": "com.amazonaws.iotwireless#NextToken", + "traits": { + "smithy.api#documentation": "To retrieve the next set of results, the nextToken value from a previous response;\n otherwise null to receive the first set of results.
The token to use to get the next set of results, or null if there are no additional results.
" + "smithy.api#documentation": "To retrieve the next set of results, the nextToken value from a previous response;\n otherwise null to receive the first set of results.
The list of device profiles.
" + "smithy.api#documentation": "Event configurations of all events for a single resource.
" } } } @@ -5076,6 +5590,77 @@ } } }, + "com.amazonaws.iotwireless#ListNetworkAnalyzerConfigurations": { + "type": "operation", + "input": { + "target": "com.amazonaws.iotwireless#ListNetworkAnalyzerConfigurationsRequest" + }, + "output": { + "target": "com.amazonaws.iotwireless#ListNetworkAnalyzerConfigurationsResponse" + }, + "errors": [ + { + "target": "com.amazonaws.iotwireless#AccessDeniedException" + }, + { + "target": "com.amazonaws.iotwireless#InternalServerException" + }, + { + "target": "com.amazonaws.iotwireless#ThrottlingException" + }, + { + "target": "com.amazonaws.iotwireless#ValidationException" + } + ], + "traits": { + "smithy.api#documentation": "Lists the network analyzer configurations.
", + "smithy.api#http": { + "method": "GET", + "uri": "/network-analyzer-configurations", + "code": 200 + }, + "smithy.api#paginated": { + "inputToken": "NextToken", + "outputToken": "NextToken", + "pageSize": "MaxResults" + } + } + }, + "com.amazonaws.iotwireless#ListNetworkAnalyzerConfigurationsRequest": { + "type": "structure", + "members": { + "MaxResults": { + "target": "com.amazonaws.iotwireless#MaxResults", + "traits": { + "smithy.api#httpQuery": "maxResults" + } + }, + "NextToken": { + "target": "com.amazonaws.iotwireless#NextToken", + "traits": { + "smithy.api#documentation": "To retrieve the next set of results, the nextToken value from a previous response; otherwise null to receive the first set of results.
The token to use to get the next set of results, or null if there are no additional results.
" + } + }, + "NetworkAnalyzerConfigurationList": { + "target": "com.amazonaws.iotwireless#NetworkAnalyzerConfigurationList", + "traits": { + "smithy.api#documentation": "The list of network analyzer configurations.
" + } + } + } + }, "com.amazonaws.iotwireless#ListPartnerAccounts": { "type": "operation", "input": { @@ -5169,7 +5754,7 @@ } ], "traits": { - "smithy.api#documentation": "The operation to list queued messages.
", + "smithy.api#documentation": "List queued messages in the downlink queue.
", "smithy.api#http": { "method": "GET", "uri": "/wireless-devices/{Id}/data", @@ -5188,7 +5773,7 @@ "Id": { "target": "com.amazonaws.iotwireless#WirelessDeviceId", "traits": { - "smithy.api#documentation": "Id of a given wireless device which the downlink packets are targeted
", + "smithy.api#documentation": "The ID of a given wireless device which the downlink message packets are being sent.
", "smithy.api#httpLabel": {}, "smithy.api#required": {} } @@ -5196,7 +5781,7 @@ "NextToken": { "target": "com.amazonaws.iotwireless#NextToken", "traits": { - "smithy.api#documentation": "To retrieve the next set of results, the nextToken value from a previous response; otherwise null to receive the first set of results.
To retrieve the next set of results, the nextToken value from a previous response; otherwise \n null to receive the first set of results.
The wireless device type, it is either Sidewalk or LoRaWAN.
", + "smithy.api#documentation": "The wireless device type, whic can be either Sidewalk or LoRaWAN.
", "smithy.api#httpQuery": "WirelessDeviceType" } } @@ -5222,13 +5807,13 @@ "NextToken": { "target": "com.amazonaws.iotwireless#NextToken", "traits": { - "smithy.api#documentation": "To retrieve the next set of results, the nextToken value from a previous response; otherwise null to receive the first set of results.
To retrieve the next set of results, the nextToken value from a previous response; \n otherwise null to receive the first set of results.
The messages in downlink queue.
" + "smithy.api#documentation": "The messages in the downlink queue.
" } } } @@ -5621,6 +6206,34 @@ } } }, + "com.amazonaws.iotwireless#LoRaWANConnectionStatusEventNotificationConfigurations": { + "type": "structure", + "members": { + "GatewayEuiEventTopic": { + "target": "com.amazonaws.iotwireless#EventNotificationTopicStatus", + "traits": { + "smithy.api#documentation": "Enum to denote whether the gateway eui connection status event topic is enabled or disabled.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Object for LoRaWAN connection status resource type event configuration.
" + } + }, + "com.amazonaws.iotwireless#LoRaWANConnectionStatusResourceTypeEventConfiguration": { + "type": "structure", + "members": { + "WirelessGatewayEventTopic": { + "target": "com.amazonaws.iotwireless#EventNotificationTopicStatus", + "traits": { + "smithy.api#documentation": "Enum to denote whether the wireless gateway connection status event topic is enabled or disabled.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Object for LoRaWAN connection status resource type event configuration.
" + } + }, "com.amazonaws.iotwireless#LoRaWANDevice": { "type": "structure", "members": { @@ -6088,6 +6701,34 @@ "smithy.api#documentation": "LoRaWANGetServiceProfileInfo object.
" } }, + "com.amazonaws.iotwireless#LoRaWANJoinEventNotificationConfigurations": { + "type": "structure", + "members": { + "DevEuiEventTopic": { + "target": "com.amazonaws.iotwireless#EventNotificationTopicStatus", + "traits": { + "smithy.api#documentation": "Enum to denote whether the dev eui join event topic is enabled or disabled.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Object for LoRaWAN join resource type event configuration.
" + } + }, + "com.amazonaws.iotwireless#LoRaWANJoinResourceTypeEventConfiguration": { + "type": "structure", + "members": { + "WirelessDeviceEventTopic": { + "target": "com.amazonaws.iotwireless#EventNotificationTopicStatus", + "traits": { + "smithy.api#documentation": "Enum to denote whether the wireless device join event topic is enabled or disabled.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Object for LoRaWAN join resource type event configuration.
" + } + }, "com.amazonaws.iotwireless#LoRaWANListDevice": { "type": "structure", "members": { @@ -6217,6 +6858,18 @@ "traits": { "smithy.api#documentation": "The ID of the service profile.
" } + }, + "AbpV1_1": { + "target": "com.amazonaws.iotwireless#UpdateAbpV1_1", + "traits": { + "smithy.api#documentation": "ABP device object for update APIs for v1.1
" + } + }, + "AbpV1_0_x": { + "target": "com.amazonaws.iotwireless#UpdateAbpV1_0_x", + "traits": { + "smithy.api#documentation": "ABP device object for update APIs for v1.0.x
" + } } }, "traits": { @@ -6278,7 +6931,7 @@ "com.amazonaws.iotwireless#LogLevel": { "type": "string", "traits": { - "smithy.api#documentation": "The log level for a log message.
", + "smithy.api#documentation": "The log level for a log message. The log levels can be disabled, or set to ERROR to display\n less verbose logs containing only error information, or to INFO for more detailed logs.
NetworkAnalyzer configuration name.
", + "smithy.api#documentation": "Name of the network analyzer configuration.
", "smithy.api#length": { "min": 1, "max": 1024 }, - "smithy.api#pattern": "^NetworkAnalyzerConfig_Default$" + "smithy.api#pattern": "^[a-zA-Z0-9-_]+$" + } + }, + "com.amazonaws.iotwireless#NetworkAnalyzerConfigurations": { + "type": "structure", + "members": { + "Arn": { + "target": "com.amazonaws.iotwireless#NetworkAnalyzerConfigurationArn", + "traits": { + "smithy.api#documentation": "The Amazon Resource Name of the new resource.
" + } + }, + "Name": { + "target": "com.amazonaws.iotwireless#NetworkAnalyzerConfigurationName" + } + }, + "traits": { + "smithy.api#documentation": "Network analyzer configurations.
" } }, "com.amazonaws.iotwireless#NextToken": { @@ -6736,12 +7421,32 @@ "traits": { "smithy.api#documentation": "Proximity event configuration object for enabling or disabling Sidewalk related event topics.
" } + }, + "WirelessDeviceIdEventTopic": { + "target": "com.amazonaws.iotwireless#EventNotificationTopicStatus", + "traits": { + "smithy.api#documentation": "Enum to denote whether the wireless device id proximity event topic is enabled or disabled.
" + } } }, "traits": { "smithy.api#documentation": "Proximity event configuration object for enabling and disabling relevant topics.
" } }, + "com.amazonaws.iotwireless#ProximityResourceTypeEventConfiguration": { + "type": "structure", + "members": { + "Sidewalk": { + "target": "com.amazonaws.iotwireless#SidewalkResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Proximity resource type event configuration object for enabling and disabling wireless device topic.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Proximity resource type event configuration object for enabling or disabling topic.
" + } + }, "com.amazonaws.iotwireless#PutResourceLogLevel": { "type": "operation", "input": { @@ -7503,6 +8208,20 @@ } } }, + "com.amazonaws.iotwireless#SidewalkResourceTypeEventConfiguration": { + "type": "structure", + "members": { + "WirelessDeviceEventTopic": { + "target": "com.amazonaws.iotwireless#EventNotificationTopicStatus", + "traits": { + "smithy.api#documentation": "Enum to denote whether the wireless device join event topic is enabled or disabled.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Sidewalk resource type event configuration object for enabling or disabling topic.
" + } + }, "com.amazonaws.iotwireless#SidewalkSendDataToDevice": { "type": "structure", "members": { @@ -8094,7 +8813,7 @@ } }, "traits": { - "smithy.api#documentation": "Trace Content for resources.
" + "smithy.api#documentation": "Trace content for your wireless gateway and wireless device resources.
" } }, "com.amazonaws.iotwireless#TransmitMode": { @@ -8195,6 +8914,34 @@ "type": "structure", "members": {} }, + "com.amazonaws.iotwireless#UpdateAbpV1_0_x": { + "type": "structure", + "members": { + "FCntStart": { + "target": "com.amazonaws.iotwireless#FCntStart", + "traits": { + "smithy.api#documentation": "The FCnt init value.
" + } + } + }, + "traits": { + "smithy.api#documentation": "ABP device object for LoRaWAN specification v1.0.x
" + } + }, + "com.amazonaws.iotwireless#UpdateAbpV1_1": { + "type": "structure", + "members": { + "FCntStart": { + "target": "com.amazonaws.iotwireless#FCntStart", + "traits": { + "smithy.api#documentation": "The FCnt init value.
" + } + } + }, + "traits": { + "smithy.api#documentation": "ABP device object for LoRaWAN specification v1.1
" + } + }, "com.amazonaws.iotwireless#UpdateDataSource": { "type": "string", "traits": { @@ -8279,6 +9026,70 @@ "type": "structure", "members": {} }, + "com.amazonaws.iotwireless#UpdateEventConfigurationByResourceTypes": { + "type": "operation", + "input": { + "target": "com.amazonaws.iotwireless#UpdateEventConfigurationByResourceTypesRequest" + }, + "output": { + "target": "com.amazonaws.iotwireless#UpdateEventConfigurationByResourceTypesResponse" + }, + "errors": [ + { + "target": "com.amazonaws.iotwireless#AccessDeniedException" + }, + { + "target": "com.amazonaws.iotwireless#InternalServerException" + }, + { + "target": "com.amazonaws.iotwireless#ThrottlingException" + }, + { + "target": "com.amazonaws.iotwireless#ValidationException" + } + ], + "traits": { + "smithy.api#documentation": "Update the event configuration by resource types.
", + "smithy.api#http": { + "method": "PATCH", + "uri": "/event-configurations-resource-types", + "code": 204 + } + } + }, + "com.amazonaws.iotwireless#UpdateEventConfigurationByResourceTypesRequest": { + "type": "structure", + "members": { + "DeviceRegistrationState": { + "target": "com.amazonaws.iotwireless#DeviceRegistrationStateResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Device registration state resource type event configuration object for enabling and disabling wireless\n gateway topic.
" + } + }, + "Proximity": { + "target": "com.amazonaws.iotwireless#ProximityResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Proximity resource type event configuration object for enabling and disabling wireless gateway topic.
" + } + }, + "Join": { + "target": "com.amazonaws.iotwireless#JoinResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Join resource type event configuration object for enabling and disabling wireless device topic.
" + } + }, + "ConnectionStatus": { + "target": "com.amazonaws.iotwireless#ConnectionStatusResourceTypeEventConfiguration", + "traits": { + "smithy.api#documentation": "Connection status resource type event configuration object for enabling and disabling wireless gateway topic.
" + } + } + } + }, + "com.amazonaws.iotwireless#UpdateEventConfigurationByResourceTypesResponse": { + "type": "structure", + "members": {} + }, "com.amazonaws.iotwireless#UpdateFuotaTask": { "type": "operation", "input": { @@ -8490,7 +9301,7 @@ } ], "traits": { - "smithy.api#documentation": "Update NetworkAnalyzer configuration.
", + "smithy.api#documentation": "Update network analyzer configuration.
", "smithy.api#http": { "method": "PATCH", "uri": "/network-analyzer-configurations/{ConfigurationName}", @@ -8514,26 +9325,29 @@ "WirelessDevicesToAdd": { "target": "com.amazonaws.iotwireless#WirelessDeviceList", "traits": { - "smithy.api#documentation": "WirelessDevices to add into NetworkAnalyzerConfiguration.
" + "smithy.api#documentation": "Wireless device resources to add to the network analyzer configuration. Provide the \n WirelessDeviceId of the resource to add in the input array.
WirelessDevices to remove from NetworkAnalyzerConfiguration.
" + "smithy.api#documentation": "Wireless device resources to remove from the network analyzer configuration. Provide the \n WirelessDeviceId of the resources to remove in the input array.
WirelessGateways to add into NetworkAnalyzerConfiguration.
" + "smithy.api#documentation": "Wireless gateway resources to add to the network analyzer configuration. Provide the \n WirelessGatewayId of the resource to add in the input array.
WirelessGateways to remove from NetworkAnalyzerConfiguration.
" + "smithy.api#documentation": "Wireless gateway resources to remove from the network analyzer configuration. Provide the \n WirelessGatewayId of the resources to remove in the input array.
Event configuration for the Proximity event
" } + }, + "Join": { + "target": "com.amazonaws.iotwireless#JoinEventConfiguration", + "traits": { + "smithy.api#documentation": "Event configuration for the join event
" + } + }, + "ConnectionStatus": { + "target": "com.amazonaws.iotwireless#ConnectionStatusEventConfiguration", + "traits": { + "smithy.api#documentation": "Event configuration for the connection status event
" + } } } }, @@ -8966,7 +9792,7 @@ "com.amazonaws.iotwireless#WirelessDeviceFrameInfo": { "type": "string", "traits": { - "smithy.api#documentation": "WirelessDevice FrameInfo for trace content.
", + "smithy.api#documentation": "FrameInfo of your wireless device resources for the trace content. Use FrameInfo to debug\n the communication between your LoRaWAN end devices and the network server.
", "smithy.api#enum": [ { "value": "ENABLED", @@ -9500,6 +10326,9 @@ { "target": "com.amazonaws.iotwireless#CreateMulticastGroup" }, + { + "target": "com.amazonaws.iotwireless#CreateNetworkAnalyzerConfiguration" + }, { "target": "com.amazonaws.iotwireless#CreateServiceProfile" }, @@ -9527,6 +10356,9 @@ { "target": "com.amazonaws.iotwireless#DeleteMulticastGroup" }, + { + "target": "com.amazonaws.iotwireless#DeleteNetworkAnalyzerConfiguration" + }, { "target": "com.amazonaws.iotwireless#DeleteQueuedMessages" }, @@ -9572,6 +10404,9 @@ { "target": "com.amazonaws.iotwireless#GetDeviceProfile" }, + { + "target": "com.amazonaws.iotwireless#GetEventConfigurationByResourceTypes" + }, { "target": "com.amazonaws.iotwireless#GetFuotaTask" }, @@ -9632,6 +10467,9 @@ { "target": "com.amazonaws.iotwireless#ListDeviceProfiles" }, + { + "target": "com.amazonaws.iotwireless#ListEventConfigurations" + }, { "target": "com.amazonaws.iotwireless#ListFuotaTasks" }, @@ -9641,6 +10479,9 @@ { "target": "com.amazonaws.iotwireless#ListMulticastGroupsByFuotaTask" }, + { + "target": "com.amazonaws.iotwireless#ListNetworkAnalyzerConfigurations" + }, { "target": "com.amazonaws.iotwireless#ListPartnerAccounts" }, @@ -9701,6 +10542,9 @@ { "target": "com.amazonaws.iotwireless#UpdateDestination" }, + { + "target": "com.amazonaws.iotwireless#UpdateEventConfigurationByResourceTypes" + }, { "target": "com.amazonaws.iotwireless#UpdateFuotaTask" }, diff --git a/codegen/sdk-codegen/aws-models/lookoutequipment.json b/codegen/sdk-codegen/aws-models/lookoutequipment.json index 4b3b74ec7a2..aadba7a216f 100644 --- a/codegen/sdk-codegen/aws-models/lookoutequipment.json +++ b/codegen/sdk-codegen/aws-models/lookoutequipment.json @@ -31,6 +31,21 @@ "shapes": { "com.amazonaws.lookoutequipment#AWSLookoutEquipmentFrontendService": { "type": "service", + "traits": { + "aws.api#service": { + "sdkId": "LookoutEquipment", + "arnNamespace": "lookoutequipment", + "cloudFormationName": "LookoutEquipment", + "cloudTrailEventSource": "lookoutequipment.amazonaws.com", + "endpointPrefix": "lookoutequipment" + }, + "aws.auth#sigv4": { + "name": "lookoutequipment" + }, + "aws.protocols#awsJson1_0": {}, + "smithy.api#documentation": "Amazon Lookout for Equipment is a machine learning service that uses advanced analytics to identify\n anomalies in machines from sensor data for use in predictive maintenance.
", + "smithy.api#title": "Amazon Lookout for Equipment" + }, "version": "2020-12-15", "operations": [ { @@ -78,6 +93,9 @@ { "target": "com.amazonaws.lookoutequipment#ListModels" }, + { + "target": "com.amazonaws.lookoutequipment#ListSensorStatistics" + }, { "target": "com.amazonaws.lookoutequipment#ListTagsForResource" }, @@ -99,22 +117,7 @@ { "target": "com.amazonaws.lookoutequipment#UpdateInferenceScheduler" } - ], - "traits": { - "aws.api#service": { - "sdkId": "LookoutEquipment", - "arnNamespace": "lookoutequipment", - "cloudFormationName": "LookoutEquipment", - "cloudTrailEventSource": "lookoutequipment.amazonaws.com", - "endpointPrefix": "lookoutequipment" - }, - "aws.auth#sigv4": { - "name": "lookoutequipment" - }, - "aws.protocols#awsJson1_0": {}, - "smithy.api#documentation": "Amazon Lookout for Equipment is a machine learning service that uses advanced analytics to identify\n anomalies in machines from sensor data for use in predictive maintenance.
", - "smithy.api#title": "Amazon Lookout for Equipment" - } + ] }, "com.amazonaws.lookoutequipment#AccessDeniedException": { "type": "structure", @@ -141,6 +144,9 @@ } } }, + "com.amazonaws.lookoutequipment#Boolean": { + "type": "boolean" + }, "com.amazonaws.lookoutequipment#BoundedLengthString": { "type": "string", "traits": { @@ -151,6 +157,37 @@ "smithy.api#pattern": "^[\\P{M}\\p{M}]{1,5000}$" } }, + "com.amazonaws.lookoutequipment#CategoricalValues": { + "type": "structure", + "members": { + "Status": { + "target": "com.amazonaws.lookoutequipment#StatisticalIssueStatus", + "traits": { + "smithy.api#documentation": "\nIndicates whether there is a potential data issue related to categorical values.\n
", + "smithy.api#required": {} + } + }, + "NumberOfCategory": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\nIndicates the number of categories in the data.\n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\nEntity that comprises information on categorical values in data.\n
" + } + }, + "com.amazonaws.lookoutequipment#ComponentName": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 1, + "max": 200 + }, + "smithy.api#pattern": "^[0-9a-zA-Z._\\-]{1,200}$" + } + }, "com.amazonaws.lookoutequipment#ComponentTimestampDelimiter": { "type": "string", "traits": { @@ -177,6 +214,28 @@ "smithy.api#httpError": 409 } }, + "com.amazonaws.lookoutequipment#CountPercent": { + "type": "structure", + "members": { + "Count": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\n\nIndicates the count of occurences of the given statistic.\n\n
", + "smithy.api#required": {} + } + }, + "Percentage": { + "target": "com.amazonaws.lookoutequipment#Float", + "traits": { + "smithy.api#documentation": "\n\nIndicates the percentage of occurances of the given statistic.\n\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nEntity that comprises information of count and percentage.\n\n
" + } + }, "com.amazonaws.lookoutequipment#CreateDataset": { "type": "operation", "input": { @@ -222,8 +281,7 @@ "DatasetSchema": { "target": "com.amazonaws.lookoutequipment#DatasetSchema", "traits": { - "smithy.api#documentation": "A JSON description of the data that is in each time series dataset, including names,\n column names, and data types.
", - "smithy.api#required": {} + "smithy.api#documentation": "A JSON description of the data that is in each time series dataset, including names,\n column names, and data types.
" } }, "ServerSideKmsKeyId": { @@ -587,7 +645,7 @@ "IngestionInputConfiguration": { "target": "com.amazonaws.lookoutequipment#IngestionInputConfiguration", "traits": { - "smithy.api#documentation": "Specifies information for the input data for the data inference job, including data S3\n location parameters.
" + "smithy.api#documentation": "Specifies information for the input data for the data inference job, including data Amazon S3\n location parameters.
" } }, "Status": { @@ -615,6 +673,58 @@ "smithy.api#documentation": "The configuration is the TargetSamplingRate, which is the sampling rate of \n the data after post processing by \n Amazon Lookout for Equipment. For example, if you provide data that \n has been collected at a 1 second level and you want the system to resample \n the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.
When providing a value for the TargetSamplingRate, you must \n attach the prefix \"PT\" to the rate you want. The value for a 1 second rate \n is therefore PT1S, the value for a 15 minute rate \n is PT15M, and the value for a 1 hour rate \n is PT1H\n
\n\nParameter that gives information about insufficient data for sensors in the dataset. This includes information about those sensors that have complete data missing and those with a short date range.\n\n
", + "smithy.api#required": {} + } + }, + "MissingSensorData": { + "target": "com.amazonaws.lookoutequipment#MissingSensorData", + "traits": { + "smithy.api#documentation": "\n\nParameter that gives information about data that is missing over all the sensors in the input data.\n\n
", + "smithy.api#required": {} + } + }, + "InvalidSensorData": { + "target": "com.amazonaws.lookoutequipment#InvalidSensorData", + "traits": { + "smithy.api#documentation": "\n\nParameter that gives information about data that is invalid over all the sensors in the input data.\n\n
", + "smithy.api#required": {} + } + }, + "UnsupportedTimestamps": { + "target": "com.amazonaws.lookoutequipment#UnsupportedTimestamps", + "traits": { + "smithy.api#documentation": "\n\nParameter that gives information about unsupported timestamps in the input data.\n\n
", + "smithy.api#required": {} + } + }, + "DuplicateTimestamps": { + "target": "com.amazonaws.lookoutequipment#DuplicateTimestamps", + "traits": { + "smithy.api#documentation": "\n\nParameter that gives information about duplicate timestamps in the input data.\n\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nDataQualitySummary gives aggregated statistics over all the sensors about a completed ingestion job. It primarily gives more information about statistics over different incorrect data like MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, DuplicateTimeStamps.\n\n
" + } + }, + "com.amazonaws.lookoutequipment#DataSizeInBytes": { + "type": "long", + "traits": { + "smithy.api#box": {}, + "smithy.api#range": { + "min": 0 + } + } + }, "com.amazonaws.lookoutequipment#DataUploadFrequency": { "type": "string", "traits": { @@ -886,7 +996,7 @@ } ], "traits": { - "smithy.api#documentation": "Provides information on a specific data ingestion job such as creation time, dataset\n ARN, status, and so on.
" + "smithy.api#documentation": "Provides information on a specific data ingestion job such as creation time, dataset\n ARN, and status.
" } }, "com.amazonaws.lookoutequipment#DescribeDataIngestionJobRequest": { @@ -945,6 +1055,39 @@ "traits": { "smithy.api#documentation": "Specifies the reason for failure when a data ingestion job has failed.
" } + }, + "DataQualitySummary": { + "target": "com.amazonaws.lookoutequipment#DataQualitySummary", + "traits": { + "smithy.api#documentation": "\nGives statistics about a completed ingestion job. These statistics primarily relate to quantifying incorrect data such as MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, and DuplicateTimeStamps.\n
" + } + }, + "IngestedFilesSummary": { + "target": "com.amazonaws.lookoutequipment#IngestedFilesSummary" + }, + "StatusDetail": { + "target": "com.amazonaws.lookoutequipment#BoundedLengthString", + "traits": { + "smithy.api#documentation": "\n Provides details about status of the ingestion job that is currently in progress.\n
" + } + }, + "IngestedDataSize": { + "target": "com.amazonaws.lookoutequipment#DataSizeInBytes", + "traits": { + "smithy.api#documentation": "\n Indicates the size of the ingested dataset.\n
" + } + }, + "DataStartTime": { + "target": "com.amazonaws.lookoutequipment#Timestamp", + "traits": { + "smithy.api#documentation": "\n Indicates the earliest timestamp corresponding to data that was successfully ingested during this specific ingestion job.\n
" + } + }, + "DataEndTime": { + "target": "com.amazonaws.lookoutequipment#Timestamp", + "traits": { + "smithy.api#documentation": "\n Indicates the latest timestamp corresponding to data that was successfully ingested during this specific ingestion job.\n
" + } } } }, @@ -974,7 +1117,7 @@ } ], "traits": { - "smithy.api#documentation": "Provides a JSON description of the data that is in each time series dataset, including names, column names, and data types.
" + "smithy.api#documentation": "Provides a JSON description of the data in each time series dataset, including names, column names, and data types.
" } }, "com.amazonaws.lookoutequipment#DescribeDatasetRequest": { @@ -1039,6 +1182,36 @@ "traits": { "smithy.api#documentation": "Specifies the S3 location configuration for the data input for the data ingestion job.
" } + }, + "DataQualitySummary": { + "target": "com.amazonaws.lookoutequipment#DataQualitySummary", + "traits": { + "smithy.api#documentation": "\nGives statistics associated with the given dataset for the latest successful associated ingestion job id. These statistics primarily relate to quantifying incorrect data such as MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, and DuplicateTimeStamps.\n
" + } + }, + "IngestedFilesSummary": { + "target": "com.amazonaws.lookoutequipment#IngestedFilesSummary", + "traits": { + "smithy.api#documentation": "\nIngestedFilesSummary associated with the given dataset for the latest successful associated ingestion job id.\n
" + } + }, + "RoleArn": { + "target": "com.amazonaws.lookoutequipment#IamRoleArn", + "traits": { + "smithy.api#documentation": "\n The Amazon Resource Name (ARN) of the IAM role that you are using for this the data ingestion job. \n
" + } + }, + "DataStartTime": { + "target": "com.amazonaws.lookoutequipment#Timestamp", + "traits": { + "smithy.api#documentation": "\n Indicates the earliest timestamp corresponding to data that was successfully ingested during the most recent ingestion of this particular dataset.\n
" + } + }, + "DataEndTime": { + "target": "com.amazonaws.lookoutequipment#Timestamp", + "traits": { + "smithy.api#documentation": "\n Indicates the latest timestamp corresponding to data that was successfully ingested during the most recent ingestion of this particular dataset.\n
" + } } } }, @@ -1338,12 +1511,30 @@ } } }, + "com.amazonaws.lookoutequipment#DuplicateTimestamps": { + "type": "structure", + "members": { + "TotalNumberOfDuplicateTimestamps": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\nIndicates the total number of duplicate timestamps.\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nEntity that comprises information abount duplicate timestamps in the dataset.\n\n
" + } + }, "com.amazonaws.lookoutequipment#FileNameTimestampFormat": { "type": "string", "traits": { "smithy.api#pattern": "^EPOCH|yyyy-MM-dd-HH-mm-ss|yyyyMMddHHmmss$" } }, + "com.amazonaws.lookoutequipment#Float": { + "type": "float" + }, "com.amazonaws.lookoutequipment#IamRoleArn": { "type": "string", "traits": { @@ -1443,7 +1634,7 @@ "DataOutputConfiguration": { "target": "com.amazonaws.lookoutequipment#InferenceOutputConfiguration", "traits": { - "smithy.api#documentation": "Specifies configuration information for the output results from for the inference\n execution, including the output S3 location.
" + "smithy.api#documentation": "Specifies configuration information for the output results from for the inference\n execution, including the output Amazon S3 location.
" } }, "CustomerResultObject": { @@ -1475,13 +1666,13 @@ "S3InputConfiguration": { "target": "com.amazonaws.lookoutequipment#InferenceS3InputConfiguration", "traits": { - "smithy.api#documentation": "Specifies configuration information for the input data for the inference, including S3\n location of input data..
" + "smithy.api#documentation": "Specifies configuration information for the input data for the inference, including Amazon S3\n location of input data.
" } }, "InputTimeZoneOffset": { "target": "com.amazonaws.lookoutequipment#TimeZoneOffset", "traits": { - "smithy.api#documentation": "Indicates the difference between your time zone and Greenwich Mean Time (GMT).
" + "smithy.api#documentation": "Indicates the difference between your time zone and Coordinated Universal Time (UTC).
" } }, "InferenceInputNameConfiguration": { @@ -1492,7 +1683,7 @@ } }, "traits": { - "smithy.api#documentation": "Specifies configuration information for the input data for the inference, including S3\n location of input data..
" + "smithy.api#documentation": "Specifies configuration information for the input data for the inference, including Amazon S3\n location of input data..
" } }, "com.amazonaws.lookoutequipment#InferenceInputNameConfiguration": { @@ -1687,6 +1878,34 @@ "smithy.api#documentation": "Contains information about the specific inference scheduler, including data delay\n offset, model name and ARN, status, and so on.
" } }, + "com.amazonaws.lookoutequipment#IngestedFilesSummary": { + "type": "structure", + "members": { + "TotalNumberOfFiles": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "Indicates the total number of files that were submitted for ingestion.
", + "smithy.api#required": {} + } + }, + "IngestedNumberOfFiles": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "Indicates the number of files that were successfully ingested.
", + "smithy.api#required": {} + } + }, + "DiscardedFiles": { + "target": "com.amazonaws.lookoutequipment#ListOfDiscardedFiles", + "traits": { + "smithy.api#documentation": "Indicates the number of files that were discarded. A file could be discarded because its format is invalid (for example, a jpg or pdf) or not readable.
" + } + } + }, + "traits": { + "smithy.api#documentation": "Gives statistics about how many files have been ingested, and which files have not been ingested, for a particular ingestion job.
" + } + }, "com.amazonaws.lookoutequipment#IngestionInputConfiguration": { "type": "structure", "members": { @@ -1746,12 +1965,46 @@ "traits": { "smithy.api#documentation": "The prefix for the S3 location being used for the input data for the data ingestion.\n
" } + }, + "KeyPattern": { + "target": "com.amazonaws.lookoutequipment#KeyPattern", + "traits": { + "smithy.api#documentation": "\nPattern for matching the Amazon S3 files which will be used for ingestion.\nIf no KeyPattern is provided, we will use the default hierarchy file structure, which is same as KeyPattern {prefix}/{component_name}/*\n
" + } } }, "traits": { "smithy.api#documentation": "Specifies S3 configuration information for the input data for the data ingestion job.\n
" } }, + "com.amazonaws.lookoutequipment#InsufficientSensorData": { + "type": "structure", + "members": { + "MissingCompleteSensorData": { + "target": "com.amazonaws.lookoutequipment#MissingCompleteSensorData", + "traits": { + "smithy.api#documentation": "\n\nParameter that describes the total number of sensors that have data completely missing for it.\n\n
", + "smithy.api#required": {} + } + }, + "SensorsWithShortDateRange": { + "target": "com.amazonaws.lookoutequipment#SensorsWithShortDateRange", + "traits": { + "smithy.api#documentation": "\n\nParameter that describes the total number of sensors that have a short date range of less than 90 days of data overall.\n\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nEntity that comprises aggregated information on sensors having insufficient data.\n\n
" + } + }, + "com.amazonaws.lookoutequipment#Integer": { + "type": "integer", + "traits": { + "smithy.api#box": {} + } + }, "com.amazonaws.lookoutequipment#InternalServerException": { "type": "structure", "members": { @@ -1768,6 +2021,37 @@ "smithy.api#httpError": 500 } }, + "com.amazonaws.lookoutequipment#InvalidSensorData": { + "type": "structure", + "members": { + "AffectedSensorCount": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\n\nIndicates the number of sensors that have at least some invalid values.\n\n
", + "smithy.api#required": {} + } + }, + "TotalNumberOfInvalidValues": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\n\nIndicates the total number of invalid values across all the sensors.\n\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nEntity that comprises aggregated information on sensors having insufficient data.\n\n
" + } + }, + "com.amazonaws.lookoutequipment#KeyPattern": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 1, + "max": 2048 + } + } + }, "com.amazonaws.lookoutequipment#KmsKeyArn": { "type": "string", "traits": { @@ -1814,6 +2098,33 @@ "smithy.api#documentation": "The location information (prefix and bucket name) for the s3 location being used for\n label data.
" } }, + "com.amazonaws.lookoutequipment#LargeTimestampGaps": { + "type": "structure", + "members": { + "Status": { + "target": "com.amazonaws.lookoutequipment#StatisticalIssueStatus", + "traits": { + "smithy.api#documentation": "\nIndicates whether there is a potential data issue related to large gaps in timestamps.\n
", + "smithy.api#required": {} + } + }, + "NumberOfLargeTimestampGaps": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\nIndicates the number of large timestamp gaps, if there are any.\n
" + } + }, + "MaxTimestampGapInDays": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\nIndicates the size of the largest timestamp gap, in days.\n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\nEntity that comprises information on large gaps between consecutive timestamps in data.\n
" + } + }, "com.amazonaws.lookoutequipment#ListDataIngestionJobs": { "type": "operation", "input": { @@ -2215,6 +2526,98 @@ } } }, + "com.amazonaws.lookoutequipment#ListOfDiscardedFiles": { + "type": "list", + "member": { + "target": "com.amazonaws.lookoutequipment#S3Object" + }, + "traits": { + "smithy.api#length": { + "min": 0 + } + } + }, + "com.amazonaws.lookoutequipment#ListSensorStatistics": { + "type": "operation", + "input": { + "target": "com.amazonaws.lookoutequipment#ListSensorStatisticsRequest" + }, + "output": { + "target": "com.amazonaws.lookoutequipment#ListSensorStatisticsResponse" + }, + "errors": [ + { + "target": "com.amazonaws.lookoutequipment#AccessDeniedException" + }, + { + "target": "com.amazonaws.lookoutequipment#InternalServerException" + }, + { + "target": "com.amazonaws.lookoutequipment#ResourceNotFoundException" + }, + { + "target": "com.amazonaws.lookoutequipment#ThrottlingException" + }, + { + "target": "com.amazonaws.lookoutequipment#ValidationException" + } + ], + "traits": { + "smithy.api#documentation": "\nLists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset. Can also be used to retreive Sensor Statistics for a previous ingestion job.\n
", + "smithy.api#paginated": { + "inputToken": "NextToken", + "outputToken": "NextToken", + "pageSize": "MaxResults" + } + } + }, + "com.amazonaws.lookoutequipment#ListSensorStatisticsRequest": { + "type": "structure", + "members": { + "DatasetName": { + "target": "com.amazonaws.lookoutequipment#DatasetName", + "traits": { + "smithy.api#documentation": "\nThe name of the dataset associated with the list of Sensor Statistics.\n
", + "smithy.api#required": {} + } + }, + "IngestionJobId": { + "target": "com.amazonaws.lookoutequipment#IngestionJobId", + "traits": { + "smithy.api#documentation": "\nThe ingestion job id associated with the list of Sensor Statistics. To get sensor statistics for a particular ingestion job id, both dataset name and ingestion job id must be submitted as inputs.\n
" + } + }, + "MaxResults": { + "target": "com.amazonaws.lookoutequipment#MaxResults", + "traits": { + "smithy.api#documentation": "\nSpecifies the maximum number of sensors for which to retrieve statistics.\n
" + } + }, + "NextToken": { + "target": "com.amazonaws.lookoutequipment#NextToken", + "traits": { + "smithy.api#documentation": "\nAn opaque pagination token indicating where to continue the listing of sensor statistics.\n
" + } + } + } + }, + "com.amazonaws.lookoutequipment#ListSensorStatisticsResponse": { + "type": "structure", + "members": { + "SensorStatisticsSummaries": { + "target": "com.amazonaws.lookoutequipment#SensorStatisticsSummaries", + "traits": { + "smithy.api#documentation": "\nProvides ingestion-based statistics regarding the specified sensor with respect to various validation types, such as whether data exists, the number and percentage of missing values, and the number and percentage of duplicate timestamps.\n
" + } + }, + "NextToken": { + "target": "com.amazonaws.lookoutequipment#NextToken", + "traits": { + "smithy.api#documentation": "\nAn opaque pagination token indicating where to continue the listing of sensor statistics.\n
" + } + } + } + }, "com.amazonaws.lookoutequipment#ListTagsForResource": { "type": "operation", "input": { @@ -2277,6 +2680,43 @@ } } }, + "com.amazonaws.lookoutequipment#MissingCompleteSensorData": { + "type": "structure", + "members": { + "AffectedSensorCount": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\n\nIndicates the number of sensors that have data missing completely.\n\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nEntity that comprises information on sensors that have sensor data completely missing.\n\n
" + } + }, + "com.amazonaws.lookoutequipment#MissingSensorData": { + "type": "structure", + "members": { + "AffectedSensorCount": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\n\nIndicates the number of sensors that have atleast some data missing.\n\n
", + "smithy.api#required": {} + } + }, + "TotalNumberOfMissingValues": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\n\nIndicates the total number of missing values across all the sensors.\n\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nEntity that comprises aggregated information on sensors having missing data.\n\n
" + } + }, "com.amazonaws.lookoutequipment#ModelArn": { "type": "string", "traits": { @@ -2366,6 +2806,61 @@ "smithy.api#documentation": "Provides information about the specified ML model, including dataset and model names and\n ARNs, as well as status.
" } }, + "com.amazonaws.lookoutequipment#MonotonicValues": { + "type": "structure", + "members": { + "Status": { + "target": "com.amazonaws.lookoutequipment#StatisticalIssueStatus", + "traits": { + "smithy.api#documentation": "\nIndicates whether there is a potential data issue related to having monotonic values.\n
", + "smithy.api#required": {} + } + }, + "Monotonicity": { + "target": "com.amazonaws.lookoutequipment#Monotonicity", + "traits": { + "smithy.api#documentation": "\nIndicates the monotonicity of values. Can be INCREASING, DECREASING, or STATIC.\n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\nEntity that comprises information on monotonic values in the data.\n
" + } + }, + "com.amazonaws.lookoutequipment#Monotonicity": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "DECREASING", + "name": "DECREASING" + }, + { + "value": "INCREASING", + "name": "INCREASING" + }, + { + "value": "STATIC", + "name": "STATIC" + } + ] + } + }, + "com.amazonaws.lookoutequipment#MultipleOperatingModes": { + "type": "structure", + "members": { + "Status": { + "target": "com.amazonaws.lookoutequipment#StatisticalIssueStatus", + "traits": { + "smithy.api#documentation": "\n Indicates whether there is a potential data issue related to having multiple operating modes.\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\nEntity that comprises information on operating modes in data.\n
" + } + }, "com.amazonaws.lookoutequipment#NameOrArn": { "type": "string", "traits": { @@ -2463,6 +2958,123 @@ "smithy.api#pattern": "^(^$)|([\\P{M}\\p{M}]{1,1023}/$)$" } }, + "com.amazonaws.lookoutequipment#SensorName": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 1, + "max": 200 + }, + "smithy.api#pattern": "^[0-9a-zA-Z:#$.\\-_]{1,200}$" + } + }, + "com.amazonaws.lookoutequipment#SensorStatisticsSummaries": { + "type": "list", + "member": { + "target": "com.amazonaws.lookoutequipment#SensorStatisticsSummary" + } + }, + "com.amazonaws.lookoutequipment#SensorStatisticsSummary": { + "type": "structure", + "members": { + "ComponentName": { + "target": "com.amazonaws.lookoutequipment#ComponentName", + "traits": { + "smithy.api#documentation": "\n\nName of the component to which the particular sensor belongs for which the statistics belong to.\n\n
" + } + }, + "SensorName": { + "target": "com.amazonaws.lookoutequipment#SensorName", + "traits": { + "smithy.api#documentation": "\n\nName of the sensor that the statistics belong to.\n\n
" + } + }, + "DataExists": { + "target": "com.amazonaws.lookoutequipment#Boolean", + "traits": { + "smithy.api#documentation": "\n\nParameter that indicates whether data exists for the sensor that the statistics belong to.\n\n
" + } + }, + "MissingValues": { + "target": "com.amazonaws.lookoutequipment#CountPercent", + "traits": { + "smithy.api#documentation": "\n\nParameter that describes the total number of, and percentage of, values that are missing for the sensor that the statistics belong to.\n\n
" + } + }, + "InvalidValues": { + "target": "com.amazonaws.lookoutequipment#CountPercent", + "traits": { + "smithy.api#documentation": "\n\nParameter that describes the total number of, and percentage of, values that are invalid for the sensor that the statistics belong to.\n\n
" + } + }, + "InvalidDateEntries": { + "target": "com.amazonaws.lookoutequipment#CountPercent", + "traits": { + "smithy.api#documentation": "\n\nParameter that describes the total number of invalid date entries associated with the sensor that the statistics belong to.\n\n
" + } + }, + "DuplicateTimestamps": { + "target": "com.amazonaws.lookoutequipment#CountPercent", + "traits": { + "smithy.api#documentation": "\nParameter that describes the total number of duplicate timestamp records associated with the sensor that the statistics belong to.\n
" + } + }, + "CategoricalValues": { + "target": "com.amazonaws.lookoutequipment#CategoricalValues", + "traits": { + "smithy.api#documentation": "\nParameter that describes potential risk about whether data associated with the sensor is categorical.\n
" + } + }, + "MultipleOperatingModes": { + "target": "com.amazonaws.lookoutequipment#MultipleOperatingModes", + "traits": { + "smithy.api#documentation": "\nParameter that describes potential risk about whether data associated with the sensor has more than one operating mode.\n
" + } + }, + "LargeTimestampGaps": { + "target": "com.amazonaws.lookoutequipment#LargeTimestampGaps", + "traits": { + "smithy.api#documentation": "\nParameter that describes potential risk about whether data associated with the sensor contains one or more large gaps between consecutive timestamps.\n
" + } + }, + "MonotonicValues": { + "target": "com.amazonaws.lookoutequipment#MonotonicValues", + "traits": { + "smithy.api#documentation": "\nParameter that describes potential risk about whether data associated with the sensor is mostly monotonic.\n
" + } + }, + "DataStartTime": { + "target": "com.amazonaws.lookoutequipment#Timestamp", + "traits": { + "smithy.api#documentation": "\nIndicates the time reference to indicate the beginning of valid data associated with the sensor that the statistics belong to.\n
" + } + }, + "DataEndTime": { + "target": "com.amazonaws.lookoutequipment#Timestamp", + "traits": { + "smithy.api#documentation": "\nIndicates the time reference to indicate the end of valid data associated with the sensor that the statistics belong to.\n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nSummary of ingestion statistics like whether data exists, number of missing values, number of invalid values and so on related to the particular sensor.\n\n
" + } + }, + "com.amazonaws.lookoutequipment#SensorsWithShortDateRange": { + "type": "structure", + "members": { + "AffectedSensorCount": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\n\nIndicates the number of sensors that have less than 90 days of data.\n\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nEntity that comprises information on sensors that have shorter date range.\n\n
" + } + }, "com.amazonaws.lookoutequipment#ServiceQuotaExceededException": { "type": "structure", "members": { @@ -2644,6 +3256,21 @@ } } }, + "com.amazonaws.lookoutequipment#StatisticalIssueStatus": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "POTENTIAL_ISSUE_DETECTED", + "name": "POTENTIAL_ISSUE_DETECTED" + }, + { + "value": "NO_ISSUE_DETECTED", + "name": "NO_ISSUE_DETECTED" + } + ] + } + }, "com.amazonaws.lookoutequipment#StopInferenceScheduler": { "type": "operation", "input": { @@ -2940,6 +3567,21 @@ "com.amazonaws.lookoutequipment#Timestamp": { "type": "timestamp" }, + "com.amazonaws.lookoutequipment#UnsupportedTimestamps": { + "type": "structure", + "members": { + "TotalNumberOfUnsupportedTimestamps": { + "target": "com.amazonaws.lookoutequipment#Integer", + "traits": { + "smithy.api#documentation": "\n\nIndicates the total number of unsupported timestamps across the ingested data.\n\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n\nEntity that comprises information abount unsupported timestamps in the dataset.\n\n
" + } + }, "com.amazonaws.lookoutequipment#UntagResource": { "type": "operation", "input": { diff --git a/codegen/sdk-codegen/aws-models/rekognition.json b/codegen/sdk-codegen/aws-models/rekognition.json index 6882826d790..5ce8b082068 100644 --- a/codegen/sdk-codegen/aws-models/rekognition.json +++ b/codegen/sdk-codegen/aws-models/rekognition.json @@ -247,7 +247,7 @@ } }, "traits": { - "smithy.api#documentation": "Identifies the bounding box around the label, face, text or personal protective equipment.\n The left (x-coordinate) and top (y-coordinate) are coordinates representing the top and\n left sides of the bounding box. Note that the upper-left corner of the image is the origin\n (0,0).
The top and left values returned are ratios of the overall\n image size. For example, if the input image is 700x200 pixels, and the top-left coordinate of\n the bounding box is 350x50 pixels, the API returns a left value of 0.5 (350/700)\n and a top value of 0.25 (50/200).
The width and height values represent the dimensions of the\n bounding box as a ratio of the overall image dimension. For example, if the input image is\n 700x200 pixels, and the bounding box width is 70 pixels, the width returned is 0.1.
The bounding box coordinates can have negative values. For example, if Amazon Rekognition is\n able to detect a face that is at the image edge and is only partially visible, the service\n can return coordinates that are outside the image bounds and, depending on the image edge,\n you might get negative values or values greater than 1 for the left or\n top values.
Identifies the bounding box around the label, face, text, object of interest, or personal protective equipment.\n The left (x-coordinate) and top (y-coordinate) are coordinates representing the top and\n left sides of the bounding box. Note that the upper-left corner of the image is the origin\n (0,0).
The top and left values returned are ratios of the overall\n image size. For example, if the input image is 700x200 pixels, and the top-left coordinate of\n the bounding box is 350x50 pixels, the API returns a left value of 0.5 (350/700)\n and a top value of 0.25 (50/200).
The width and height values represent the dimensions of the\n bounding box as a ratio of the overall image dimension. For example, if the input image is\n 700x200 pixels, and the bounding box width is 70 pixels, the width returned is 0.1.
The bounding box coordinates can have negative values. For example, if Amazon Rekognition is\n able to detect a face that is at the image edge and is only partially visible, the service\n can return coordinates that are outside the image bounds and, depending on the image edge,\n you might get negative values or values greater than 1 for the left or\n top values.
Type that describes the face Amazon Rekognition chose to compare with the faces in the target.\n This contains a bounding box for the selected face and confidence level that the bounding box\n contains a face. Note that Amazon Rekognition selects the largest face in the source image for this\n comparison.
" } }, + "com.amazonaws.rekognition#ConnectedHomeLabel": { + "type": "string" + }, + "com.amazonaws.rekognition#ConnectedHomeLabels": { + "type": "list", + "member": { + "target": "com.amazonaws.rekognition#ConnectedHomeLabel" + }, + "traits": { + "smithy.api#length": { + "min": 1, + "max": 128 + } + } + }, + "com.amazonaws.rekognition#ConnectedHomeSettings": { + "type": "structure", + "members": { + "Labels": { + "target": "com.amazonaws.rekognition#ConnectedHomeLabels", + "traits": { + "smithy.api#documentation": "\n Specifies what you want to detect in the video, such as people, packages, or pets. The current valid labels you can include in this list are: \"PERSON\", \"PET\", \"PACKAGE\", and \"ALL\".\n
", + "smithy.api#required": {} + } + }, + "MinConfidence": { + "target": "com.amazonaws.rekognition#Percent", + "traits": { + "smithy.api#documentation": "\n The minimum confidence required to label an object in the video. \n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\n Label detection settings to use on a streaming video. Defining the settings is required in the request parameter for CreateStreamProcessor.\n Including this setting in the CreateStreamProcessor request enables you to use the stream processor for label detection. \n You can then select what you want the stream processor to detect, such as people or pets. When the stream processor has started, one notification\n is sent for each object class specified. For example, if packages and pets are selected, one SNS notification is published the first time a package is detected \n and one SNS notification is published the first time a pet is detected, as well as an end-of-session summary. \n
\n Specifies what you want to detect in the video, such as people, packages, or pets. The current valid labels you can include in this list are: \"PERSON\", \"PET\", \"PACKAGE\", and \"ALL\".\n
" + } + }, + "MinConfidence": { + "target": "com.amazonaws.rekognition#Percent", + "traits": { + "smithy.api#documentation": "\n The minimum confidence required to label an object in the video. \n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\n The label detection settings you want to use in your stream processor. This includes the labels you want the stream processor to detect and the minimum confidence level allowed to label objects. \n
" + } + }, "com.amazonaws.rekognition#ContentClassifier": { "type": "string", "traits": { @@ -805,7 +861,7 @@ "FaceModelVersion": { "target": "com.amazonaws.rekognition#String", "traits": { - "smithy.api#documentation": "Latest face model being used with the collection. For more information, see Model versioning.
" + "smithy.api#documentation": "Version number of the face detection model associated with the collection you are creating.
" } } } @@ -1082,7 +1138,7 @@ } ], "traits": { - "smithy.api#documentation": "Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces in a streaming video.
\nAmazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. Amazon Rekognition Video sends analysis results to Amazon Kinesis Data Streams.
\nYou provide as input a Kinesis video stream (Input) and a Kinesis data stream (Output) stream. You also specify the\n face recognition criteria in Settings. For example, the collection containing faces that you want to recognize.\n Use Name to assign an identifier for the stream processor. You use Name\n to manage the stream processor. For example, you can start processing the source video by calling StartStreamProcessor with\n the Name field.
After you have finished analyzing a streaming video, use StopStreamProcessor to\n stop processing. You can delete the stream processor by calling DeleteStreamProcessor.
\nThis operation requires permissions to perform the\n rekognition:CreateStreamProcessor action. If you want to tag your stream processor, you also require permission to perform the rekognition:TagResource operation.
Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video.
\nAmazon Rekognition Video is a consumer of live video from Amazon Kinesis Video Streams. There are two different settings for stream processors in Amazon Rekognition: detecting faces and detecting labels.
\nIf you are creating a stream processor for detecting faces, you provide as input a Kinesis video stream (Input) and a Kinesis data stream (Output) stream. You also specify the\n face recognition criteria in Settings. For example, the collection containing faces that you want to recognize. After you have finished analyzing a streaming video, use StopStreamProcessor to\n stop processing.
If you are creating a stream processor to detect labels, you provide as input a Kinesis video stream (Input), Amazon S3 bucket information (Output), and an\n Amazon SNS topic ARN (NotificationChannel). You can also provide a KMS key ID to encrypt the data sent to your Amazon S3 bucket.\n You specify what you want to detect in ConnectedHomeSettings, such as people, packages and people, or pets, people, and packages. You can also specify where in the frame you want Amazon Rekognition to monitor with RegionsOfInterest. \n When you run the StartStreamProcessor operation on a label detection stream processor, you input start and stop information to determine the length of the processing time.
\n Use Name to assign an identifier for the stream processor. You use Name\n to manage the stream processor. For example, you can start processing the source video by calling StartStreamProcessor with\n the Name field.
This operation requires permissions to perform the\n rekognition:CreateStreamProcessor action. If you want to tag your stream processor, you also require permission to perform the rekognition:TagResource operation.
Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is StreamProcessorInput.
Kinesis video stream stream that provides the source streaming video. If you are using the AWS CLI, the parameter name is StreamProcessorInput. This is required for both face search and label detection stream processors.
Kinesis data stream stream to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is StreamProcessorOutput.
Kinesis data stream stream or Amazon S3 bucket location to which Amazon Rekognition Video puts the analysis results. If you are using the AWS CLI, the parameter name is StreamProcessorOutput. \n This must be a S3Destination of an Amazon S3 bucket that you own for a label detection stream processor or a Kinesis data stream ARN for a face search stream processor.
An identifier you assign to the stream processor. You can use Name to\n manage the stream processor. For example, you can get the current status of the stream processor by calling DescribeStreamProcessor.\n Name is idempotent.\n
An identifier you assign to the stream processor. You can use Name to\n manage the stream processor. For example, you can get the current status of the stream processor by calling DescribeStreamProcessor.\n Name is idempotent. This is required for both face search and label detection stream processors.\n
Face recognition input parameters to be used by the stream processor. Includes the collection to use for face recognition and the face\n attributes to detect.
", + "smithy.api#documentation": "Input parameters used in a streaming video analyzed by a stream processor. You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.
ARN of the IAM role that allows access to the stream processor.
", + "smithy.api#documentation": "The Amazon Resource Number (ARN) of the IAM role that allows access to the stream processor. \n The IAM role provides Rekognition read permissions for a Kinesis stream. \n It also provides write permissions to an Amazon S3 bucket and Amazon Simple Notification Service topic for a label detection stream processor. This is required for both face search and label detection stream processors.
", "smithy.api#required": {} } }, @@ -1128,6 +1184,27 @@ "traits": { "smithy.api#documentation": "\n A set of tags (key-value pairs) that you want to attach to the stream processor.\n
" } + }, + "NotificationChannel": { + "target": "com.amazonaws.rekognition#StreamProcessorNotificationChannel" + }, + "KmsKeyId": { + "target": "com.amazonaws.rekognition#KmsKeyId", + "traits": { + "smithy.api#documentation": "\n The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor.\n You can supply the Amazon Resource Name (ARN) of your KMS key, the ID of your KMS key, an alias for your KMS key, or an alias ARN. \n The key is used to encrypt results and data published to your Amazon S3 bucket, which includes image frames and hero images. Your source images are unaffected. \n
\n\n
" + } + }, + "RegionsOfInterest": { + "target": "com.amazonaws.rekognition#RegionsOfInterest", + "traits": { + "smithy.api#documentation": "\n Specifies locations in the frames where Amazon Rekognition checks for objects or people. You can specify up to 10 regions of interest. This is an optional parameter for label detection stream processors and should not be used to create a face search stream processor.\n
" + } + }, + "DataSharingPreference": { + "target": "com.amazonaws.rekognition#StreamProcessorDataSharingPreference", + "traits": { + "smithy.api#documentation": "\n Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis.\n Note that if you opt out at the account level this setting is ignored on individual streams.\n
" + } } } }, @@ -1137,7 +1214,7 @@ "StreamProcessorArn": { "target": "com.amazonaws.rekognition#StreamProcessorArn", "traits": { - "smithy.api#documentation": "ARN for the newly create stream processor.
" + "smithy.api#documentation": "Amazon Resource Number for the newly created stream processor.
" } } } @@ -1537,7 +1614,7 @@ } ], "traits": { - "smithy.api#documentation": "Deletes the specified collection. Note that this operation\n removes all faces in the collection. For an example, see delete-collection-procedure.
\n\nThis operation requires permissions to perform the\n rekognition:DeleteCollection action.
Deletes the specified collection. Note that this operation\n removes all faces in the collection. For an example, see Deleting a collection.
\n\nThis operation requires permissions to perform the\n rekognition:DeleteCollection action.
The version of the face model that's used by the collection for face detection.
\n \nFor more information, see Model Versioning in the \n Amazon Rekognition Developer Guide.
" + "smithy.api#documentation": "The version of the face model that's used by the collection for face detection.
\n \nFor more information, see Model versioning in the \n Amazon Rekognition Developer Guide.
" } }, "CollectionARN": { @@ -2298,7 +2375,28 @@ "Settings": { "target": "com.amazonaws.rekognition#StreamProcessorSettings", "traits": { - "smithy.api#documentation": "Face recognition input parameters that are being used by the stream processor.\n Includes the collection to use for face recognition and the face\n attributes to detect.
" + "smithy.api#documentation": "Input parameters used in a streaming video analyzed by a stream processor. You can use FaceSearch to recognize faces\n in a streaming video, or you can use ConnectedHome to detect labels.
\n The identifier for your AWS Key Management Service key (AWS KMS key). This is an optional parameter for label detection stream processors.\n
" + } + }, + "RegionsOfInterest": { + "target": "com.amazonaws.rekognition#RegionsOfInterest", + "traits": { + "smithy.api#documentation": "\n Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.\n
" + } + }, + "DataSharingPreference": { + "target": "com.amazonaws.rekognition#StreamProcessorDataSharingPreference", + "traits": { + "smithy.api#documentation": "\n Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis.\n Note that if you opt out at the account level this setting is ignored on individual streams.\n
" } } } @@ -2499,7 +2597,7 @@ } ], "traits": { - "smithy.api#documentation": "Detects instances of real-world entities within an image (JPEG or PNG)\n provided as input. This includes objects like flower, tree, and table; events like\n wedding, graduation, and birthday party; and concepts like landscape, evening, and nature.\n
\n \nFor an example, see Analyzing Images Stored in an Amazon S3 Bucket in the Amazon Rekognition Developer Guide.
\n\n DetectLabels does not support the detection of activities. However, activity detection\n is supported for label detection in videos. For more information, see StartLabelDetection in the Amazon Rekognition Developer Guide.
You pass the input image as base64-encoded image bytes or as a reference to an image in\n an Amazon S3 bucket. If you use the\n AWS\n CLI to call Amazon Rekognition operations, passing image bytes is not\n supported. The image must be either a PNG or JPEG formatted file.
\nFor each object, scene, and concept the API returns one or more labels. Each label\n provides the object name, and the level of confidence that the image contains the object. For\n example, suppose the input image has a lighthouse, the sea, and a rock. The response includes\n all three labels, one for each object.
\n \n\n {Name: lighthouse, Confidence: 98.4629}\n
\n {Name: rock,Confidence: 79.2097}\n
\n {Name: sea,Confidence: 75.061}\n
In the preceding example, the operation returns one label for each of the three\n objects. The operation can also return multiple labels for the same object in the image. For\n example, if the input image shows a flower (for example, a tulip), the operation might return\n the following three labels.
\n\n {Name: flower,Confidence: 99.0562}\n
\n {Name: plant,Confidence: 99.0562}\n
\n {Name: tulip,Confidence: 99.0562}\n
In this example, the detection algorithm more precisely identifies the flower as a\n tulip.
\nIn response, the API returns an array of labels. In addition, the response also\n includes the orientation correction. Optionally, you can specify MinConfidence to\n control the confidence threshold for the labels returned. The default is 55%. You can also add\n the MaxLabels parameter to limit the number of labels returned.
If the object detected is a person, the operation doesn't provide the same facial\n details that the DetectFaces operation provides.
\n\n DetectLabels returns bounding boxes for instances of common object labels in an array of\n Instance objects. An Instance object contains a \n BoundingBox object, for the location of the label on the image. It also includes \n the confidence by which the bounding box was detected.
\n DetectLabels also returns a hierarchical taxonomy of detected labels. For example,\n a detected car might be assigned the label car. The label car\n has two parent labels: Vehicle (its parent) and Transportation (its\n grandparent). \n The response returns the entire list of ancestors for a label. Each ancestor is a unique label in the response.\n In the previous example, Car, Vehicle, and Transportation\n are returned as unique labels in the response.\n
This is a stateless API operation. That is, the operation does not persist any\n data.
\nThis operation requires permissions to perform the\n rekognition:DetectLabels action.
Detects instances of real-world entities within an image (JPEG or PNG)\n provided as input. This includes objects like flower, tree, and table; events like\n wedding, graduation, and birthday party; and concepts like landscape, evening, and nature.\n
\n \nFor an example, see Analyzing images stored in an Amazon S3 bucket in the Amazon Rekognition Developer Guide.
\n\n DetectLabels does not support the detection of activities. However, activity detection\n is supported for label detection in videos. For more information, see StartLabelDetection in the Amazon Rekognition Developer Guide.
You pass the input image as base64-encoded image bytes or as a reference to an image in\n an Amazon S3 bucket. If you use the\n AWS\n CLI to call Amazon Rekognition operations, passing image bytes is not\n supported. The image must be either a PNG or JPEG formatted file.
\nFor each object, scene, and concept the API returns one or more labels. Each label\n provides the object name, and the level of confidence that the image contains the object. For\n example, suppose the input image has a lighthouse, the sea, and a rock. The response includes\n all three labels, one for each object.
\n \n\n {Name: lighthouse, Confidence: 98.4629}\n
\n {Name: rock,Confidence: 79.2097}\n
\n {Name: sea,Confidence: 75.061}\n
In the preceding example, the operation returns one label for each of the three\n objects. The operation can also return multiple labels for the same object in the image. For\n example, if the input image shows a flower (for example, a tulip), the operation might return\n the following three labels.
\n\n {Name: flower,Confidence: 99.0562}\n
\n {Name: plant,Confidence: 99.0562}\n
\n {Name: tulip,Confidence: 99.0562}\n
In this example, the detection algorithm more precisely identifies the flower as a\n tulip.
\nIn response, the API returns an array of labels. In addition, the response also\n includes the orientation correction. Optionally, you can specify MinConfidence to\n control the confidence threshold for the labels returned. The default is 55%. You can also add\n the MaxLabels parameter to limit the number of labels returned.
If the object detected is a person, the operation doesn't provide the same facial\n details that the DetectFaces operation provides.
\n\n DetectLabels returns bounding boxes for instances of common object labels in an array of\n Instance objects. An Instance object contains a \n BoundingBox object, for the location of the label on the image. It also includes \n the confidence by which the bounding box was detected.
\n DetectLabels also returns a hierarchical taxonomy of detected labels. For example,\n a detected car might be assigned the label car. The label car\n has two parent labels: Vehicle (its parent) and Transportation (its\n grandparent). \n The response returns the entire list of ancestors for a label. Each ancestor is a unique label in the response.\n In the previous example, Car, Vehicle, and Transportation\n are returned as unique labels in the response.\n
This is a stateless API operation. That is, the operation does not persist any\n data.
\nThis operation requires permissions to perform the\n rekognition:DetectLabels action.
Detects text in the input image and converts it into machine-readable text.
\nPass the input image as base64-encoded image bytes or as a reference to an image in an\n Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a\n reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not\n supported. The image must be either a .png or .jpeg formatted file.
\nThe DetectText operation returns text in an array of TextDetection elements, TextDetections. Each\n TextDetection element provides information about a single word or line of text\n that was detected in the image.
A word is one or more script characters that are not separated by spaces.\n DetectText can detect up to 100 words in an image.
A line is a string of equally spaced words. A line isn't necessarily a complete\n sentence. For example, a driver's license number is detected as a line. A line ends when there\n is no aligned text after it. Also, a line ends when there is a large gap between words,\n relative to the length of the words. This means, depending on the gap between words, Amazon Rekognition\n may detect multiple lines in text aligned in the same direction. Periods don't represent the\n end of a line. If a sentence spans multiple lines, the DetectText operation\n returns multiple lines.
To determine whether a TextDetection element is a line of text or a word,\n use the TextDetection object Type field.
To be detected, text must be within +/- 90 degrees orientation of the horizontal axis.
\n \nFor more information, see DetectText in the Amazon Rekognition Developer Guide.
" + "smithy.api#documentation": "Detects text in the input image and converts it into machine-readable text.
\nPass the input image as base64-encoded image bytes or as a reference to an image in an\n Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, you must pass it as a\n reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image bytes is not\n supported. The image must be either a .png or .jpeg formatted file.
\nThe DetectText operation returns text in an array of TextDetection elements, TextDetections. Each\n TextDetection element provides information about a single word or line of text\n that was detected in the image.
A word is one or more script characters that are not separated by spaces.\n DetectText can detect up to 100 words in an image.
A line is a string of equally spaced words. A line isn't necessarily a complete\n sentence. For example, a driver's license number is detected as a line. A line ends when there\n is no aligned text after it. Also, a line ends when there is a large gap between words,\n relative to the length of the words. This means, depending on the gap between words, Amazon Rekognition\n may detect multiple lines in text aligned in the same direction. Periods don't represent the\n end of a line. If a sentence spans multiple lines, the DetectText operation\n returns multiple lines.
To determine whether a TextDetection element is a line of text or a word,\n use the TextDetection object Type field.
To be detected, text must be within +/- 90 degrees orientation of the horizontal axis.
\n \nFor more information, see Detecting text in the Amazon Rekognition Developer Guide.
" } }, "com.amazonaws.rekognition#DetectTextFilters": { @@ -2812,7 +2910,7 @@ "MinConfidence": { "target": "com.amazonaws.rekognition#Percent", "traits": { - "smithy.api#documentation": "Sets the confidence of word detection. Words with detection confidence below this will be excluded \n from the result. Values should be between 50 and 100 as Text in Video will not return any result below \n 50.
" + "smithy.api#documentation": "Sets the confidence of word detection. Words with detection confidence below this will be\n excluded from the result. Values should be between 0 and 100. The default MinConfidence is\n 80.
" } }, "MinBoundingBoxHeight": { @@ -3384,7 +3482,7 @@ } }, "traits": { - "smithy.api#documentation": "Input face recognition parameters for an Amazon Rekognition stream processor. FaceRecognitionSettings is a request\n parameter for CreateStreamProcessor.
Input face recognition parameters for an Amazon Rekognition stream processor. \n Includes the collection to use for face recognition and the face attributes to detect. \n Defining the settings is required in the request parameter for CreateStreamProcessor.
" } }, "com.amazonaws.rekognition#FaceSearchSortBy": { @@ -3434,7 +3532,7 @@ } }, "traits": { - "smithy.api#documentation": "The predicted gender of a detected face. \n
\n \n \nAmazon Rekognition makes gender binary (male/female) predictions based on the physical appearance\n of a face in a particular image. This kind of prediction is not designed to categorize a person’s gender\n identity, and you shouldn't use Amazon Rekognition to make such a determination. For example, a male actor\n wearing a long-haired wig and earrings for a role might be predicted as female.
\n \nUsing Amazon Rekognition to make gender binary predictions is best suited for use cases where aggregate gender distribution statistics need to be \n analyzed without identifying specific users. For example, the percentage of female users compared to male users on a social media platform.
\n \nWe don't recommend using gender binary predictions to make decisions that impact\u2028 an individual's rights, privacy, or access to services.
" + "smithy.api#documentation": "The predicted gender of a detected face. \n
\n \n \nAmazon Rekognition makes gender binary (male/female) predictions based on the physical appearance\n of a face in a particular image. This kind of prediction is not designed to categorize a person’s gender\n identity, and you shouldn't use Amazon Rekognition to make such a determination. For example, a male actor\n wearing a long-haired wig and earrings for a role might be predicted as female.
\n \nUsing Amazon Rekognition to make gender binary predictions is best suited for use cases where aggregate gender distribution statistics need to be \n analyzed without identifying specific users. For example, the percentage of female users compared to male users on a social media platform.
\n \nWe don't recommend using gender binary predictions to make decisions that impact an individual's rights, privacy, or access to services.
" } }, "com.amazonaws.rekognition#GenderType": { @@ -3501,7 +3599,7 @@ } ], "traits": { - "smithy.api#documentation": "Gets the name and additional information about a celebrity based on their Amazon Rekognition ID.\n The additional information is returned as an array of URLs. If there is no additional\n information about the celebrity, this list is empty.
\n \nFor more information, see Recognizing Celebrities in an Image in\n the Amazon Rekognition Developer Guide.
\nThis operation requires permissions to perform the\n rekognition:GetCelebrityInfo action.
Gets the name and additional information about a celebrity based on their Amazon Rekognition ID.\n The additional information is returned as an array of URLs. If there is no additional\n information about the celebrity, this list is empty.
\n \nFor more information, see Getting information about a celebrity in\n the Amazon Rekognition Developer Guide.
\nThis operation requires permissions to perform the\n rekognition:GetCelebrityInfo action.
Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by\n StartContentModeration. For a list of moderation labels in Amazon Rekognition, see\n Using the image and video moderation APIs.
\n\nAmazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling\n StartContentModeration which returns a job identifier (JobId).\n When analysis finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service\n topic registered in the initial call to StartContentModeration.\n To get the results of the content analysis, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier\n (JobId) from the initial call to StartContentModeration.
For more information, see Working with Stored Videos in the\n Amazon Rekognition Devlopers Guide.
\n\n GetContentModeration returns detected inappropriate, unwanted, or offensive content moderation labels,\n and the time they are detected, in an array, ModerationLabels, of\n ContentModerationDetection objects.\n
By default, the moderated labels are returned sorted by time, in milliseconds from the start of the\n video. You can also sort them by moderated label by specifying NAME for the SortBy\n input parameter.
Since video analysis can return a large number of results, use the MaxResults parameter to limit\n the number of labels returned in a single call to GetContentModeration. If there are more results than\n specified in MaxResults, the value of NextToken in the operation response contains a\n pagination token for getting the next set of results. To get the next page of results, call GetContentModeration\n and populate the NextToken request parameter with the value of NextToken\n returned from the previous call to GetContentModeration.
For more information, see Content moderation in the Amazon Rekognition Developer Guide.
", + "smithy.api#documentation": "Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by\n StartContentModeration. For a list of moderation labels in Amazon Rekognition, see\n Using the image and video moderation APIs.
\n\nAmazon Rekognition Video inappropriate or offensive content detection in a stored video is an asynchronous operation. You start analysis by calling\n StartContentModeration which returns a job identifier (JobId).\n When analysis finishes, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service\n topic registered in the initial call to StartContentModeration.\n To get the results of the content analysis, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier\n (JobId) from the initial call to StartContentModeration.
For more information, see Working with Stored Videos in the\n Amazon Rekognition Devlopers Guide.
\n\n GetContentModeration returns detected inappropriate, unwanted, or offensive content moderation labels,\n and the time they are detected, in an array, ModerationLabels, of\n ContentModerationDetection objects.\n
By default, the moderated labels are returned sorted by time, in milliseconds from the start of the\n video. You can also sort them by moderated label by specifying NAME for the SortBy\n input parameter.
Since video analysis can return a large number of results, use the MaxResults parameter to limit\n the number of labels returned in a single call to GetContentModeration. If there are more results than\n specified in MaxResults, the value of NextToken in the operation response contains a\n pagination token for getting the next set of results. To get the next page of results, call GetContentModeration\n and populate the NextToken request parameter with the value of NextToken\n returned from the previous call to GetContentModeration.
For more information, see moderating content in the Amazon Rekognition Developer Guide.
", "smithy.api#paginated": { "inputToken": "NextToken", "outputToken": "NextToken", @@ -4207,7 +4305,7 @@ } ], "traits": { - "smithy.api#documentation": "Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.
\nSegment detection with Amazon Rekognition Video is an asynchronous operation. You start segment detection by \n calling StartSegmentDetection which returns a job identifier (JobId).\n When the segment detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service\n topic registered in the initial call to StartSegmentDetection. To get the results\n of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. \n if so, call GetSegmentDetection and pass the job identifier (JobId) from the initial call\n of StartSegmentDetection.
\n GetSegmentDetection returns detected segments in an array (Segments)\n of SegmentDetection objects. Segments is sorted by the segment types \n specified in the SegmentTypes input parameter of StartSegmentDetection. \n Each element of the array includes the detected segment, the precentage confidence in the acuracy \n of the detected segment, the type of the segment, and the frame in which the segment was detected.
Use SelectedSegmentTypes to find out the type of segment detection requested in the \n call to StartSegmentDetection.
Use the MaxResults parameter to limit the number of segment detections returned. If there are more results than \n specified in MaxResults, the value of NextToken in the operation response contains\n a pagination token for getting the next set of results. To get the next page of results, call GetSegmentDetection\n and populate the NextToken request parameter with the token value returned from the previous \n call to GetSegmentDetection.
For more information, see Detecting Video Segments in Stored Video in the Amazon Rekognition Developer Guide.
", + "smithy.api#documentation": "Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection.
\nSegment detection with Amazon Rekognition Video is an asynchronous operation. You start segment detection by \n calling StartSegmentDetection which returns a job identifier (JobId).\n When the segment detection operation finishes, Amazon Rekognition publishes a completion status to the Amazon Simple Notification Service\n topic registered in the initial call to StartSegmentDetection. To get the results\n of the segment detection operation, first check that the status value published to the Amazon SNS topic is SUCCEEDED. \n if so, call GetSegmentDetection and pass the job identifier (JobId) from the initial call\n of StartSegmentDetection.
\n GetSegmentDetection returns detected segments in an array (Segments)\n of SegmentDetection objects. Segments is sorted by the segment types \n specified in the SegmentTypes input parameter of StartSegmentDetection. \n Each element of the array includes the detected segment, the precentage confidence in the acuracy \n of the detected segment, the type of the segment, and the frame in which the segment was detected.
Use SelectedSegmentTypes to find out the type of segment detection requested in the \n call to StartSegmentDetection.
Use the MaxResults parameter to limit the number of segment detections returned. If there are more results than \n specified in MaxResults, the value of NextToken in the operation response contains\n a pagination token for getting the next set of results. To get the next page of results, call GetSegmentDetection\n and populate the NextToken request parameter with the token value returned from the previous \n call to GetSegmentDetection.
For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.
", "smithy.api#paginated": { "inputToken": "NextToken", "outputToken": "NextToken", @@ -4593,7 +4691,7 @@ } }, "traits": { - "smithy.api#documentation": "Provides the input image either as bytes or an S3 object.
\nYou pass image bytes to an Amazon Rekognition API operation by using the Bytes\n property. For example, you would use the Bytes property to pass an image loaded\n from a local file system. Image bytes passed by using the Bytes property must be\n base64-encoded. Your code may not need to encode image bytes if you are using an AWS SDK to\n call Amazon Rekognition API operations.
For more information, see Analyzing an Image Loaded from a Local File System \n in the Amazon Rekognition Developer Guide.
\n You pass images stored in an S3 bucket to an Amazon Rekognition API operation by using the\n S3Object property. Images stored in an S3 bucket do not need to be\n base64-encoded.
The region for the S3 bucket containing the S3 object must match the region you use for\n Amazon Rekognition operations.
\nIf you use the\n AWS\n CLI to call Amazon Rekognition operations, passing image bytes using the Bytes\n property is not supported. You must first upload the image to an Amazon S3 bucket and then\n call the operation using the S3Object property.
\n \nFor Amazon Rekognition to process an S3 object, the user must have permission to access the S3\n object. For more information, see Resource Based Policies in the Amazon Rekognition Developer Guide.\n
" + "smithy.api#documentation": "Provides the input image either as bytes or an S3 object.
\nYou pass image bytes to an Amazon Rekognition API operation by using the Bytes\n property. For example, you would use the Bytes property to pass an image loaded\n from a local file system. Image bytes passed by using the Bytes property must be\n base64-encoded. Your code may not need to encode image bytes if you are using an AWS SDK to\n call Amazon Rekognition API operations.
For more information, see Analyzing an Image Loaded from a Local File System \n in the Amazon Rekognition Developer Guide.
\n You pass images stored in an S3 bucket to an Amazon Rekognition API operation by using the\n S3Object property. Images stored in an S3 bucket do not need to be\n base64-encoded.
The region for the S3 bucket containing the S3 object must match the region you use for\n Amazon Rekognition operations.
\nIf you use the\n AWS\n CLI to call Amazon Rekognition operations, passing image bytes using the Bytes\n property is not supported. You must first upload the image to an Amazon S3 bucket and then\n call the operation using the S3Object property.
\n \nFor Amazon Rekognition to process an S3 object, the user must have permission to access the S3\n object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.\n
" } }, "com.amazonaws.rekognition#ImageBlob": { @@ -4648,7 +4746,7 @@ } }, "traits": { - "smithy.api#documentation": "The input image size exceeds the allowed limit. If you are calling\n DetectProtectiveEquipment, the image size or resolution exceeds the allowed limit. For more information, see \n Limits in Amazon Rekognition in the Amazon Rekognition Developer Guide.
", + "smithy.api#documentation": "The input image size exceeds the allowed limit. If you are calling\n DetectProtectiveEquipment, the image size or resolution exceeds the allowed limit. For more information, see \n Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide.
", "smithy.api#error": "client" } }, @@ -4693,7 +4791,7 @@ } ], "traits": { - "smithy.api#documentation": "Detects faces in the input image and adds them to the specified collection.
\nAmazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying\n detection algorithm first detects the faces in the input image. For each face, the algorithm\n extracts facial features into a feature vector, and stores it in the backend database.\n Amazon Rekognition uses feature vectors when it performs face match and search operations using the\n SearchFaces and SearchFacesByImage\n operations.
\n \nFor more information, see Adding Faces to a Collection in the Amazon Rekognition\n Developer Guide.
\nTo get the number of faces in a collection, call DescribeCollection.
\n\nIf you're using version 1.0 of the face detection model, IndexFaces\n indexes the 15 largest faces in the input image. Later versions of the face detection model\n index the 100 largest faces in the input image.
If you're using version 4 or later of the face model, image orientation information\n is not returned in the OrientationCorrection field.
To determine which version of the model you're using, call DescribeCollection\n and supply the collection ID. You can also get the model version from the value of FaceModelVersion in the response\n from IndexFaces\n
For more information, see Model Versioning in the Amazon Rekognition Developer\n Guide.
\nIf you provide the optional ExternalImageId for the input image you\n provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the ListFaces operation, the response returns the external ID. You can use this\n external image ID to create a client-side index to associate the faces with each image. You\n can then use the index to find all faces in an image.
You can specify the maximum number of faces to index with the MaxFaces input\n parameter. This is useful when you want to index the largest faces in an image and don't want to index\n smaller faces, such as those belonging to people standing in the background.
The QualityFilter input parameter allows you to filter out detected faces\n that don’t meet a required quality bar. The quality bar is based on a\n variety of common use cases. By default, IndexFaces chooses the quality bar that's \n used to filter faces. You can also explicitly choose\n the quality bar. Use QualityFilter, to set the quality bar\n by specifying LOW, MEDIUM, or HIGH.\n If you do not want to filter detected faces, specify NONE.
To use quality filtering, you need a collection associated with version 3 of the \n face model or higher. To get the version of the face model associated with a collection, call \n DescribeCollection.
\nInformation about faces detected in an image, but not indexed, is returned in an array of\n UnindexedFace objects, UnindexedFaces. Faces aren't\n indexed for reasons such as:
The number of faces detected exceeds the value of the MaxFaces request\n parameter.
The face is too small compared to the image dimensions.
\nThe face is too blurry.
\nThe image is too dark.
\nThe face has an extreme pose.
\nThe face doesn’t have enough detail to be suitable for face search.
\nIn response, the IndexFaces operation returns an array of metadata for \n all detected faces, FaceRecords. This includes:
The bounding box, BoundingBox, of the detected face.
A confidence value, Confidence, which indicates the confidence that the\n bounding box contains a face.
A face ID, FaceId, assigned by the service for each face that's detected\n and stored.
An image ID, ImageId, assigned by the service for the input image.
If you request all facial attributes (by using the detectionAttributes\n parameter), Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for\n example, location of eye and mouth) and other facial attributes. If you provide\n the same image, specify the same collection, use the same external ID, and use the same model version in the\n IndexFaces operation, Amazon Rekognition doesn't save duplicate face metadata.
The input image is passed either as base64-encoded image bytes, or as a reference to an\n image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations,\n passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.
\nThis operation requires permissions to perform the rekognition:IndexFaces\n action.
Detects faces in the input image and adds them to the specified collection.
\nAmazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying\n detection algorithm first detects the faces in the input image. For each face, the algorithm\n extracts facial features into a feature vector, and stores it in the backend database.\n Amazon Rekognition uses feature vectors when it performs face match and search operations using the\n SearchFaces and SearchFacesByImage\n operations.
\n \nFor more information, see Adding faces to a collection in the Amazon Rekognition\n Developer Guide.
\nTo get the number of faces in a collection, call DescribeCollection.
\n\nIf you're using version 1.0 of the face detection model, IndexFaces\n indexes the 15 largest faces in the input image. Later versions of the face detection model\n index the 100 largest faces in the input image.
If you're using version 4 or later of the face model, image orientation information\n is not returned in the OrientationCorrection field.
To determine which version of the model you're using, call DescribeCollection\n and supply the collection ID. You can also get the model version from the value of FaceModelVersion in the response\n from IndexFaces\n
For more information, see Model Versioning in the Amazon Rekognition Developer\n Guide.
\nIf you provide the optional ExternalImageId for the input image you\n provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the ListFaces operation, the response returns the external ID. You can use this\n external image ID to create a client-side index to associate the faces with each image. You\n can then use the index to find all faces in an image.
You can specify the maximum number of faces to index with the MaxFaces input\n parameter. This is useful when you want to index the largest faces in an image and don't want to index\n smaller faces, such as those belonging to people standing in the background.
The QualityFilter input parameter allows you to filter out detected faces\n that don’t meet a required quality bar. The quality bar is based on a\n variety of common use cases. By default, IndexFaces chooses the quality bar that's \n used to filter faces. You can also explicitly choose\n the quality bar. Use QualityFilter, to set the quality bar\n by specifying LOW, MEDIUM, or HIGH.\n If you do not want to filter detected faces, specify NONE.
To use quality filtering, you need a collection associated with version 3 of the \n face model or higher. To get the version of the face model associated with a collection, call \n DescribeCollection.
\nInformation about faces detected in an image, but not indexed, is returned in an array of\n UnindexedFace objects, UnindexedFaces. Faces aren't\n indexed for reasons such as:
The number of faces detected exceeds the value of the MaxFaces request\n parameter.
The face is too small compared to the image dimensions.
\nThe face is too blurry.
\nThe image is too dark.
\nThe face has an extreme pose.
\nThe face doesn’t have enough detail to be suitable for face search.
\nIn response, the IndexFaces operation returns an array of metadata for \n all detected faces, FaceRecords. This includes:
The bounding box, BoundingBox, of the detected face.
A confidence value, Confidence, which indicates the confidence that the\n bounding box contains a face.
A face ID, FaceId, assigned by the service for each face that's detected\n and stored.
An image ID, ImageId, assigned by the service for the input image.
If you request all facial attributes (by using the detectionAttributes\n parameter), Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for\n example, location of eye and mouth) and other facial attributes. If you provide\n the same image, specify the same collection, and use the same external ID in the\n IndexFaces operation, Amazon Rekognition doesn't save duplicate face metadata.
The input image is passed either as base64-encoded image bytes, or as a reference to an\n image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations,\n passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.
\nThis operation requires permissions to perform the rekognition:IndexFaces\n action.
Latest face model being used with the collection. For more information, see Model versioning.
" + "smithy.api#documentation": "The version number of the face detection model that's associated with the input\n collection (CollectionId).
Kinesis video stream stream that provides the source streaming video for a Amazon Rekognition Video stream processor. For more information, see\n CreateStreamProcessor in the Amazon Rekognition Developer Guide.
" } }, + "com.amazonaws.rekognition#KinesisVideoStreamFragmentNumber": { + "type": "string", + "traits": { + "smithy.api#length": { + "min": 1, + "max": 128 + }, + "smithy.api#pattern": "^[0-9]+$" + } + }, + "com.amazonaws.rekognition#KinesisVideoStreamStartSelector": { + "type": "structure", + "members": { + "ProducerTimestamp": { + "target": "com.amazonaws.rekognition#ULong", + "traits": { + "smithy.api#documentation": "\n The timestamp from the producer corresponding to the fragment.\n
" + } + }, + "FragmentNumber": { + "target": "com.amazonaws.rekognition#KinesisVideoStreamFragmentNumber", + "traits": { + "smithy.api#documentation": "\n The unique identifier of the fragment. This value monotonically increases based on the ingestion order.\n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\n Specifies the starting point in a Kinesis stream to start processing. \n You can use the producer timestamp or the fragment number.\n For more information, see Fragment. \n
" + } + }, "com.amazonaws.rekognition#KmsKeyId": { "type": "string", "traits": { @@ -5319,7 +5447,7 @@ } ], "traits": { - "smithy.api#documentation": "Returns list of collection IDs in your account.\n If the result is truncated, the response also provides a NextToken\n that you can use in the subsequent request to fetch the next set of collection IDs.
For an example, see Listing Collections in the Amazon Rekognition Developer Guide.
\nThis operation requires permissions to perform the rekognition:ListCollections action.
Returns list of collection IDs in your account.\n If the result is truncated, the response also provides a NextToken\n that you can use in the subsequent request to fetch the next set of collection IDs.
For an example, see Listing collections in the Amazon Rekognition Developer Guide.
\nThis operation requires permissions to perform the rekognition:ListCollections action.
Latest face models being used with the corresponding collections in the array. For more information, see Model versioning. \n For example, the value of FaceModelVersions[2] is the version number for the face detection model used\n by the collection in CollectionId[2].
Version numbers of the face detection models associated with the collections in the array CollectionIds.\n For example, the value of FaceModelVersions[2] is the version number for the face detection model used\n by the collection in CollectionId[2].
Latest face model being used with the collection. For more information, see Model versioning.
" + "smithy.api#documentation": "Version number of the face detection model associated with the input collection (CollectionId).
The Amazon SNS topic to which Amazon Rekognition to posts the completion status.
", + "smithy.api#documentation": "The Amazon SNS topic to which Amazon Rekognition posts the completion status.
", "smithy.api#required": {} } }, @@ -5941,7 +6079,7 @@ } }, "traits": { - "smithy.api#documentation": "The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the completion status of a video analysis operation. For more information, see\n api-video. Note that the Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.\n For more information, see Giving access to multiple Amazon SNS topics.
" + "smithy.api#documentation": "The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the completion status of a video analysis operation. For more information, see\n Calling Amazon Rekognition Video operations. Note that the Amazon SNS topic must have a topic name that begins with AmazonRekognition if you are using the AmazonRekognitionServiceRole permissions policy to access the topic.\n For more information, see Giving access to multiple Amazon SNS topics.
" } }, "com.amazonaws.rekognition#OrientationCorrection": { @@ -6155,7 +6293,7 @@ } }, "traits": { - "smithy.api#documentation": "The X and Y coordinates of a point on an image. The X and Y values returned are ratios\n of the overall image size. For example, if the input image is 700x200 and the \n operation returns X=0.5 and Y=0.25, then the point is at the (350,50) pixel coordinate on the image.
\n \nAn array of Point objects,\n Polygon, is returned by DetectText and by DetectCustomLabels. Polygon\n represents a fine-grained polygon around a detected item. For more information, see Geometry in the\n Amazon Rekognition Developer Guide.
The X and Y coordinates of a point on an image or video frame. The X and Y values are ratios\n of the overall image size or video resolution. For example, if an input image is 700x200 and the \n values are X=0.5 and Y=0.25, then the point is at the (350,50) pixel coordinate on the image.
\n \nAn array of Point objects makes up a Polygon.\n A Polygon is returned by DetectText and by DetectCustomLabels\n Polygon\n represents a fine-grained polygon around a detected item. For more information, see Geometry in the\n Amazon Rekognition Developer Guide.
Returns an array of celebrities recognized in the input image. For more information, see Recognizing Celebrities\n in the Amazon Rekognition Developer Guide.
\n\n RecognizeCelebrities returns the 64 largest faces in the image. It lists the\n recognized celebrities in the CelebrityFaces array and any unrecognized faces in\n the UnrecognizedFaces array. RecognizeCelebrities doesn't return\n celebrities whose faces aren't among the largest 64 faces in the image.
For each celebrity recognized, RecognizeCelebrities returns a\n Celebrity object. The Celebrity object contains the celebrity\n name, ID, URL links to additional information, match confidence, and a\n ComparedFace object that you can use to locate the celebrity's face on the\n image.
Amazon Rekognition doesn't retain information about which images a celebrity has been recognized\n in. Your application must store this information and use the Celebrity ID\n property as a unique identifier for the celebrity. If you don't store the celebrity name or\n additional information URLs returned by RecognizeCelebrities, you will need the\n ID to identify the celebrity in a call to the GetCelebrityInfo\n operation.
You pass the input image either as base64-encoded image bytes or as a reference to an\n image in an Amazon S3 bucket. If you use the\n AWS\n CLI to call Amazon Rekognition operations, passing image bytes is not\n supported. The image must be either a PNG or JPEG formatted file.
\n\n\n\n \nFor an example, see Recognizing Celebrities in an Image in the Amazon Rekognition Developer Guide.
\nThis operation requires permissions to perform the\n rekognition:RecognizeCelebrities operation.
Returns an array of celebrities recognized in the input image. For more information, see Recognizing celebrities\n in the Amazon Rekognition Developer Guide.
\n\n RecognizeCelebrities returns the 64 largest faces in the image. It lists the\n recognized celebrities in the CelebrityFaces array and any unrecognized faces in\n the UnrecognizedFaces array. RecognizeCelebrities doesn't return\n celebrities whose faces aren't among the largest 64 faces in the image.
For each celebrity recognized, RecognizeCelebrities returns a\n Celebrity object. The Celebrity object contains the celebrity\n name, ID, URL links to additional information, match confidence, and a\n ComparedFace object that you can use to locate the celebrity's face on the\n image.
Amazon Rekognition doesn't retain information about which images a celebrity has been recognized\n in. Your application must store this information and use the Celebrity ID\n property as a unique identifier for the celebrity. If you don't store the celebrity name or\n additional information URLs returned by RecognizeCelebrities, you will need the\n ID to identify the celebrity in a call to the GetCelebrityInfo\n operation.
You pass the input image either as base64-encoded image bytes or as a reference to an\n image in an Amazon S3 bucket. If you use the\n AWS\n CLI to call Amazon Rekognition operations, passing image bytes is not\n supported. The image must be either a PNG or JPEG formatted file.
\n\n\n\n \nFor an example, see Recognizing celebrities in an image in the Amazon Rekognition Developer Guide.
\nThis operation requires permissions to perform the\n rekognition:RecognizeCelebrities operation.
The box representing a region of interest on screen.
" } + }, + "Polygon": { + "target": "com.amazonaws.rekognition#Polygon", + "traits": { + "smithy.api#documentation": "\n Specifies a shape made up of up to 10 Point objects to define a region of interest.\n
Specifies a location within the frame that Rekognition checks for text. Uses a BoundingBox\n object to set a region of the screen.
A word is included in the region if the word is more than half in that region. If there is more than\n one region, the word will be compared with all regions of the screen. Any word more than half in a region\n is kept in the results.
" + "smithy.api#documentation": "Specifies a location within the frame that Rekognition checks for objects of interest such as text, labels, or faces. It uses a BoundingBox\n or object or Polygon to set a region of the screen.
A word, face, or label is included in the region if it is more than half in that region. If there is more than\n one region, the word, face, or label is compared with all regions of the screen. Any object of interest that is more than half in a region\n is kept in the results.
" } }, "com.amazonaws.rekognition#RegionsOfInterest": { @@ -6799,7 +6943,7 @@ "name": "rekognition" }, "aws.protocols#awsJson1_1": {}, - "smithy.api#documentation": "This is the Amazon Rekognition API reference.
", + "smithy.api#documentation": "This is the API Reference for Amazon Rekognition Image, \n Amazon Rekognition Custom Labels,\n Amazon Rekognition Stored Video, \n Amazon Rekognition Streaming Video.\n It provides descriptions of actions, data types, common parameters,\n and common errors.
\n\n\n Amazon Rekognition Image \n
\n\n\n Amazon Rekognition Custom Labels \n
\n\n Amazon Rekognition Video Stored Video \n
\n\n\n Amazon Rekognition Video Streaming Video \n
\n\n\n The name of the Amazon S3 bucket you want to associate with the streaming video project. You must be the owner of the Amazon S3 bucket.\n
" + } + }, + "KeyPrefix": { + "target": "com.amazonaws.rekognition#S3KeyPrefix", + "traits": { + "smithy.api#documentation": "\n The prefix value of the location within the bucket that you want the information to be published to. \n For more information, see Using prefixes.\n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\n The Amazon S3 bucket location to which Amazon Rekognition publishes the detailed inference results of a video analysis operation.\n These results include the name of the stream processor resource, the session ID of the stream processing session, \n and labeled timestamps and bounding boxes for detected labels. \n
" + } + }, "com.amazonaws.rekognition#S3KeyPrefix": { "type": "string", "traits": { @@ -7127,7 +7294,7 @@ } }, "traits": { - "smithy.api#documentation": "Provides the S3 bucket name and object name.
\nThe region for the S3 bucket containing the S3 object must match the region you use for\n Amazon Rekognition operations.
\n \nFor Amazon Rekognition to process an S3 object, the user must have permission to\n access the S3 object. For more information, see Resource-Based Policies in the Amazon Rekognition\n Developer Guide.
" + "smithy.api#documentation": "Provides the S3 bucket name and object name.
\nThe region for the S3 bucket containing the S3 object must match the region you use for\n Amazon Rekognition operations.
\n \nFor Amazon Rekognition to process an S3 object, the user must have permission to\n access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition\n Developer Guide.
" } }, "com.amazonaws.rekognition#S3ObjectName": { @@ -7183,7 +7350,7 @@ } ], "traits": { - "smithy.api#documentation": "For a given input face ID, searches for matching faces in the collection the face\n belongs to. You get a face ID when you add a face to the collection using the IndexFaces operation. The operation compares the features of the input face with\n faces in the specified collection.
\nYou can also search faces without indexing faces by using the\n SearchFacesByImage operation.
\n The operation response returns\n an array of faces that match, ordered by similarity score with the highest\n similarity first. More specifically, it is an\n array of metadata for each face match that is found. Along with the metadata, the response also\n includes a confidence value for each face match, indicating the confidence\n that the specific face matches the input face.\n
For an example, see Searching for a Face Using Its Face ID in the Amazon Rekognition Developer Guide.
\n\nThis operation requires permissions to perform the rekognition:SearchFaces\n action.
For a given input face ID, searches for matching faces in the collection the face\n belongs to. You get a face ID when you add a face to the collection using the IndexFaces operation. The operation compares the features of the input face with\n faces in the specified collection.
\nYou can also search faces without indexing faces by using the\n SearchFacesByImage operation.
\n The operation response returns\n an array of faces that match, ordered by similarity score with the highest\n similarity first. More specifically, it is an\n array of metadata for each face match that is found. Along with the metadata, the response also\n includes a confidence value for each face match, indicating the confidence\n that the specific face matches the input face.\n
For an example, see Searching for a face using its face ID in the Amazon Rekognition Developer Guide.
\n\nThis operation requires permissions to perform the rekognition:SearchFaces\n action.
Latest face model being used with the collection. For more information, see Model versioning.
" + "smithy.api#documentation": "Version number of the face detection model associated with the input collection (CollectionId).
Latest face model being used with the collection. For more information, see Model versioning.
" + "smithy.api#documentation": "Version number of the face detection model associated with the input collection (CollectionId).
The size of the collection exceeds the allowed limit. For more information, see \n Limits in Amazon Rekognition in the Amazon Rekognition Developer Guide.
", + "smithy.api#documentation": "\n \n \nThe size of the collection exceeds the allowed limit. For more information, see \n Guidelines and quotas in Amazon Rekognition in the Amazon Rekognition Developer Guide.
", "smithy.api#error": "client" } }, @@ -7594,7 +7761,7 @@ } ], "traits": { - "smithy.api#documentation": "Starts asynchronous recognition of celebrities in a stored video.
\nAmazon Rekognition Video can detect celebrities in a video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name\n and the filename of the video.\n StartCelebrityRecognition\n returns a job identifier (JobId) which you use to get the results of the analysis.\n When celebrity recognition analysis is finished, Amazon Rekognition Video publishes a completion status\n to the Amazon Simple Notification Service topic that you specify in NotificationChannel.\n To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetCelebrityRecognition and pass the job identifier\n (JobId) from the initial call to StartCelebrityRecognition.
For more information, see Recognizing Celebrities in the Amazon Rekognition Developer Guide.
", + "smithy.api#documentation": "Starts asynchronous recognition of celebrities in a stored video.
\nAmazon Rekognition Video can detect celebrities in a video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name\n and the filename of the video.\n StartCelebrityRecognition\n returns a job identifier (JobId) which you use to get the results of the analysis.\n When celebrity recognition analysis is finished, Amazon Rekognition Video publishes a completion status\n to the Amazon Simple Notification Service topic that you specify in NotificationChannel.\n To get the results of the celebrity recognition analysis, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetCelebrityRecognition and pass the job identifier\n (JobId) from the initial call to StartCelebrityRecognition.
For more information, see Recognizing celebrities in the Amazon Rekognition Developer Guide.
", "smithy.api#idempotent": {} } }, @@ -7677,7 +7844,7 @@ } ], "traits": { - "smithy.api#documentation": "Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video. For a list of moderation labels in Amazon Rekognition, see\n Using the image and video moderation APIs.
\nAmazon Rekognition Video can moderate content in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name\n and the filename of the video. StartContentModeration\n returns a job identifier (JobId) which you use to get the results of the analysis.\n When content analysis is finished, Amazon Rekognition Video publishes a completion status\n to the Amazon Simple Notification Service topic that you specify in NotificationChannel.
To get the results of the content analysis, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier\n (JobId) from the initial call to StartContentModeration.
For more information, see Content moderation in the Amazon Rekognition Developer Guide.
", + "smithy.api#documentation": "Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video. For a list of moderation labels in Amazon Rekognition, see\n Using the image and video moderation APIs.
\nAmazon Rekognition Video can moderate content in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name\n and the filename of the video. StartContentModeration\n returns a job identifier (JobId) which you use to get the results of the analysis.\n When content analysis is finished, Amazon Rekognition Video publishes a completion status\n to the Amazon Simple Notification Service topic that you specify in NotificationChannel.
To get the results of the content analysis, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetContentModeration and pass the job identifier\n (JobId) from the initial call to StartContentModeration.
For more information, see Moderating content in the Amazon Rekognition Developer Guide.
", "smithy.api#idempotent": {} } }, @@ -7766,7 +7933,7 @@ } ], "traits": { - "smithy.api#documentation": "Starts asynchronous detection of faces in a stored video.
\nAmazon Rekognition Video can detect faces in a video stored in an Amazon S3 bucket.\n Use Video to specify the bucket name and the filename of the video.\n StartFaceDetection returns a job identifier (JobId) that you\n use to get the results of the operation.\n When face detection is finished, Amazon Rekognition Video publishes a completion status\n to the Amazon Simple Notification Service topic that you specify in NotificationChannel.\n To get the results of the face detection operation, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetFaceDetection and pass the job identifier\n (JobId) from the initial call to StartFaceDetection.
For more information, see Detecting Faces in a Stored Video in the \n Amazon Rekognition Developer Guide.
", + "smithy.api#documentation": "Starts asynchronous detection of faces in a stored video.
\nAmazon Rekognition Video can detect faces in a video stored in an Amazon S3 bucket.\n Use Video to specify the bucket name and the filename of the video.\n StartFaceDetection returns a job identifier (JobId) that you\n use to get the results of the operation.\n When face detection is finished, Amazon Rekognition Video publishes a completion status\n to the Amazon Simple Notification Service topic that you specify in NotificationChannel.\n To get the results of the face detection operation, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetFaceDetection and pass the job identifier\n (JobId) from the initial call to StartFaceDetection.
For more information, see Detecting faces in a stored video in the \n Amazon Rekognition Developer Guide.
", "smithy.api#idempotent": {} } }, @@ -7858,7 +8025,7 @@ } ], "traits": { - "smithy.api#documentation": "Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.
\nThe video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name\n and the filename of the video. StartFaceSearch\n returns a job identifier (JobId) which you use to get the search results once the search has completed.\n When searching is finished, Amazon Rekognition Video publishes a completion status\n to the Amazon Simple Notification Service topic that you specify in NotificationChannel.\n To get the search results, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetFaceSearch and pass the job identifier\n (JobId) from the initial call to StartFaceSearch. For more information, see\n procedure-person-search-videos.
Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video.
\nThe video must be stored in an Amazon S3 bucket. Use Video to specify the bucket name\n and the filename of the video. StartFaceSearch\n returns a job identifier (JobId) which you use to get the search results once the search has completed.\n When searching is finished, Amazon Rekognition Video publishes a completion status\n to the Amazon Simple Notification Service topic that you specify in NotificationChannel.\n To get the search results, first check that the status value published to the Amazon SNS\n topic is SUCCEEDED. If so, call GetFaceSearch and pass the job identifier\n (JobId) from the initial call to StartFaceSearch. For more information, see\n Searching stored videos for faces.\n
Starts asynchronous detection of segment detection in a stored video.
\nAmazon Rekognition Video can detect segments in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and \n the filename of the video. StartSegmentDetection returns a job identifier (JobId) which you use to get \n the results of the operation. When segment detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic\n that you specify in NotificationChannel.
You can use the Filters (StartSegmentDetectionFilters) \n input parameter to specify the minimum detection confidence returned in the response. \n Within Filters, use ShotFilter (StartShotDetectionFilter)\n to filter detected shots. Use TechnicalCueFilter (StartTechnicalCueDetectionFilter)\n to filter technical cues.
To get the results of the segment detection operation, first check that the status value published to the Amazon SNS \n topic is SUCCEEDED. if so, call GetSegmentDetection and pass the job identifier (JobId) \n from the initial call to StartSegmentDetection.
For more information, see Detecting Video Segments in Stored Video in the Amazon Rekognition Developer Guide.
", + "smithy.api#documentation": "Starts asynchronous detection of segment detection in a stored video.
\nAmazon Rekognition Video can detect segments in a video stored in an Amazon S3 bucket. Use Video to specify the bucket name and \n the filename of the video. StartSegmentDetection returns a job identifier (JobId) which you use to get \n the results of the operation. When segment detection is finished, Amazon Rekognition Video publishes a completion status to the Amazon Simple Notification Service topic\n that you specify in NotificationChannel.
You can use the Filters (StartSegmentDetectionFilters) \n input parameter to specify the minimum detection confidence returned in the response. \n Within Filters, use ShotFilter (StartShotDetectionFilter)\n to filter detected shots. Use TechnicalCueFilter (StartTechnicalCueDetectionFilter)\n to filter technical cues.
To get the results of the segment detection operation, first check that the status value published to the Amazon SNS \n topic is SUCCEEDED. if so, call GetSegmentDetection and pass the job identifier (JobId) \n from the initial call to StartSegmentDetection.
For more information, see Detecting video segments in stored video in the Amazon Rekognition Developer Guide.
", "smithy.api#idempotent": {} } }, @@ -8317,7 +8484,7 @@ } ], "traits": { - "smithy.api#documentation": "Starts processing a stream processor. You create a stream processor by calling CreateStreamProcessor.\n To tell StartStreamProcessor which stream processor to start, use the value of the Name field specified in the call to\n CreateStreamProcessor.
Starts processing a stream processor. You create a stream processor by calling CreateStreamProcessor.\n To tell StartStreamProcessor which stream processor to start, use the value of the Name field specified in the call to\n CreateStreamProcessor.
If you are using a label detection stream processor to detect labels, you need to provide a Start selector and a Stop selector to determine the length of the stream processing time.
The name of the stream processor to start processing.
", "smithy.api#required": {} } + }, + "StartSelector": { + "target": "com.amazonaws.rekognition#StreamProcessingStartSelector", + "traits": { + "smithy.api#documentation": "\n Specifies the starting point in the Kinesis stream to start processing. \n You can use the producer timestamp or the fragment number. \n For more information, see Fragment. \n
\nThis is a required parameter for label detection stream processors and should not be used to start a face search stream processor.
" + } + }, + "StopSelector": { + "target": "com.amazonaws.rekognition#StreamProcessingStopSelector", + "traits": { + "smithy.api#documentation": "\n Specifies when to stop processing the stream. You can specify a \n maximum amount of time to process the video. \n
\nThis is a required parameter for label detection stream processors and should not be used to start a face search stream processor.
" + } } } }, "com.amazonaws.rekognition#StartStreamProcessorResponse": { "type": "structure", - "members": {} + "members": { + "SessionId": { + "target": "com.amazonaws.rekognition#StartStreamProcessorSessionId", + "traits": { + "smithy.api#documentation": "\n A unique identifier for the stream processing session. \n
" + } + } + } + }, + "com.amazonaws.rekognition#StartStreamProcessorSessionId": { + "type": "string" }, "com.amazonaws.rekognition#StartTechnicalCueDetectionFilter": { "type": "structure", @@ -8573,6 +8762,34 @@ "type": "structure", "members": {} }, + "com.amazonaws.rekognition#StreamProcessingStartSelector": { + "type": "structure", + "members": { + "KVSStreamStartSelector": { + "target": "com.amazonaws.rekognition#KinesisVideoStreamStartSelector", + "traits": { + "smithy.api#documentation": "\n Specifies the starting point in the stream to start processing. This can be done with a timestamp or a fragment number in a Kinesis stream.\n
" + } + } + }, + "traits": { + "smithy.api#documentation": "" + } + }, + "com.amazonaws.rekognition#StreamProcessingStopSelector": { + "type": "structure", + "members": { + "MaxDurationInSeconds": { + "target": "com.amazonaws.rekognition#MaxDurationInSecondsULong", + "traits": { + "smithy.api#documentation": "\n Specifies the maximum amount of time in seconds that you want the stream to be processed. The largest amount of time is 2 minutes. The default is 10 seconds.\n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\n Specifies when to stop processing the stream. You can specify a maximum amount\n of time to process the video. \n
" + } + }, "com.amazonaws.rekognition#StreamProcessor": { "type": "structure", "members": { @@ -8590,7 +8807,7 @@ } }, "traits": { - "smithy.api#documentation": "An object that recognizes faces in a streaming video. An Amazon Rekognition stream processor is created by a call to CreateStreamProcessor. The request\n parameters for CreateStreamProcessor describe the Kinesis video stream source for the streaming video, face recognition parameters, and where to stream the analysis resullts.\n\n
An object that recognizes faces or labels in a streaming video. An Amazon Rekognition stream processor is created by a call to CreateStreamProcessor. The request\n parameters for CreateStreamProcessor describe the Kinesis video stream source for the streaming video, face recognition parameters, and where to stream the analysis resullts.\n\n
\n If this option is set to true, you choose to share data with Rekognition to improve model performance.\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "\n Allows you to opt in or opt out to share data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis.\n Note that if you opt out at the account level this setting is ignored on individual streams.\n \n
" + } + }, "com.amazonaws.rekognition#StreamProcessorInput": { "type": "structure", "members": { @@ -8629,6 +8861,21 @@ "smithy.api#pattern": "^[a-zA-Z0-9_.\\-]+$" } }, + "com.amazonaws.rekognition#StreamProcessorNotificationChannel": { + "type": "structure", + "members": { + "SNSTopicArn": { + "target": "com.amazonaws.rekognition#SNSTopicArn", + "traits": { + "smithy.api#documentation": "\n The Amazon Resource Number (ARN) of the Amazon Amazon Simple Notification Service topic to which Amazon Rekognition posts the completion status.\n
", + "smithy.api#required": {} + } + } + }, + "traits": { + "smithy.api#documentation": "The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.
\nAmazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition\n detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications,\n one for a person at second 2 and one for a pet at second 4.
\nAmazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.
" + } + }, "com.amazonaws.rekognition#StreamProcessorOutput": { "type": "structure", "members": { @@ -8637,12 +8884,39 @@ "traits": { "smithy.api#documentation": "The Amazon Kinesis Data Streams stream to which the Amazon Rekognition stream processor streams the analysis results.
" } + }, + "S3Destination": { + "target": "com.amazonaws.rekognition#S3Destination", + "traits": { + "smithy.api#documentation": "\n The Amazon S3 bucket location to which Amazon Rekognition publishes the detailed inference results of a video analysis operation.\n
" + } } }, "traits": { "smithy.api#documentation": "Information about the Amazon Kinesis Data Streams stream to which a Amazon Rekognition Video stream processor streams the results of a video analysis. For more\n information, see CreateStreamProcessor in the Amazon Rekognition Developer Guide.
" } }, + "com.amazonaws.rekognition#StreamProcessorParameterToDelete": { + "type": "string", + "traits": { + "smithy.api#enum": [ + { + "value": "ConnectedHomeMinConfidence", + "name": "ConnectedHomeMinConfidence" + }, + { + "value": "RegionsOfInterest", + "name": "RegionsOfInterest" + } + ] + } + }, + "com.amazonaws.rekognition#StreamProcessorParametersToDelete": { + "type": "list", + "member": { + "target": "com.amazonaws.rekognition#StreamProcessorParameterToDelete" + } + }, "com.amazonaws.rekognition#StreamProcessorSettings": { "type": "structure", "members": { @@ -8651,10 +8925,27 @@ "traits": { "smithy.api#documentation": "Face search settings to use on a streaming video.
" } + }, + "ConnectedHome": { + "target": "com.amazonaws.rekognition#ConnectedHomeSettings" } }, "traits": { - "smithy.api#documentation": "Input parameters used to recognize faces in a streaming video analyzed by a Amazon Rekognition stream processor.
" + "smithy.api#documentation": "Input parameters used in a streaming video analyzed by a Amazon Rekognition stream processor. \n You can use FaceSearch to recognize faces in a streaming video, or you can use ConnectedHome to detect labels.
\n The label detection settings you want to use for your stream processor.\n
" + } + } + }, + "traits": { + "smithy.api#documentation": "\n The stream processor settings that you want to update. ConnectedHome settings can be updated to detect different labels with a different minimum confidence.\n
Information about a word or line of text detected by DetectText.
\nThe DetectedText field contains the text that Amazon Rekognition detected in the\n image.
Every word and line has an identifier (Id). Each word belongs to a line\n and has a parent identifier (ParentId) that identifies the line of text in which\n the word appears. The word Id is also an index for the word within a line of\n words.
For more information, see Detecting Text in the Amazon Rekognition Developer Guide.
" + "smithy.api#documentation": "Information about a word or line of text detected by DetectText.
\nThe DetectedText field contains the text that Amazon Rekognition detected in the\n image.
Every word and line has an identifier (Id). Each word belongs to a line\n and has a parent identifier (ParentId) that identifies the line of text in which\n the word appears. The word Id is also an index for the word within a line of\n words.
For more information, see Detecting text in the Amazon Rekognition Developer Guide.
" } }, "com.amazonaws.rekognition#TextDetectionList": { @@ -9252,6 +9547,78 @@ "type": "structure", "members": {} }, + "com.amazonaws.rekognition#UpdateStreamProcessor": { + "type": "operation", + "input": { + "target": "com.amazonaws.rekognition#UpdateStreamProcessorRequest" + }, + "output": { + "target": "com.amazonaws.rekognition#UpdateStreamProcessorResponse" + }, + "errors": [ + { + "target": "com.amazonaws.rekognition#AccessDeniedException" + }, + { + "target": "com.amazonaws.rekognition#InternalServerError" + }, + { + "target": "com.amazonaws.rekognition#InvalidParameterException" + }, + { + "target": "com.amazonaws.rekognition#ProvisionedThroughputExceededException" + }, + { + "target": "com.amazonaws.rekognition#ResourceNotFoundException" + }, + { + "target": "com.amazonaws.rekognition#ThrottlingException" + } + ], + "traits": { + "smithy.api#documentation": "\n Allows you to update a stream processor. You can change some settings and regions of interest and delete certain parameters.\n
" + } + }, + "com.amazonaws.rekognition#UpdateStreamProcessorRequest": { + "type": "structure", + "members": { + "Name": { + "target": "com.amazonaws.rekognition#StreamProcessorName", + "traits": { + "smithy.api#documentation": "\n Name of the stream processor that you want to update.\n
", + "smithy.api#required": {} + } + }, + "SettingsForUpdate": { + "target": "com.amazonaws.rekognition#StreamProcessorSettingsForUpdate", + "traits": { + "smithy.api#documentation": "\n The stream processor settings that you want to update. Label detection settings can be updated to detect different labels with a different minimum confidence.\n
" + } + }, + "RegionsOfInterestForUpdate": { + "target": "com.amazonaws.rekognition#RegionsOfInterest", + "traits": { + "smithy.api#documentation": "\n Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.\n
" + } + }, + "DataSharingPreferenceForUpdate": { + "target": "com.amazonaws.rekognition#StreamProcessorDataSharingPreference", + "traits": { + "smithy.api#documentation": "\n Shows whether you are sharing data with Rekognition to improve model performance. You can choose this option at the account level or on a per-stream basis.\n Note that if you opt out at the account level this setting is ignored on individual streams.\n
" + } + }, + "ParametersToDelete": { + "target": "com.amazonaws.rekognition#StreamProcessorParametersToDelete", + "traits": { + "smithy.api#documentation": "\n A list of parameters you want to delete from the stream processor.\n
" + } + } + } + }, + "com.amazonaws.rekognition#UpdateStreamProcessorResponse": { + "type": "structure", + "members": {} + }, "com.amazonaws.rekognition#Url": { "type": "string" }, diff --git a/codegen/sdk-codegen/aws-models/sagemaker.json b/codegen/sdk-codegen/aws-models/sagemaker.json index 56c303ecb90..9c07492f052 100644 --- a/codegen/sdk-codegen/aws-models/sagemaker.json +++ b/codegen/sdk-codegen/aws-models/sagemaker.json @@ -240,7 +240,7 @@ "target": "com.amazonaws.sagemaker#AddTagsOutput" }, "traits": { - "smithy.api#documentation": "Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add\n tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform\n jobs, models, labeling jobs, work teams, endpoint configurations, and\n endpoints.
\nEach tag consists of a key and an optional value. Tag keys must be unique per\n resource. For more information about tags, see For more information, see Amazon Web Services\n Tagging Strategies.
\nTags that you add to a hyperparameter tuning job by calling this API are also\n added to any training jobs that the hyperparameter tuning job launches after you\n call this API, but not to training jobs that the hyperparameter tuning job launched\n before you called this API. To make sure that the tags associated with a\n hyperparameter tuning job are also added to all training jobs that the\n hyperparameter tuning job launches, add the tags when you first create the tuning\n job by specifying them in the Tags parameter of CreateHyperParameterTuningJob\n
Tags that you add to a SageMaker Studio Domain or User Profile by calling this API\n are also added to any Apps that the Domain or User Profile launches after you call\n this API, but not to Apps that the Domain or User Profile launched before you called\n this API. To make sure that the tags associated with a Domain or User Profile are\n also added to all Apps that the Domain or User Profile launches, add the tags when\n you first create the Domain or User Profile by specifying them in the\n Tags parameter of CreateDomain or CreateUserProfile.
Adds or overwrites one or more tags for the specified SageMaker resource. You can add\n tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform\n jobs, models, labeling jobs, work teams, endpoint configurations, and\n endpoints.
\nEach tag consists of a key and an optional value. Tag keys must be unique per\n resource. For more information about tags, see For more information, see Amazon Web Services\n Tagging Strategies.
\nTags that you add to a hyperparameter tuning job by calling this API are also\n added to any training jobs that the hyperparameter tuning job launches after you\n call this API, but not to training jobs that the hyperparameter tuning job launched\n before you called this API. To make sure that the tags associated with a\n hyperparameter tuning job are also added to all training jobs that the\n hyperparameter tuning job launches, add the tags when you first create the tuning\n job by specifying them in the Tags parameter of CreateHyperParameterTuningJob\n
Tags that you add to a SageMaker Studio Domain or User Profile by calling this API\n are also added to any Apps that the Domain or User Profile launches after you call\n this API, but not to Apps that the Domain or User Profile launched before you called\n this API. To make sure that the tags associated with a Domain or User Profile are\n also added to all Apps that the Domain or User Profile launches, add the tags when\n you first create the Domain or User Profile by specifying them in the\n Tags parameter of CreateDomain or CreateUserProfile.
A list of tags associated with the Amazon SageMaker resource.
" + "smithy.api#documentation": "A list of tags associated with the SageMaker resource.
" } } } @@ -454,7 +454,7 @@ "TrainingImage": { "target": "com.amazonaws.sagemaker#AlgorithmImage", "traits": { - "smithy.api#documentation": "The registry path of the Docker image\n that contains the training algorithm.\n For information about docker registry paths for built-in algorithms, see Algorithms\n Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with Amazon\n SageMaker.
The registry path of the Docker image\n that contains the training algorithm.\n For information about docker registry paths for built-in algorithms, see Algorithms\n Provided by Amazon SageMaker: Common Parameters. SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with Amazon\n SageMaker.
A list of metric definition objects. Each object specifies the metric name and regular\n expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
" + "smithy.api#documentation": "A list of metric definition objects. Each object specifies the metric name and regular\n expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
" } }, "EnableSageMakerMetricsTimeSeries": { "target": "com.amazonaws.sagemaker#Boolean", "traits": { - "smithy.api#documentation": "To generate and save time-series metrics during training, set to true.\n The default is false and time-series metrics aren't generated except in the\n following cases:
You use one of the Amazon SageMaker built-in algorithms
\nYou use one of the following Prebuilt Amazon SageMaker Docker Images:
\nTensorflow (version >= 1.15)
\nMXNet (version >= 1.6)
\nPyTorch (version >= 1.3)
\nYou specify at least one MetricDefinition\n
\nTo generate and save time-series metrics during training, set to true.\n The default is false and time-series metrics aren't generated except in the\n following cases:
You use one of the SageMaker built-in algorithms
\nYou use one of the following Prebuilt SageMaker Docker Images:
\nTensorflow (version >= 1.15)
\nMXNet (version >= 1.6)
\nPyTorch (version >= 1.3)
\nYou specify at least one MetricDefinition\n
\nSpecifies the training algorithm to use in a CreateTrainingJob\n request.
\nFor more information about algorithms provided by Amazon SageMaker, see Algorithms. For\n information about using your own algorithms, see Using Your Own Algorithms with Amazon\n SageMaker.
" + "smithy.api#documentation": "Specifies the training algorithm to use in a CreateTrainingJob\n request.
\nFor more information about algorithms provided by SageMaker, see Algorithms. For\n information about using your own algorithms, see Using Your Own Algorithms with Amazon\n SageMaker.
" } }, "com.amazonaws.sagemaker#AlgorithmStatus": { @@ -628,19 +628,19 @@ "TrainingJobDefinition": { "target": "com.amazonaws.sagemaker#TrainingJobDefinition", "traits": { - "smithy.api#documentation": "The TrainingJobDefinition object that describes the training job that\n Amazon SageMaker runs to validate your algorithm.
The TrainingJobDefinition object that describes the training job that\n SageMaker runs to validate your algorithm.
The TransformJobDefinition object that describes the transform job that\n Amazon SageMaker runs to validate your algorithm.
The TransformJobDefinition object that describes the transform job that\n SageMaker runs to validate your algorithm.
Defines a training job and a batch transform job that Amazon SageMaker runs to validate your\n algorithm.
\nThe data provided in the validation profile is made available to your buyers on Amazon Web Services\n Marketplace.
" + "smithy.api#documentation": "Defines a training job and a batch transform job that SageMaker runs to validate your\n algorithm.
\nThe data provided in the validation profile is made available to your buyers on Amazon Web Services\n Marketplace.
" } }, "com.amazonaws.sagemaker#AlgorithmValidationProfiles": { @@ -661,20 +661,20 @@ "ValidationRole": { "target": "com.amazonaws.sagemaker#RoleArn", "traits": { - "smithy.api#documentation": "The IAM roles that Amazon SageMaker uses to run the training jobs.
", + "smithy.api#documentation": "The IAM roles that SageMaker uses to run the training jobs.
", "smithy.api#required": {} } }, "ValidationProfiles": { "target": "com.amazonaws.sagemaker#AlgorithmValidationProfiles", "traits": { - "smithy.api#documentation": "An array of AlgorithmValidationProfile objects, each of which specifies a\n training job and batch transform job that Amazon SageMaker runs to validate your algorithm.
An array of AlgorithmValidationProfile objects, each of which specifies a\n training job and batch transform job that SageMaker runs to validate your algorithm.
Specifies configurations for one or more training jobs that Amazon SageMaker runs to test the\n algorithm.
" + "smithy.api#documentation": "Specifies configurations for one or more training jobs that SageMaker runs to test the\n algorithm.
" } }, "com.amazonaws.sagemaker#AnnotationConsolidationConfig": { @@ -1506,12 +1506,12 @@ "MaxConcurrentInvocationsPerInstance": { "target": "com.amazonaws.sagemaker#MaxConcurrentInvocationsPerInstance", "traits": { - "smithy.api#documentation": "The maximum number of concurrent requests sent by the SageMaker client to the \n model container. If no value is provided, Amazon SageMaker will choose an optimal value for you.
" + "smithy.api#documentation": "The maximum number of concurrent requests sent by the SageMaker client to the \n model container. If no value is provided, SageMaker chooses an optimal value.
" } } }, "traits": { - "smithy.api#documentation": "Configures the behavior of the client used by Amazon SageMaker to interact with the \n model container during asynchronous inference.
" + "smithy.api#documentation": "Configures the behavior of the client used by SageMaker to interact with the \n model container during asynchronous inference.
" } }, "com.amazonaws.sagemaker#AsyncInferenceConfig": { @@ -1520,7 +1520,7 @@ "ClientConfig": { "target": "com.amazonaws.sagemaker#AsyncInferenceClientConfig", "traits": { - "smithy.api#documentation": "Configures the behavior of the client used by Amazon SageMaker to interact \n with the model container during asynchronous inference.
" + "smithy.api#documentation": "Configures the behavior of the client used by SageMaker to interact \n with the model container during asynchronous inference.
" } }, "OutputConfig": { @@ -1561,7 +1561,7 @@ "KmsKeyId": { "target": "com.amazonaws.sagemaker#KmsKeyId", "traits": { - "smithy.api#documentation": "The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that\n Amazon SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
\n " + "smithy.api#documentation": "The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that\n SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
\n " } }, "S3OutputPath": { @@ -1903,12 +1903,33 @@ "ContentType": { "target": "com.amazonaws.sagemaker#ContentType", "traits": { - "smithy.api#documentation": "The content type of the data from the input source. You can use\n text/csv;header=present or x-application/vnd.amazon+parquet.\n The default value is text/csv;header=present.
The content type of the data from the input source. You can use\n text/csv;header=present or x-application/vnd.amazon+parquet.\n The default value is text/csv;header=present.
The channel type (optional) is an enum string. The default value is\n training. Channels for training and validation must share the same\n ContentType and TargetAttributeName.
A channel is a named input source that training algorithms can consume. For more\n information, see .
" + "smithy.api#documentation": "A channel is a named input source that training algorithms can consume. The\n validation dataset size is limited to less than 2 GB. The training dataset size must be\n less than 100 GB. For more information, see .
\nA validation dataset must contain the same headers as the training dataset.
\nThe data source for the Autopilot job.
" } }, + "com.amazonaws.sagemaker#AutoMLDataSplitConfig": { + "type": "structure", + "members": { + "ValidationFraction": { + "target": "com.amazonaws.sagemaker#ValidationFraction", + "traits": { + "smithy.api#documentation": "The validation fraction (optional) is a float that specifies the portion of the training\n dataset to be used for validation. The default value is 0.2, and values can range from 0 to\n 1. We recommend setting this value to be less than 0.5.
" + } + } + }, + "traits": { + "smithy.api#documentation": "This structure specifies how to split the data into train and test datasets. The\n validation and training datasets must contain the same headers. The validation dataset must\n be less than 2 GB in size.
" + } + }, "com.amazonaws.sagemaker#AutoMLFailureReason": { "type": "string", "traits": { @@ -2057,6 +2092,12 @@ "traits": { "smithy.api#documentation": "The security configuration for traffic encryption or Amazon VPC settings.
" } + }, + "DataSplitConfig": { + "target": "com.amazonaws.sagemaker#AutoMLDataSplitConfig", + "traits": { + "smithy.api#documentation": "The configuration for splitting the input training dataset.
\nType: AutoMLDataSplitConfig
" + } } }, "traits": { @@ -2702,7 +2743,7 @@ "MaximumExecutionTimeoutInSeconds": { "target": "com.amazonaws.sagemaker#MaximumExecutionTimeoutInSeconds", "traits": { - "smithy.api#documentation": "Maximum execution timeout for the deployment. Note that the timeout value should be larger\n than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds.
Maximum execution timeout for the deployment. Note that the timeout value should be larger\n than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds.
The Amazon S3 prefix to the model insight artifacts generated for the AutoML\n candidate.
" + "smithy.api#documentation": "The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
" } } }, @@ -2952,7 +2993,7 @@ "Type": { "target": "com.amazonaws.sagemaker#CapacitySizeType", "traits": { - "smithy.api#documentation": "Specifies the endpoint capacity type.
\n\n INSTANCE_COUNT: The endpoint activates based on\n the number of instances.
\n CAPACITY_PERCENT: The endpoint activates based on\n the specified percentage of capacity.
Specifies the endpoint capacity type.
\n\n INSTANCE_COUNT: The endpoint activates based on\n the number of instances.
\n CAPACITY_PERCENT: The endpoint activates based on\n the specified percentage of capacity.
Specify RecordIO as the value when input data is in raw format but the training\n algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3\n object in a RecordIO record. If the input data is already in RecordIO format, you don't\n need to set this attribute. For more information, see Create\n a Dataset Using RecordIO.
\nIn File mode, leave this field unset or set it to None.
" + "smithy.api#documentation": "\nSpecify RecordIO as the value when input data is in raw format but the training\n algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3\n object in a RecordIO record. If the input data is already in RecordIO format, you don't\n need to set this attribute. For more information, see Create\n a Dataset Using RecordIO.
\nIn File mode, leave this field unset or set it to None.
" } }, "InputMode": { "target": "com.amazonaws.sagemaker#TrainingInputMode", "traits": { - "smithy.api#documentation": "(Optional) The input mode to use for the data channel in a training job. If you don't\n set a value for InputMode, Amazon SageMaker uses the value set for\n TrainingInputMode. Use this parameter to override the\n TrainingInputMode setting in a AlgorithmSpecification\n request when you have a channel that needs a different input mode from the training\n job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML\n storage volume, and mount the directory to a Docker volume, use File input\n mode. To stream data directly from Amazon S3 to the container, choose Pipe input\n mode.
To use a model for incremental training, choose File input model.
(Optional) The input mode to use for the data channel in a training job. If you don't\n set a value for InputMode, SageMaker uses the value set for\n TrainingInputMode. Use this parameter to override the\n TrainingInputMode setting in a AlgorithmSpecification\n request when you have a channel that needs a different input mode from the training\n job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML\n storage volume, and mount the directory to a Docker volume, use File input\n mode. To stream data directly from Amazon S3 to the container, choose Pipe input\n mode.
To use a model for incremental training, choose File input model.
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example,\n s3://bucket-name/key-name-prefix.
Identifies the S3 path where you want SageMaker to store checkpoints. For example,\n s3://bucket-name/key-name-prefix.
This flag indicates if the drift check against the previous baseline will be skipped or not. \n If it is set to False, the previous baseline of the configured check type must be available.
This flag indicates if the drift check against the previous baseline will be skipped or not. \n If it is set to False, the previous baseline of the configured check type must be available.
This flag indicates if a newly calculated baseline can be accessed through step properties \n BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. \n If it is set to False, the previous baseline of the configured check type must also be available. \n These can be accessed through the BaselineUsedForDriftCheckConstraints property.
This flag indicates if a newly calculated baseline can be accessed through step properties \n BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. \n If it is set to False, the previous baseline of the configured check type must also be available. \n These can be accessed through the BaselineUsedForDriftCheckConstraints property.
The container for the metadata for the ClarifyCheck step. For more information, \n see the topic on ClarifyCheck step in the Amazon SageMaker Developer Guide.\n
" + "smithy.api#documentation": "The container for the metadata for the ClarifyCheck step. For more information, \n see the topic on ClarifyCheck step in the Amazon SageMaker Developer Guide.\n
" } }, "com.amazonaws.sagemaker#ClientId": { @@ -3932,7 +3973,7 @@ "Image": { "target": "com.amazonaws.sagemaker#ContainerImage", "traits": { - "smithy.api#documentation": "The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a\n Docker registry that is accessible from the same VPC that you configure for your\n endpoint. If you are using your own custom algorithm instead of an algorithm provided by\n Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with Amazon\n SageMaker\n
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a\n Docker registry that is accessible from the same VPC that you configure for your\n endpoint. If you are using your own custom algorithm instead of an algorithm provided by\n SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with Amazon\n SageMaker\n
The S3 path where the model artifacts, which result from model training, are stored.\n This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3\n path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms.\n For more information on built-in algorithms, see Common\n Parameters.
\nThe model artifacts must be in an S3 bucket that is in the same region as the\n model or endpoint you are creating.
\nIf you provide a value for this parameter, Amazon SageMaker uses Amazon Web Services Security Token Service to\n download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your\n IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you\n need to reactivate Amazon Web Services STS for that region. For more information, see Activating and\n Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User\n Guide.
\nIf you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide\n a S3 path to the model artifacts in ModelDataUrl.
The S3 path where the model artifacts, which result from model training, are stored.\n This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3\n path is required for SageMaker built-in algorithms, but not if you use your own algorithms.\n For more information on built-in algorithms, see Common\n Parameters.
\nThe model artifacts must be in an S3 bucket that is in the same region as the\n model or endpoint you are creating.
\nIf you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to\n download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your\n IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you\n need to reactivate Amazon Web Services STS for that region. For more information, see Activating and\n Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User\n Guide.
\nIf you use a built-in algorithm to create a model, SageMaker requires that you provide\n a S3 path to the model artifacts in ModelDataUrl.
The scale that hyperparameter tuning uses to search the hyperparameter range. For\n information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
\nAmazon SageMaker hyperparameter tuning chooses the best scale for the\n hyperparameter.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a linear scale.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a logarithmic scale.
\nLogarithmic scaling works only for ranges that have only values greater\n than 0.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a reverse logarithmic scale.
\nReverse logarithmic scaling works only for ranges that are entirely within\n the range 0<=x<1.0.
\nThe scale that hyperparameter tuning uses to search the hyperparameter range. For\n information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
\nSageMaker hyperparameter tuning chooses the best scale for the\n hyperparameter.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a linear scale.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a logarithmic scale.
\nLogarithmic scaling works only for ranges that have only values greater\n than 0.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a reverse logarithmic scale.
\nReverse logarithmic scaling works only for ranges that are entirely within\n the range 0<=x<1.0.
\nCreate a machine learning algorithm that you can use in Amazon SageMaker and list in the Amazon Web Services\n Marketplace.
" + "smithy.api#documentation": "Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services\n Marketplace.
" } }, "com.amazonaws.sagemaker#CreateAlgorithmInput": { @@ -4385,7 +4426,7 @@ "ValidationSpecification": { "target": "com.amazonaws.sagemaker#AlgorithmValidationSpecification", "traits": { - "smithy.api#documentation": "Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the\n algorithm's training code and, optionally, one or more batch transform jobs that Amazon SageMaker\n runs to test the algorithm's inference code.
" + "smithy.api#documentation": "Specifies configurations for one or more training jobs and that SageMaker runs to test the\n algorithm's training code and, optionally, one or more batch transform jobs that SageMaker\n runs to test the algorithm's inference code.
" } }, "CertifyForMarketplace": { @@ -4720,7 +4761,7 @@ "target": "com.amazonaws.sagemaker#CreateCodeRepositoryOutput" }, "traits": { - "smithy.api#documentation": "Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the\n repository with notebook instances so that you can use Git source control for the\n notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can\n be associated with more than one notebook instance, and it persists independently from\n the lifecycle of any notebook instances it is associated with.
\nThe repository can be hosted either in Amazon Web Services CodeCommit or in any\n other Git repository.
" + "smithy.api#documentation": "Creates a Git repository as a resource in your SageMaker account. You can associate the\n repository with notebook instances so that you can use Git source control for the\n notebooks you create. The Git repository is a resource in your SageMaker account, so it can\n be associated with more than one notebook instance, and it persists independently from\n the lifecycle of any notebook instances it is associated with.
\nThe repository can be hosted either in Amazon Web Services CodeCommit or in any\n other Git repository.
" } }, "com.amazonaws.sagemaker#CreateCodeRepositoryInput": { @@ -5288,7 +5329,7 @@ } ], "traits": { - "smithy.api#documentation": "Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker\n uses the endpoint to provision resources and deploy models. You create the endpoint\n configuration with the CreateEndpointConfig API.
\nUse this API to deploy models using Amazon SageMaker hosting services.
\nFor an example that calls this method when deploying a model to Amazon SageMaker hosting services,\n see the Create Endpoint example notebook.\n
\n You must not delete an EndpointConfig that is in use by an endpoint\n that is live or while the UpdateEndpoint or CreateEndpoint\n operations are being performed on the endpoint. To update an endpoint, you must\n create a new EndpointConfig.
The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
\nWhen it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML\n compute instances), and deploys the model(s) on them.
\n \nWhen you call CreateEndpoint, a load call is made to DynamoDB to\n verify that your endpoint configuration exists. When you read data from a DynamoDB\n table supporting \n Eventually Consistent Reads\n , the response might not\n reflect the results of a recently completed write operation. The response might\n include some stale data. If the dependent entities are not yet in DynamoDB, this\n causes a validation error. If you repeat your read request after a short time, the\n response should return the latest data. So retry logic is recommended to handle\n these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
When Amazon SageMaker receives the request, it sets the endpoint status to\n Creating. After it creates the endpoint, it sets the status to\n InService. Amazon SageMaker can then process incoming requests for inferences. To\n check the status of an endpoint, use the DescribeEndpoint\n API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location,\n Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you\n provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously\n deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For\n more information, see Activating and\n Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User\n Guide.
\nTo add the IAM role policies for using this API operation, go to the IAM console, and choose\n Roles in the left navigation pane. Search the IAM role that you want to grant\n access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to\n the role.
\nOption 1: For a full SageMaker access, search and attach the\n AmazonSageMakerFullAccess policy.
Option 2: For granting a limited access to an IAM role, paste the\n following Action elements manually into the JSON file of the IAM role:
\n\n \"Action\": [\"sagemaker:CreateEndpoint\",\n \"sagemaker:CreateEndpointConfig\"]\n
\n \"Resource\": [\n
\n \"arn:aws:sagemaker:region:account-id:endpoint/endpointName\"\n
\n \"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName\"\n
\n ]\n
For more information, see SageMaker API\n Permissions: Actions, Permissions, and Resources\n Reference.
\nCreates an endpoint using the endpoint configuration specified in the request. SageMaker\n uses the endpoint to provision resources and deploy models. You create the endpoint\n configuration with the CreateEndpointConfig API.
\nUse this API to deploy models using SageMaker hosting services.
\nFor an example that calls this method when deploying a model to SageMaker hosting services,\n see the Create Endpoint example notebook.\n
\n You must not delete an EndpointConfig that is in use by an endpoint\n that is live or while the UpdateEndpoint or CreateEndpoint\n operations are being performed on the endpoint. To update an endpoint, you must\n create a new EndpointConfig.
The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
\nWhen it receives the request, SageMaker creates the endpoint, launches the resources (ML\n compute instances), and deploys the model(s) on them.
\n \nWhen you call CreateEndpoint, a load call is made to DynamoDB to\n verify that your endpoint configuration exists. When you read data from a DynamoDB\n table supporting \n Eventually Consistent Reads\n , the response might not\n reflect the results of a recently completed write operation. The response might\n include some stale data. If the dependent entities are not yet in DynamoDB, this\n causes a validation error. If you repeat your read request after a short time, the\n response should return the latest data. So retry logic is recommended to handle\n these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
When SageMaker receives the request, it sets the endpoint status to\n Creating. After it creates the endpoint, it sets the status to\n InService. SageMaker can then process incoming requests for inferences. To\n check the status of an endpoint, use the DescribeEndpoint\n API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location,\n SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you\n provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously\n deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For\n more information, see Activating and\n Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User\n Guide.
\nTo add the IAM role policies for using this API operation, go to the IAM console, and choose\n Roles in the left navigation pane. Search the IAM role that you want to grant\n access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to\n the role.
\nOption 1: For a full SageMaker access, search and attach the\n AmazonSageMakerFullAccess policy.
Option 2: For granting a limited access to an IAM role, paste the\n following Action elements manually into the JSON file of the IAM role:
\n\n \"Action\": [\"sagemaker:CreateEndpoint\",\n \"sagemaker:CreateEndpointConfig\"]\n
\n \"Resource\": [\n
\n \"arn:aws:sagemaker:region:account-id:endpoint/endpointName\"\n
\n \"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName\"\n
\n ]\n
For more information, see SageMaker API\n Permissions: Actions, Permissions, and Resources\n Reference.
\nCreates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In\n the configuration, you identify one or more models, created using the\n CreateModel API, to deploy and the resources that you want Amazon SageMaker to\n provision. Then you call the CreateEndpoint API.
Use this API if you want to use Amazon SageMaker hosting services to deploy models into\n production.
\nIn the request, you define a ProductionVariant, for each model that you\n want to deploy. Each ProductionVariant parameter also describes the\n resources that you want Amazon SageMaker to provision. This includes the number and type of ML\n compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight to\n specify how much traffic you want to allocate to each model. For example, suppose that\n you want to host two models, A and B, and you assign traffic weight 2 for model A and 1\n for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to\n model B.
When you call CreateEndpoint, a load call is made to DynamoDB to\n verify that your endpoint configuration exists. When you read data from a DynamoDB\n table supporting \n Eventually Consistent Reads\n , the response might not\n reflect the results of a recently completed write operation. The response might\n include some stale data. If the dependent entities are not yet in DynamoDB, this\n causes a validation error. If you repeat your read request after a short time, the\n response should return the latest data. So retry logic is recommended to handle\n these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In\n the configuration, you identify one or more models, created using the\n CreateModel API, to deploy and the resources that you want SageMaker to\n provision. Then you call the CreateEndpoint API.
Use this API if you want to use SageMaker hosting services to deploy models into\n production.
\nIn the request, you define a ProductionVariant, for each model that you\n want to deploy. Each ProductionVariant parameter also describes the\n resources that you want SageMaker to provision. This includes the number and type of ML\n compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight to\n specify how much traffic you want to allocate to each model. For example, suppose that\n you want to host two models, A and B, and you assign traffic weight 2 for model A and 1\n for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to\n model B.
When you call CreateEndpoint, a load call is made to DynamoDB to\n verify that your endpoint configuration exists. When you read data from a DynamoDB\n table supporting \n Eventually Consistent Reads\n , the response might not\n reflect the results of a recently completed write operation. The response might\n include some stale data. If the dependent entities are not yet in DynamoDB, this\n causes a validation error. If you repeat your read request after a short time, the\n response should return the latest data. So retry logic is recommended to handle\n these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on\n the storage volume attached to the ML compute instance that hosts the endpoint.
\nThe KmsKeyId can be any of the following formats:
\nKey ID: 1234abcd-12ab-34cd-56ef-1234567890ab\n
Key ARN:\n arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab\n
Alias name: alias/ExampleAlias\n
Alias name ARN:\n arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias\n
The KMS key policy must grant permission to the IAM role that you specify in your\n CreateEndpoint, UpdateEndpoint requests. For more\n information, refer to the Amazon Web Services Key Management Service section Using Key\n Policies in Amazon Web Services KMS \n
Certain Nitro-based instances include local storage, dependent on the instance\n type. Local storage volumes are encrypted using a hardware module on the instance.\n You can't request a KmsKeyId when using an instance type with local\n storage. If any of the models that you specify in the\n ProductionVariants parameter use nitro-based instances with local\n storage, do not specify a value for the KmsKeyId parameter. If you\n specify a value for KmsKeyId when using any nitro-based instances with\n local storage, the call to CreateEndpointConfig fails.
For a list of instance types that support local instance storage, see Instance Store Volumes.
\nFor more information about local instance storage encryption, see SSD\n Instance Store Volumes.
\nThe Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on\n the storage volume attached to the ML compute instance that hosts the endpoint.
\nThe KmsKeyId can be any of the following formats:
\nKey ID: 1234abcd-12ab-34cd-56ef-1234567890ab\n
Key ARN:\n arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab\n
Alias name: alias/ExampleAlias\n
Alias name ARN:\n arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias\n
The KMS key policy must grant permission to the IAM role that you specify in your\n CreateEndpoint, UpdateEndpoint requests. For more\n information, refer to the Amazon Web Services Key Management Service section Using Key\n Policies in Amazon Web Services KMS \n
Certain Nitro-based instances include local storage, dependent on the instance\n type. Local storage volumes are encrypted using a hardware module on the instance.\n You can't request a KmsKeyId when using an instance type with local\n storage. If any of the models that you specify in the\n ProductionVariants parameter use nitro-based instances with local\n storage, do not specify a value for the KmsKeyId parameter. If you\n specify a value for KmsKeyId when using any nitro-based instances with\n local storage, the call to CreateEndpointConfig fails.
For a list of instance types that support local instance storage, see Instance Store Volumes.
\nFor more information about local instance storage encryption, see SSD\n Instance Store Volumes.
\nThe HyperParameterTrainingJobDefinition object that describes the\n training jobs that this tuning job launches,\n including\n static hyperparameters, input data configuration, output data configuration, resource\n configuration, and stopping condition.
" + "smithy.api#documentation": "The HyperParameterTrainingJobDefinition object that describes the\n training jobs that this tuning job launches, including static hyperparameters, input\n data configuration, output data configuration, resource configuration, and stopping\n condition.
" } }, "TrainingJobDefinitions": { @@ -5761,7 +5802,7 @@ "HyperParameterTuningJobArn": { "target": "com.amazonaws.sagemaker#HyperParameterTuningJobArn", "traits": { - "smithy.api#documentation": "The Amazon Resource Name (ARN) of the tuning job. Amazon SageMaker assigns an ARN to a\n hyperparameter tuning job when you create it.
", + "smithy.api#documentation": "The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a\n hyperparameter tuning job when you create it.
", "smithy.api#required": {} } } @@ -5784,7 +5825,7 @@ } ], "traits": { - "smithy.api#documentation": "Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image\n version represents a container image stored in Amazon Container Registry (ECR). For more information, see\n Bring your own SageMaker image.
" + "smithy.api#documentation": "Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image\n version represents a container image stored in Amazon Elastic Container Registry (ECR). For more information, see\n Bring your own SageMaker image.
" } }, "com.amazonaws.sagemaker#CreateImageRequest": { @@ -5855,7 +5896,7 @@ } ], "traits": { - "smithy.api#documentation": "Creates a version of the SageMaker image specified by ImageName. The version\n represents the Amazon Container Registry (ECR) container image specified by BaseImage.
Creates a version of the SageMaker image specified by ImageName. The version\n represents the Amazon Elastic Container Registry (ECR) container image specified by BaseImage.
The registry path of the container image to use as the starting point for this\n version. The path is an Amazon Container Registry (ECR) URI in the following format:
\n\n
The registry path of the container image to use as the starting point for this\n version. The path is an Amazon Elastic Container Registry (ECR) URI in the following format:
\n\n
Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary\n container. For the primary container, you specify the Docker image that\n contains inference code, artifacts (from prior training), and a custom environment map\n that the inference code uses when you deploy the model for predictions.
\nUse this API to create a model if you want to use Amazon SageMaker hosting services or run a batch\n transform job.
\nTo host your model, you create an endpoint configuration with the\n CreateEndpointConfig API, and then create an endpoint with the\n CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you\n defined for the model in the hosting environment.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services,\n see Deploy the\n Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto\n 3)).\n
\nTo run a batch transform using your model, you start a job with the\n CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get\n inferences which are then saved to a specified S3 location.
In the CreateModel request, you must define a container with the\n PrimaryContainer parameter.
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model\n artifacts and docker image for deployment on ML compute hosting instances or for batch\n transform jobs. In addition, you also use the IAM role to manage permissions the\n inference code needs. For example, if the inference code access any other Amazon Web Services resources,\n you grant necessary permissions via this role.
" + "smithy.api#documentation": "Creates a model in SageMaker. In the request, you name the model and describe a primary\n container. For the primary container, you specify the Docker image that\n contains inference code, artifacts (from prior training), and a custom environment map\n that the inference code uses when you deploy the model for predictions.
\nUse this API to create a model if you want to use SageMaker hosting services or run a batch\n transform job.
\nTo host your model, you create an endpoint configuration with the\n CreateEndpointConfig API, and then create an endpoint with the\n CreateEndpoint API. SageMaker then deploys all of the containers that you\n defined for the model in the hosting environment.
For an example that calls this method when deploying a model to SageMaker hosting services,\n see Deploy the\n Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto\n 3)).\n
\nTo run a batch transform using your model, you start a job with the\n CreateTransformJob API. SageMaker uses your model and your dataset to get\n inferences which are then saved to a specified S3 location.
In the request, you also provide an IAM role that SageMaker can assume to access model\n artifacts and docker image for deployment on ML compute hosting instances or for batch\n transform jobs. In addition, you also use the IAM role to manage permissions the\n inference code needs. For example, if the inference code access any other Amazon Web Services resources,\n you grant necessary permissions via this role.
" } }, "com.amazonaws.sagemaker#CreateModelBiasJobDefinition": { @@ -6332,7 +6373,7 @@ "ExecutionRoleArn": { "target": "com.amazonaws.sagemaker#RoleArn", "traits": { - "smithy.api#documentation": "The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model\n artifacts and docker image for deployment on ML compute instances or for batch transform\n jobs. Deploying on ML compute instances is part of model hosting. For more information,\n see Amazon SageMaker\n Roles.
\nTo be able to pass this role to Amazon SageMaker, the caller of this API must have the\n iam:PassRole permission.
The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model\n artifacts and docker image for deployment on ML compute instances or for batch transform\n jobs. Deploying on ML compute instances is part of model hosting. For more information,\n see SageMaker\n Roles.
\nTo be able to pass this role to SageMaker, the caller of this API must have the\n iam:PassRole permission.
The ARN of the model created in Amazon SageMaker.
", + "smithy.api#documentation": "The ARN of the model created in SageMaker.
", "smithy.api#required": {} } } @@ -6385,7 +6426,7 @@ } ], "traits": { - "smithy.api#documentation": "Creates a model package that you can use to create Amazon SageMaker models or list on Amazon Web Services\n Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to\n model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.
\nTo create a model package by specifying a Docker container that contains your\n inference code and the Amazon S3 location of your model artifacts, provide values for\n InferenceSpecification. To create a model from an algorithm resource\n that you created or subscribed to in Amazon Web Services Marketplace, provide a value for\n SourceAlgorithmSpecification.
There are two types of model packages:
\nVersioned - a model that is part of a model group in the model\n registry.
\nUnversioned - a model package that is not part of a model group.
\nCreates a model package that you can use to create SageMaker models or list on Amazon Web Services\n Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to\n model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
\nTo create a model package by specifying a Docker container that contains your\n inference code and the Amazon S3 location of your model artifacts, provide values for\n InferenceSpecification. To create a model from an algorithm resource\n that you created or subscribed to in Amazon Web Services Marketplace, provide a value for\n SourceAlgorithmSpecification.
There are two types of model packages:
\nVersioned - a model that is part of a model group in the model\n registry.
\nUnversioned - a model package that is not part of a model group.
\nSpecifies configurations for one or more transform jobs that Amazon SageMaker runs to test the\n model package.
" + "smithy.api#documentation": "Specifies configurations for one or more transform jobs that SageMaker runs to test the\n model package.
" } }, "SourceAlgorithmSpecification": { @@ -6523,7 +6564,7 @@ "DriftCheckBaselines": { "target": "com.amazonaws.sagemaker#DriftCheckBaselines", "traits": { - "smithy.api#documentation": "Represents the drift check baselines that can be used when the model monitor is set using the model package.\n For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.\n
" + "smithy.api#documentation": "Represents the drift check baselines that can be used when the model monitor is set using the model package.\n For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.\n
" } }, "Domain": { @@ -6733,7 +6774,7 @@ } ], "traits": { - "smithy.api#documentation": "Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML)\n compute instance running on a Jupyter notebook.
\nIn a CreateNotebookInstance request, specify the type of ML compute\n instance that you want to run. Amazon SageMaker launches the instance, installs common libraries\n that you can use to explore datasets for model training, and attaches an ML storage\n volume to the notebook instance.
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to\n use Amazon SageMaker with a specific algorithm or with a machine learning framework.
\nAfter receiving the request, Amazon SageMaker does the following:
\nCreates a network interface in the Amazon SageMaker VPC.
\n(Option) If you specified SubnetId, Amazon SageMaker creates a network\n interface in your own VPC, which is inferred from the subnet ID that you provide\n in the input. When creating this network interface, Amazon SageMaker attaches the security\n group that you specified in the request to the network interface that it creates\n in your VPC.
Launches an EC2 instance of the type specified in the request in the Amazon SageMaker\n VPC. If you specified SubnetId of your VPC, Amazon SageMaker specifies both\n network interfaces when launching this instance. This enables inbound traffic\n from your own VPC to the notebook instance, assuming that the security groups\n allow it.
After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).\n You can't change the name of a notebook instance after you create it.
\nAfter Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and\n work in Jupyter notebooks. For example, you can write code to explore a dataset that you\n can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and\n validate hosted models.
\nFor more information, see How It Works.
" + "smithy.api#documentation": "Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML)\n compute instance running on a Jupyter notebook.
\nIn a CreateNotebookInstance request, specify the type of ML compute\n instance that you want to run. SageMaker launches the instance, installs common libraries\n that you can use to explore datasets for model training, and attaches an ML storage\n volume to the notebook instance.
SageMaker also provides a set of example notebooks. Each notebook demonstrates how to\n use SageMaker with a specific algorithm or with a machine learning framework.
\nAfter receiving the request, SageMaker does the following:
\nCreates a network interface in the SageMaker VPC.
\n(Option) If you specified SubnetId, SageMaker creates a network\n interface in your own VPC, which is inferred from the subnet ID that you provide\n in the input. When creating this network interface, SageMaker attaches the security\n group that you specified in the request to the network interface that it creates\n in your VPC.
Launches an EC2 instance of the type specified in the request in the SageMaker\n VPC. If you specified SubnetId of your VPC, SageMaker specifies both\n network interfaces when launching this instance. This enables inbound traffic\n from your own VPC to the notebook instance, assuming that the security groups\n allow it.
After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN).\n You can't change the name of a notebook instance after you create it.
\nAfter SageMaker creates the notebook instance, you can connect to the Jupyter server and\n work in Jupyter notebooks. For example, you can write code to explore a dataset that you\n can use for model training, train a model, host models by creating SageMaker endpoints, and\n validate hosted models.
\nFor more information, see How It Works.
" } }, "com.amazonaws.sagemaker#CreateNotebookInstanceInput": { @@ -6768,14 +6809,14 @@ "RoleArn": { "target": "com.amazonaws.sagemaker#RoleArn", "traits": { - "smithy.api#documentation": "When you send any requests to Amazon Web Services resources from the notebook instance, Amazon SageMaker\n assumes this role to perform tasks on your behalf. You must grant this role necessary\n permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service\n principal (sagemaker.amazonaws.com) permissions to assume this role. For more\n information, see Amazon SageMaker Roles.
\nTo be able to pass this role to Amazon SageMaker, the caller of this API must have the\n iam:PassRole permission.
When you send any requests to Amazon Web Services resources from the notebook instance, SageMaker\n assumes this role to perform tasks on your behalf. You must grant this role necessary\n permissions so SageMaker can perform these tasks. The policy must allow the SageMaker service\n principal (sagemaker.amazonaws.com) permissions to assume this role. For more\n information, see SageMaker Roles.
\nTo be able to pass this role to SageMaker, the caller of this API must have the\n iam:PassRole permission.
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on\n the storage volume attached to your notebook instance. The KMS key you provide must be\n enabled. For information, see Enabling and Disabling\n Keys in the Amazon Web Services Key Management Service Developer Guide.
" + "smithy.api#documentation": "The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on\n the storage volume attached to your notebook instance. The KMS key you provide must be\n enabled. For information, see Enabling and Disabling\n Keys in the Amazon Web Services Key Management Service Developer Guide.
" } }, "Tags": { @@ -6793,7 +6834,7 @@ "DirectInternetAccess": { "target": "com.amazonaws.sagemaker#DirectInternetAccess", "traits": { - "smithy.api#documentation": "Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this\n to Disabled this notebook instance is able to access resources only in your\n VPC, and is not be able to connect to Amazon SageMaker training and endpoint services unless you\n configure a NAT Gateway in your VPC.
For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value\n of this parameter to Disabled only if you set a value for the\n SubnetId parameter.
Sets whether SageMaker provides internet access to the notebook instance. If you set this\n to Disabled this notebook instance is able to access resources only in your\n VPC, and is not be able to connect to SageMaker training and endpoint services unless you\n configure a NAT Gateway in your VPC.
For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value\n of this parameter to Disabled only if you set a value for the\n SubnetId parameter.
A Git repository to associate with the notebook instance as its default code\n repository. This can be either the name of a Git repository stored as a resource in your\n account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any\n other Git repository. When you open a notebook instance, it opens in the directory that\n contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker\n Notebook Instances.
" + "smithy.api#documentation": "A Git repository to associate with the notebook instance as its default code\n repository. This can be either the name of a Git repository stored as a resource in your\n account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any\n other Git repository. When you open a notebook instance, it opens in the directory that\n contains this repository. For more information, see Associating Git Repositories with SageMaker\n Notebook Instances.
" } }, "AdditionalCodeRepositories": { "target": "com.amazonaws.sagemaker#AdditionalCodeRepositoryNamesOrUrls", "traits": { - "smithy.api#documentation": "An array of up to three Git repositories to associate with the notebook instance.\n These can be either the names of Git repositories stored as resources in your account,\n or the URL of Git repositories in Amazon Web Services CodeCommit or in any\n other Git repository. These repositories are cloned at the same level as the default\n repository of your notebook instance. For more information, see Associating Git\n Repositories with Amazon SageMaker Notebook Instances.
" + "smithy.api#documentation": "An array of up to three Git repositories to associate with the notebook instance.\n These can be either the names of Git repositories stored as resources in your account,\n or the URL of Git repositories in Amazon Web Services CodeCommit or in any\n other Git repository. These repositories are cloned at the same level as the default\n repository of your notebook instance. For more information, see Associating Git\n Repositories with SageMaker Notebook Instances.
" } }, "RootAccess": { @@ -7059,7 +7100,7 @@ "target": "com.amazonaws.sagemaker#CreatePresignedNotebookInstanceUrlOutput" }, "traits": { - "smithy.api#documentation": "Returns a URL that you can use to connect to the Jupyter server from a notebook\n instance. In the Amazon SageMaker console, when you choose Open next to a notebook\n instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook\n instance. The console uses this API to get the URL and show the page.
The IAM role or user used to call this API defines the permissions to access the\n notebook instance. Once the presigned URL is created, no additional permission is\n required to access this URL. IAM authorization policies for this API are also enforced\n for every HTTP request and WebSocket frame that attempts to connect to the notebook\n instance.
\nYou can restrict access to this API and to the URL that it returns to a list of IP\n addresses that you specify. Use the NotIpAddress condition operator and the\n aws:SourceIP condition context key to specify the list of IP addresses\n that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If\n you try to use the URL after the 5-minute limit expires, you are directed to the\n Amazon Web Services console sign-in page.
\nReturns a URL that you can use to connect to the Jupyter server from a notebook\n instance. In the SageMaker console, when you choose Open next to a notebook\n instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook\n instance. The console uses this API to get the URL and show the page.
The IAM role or user used to call this API defines the permissions to access the\n notebook instance. Once the presigned URL is created, no additional permission is\n required to access this URL. IAM authorization policies for this API are also enforced\n for every HTTP request and WebSocket frame that attempts to connect to the notebook\n instance.
\nYou can restrict access to this API and to the URL that it returns to a list of IP\n addresses that you specify. Use the NotIpAddress condition operator and the\n aws:SourceIP condition context key to specify the list of IP addresses\n that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If\n you try to use the URL after the 5-minute limit expires, you are directed to the\n Amazon Web Services console sign-in page.
\nStarts a model training job. After training completes, Amazon SageMaker saves the resulting\n model artifacts to an Amazon S3 location that you specify.
\nIf you choose to host your model using Amazon SageMaker hosting services, you can use the\n resulting model artifacts as part of the model. You can also use the artifacts in a\n machine learning service other than Amazon SageMaker, provided that you know how to use them for\n inference. \n
\nIn the request body, you provide the following:
\n\n AlgorithmSpecification - Identifies the training algorithm to\n use.\n
\n HyperParameters - Specify these algorithm-specific parameters to\n enable the estimation of model parameters during training. Hyperparameters can\n be tuned to optimize this learning process. For a list of hyperparameters for\n each training algorithm provided by Amazon SageMaker, see Algorithms.
\n InputDataConfig - Describes the training dataset and the Amazon S3,\n EFS, or FSx location where it is stored.
\n OutputDataConfig - Identifies the Amazon S3 bucket where you want\n Amazon SageMaker to save the results of model training.
\n ResourceConfig - Identifies the resources, ML compute\n instances, and ML storage volumes to deploy for model training. In distributed\n training, you specify more than one instance.
\n EnableManagedSpotTraining - Optimize the cost of training machine\n learning models by up to 80% by using Amazon EC2 Spot instances. For more\n information, see Managed Spot\n Training.
\n RoleArn - The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on\n your behalf during model training.\n \n You must grant this role the necessary permissions so that Amazon SageMaker can successfully\n complete model training.
\n StoppingCondition - To help cap training costs, use\n MaxRuntimeInSeconds to set a time limit for training. Use\n MaxWaitTimeInSeconds to specify how long a managed spot\n training job has to complete.
\n Environment - The environment variables to set in the Docker\n container.
\n RetryStrategy - The number of times to retry the job when the job\n fails due to an InternalServerError.
For more information about Amazon SageMaker, see How It Works.
" + "smithy.api#documentation": "Starts a model training job. After training completes, SageMaker saves the resulting\n model artifacts to an Amazon S3 location that you specify.
\nIf you choose to host your model using SageMaker hosting services, you can use the\n resulting model artifacts as part of the model. You can also use the artifacts in a\n machine learning service other than SageMaker, provided that you know how to use them for\n inference. \n
\nIn the request body, you provide the following:
\n\n AlgorithmSpecification - Identifies the training algorithm to\n use.\n
\n HyperParameters - Specify these algorithm-specific parameters to\n enable the estimation of model parameters during training. Hyperparameters can\n be tuned to optimize this learning process. For a list of hyperparameters for\n each training algorithm provided by SageMaker, see Algorithms.
\n InputDataConfig - Describes the training dataset and the Amazon S3,\n EFS, or FSx location where it is stored.
\n OutputDataConfig - Identifies the Amazon S3 bucket where you want\n SageMaker to save the results of model training.
\n ResourceConfig - Identifies the resources, ML compute\n instances, and ML storage volumes to deploy for model training. In distributed\n training, you specify more than one instance.
\n EnableManagedSpotTraining - Optimize the cost of training machine\n learning models by up to 80% by using Amazon EC2 Spot instances. For more\n information, see Managed Spot\n Training.
\n RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on\n your behalf during model training.\n \n You must grant this role the necessary permissions so that SageMaker can successfully\n complete model training.
\n StoppingCondition - To help cap training costs, use\n MaxRuntimeInSeconds to set a time limit for training. Use\n MaxWaitTimeInSeconds to specify how long a managed spot\n training job has to complete.
\n Environment - The environment variables to set in the Docker\n container.
\n RetryStrategy - The number of times to retry the job when the job\n fails due to an InternalServerError.
For more information about SageMaker, see How It Works.
" } }, "com.amazonaws.sagemaker#CreateTrainingJobRequest": { @@ -7361,40 +7402,40 @@ "HyperParameters": { "target": "com.amazonaws.sagemaker#HyperParameters", "traits": { - "smithy.api#documentation": "Algorithm-specific parameters that influence the quality of the model. You set\n hyperparameters before you start the learning process. For a list of hyperparameters for\n each training algorithm provided by Amazon SageMaker, see Algorithms.
\nYou can specify a maximum of 100 hyperparameters. Each hyperparameter is a\n key-value pair. Each key and value is limited to 256 characters, as specified by the\n Length Constraint.
Algorithm-specific parameters that influence the quality of the model. You set\n hyperparameters before you start the learning process. For a list of hyperparameters for\n each training algorithm provided by SageMaker, see Algorithms.
\nYou can specify a maximum of 100 hyperparameters. Each hyperparameter is a\n key-value pair. Each key and value is limited to 256 characters, as specified by the\n Length Constraint.
The registry path of the Docker image that contains the training algorithm and\n algorithm-specific metadata, including the input mode. For more information about\n algorithms provided by Amazon SageMaker, see Algorithms. For information about\n providing your own algorithms, see Using Your Own Algorithms with Amazon\n SageMaker.
", + "smithy.api#documentation": "The registry path of the Docker image that contains the training algorithm and\n algorithm-specific metadata, including the input mode. For more information about\n algorithms provided by SageMaker, see Algorithms. For information about\n providing your own algorithms, see Using Your Own Algorithms with Amazon\n SageMaker.
", "smithy.api#required": {} } }, "RoleArn": { "target": "com.amazonaws.sagemaker#RoleArn", "traits": { - "smithy.api#documentation": "The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform\n tasks on your behalf.
\nDuring model training, Amazon SageMaker needs your permission to read input data from an S3\n bucket, download a Docker image that contains training code, write model artifacts to an\n S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant\n permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker\n Roles.
\nTo be able to pass this role to Amazon SageMaker, the caller of this API must have the\n iam:PassRole permission.
The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform\n tasks on your behalf.
\nDuring model training, SageMaker needs your permission to read input data from an S3\n bucket, download a Docker image that contains training code, write model artifacts to an\n S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant\n permissions for all of these tasks to an IAM role. For more information, see SageMaker\n Roles.
\nTo be able to pass this role to SageMaker, the caller of this API must have the\n iam:PassRole permission.
An array of Channel objects. Each channel is a named input source.\n InputDataConfig\n describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an\n algorithm might have two channels of input data, training_data and\n validation_data. The configuration for each channel provides the S3,\n EFS, or FSx location where the input data is stored. It also provides information about\n the stored data: the MIME type, compression method, and whether the data is wrapped in\n RecordIO format.
Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input\n data files from an S3 bucket to a local directory in the Docker container, or makes it\n available as input streams. For example, if you specify an EFS location, input data\n files will be made available as input streams. They do not need to be\n downloaded.
" + "smithy.api#documentation": "An array of Channel objects. Each channel is a named input source.\n InputDataConfig describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an\n algorithm might have two channels of input data, training_data and\n validation_data. The configuration for each channel provides the S3,\n EFS, or FSx location where the input data is stored. It also provides information about\n the stored data: the MIME type, compression method, and whether the data is wrapped in\n RecordIO format.
Depending on the input mode that the algorithm supports, SageMaker either copies input\n data files from an S3 bucket to a local directory in the Docker container, or makes it\n available as input streams. For example, if you specify an EFS location, input data\n files are available as input streams. They do not need to be\n downloaded.
" } }, "OutputDataConfig": { "target": "com.amazonaws.sagemaker#OutputDataConfig", "traits": { - "smithy.api#documentation": "Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker\n creates subfolders for the artifacts.
", + "smithy.api#documentation": "Specifies the path to the S3 location where you want to store model artifacts. SageMaker\n creates subfolders for the artifacts.
", "smithy.api#required": {} } }, "ResourceConfig": { "target": "com.amazonaws.sagemaker#ResourceConfig", "traits": { - "smithy.api#documentation": "The resources, including the ML compute instances and ML storage volumes, to use\n for model training.
\nML storage volumes store model artifacts and incremental states. Training\n algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use\n the ML storage volume to store the training data, choose File as the\n TrainingInputMode in the algorithm specification. For distributed\n training algorithms, specify an instance count greater than 1.
The resources, including the ML compute instances and ML storage volumes, to use\n for model training.
\nML storage volumes store model artifacts and incremental states. Training\n algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use\n the ML storage volume to store the training data, choose File as the\n TrainingInputMode in the algorithm specification. For distributed\n training algorithms, specify an instance count greater than 1.
Specifies a limit to how long a model training job can run. It also specifies how long\n a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker\n ends the training job. Use this API to cap model training costs.
\nTo stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays\n job termination for 120 seconds. Algorithms can use this 120-second window to save the\n model artifacts, so the results of training are not lost.
Specifies a limit to how long a model training job can run. It also specifies how long\n a managed Spot training job has to complete. When the job reaches the time limit, SageMaker\n ends the training job. Use this API to cap model training costs.
\nTo stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays\n job termination for 120 seconds. Algorithms can use this 120-second window to save the\n model artifacts, so the results of training are not lost.
Isolates the training container. No inbound or outbound network calls can be made,\n except for calls between peers within a training cluster for distributed training. If\n you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker\n downloads and uploads customer data and model artifacts through the specified VPC, but\n the training container does not have network access.
" + "smithy.api#documentation": "Isolates the training container. No inbound or outbound network calls can be made,\n except for calls between peers within a training cluster for distributed training. If\n you enable network isolation for training jobs that are configured to use a VPC, SageMaker\n downloads and uploads customer data and model artifacts through the specified VPC, but\n the training container does not have network access.
" } }, "EnableInterContainerTrafficEncryption": { @@ -7546,7 +7587,7 @@ "MaxPayloadInMB": { "target": "com.amazonaws.sagemaker#MaxPayloadInMB", "traits": { - "smithy.api#documentation": "The maximum allowed size of the payload, in MB. A payload is the\n data portion of a record (without metadata). The value in MaxPayloadInMB\n must be greater than, or equal to, the size of a single record. To estimate the size of\n a record in MB, divide the size of your dataset by the number of records. To ensure that\n the records fit within the maximum payload size, we recommend using a slightly larger\n value. The default value is 6 MB.\n
For cases where the payload might be arbitrarily large and is transmitted using HTTP\n chunked encoding, set the value to 0.\n This\n feature works only in supported algorithms. Currently, Amazon SageMaker built-in\n algorithms do not support HTTP chunked encoding.
The maximum allowed size of the payload, in MB. A payload is the\n data portion of a record (without metadata). The value in MaxPayloadInMB\n must be greater than, or equal to, the size of a single record. To estimate the size of\n a record in MB, divide the size of your dataset by the number of records. To ensure that\n the records fit within the maximum payload size, we recommend using a slightly larger\n value. The default value is 6 MB.\n
The value of MaxPayloadInMB cannot be greater than 100 MB. If you specify\n the MaxConcurrentTransforms parameter, the value of\n (MaxConcurrentTransforms * MaxPayloadInMB) also cannot exceed 100\n MB.
For cases where the payload might be arbitrarily large and is transmitted using HTTP\n chunked encoding, set the value to 0.\n This\n feature works only in supported algorithms. Currently, Amazon SageMaker built-in\n algorithms do not support HTTP chunked encoding.
A JSONPath expression used to select a portion of the input data to pass to\n the algorithm. Use the InputFilter parameter to exclude fields, such as an\n ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the\n algorithm, accept the default value $.
Examples: \"$\", \"$[1:]\", \"$.features\"\n
A JSONPath expression used to select a portion of the input data to pass to\n the algorithm. Use the InputFilter parameter to exclude fields, such as an\n ID column, from the input. If you want SageMaker to pass the entire input dataset to the\n algorithm, accept the default value $.
Examples: \"$\", \"$[1:]\", \"$.features\"\n
A JSONPath expression used to select a portion of the joined dataset to save\n in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input\n dataset in the output file, leave the default value, $. If you specify\n indexes that aren't within the dimension size of the joined dataset, you get an\n error.
Examples: \"$\", \"$[0,5:]\",\n \"$['id','SageMakerOutput']\"\n
A JSONPath expression used to select a portion of the joined dataset to save\n in the output file for a batch transform job. If you want SageMaker to store the entire input\n dataset in the output file, leave the default value, $. If you specify\n indexes that aren't within the dimension size of the joined dataset, you get an\n error.
Examples: \"$\", \"$[0,5:]\",\n \"$['id','SageMakerOutput']\"\n
Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the\n endpoint was created.
\nAmazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't\n need to use the RevokeGrant API call.
" + "smithy.api#documentation": "Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the\n endpoint was created.
\nSageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't\n need to use the RevokeGrant API call.
\nWhen you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants.\n You might still see these resources in your account for a few minutes after deleting your endpoint.\n Do not delete or revoke the permissions for your\n \n ExecutionRoleArn\n ,\n otherwise SageMaker cannot delete these resources.
Deletes a model. The DeleteModel API deletes only the model entry that\n was created in Amazon SageMaker when you called the CreateModel API. It does not\n delete model artifacts, inference code, or the IAM role that you specified when\n creating the model.
Deletes a model. The DeleteModel API deletes only the model entry that\n was created in SageMaker when you called the CreateModel API. It does not\n delete model artifacts, inference code, or the IAM role that you specified when\n creating the model.
Deletes a model package.
\nA model package is used to create Amazon SageMaker models or list on Amazon Web Services Marketplace. Buyers can\n subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.
" + "smithy.api#documentation": "Deletes a model package.
\nA model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can\n subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
" } }, "com.amazonaws.sagemaker#DeleteModelPackageGroup": { @@ -9391,7 +9432,7 @@ "ModelPackageName": { "target": "com.amazonaws.sagemaker#VersionedArnOrName", "traits": { - "smithy.api#documentation": "The name or Amazon Resource Name (ARN) of the model package to delete.
\nWhen you specify a name, the name must have 1 to 63 characters. Valid\n characters are a-z, A-Z, 0-9, and - (hyphen).
", + "smithy.api#documentation": "The name or Amazon Resource Name (ARN) of the model package to delete.
\nWhen you specify a name, the name must have 1 to 63 characters. Valid\n characters are a-z, A-Z, 0-9, and - (hyphen).
", "smithy.api#required": {} } } @@ -9464,7 +9505,7 @@ "target": "smithy.api#Unit" }, "traits": { - "smithy.api#documentation": " Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you\n must call the StopNotebookInstance API.
When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes\n the ML compute instance, and deletes the ML storage volume and the network interface\n associated with the notebook instance.
\n Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you\n must call the StopNotebookInstance API.
When you delete a notebook instance, you lose all of your data. SageMaker removes\n the ML compute instance, and deletes the ML storage volume and the network interface\n associated with the notebook instance.
\nThe name of the Amazon SageMaker notebook instance to delete.
", + "smithy.api#documentation": "The name of the SageMaker notebook instance to delete.
", "smithy.api#required": {} } } @@ -9621,7 +9662,7 @@ "target": "com.amazonaws.sagemaker#DeleteTagsOutput" }, "traits": { - "smithy.api#documentation": "Deletes the specified tags from an Amazon SageMaker resource.
\nTo list a resource's tags, use the ListTags API.
When you call this API to delete tags from a hyperparameter tuning job, the\n deleted tags are not removed from training jobs that the hyperparameter tuning job\n launched before you called this API.
\nWhen you call this API to delete tags from a SageMaker Studio Domain or User\n Profile, the deleted tags are not removed from Apps that the SageMaker Studio Domain\n or User Profile launched before you called this API.
\nDeletes the specified tags from an SageMaker resource.
\nTo list a resource's tags, use the ListTags API.
When you call this API to delete tags from a hyperparameter tuning job, the\n deleted tags are not removed from training jobs that the hyperparameter tuning job\n launched before you called this API.
\nWhen you call this API to delete tags from a SageMaker Studio Domain or User\n Profile, the deleted tags are not removed from Apps that the SageMaker Studio Domain\n or User Profile launched before you called this API.
\nDetails about configurations for one or more training jobs that Amazon SageMaker runs to test the\n algorithm.
" + "smithy.api#documentation": "Details about configurations for one or more training jobs that SageMaker runs to test the\n algorithm.
" } }, "AlgorithmStatus": { @@ -11561,7 +11602,7 @@ "EndpointConfigName": { "target": "com.amazonaws.sagemaker#EndpointConfigName", "traits": { - "smithy.api#documentation": "Name of the Amazon SageMaker endpoint configuration.
", + "smithy.api#documentation": "Name of the SageMaker endpoint configuration.
", "smithy.api#required": {} } }, @@ -12782,7 +12823,7 @@ "RoleArn": { "target": "com.amazonaws.sagemaker#RoleArn", "traits": { - "smithy.api#documentation": "The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf\n during data labeling.
", + "smithy.api#documentation": "The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf\n during data labeling.
", "smithy.api#required": {} } }, @@ -13137,7 +13178,7 @@ "ModelName": { "target": "com.amazonaws.sagemaker#ModelName", "traits": { - "smithy.api#documentation": "Name of the Amazon SageMaker model.
", + "smithy.api#documentation": "Name of the SageMaker model.
", "smithy.api#required": {} } }, @@ -13224,7 +13265,7 @@ "ModelPackageGroupName": { "target": "com.amazonaws.sagemaker#ArnOrName", "traits": { - "smithy.api#documentation": "The name of the model group to describe.
", + "smithy.api#documentation": "The name of gthe model group to describe.
", "smithy.api#required": {} } } @@ -13388,7 +13429,7 @@ "LastModifiedTime": { "target": "com.amazonaws.sagemaker#Timestamp", "traits": { - "smithy.api#documentation": "The last time the model package was modified.
" + "smithy.api#documentation": "The last time that the model package was modified.
" } }, "LastModifiedBy": { @@ -13409,7 +13450,7 @@ "DriftCheckBaselines": { "target": "com.amazonaws.sagemaker#DriftCheckBaselines", "traits": { - "smithy.api#documentation": "Represents the drift check baselines that can be used when the model monitor is set using the model package. \n For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.\n
" + "smithy.api#documentation": "Represents the drift check baselines that can be used when the model monitor is set using the model package. \n For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.\n
" } }, "Domain": { @@ -13818,7 +13859,7 @@ "NotebookInstanceName": { "target": "com.amazonaws.sagemaker#NotebookInstanceName", "traits": { - "smithy.api#documentation": "The name of the Amazon SageMaker notebook instance.
" + "smithy.api#documentation": "The name of the SageMaker notebook instance.
" } }, "NotebookInstanceStatus": { @@ -13866,13 +13907,13 @@ "KmsKeyId": { "target": "com.amazonaws.sagemaker#KmsKeyId", "traits": { - "smithy.api#documentation": "The Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage\n volume attached to the instance.
" + "smithy.api#documentation": "The Amazon Web Services KMS key ID SageMaker uses to encrypt data when storing it on the ML storage\n volume attached to the instance.
" } }, "NetworkInterfaceId": { "target": "com.amazonaws.sagemaker#NetworkInterfaceId", "traits": { - "smithy.api#documentation": "The network interface IDs that Amazon SageMaker created at the time of creating the instance.\n
" + "smithy.api#documentation": "The network interface IDs that SageMaker created at the time of creating the instance.\n
" } }, "LastModifiedTime": { @@ -13896,7 +13937,7 @@ "DirectInternetAccess": { "target": "com.amazonaws.sagemaker#DirectInternetAccess", "traits": { - "smithy.api#documentation": "Describes whether Amazon SageMaker provides internet access to the notebook instance. If this\n value is set to Disabled, the notebook instance does not have\n internet access, and cannot connect to Amazon SageMaker training and endpoint services.
\nFor more information, see Notebook Instances Are Internet-Enabled by Default.
" + "smithy.api#documentation": "Describes whether SageMaker provides internet access to the notebook instance. If this\n value is set to Disabled, the notebook instance does not have\n internet access, and cannot connect to SageMaker training and endpoint services.
\nFor more information, see Notebook Instances Are Internet-Enabled by Default.
" } }, "VolumeSizeInGB": { @@ -13914,13 +13955,13 @@ "DefaultCodeRepository": { "target": "com.amazonaws.sagemaker#CodeRepositoryNameOrUrl", "traits": { - "smithy.api#documentation": "The Git repository associated with the notebook instance as its default code\n repository. This can be either the name of a Git repository stored as a resource in your\n account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any\n other Git repository. When you open a notebook instance, it opens in the directory that\n contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker\n Notebook Instances.
" + "smithy.api#documentation": "The Git repository associated with the notebook instance as its default code\n repository. This can be either the name of a Git repository stored as a resource in your\n account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any\n other Git repository. When you open a notebook instance, it opens in the directory that\n contains this repository. For more information, see Associating Git Repositories with SageMaker\n Notebook Instances.
" } }, "AdditionalCodeRepositories": { "target": "com.amazonaws.sagemaker#AdditionalCodeRepositoryNamesOrUrls", "traits": { - "smithy.api#documentation": "An array of up to three Git repositories associated with the notebook instance. These\n can be either the names of Git repositories stored as resources in your account, or the\n URL of Git repositories in Amazon Web Services CodeCommit or in any\n other Git repository. These repositories are cloned at the same level as the default\n repository of your notebook instance. For more information, see Associating Git\n Repositories with Amazon SageMaker Notebook Instances.
" + "smithy.api#documentation": "An array of up to three Git repositories associated with the notebook instance. These\n can be either the names of Git repositories stored as resources in your account, or the\n URL of Git repositories in Amazon Web Services CodeCommit or in any\n other Git repository. These repositories are cloned at the same level as the default\n repository of your notebook instance. For more information, see Associating Git\n Repositories with SageMaker Notebook Instances.
" } }, "RootAccess": { @@ -14700,7 +14741,7 @@ "LabelingJobArn": { "target": "com.amazonaws.sagemaker#LabelingJobArn", "traits": { - "smithy.api#documentation": "The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the\n transform or training job.
" + "smithy.api#documentation": "The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the\n transform or training job.
" } }, "AutoMLJobArn": { @@ -14719,14 +14760,14 @@ "TrainingJobStatus": { "target": "com.amazonaws.sagemaker#TrainingJobStatus", "traits": { - "smithy.api#documentation": "The status of the\n training\n job.
\nAmazon SageMaker provides the following training job statuses:
\n\n InProgress - The training is in progress.
\n Completed - The training job has completed.
\n Failed - The training job has failed. To see the reason for the\n failure, see the FailureReason field in the response to a\n DescribeTrainingJobResponse call.
\n Stopping - The training job is stopping.
\n Stopped - The training job has stopped.
For\n more detailed information, see SecondaryStatus.
The status of the training job.
\nSageMaker provides the following training job statuses:
\n\n InProgress - The training is in progress.
\n Completed - The training job has completed.
\n Failed - The training job has failed. To see the reason for the\n failure, see the FailureReason field in the response to a\n DescribeTrainingJobResponse call.
\n Stopping - The training job is stopping.
\n Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.
Provides detailed information about the state of the training job. For detailed\n information on the secondary status of the training job, see StatusMessage\n under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of\n them:
\n\n Starting\n - Starting the training job.
\n Downloading - An optional stage for algorithms that\n support File training input mode. It indicates that\n data is being downloaded to the ML storage volumes.
\n Training - Training is in progress.
\n Interrupted - The job stopped because the managed\n spot training instances were interrupted.
\n Uploading - Training is complete and the model\n artifacts are being uploaded to the S3 location.
\n Completed - The training job has completed.
\n Failed - The training job has failed. The reason for\n the failure is returned in the FailureReason field of\n DescribeTrainingJobResponse.
\n MaxRuntimeExceeded - The job stopped because it\n exceeded the maximum allowed runtime.
\n MaxWaitTimeExceeded - The job stopped because it\n exceeded the maximum allowed wait time.
\n Stopped - The training job has stopped.
\n Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
\n\n LaunchingMLInstances\n
\n PreparingTraining\n
\n DownloadingTrainingImage\n
Provides detailed information about the state of the training job. For detailed\n information on the secondary status of the training job, see StatusMessage\n under SecondaryStatusTransition.
SageMaker provides primary statuses and secondary statuses that apply to each of\n them:
\n\n Starting\n - Starting the training job.
\n Downloading - An optional stage for algorithms that\n support File training input mode. It indicates that\n data is being downloaded to the ML storage volumes.
\n Training - Training is in progress.
\n Interrupted - The job stopped because the managed\n spot training instances were interrupted.
\n Uploading - Training is complete and the model\n artifacts are being uploaded to the S3 location.
\n Completed - The training job has completed.
\n Failed - The training job has failed. The reason for\n the failure is returned in the FailureReason field of\n DescribeTrainingJobResponse.
\n MaxRuntimeExceeded - The job stopped because it\n exceeded the maximum allowed runtime.
\n MaxWaitTimeExceeded - The job stopped because it\n exceeded the maximum allowed wait time.
\n Stopped - The training job has stopped.
\n Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
\n\n LaunchingMLInstances\n
\n PreparingTraining\n
\n DownloadingTrainingImage\n
The S3 path where model artifacts that you configured when creating the job are\n stored. Amazon SageMaker creates subfolders for model artifacts.
" + "smithy.api#documentation": "The S3 path where model artifacts that you configured when creating the job are\n stored. SageMaker creates subfolders for model artifacts.
" } }, "ResourceConfig": { @@ -14783,7 +14824,7 @@ "StoppingCondition": { "target": "com.amazonaws.sagemaker#StoppingCondition", "traits": { - "smithy.api#documentation": "Specifies a limit to how long a model training job can run. It also specifies how long\n a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker\n ends the training job. Use this API to cap model training costs.
\nTo stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays\n job termination for 120 seconds. Algorithms can use this 120-second window to save the\n model artifacts, so the results of training are not lost.
Specifies a limit to how long a model training job can run. It also specifies how long\n a managed Spot training job has to complete. When the job reaches the time limit, SageMaker\n ends the training job. Use this API to cap model training costs.
\nTo stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays\n job termination for 120 seconds. Algorithms can use this 120-second window to save the\n model artifacts, so the results of training are not lost.
Indicates the time when the training job ends on training instances. You are billed\n for the time interval between the value of TrainingStartTime and this time.\n For successful jobs and stopped jobs, this is the time after model artifacts are\n uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
Indicates the time when the training job ends on training instances. You are billed\n for the time interval between the value of TrainingStartTime and this time.\n For successful jobs and stopped jobs, this is the time after model artifacts are\n uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
If you want to allow inbound or outbound network calls, except for calls between peers\n within a training cluster for distributed training, choose True. If you\n enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker\n downloads and uploads customer data and model artifacts through the specified VPC, but\n the training container does not have network access.
If you want to allow inbound or outbound network calls, except for calls between peers\n within a training cluster for distributed training, choose True. If you\n enable network isolation for training jobs that are configured to use a VPC, SageMaker\n downloads and uploads customer data and model artifacts through the specified VPC, but\n the training container does not have network access.
The billable time in seconds. Billable time refers to the absolute wall-clock\n time.
\nMultiply BillableTimeInSeconds by the number of instances\n (InstanceCount) in your training cluster to get the total compute time\n SageMaker will bill you if you run distributed training. The formula is as follows:\n BillableTimeInSeconds * InstanceCount .
You can calculate the savings from using managed spot training using the formula\n (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example,\n if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is\n 500, the savings is 80%.
The billable time in seconds. Billable time refers to the absolute wall-clock\n time.
\nMultiply BillableTimeInSeconds by the number of instances\n (InstanceCount) in your training cluster to get the total compute time\n SageMaker bills you if you run distributed training. The formula is as follows:\n BillableTimeInSeconds * InstanceCount .
You can calculate the savings from using managed spot training using the formula\n (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example,\n if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is\n 500, the savings is 80%.
The name of the\n variant\n to update.
", + "smithy.api#documentation": "The name of the variant to update.
", "smithy.api#required": {} } }, @@ -16156,30 +16197,30 @@ "Bias": { "target": "com.amazonaws.sagemaker#DriftCheckBias", "traits": { - "smithy.api#documentation": "Represents the drift check bias baselines that can be used when the model monitor is set using the model \n package.
" + "smithy.api#documentation": "Represents the drift check bias baselines that can be used when the model monitor is set using the model \n package.
" } }, "Explainability": { "target": "com.amazonaws.sagemaker#DriftCheckExplainability", "traits": { - "smithy.api#documentation": "Represents the drift check explainability baselines that can be used when the model monitor is set using \n the model package.
" + "smithy.api#documentation": "Represents the drift check explainability baselines that can be used when the model monitor is set using \n the model package.
" } }, "ModelQuality": { "target": "com.amazonaws.sagemaker#DriftCheckModelQuality", "traits": { - "smithy.api#documentation": "Represents the drift check model quality baselines that can be used when the model monitor is set using \n the model package.
" + "smithy.api#documentation": "Represents the drift check model quality baselines that can be used when the model monitor is set using \n the model package.
" } }, "ModelDataQuality": { "target": "com.amazonaws.sagemaker#DriftCheckModelDataQuality", "traits": { - "smithy.api#documentation": "Represents the drift check model data quality baselines that can be used when the model monitor is set \n using the model package.
" + "smithy.api#documentation": "Represents the drift check model data quality baselines that can be used when the model monitor is set \n using the model package.
" } } }, "traits": { - "smithy.api#documentation": "Represents the drift check baselines that can be used when the model monitor is set using the model \n package.
" + "smithy.api#documentation": "Represents the drift check baselines that can be used when the model monitor is set using the model \n package.
" } }, "com.amazonaws.sagemaker#DriftCheckBias": { @@ -16199,7 +16240,7 @@ } }, "traits": { - "smithy.api#documentation": "Represents the drift check bias baselines that can be used when the model monitor is set using the \n model package.
" + "smithy.api#documentation": "Represents the drift check bias baselines that can be used when the model monitor is set using the \n model package.
" } }, "com.amazonaws.sagemaker#DriftCheckExplainability": { @@ -16216,7 +16257,7 @@ } }, "traits": { - "smithy.api#documentation": "Represents the drift check explainability baselines that can be used when the model monitor is set \n using the model package.
" + "smithy.api#documentation": "Represents the drift check explainability baselines that can be used when the model monitor is set \n using the model package.
" } }, "com.amazonaws.sagemaker#DriftCheckModelDataQuality": { @@ -16230,7 +16271,7 @@ } }, "traits": { - "smithy.api#documentation": "Represents the drift check data quality baselines that can be used when the model monitor is set using \n the model package.
" + "smithy.api#documentation": "Represents the drift check data quality baselines that can be used when the model monitor is set using \n the model package.
" } }, "com.amazonaws.sagemaker#DriftCheckModelQuality": { @@ -16244,7 +16285,7 @@ } }, "traits": { - "smithy.api#documentation": "Represents the drift check model quality baselines that can be used when the model monitor is set using \n the model package.
" + "smithy.api#documentation": "Represents the drift check model quality baselines that can be used when the model monitor is set using \n the model package.
" } }, "com.amazonaws.sagemaker#EMRStepMetadata": { @@ -18917,7 +18958,7 @@ "TrainingImage": { "target": "com.amazonaws.sagemaker#AlgorithmImage", "traits": { - "smithy.api#documentation": " The registry path of the Docker image that contains the training algorithm. For\n information about Docker registry paths for built-in algorithms, see Algorithms\n Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with Amazon\n SageMaker.
The registry path of the Docker image that contains the training algorithm. For\n information about Docker registry paths for built-in algorithms, see Algorithms\n Provided by Amazon SageMaker: Common Parameters. SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with Amazon\n SageMaker.
The resources,\n including\n the compute instances and storage volumes, to use for the training\n jobs that the tuning job launches.
\nStorage\n volumes store model artifacts and\n incremental\n states. Training algorithms might also use storage volumes for\n scratch\n space. If you want Amazon SageMaker to use the storage volume\n to store the training data, choose File as the\n TrainingInputMode in the algorithm specification. For distributed\n training algorithms, specify an instance count greater than 1.
The resources,\n including\n the compute instances and storage volumes, to use for the training\n jobs that the tuning job launches.
\nStorage volumes store model artifacts and\n incremental\n states. Training algorithms might also use storage volumes for\n scratch\n space. If you want SageMaker to use the storage volume to store the\n training data, choose File as the TrainingInputMode in the\n algorithm specification. For distributed training algorithms, specify an instance count\n greater than 1.
Specifies a limit to how long a model hyperparameter training job can run. It also\n specifies how long a managed spot training job has to complete. When the job reaches the\n time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
", + "smithy.api#documentation": "Specifies a limit to how long a model hyperparameter training job can run. It also\n specifies how long a managed spot training job has to complete. When the job reaches the\n time limit, SageMaker ends the training job. Use this API to cap model training costs.
", "smithy.api#required": {} } }, "EnableNetworkIsolation": { "target": "com.amazonaws.sagemaker#Boolean", "traits": { - "smithy.api#documentation": "Isolates the training container. No inbound or outbound network calls can be made,\n except for calls between peers within a training cluster for distributed training. If\n network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker\n downloads and uploads customer data and model artifacts through the specified VPC, but\n the training container does not have network access.
" + "smithy.api#documentation": "Isolates the training container. No inbound or outbound network calls can be made,\n except for calls between peers within a training cluster for distributed training. If\n network isolation is used for training jobs that are configured to use a VPC, SageMaker\n downloads and uploads customer data and model artifacts through the specified VPC, but\n the training container does not have network access.
" } }, "EnableInterContainerTrafficEncryption": { @@ -19187,7 +19228,7 @@ "TrainingJobArn": { "target": "com.amazonaws.sagemaker#TrainingJobArn", "traits": { - "smithy.api#documentation": "The\n Amazon\n Resource Name (ARN) of the training job.
", + "smithy.api#documentation": "The Amazon Resource Name (ARN) of the training job.
", "smithy.api#required": {} } }, @@ -19213,7 +19254,7 @@ "TrainingEndTime": { "target": "com.amazonaws.sagemaker#Timestamp", "traits": { - "smithy.api#documentation": "Specifies the time when the training job ends on training instances. You are billed\n for the time interval between the value of TrainingStartTime and this time.\n For successful jobs and stopped jobs, this is the time after model artifacts are\n uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
Specifies the time when the training job ends on training instances. You are billed\n for the time interval between the value of TrainingStartTime and this time.\n For successful jobs and stopped jobs, this is the time after model artifacts are\n uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
Specifies\n summary information about a training job.
" + "smithy.api#documentation": "The container for the summary information about a training job.
" } }, "com.amazonaws.sagemaker#HyperParameterTuningJobArn": { @@ -19295,7 +19336,7 @@ "TrainingJobEarlyStoppingType": { "target": "com.amazonaws.sagemaker#TrainingJobEarlyStoppingType", "traits": { - "smithy.api#documentation": "Specifies whether to use early stopping for training jobs launched by the\n hyperparameter tuning job. This can be one of the following values (the default value is\n OFF):
Training jobs launched by the hyperparameter tuning job do not use early\n stopping.
\nAmazon SageMaker stops training jobs launched by the hyperparameter tuning job when\n they are unlikely to perform better than previously completed training jobs.\n For more information, see Stop Training Jobs Early.
\nSpecifies whether to use early stopping for training jobs launched by the\n hyperparameter tuning job. This can be one of the following values (the default value is\n OFF):
Training jobs launched by the hyperparameter tuning job do not use early\n stopping.
\nSageMaker stops training jobs launched by the hyperparameter tuning job when\n they are unlikely to perform better than previously completed training jobs.\n For more information, see Stop Training Jobs Early.
\nThe scale that hyperparameter tuning uses to search the hyperparameter range. For\n information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
\nAmazon SageMaker hyperparameter tuning chooses the best scale for the\n hyperparameter.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a linear scale.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a logarithmic scale.
\nLogarithmic scaling works only for ranges that have only values greater\n than 0.
\nThe scale that hyperparameter tuning uses to search the hyperparameter range. For\n information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
\nSageMaker hyperparameter tuning chooses the best scale for the\n hyperparameter.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a linear scale.
\nHyperparameter tuning searches the values in the hyperparameter range by\n using a logarithmic scale.
\nLogarithmic scaling works only for ranges that have only values greater\n than 0.
\nThe default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app.
" + "smithy.api#documentation": "The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp.
" + "smithy.api#documentation": " The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
" + "smithy.api#documentation": "The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
\nThe Amazon SageMaker Studio UI does not use the default instance type value set here. The default\n instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation\n and the instance type parameter value is not passed.
\nThe Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
" + "smithy.api#documentation": "The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
\nTo remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
Declares that your content is free of personally identifiable information or adult\n content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task\n based on this information.
" + "smithy.api#documentation": "Declares that your content is free of personally identifiable information or adult\n content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task\n based on this information.
" } } }, @@ -21146,7 +21187,7 @@ "FinalActiveLearningModelArn": { "target": "com.amazonaws.sagemaker#ModelArn", "traits": { - "smithy.api#documentation": "The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of\n automated data labeling.
" + "smithy.api#documentation": "The Amazon Resource Name (ARN) for the most recent SageMaker model trained as part of\n automated data labeling.
" } } }, @@ -21682,7 +21723,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#NextToken", "traits": { - "smithy.api#documentation": "If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of\n algorithms, use it in the subsequent request.
" + "smithy.api#documentation": "If the response is truncated, SageMaker returns this token. To retrieve the next set of\n algorithms, use it in the subsequent request.
" } } } @@ -23103,7 +23144,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#PaginationToken", "traits": { - "smithy.api#documentation": "If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of\n endpoint configurations, use it in the subsequent request
" + "smithy.api#documentation": "If the response is truncated, SageMaker returns this token. To retrieve the next set of\n endpoint configurations, use it in the subsequent request
" } } } @@ -23204,7 +23245,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#PaginationToken", "traits": { - "smithy.api#documentation": "If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of\n training jobs, use it in the subsequent request.
" + "smithy.api#documentation": "If the response is truncated, SageMaker returns this token. To retrieve the next set of\n training jobs, use it in the subsequent request.
" } } } @@ -23556,13 +23597,13 @@ "SortBy": { "target": "com.amazonaws.sagemaker#HyperParameterTuningJobSortByOptions", "traits": { - "smithy.api#documentation": "The\n field\n to sort results by. The default is Name.
The field to sort results by. The default is Name.
The sort\n order\n for results. The default is Ascending.
The sort order for results. The default is Ascending.
A filter that returns only tuning jobs that were created after the\n specified\n time.
" + "smithy.api#documentation": "A filter that returns only tuning jobs that were created after the specified\n time.
" } }, "CreationTimeBefore": { "target": "com.amazonaws.sagemaker#Timestamp", "traits": { - "smithy.api#documentation": "A filter that returns only tuning jobs that were created before the\n specified\n time.
" + "smithy.api#documentation": "A filter that returns only tuning jobs that were created before the specified\n time.
" } }, "LastModifiedTimeAfter": { @@ -23598,7 +23639,7 @@ "StatusEquals": { "target": "com.amazonaws.sagemaker#HyperParameterTuningJobStatus", "traits": { - "smithy.api#documentation": "A filter that returns only tuning jobs with the\n specified\n status.
" + "smithy.api#documentation": "A filter that returns only tuning jobs with the specified status.
" } } } @@ -24043,7 +24084,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#NextToken", "traits": { - "smithy.api#documentation": "If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of\n labeling jobs, use it in the subsequent request.
" + "smithy.api#documentation": "If the response is truncated, SageMaker returns this token. To retrieve the next set of\n labeling jobs, use it in the subsequent request.
" } } } @@ -24136,7 +24177,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#NextToken", "traits": { - "smithy.api#documentation": "If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of\n labeling jobs, use it in the subsequent request.
" + "smithy.api#documentation": "If the response is truncated, SageMaker returns this token. To retrieve the next set of\n labeling jobs, use it in the subsequent request.
" } } } @@ -24647,7 +24688,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#NextToken", "traits": { - "smithy.api#documentation": "If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of\n model packages, use it in the subsequent request.
" + "smithy.api#documentation": "If the response is truncated, SageMaker returns this token. To retrieve the next set of\n model packages, use it in the subsequent request.
" } } } @@ -24819,7 +24860,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#PaginationToken", "traits": { - "smithy.api#documentation": "If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of\n models, use it in the subsequent request.
" + "smithy.api#documentation": "If the response is truncated, SageMaker returns this token. To retrieve the next set of\n models, use it in the subsequent request.
" } } } @@ -25157,7 +25198,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#NextToken", "traits": { - "smithy.api#documentation": "If the response is truncated, Amazon SageMaker returns this token. To get the next set of\n lifecycle configurations, use it in the next request.
" + "smithy.api#documentation": "If the response is truncated, SageMaker returns this token. To get the next set of\n lifecycle configurations, use it in the next request.
" } }, "NotebookInstanceLifecycleConfigs": { @@ -25177,7 +25218,7 @@ "target": "com.amazonaws.sagemaker#ListNotebookInstancesOutput" }, "traits": { - "smithy.api#documentation": "Returns a list of the Amazon SageMaker notebook instances in the requester's account in an Amazon Web Services\n Region.
", + "smithy.api#documentation": "Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services\n Region.
", "smithy.api#paginated": { "inputToken": "NextToken", "outputToken": "NextToken", @@ -25275,7 +25316,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#NextToken", "traits": { - "smithy.api#documentation": "If the response to the previous ListNotebookInstances request was\n truncated, Amazon SageMaker returns this token. To retrieve the next set of notebook instances, use\n the token in the next request.
If the response to the previous ListNotebookInstances request was\n truncated, SageMaker returns this token. To retrieve the next set of notebook instances, use\n the token in the next request.
Returns the tags for the specified Amazon SageMaker resource.
", + "smithy.api#documentation": "Returns the tags for the specified SageMaker resource.
", "smithy.api#paginated": { "inputToken": "NextToken", "outputToken": "NextToken", @@ -25969,7 +26010,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#NextToken", "traits": { - "smithy.api#documentation": " If the response to the previous ListTags request is truncated, Amazon SageMaker\n returns this token. To retrieve the next set of tags, use it in the subsequent request.\n
If the response to the previous ListTags request is truncated, SageMaker\n returns this token. To retrieve the next set of tags, use it in the subsequent request.\n
If response is truncated, Amazon SageMaker includes a token in the response. You can use this\n token in your subsequent request to fetch next set of tokens.
" + "smithy.api#documentation": "If response is truncated, SageMaker includes a token in the response. You can use this\n token in your subsequent request to fetch next set of tokens.
" } } } @@ -26072,19 +26113,19 @@ "StatusEquals": { "target": "com.amazonaws.sagemaker#TrainingJobStatus", "traits": { - "smithy.api#documentation": "A filter that returns only training jobs with the\n specified\n status.
" + "smithy.api#documentation": "A filter that returns only training jobs with the specified status.
" } }, "SortBy": { "target": "com.amazonaws.sagemaker#TrainingJobSortByOptions", "traits": { - "smithy.api#documentation": "The field to sort\n results\n by. The default is Name.
If the value of this field is FinalObjectiveMetricValue, any training\n jobs that did not return an objective metric are not listed.
The field to sort results by. The default is Name.
If the value of this field is FinalObjectiveMetricValue, any training\n jobs that did not return an objective metric are not listed.
The sort order\n for\n results. The default is Ascending.
The sort order for results. The default is Ascending.
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of\n training jobs, use it in the subsequent request.
" + "smithy.api#documentation": "If the response is truncated, SageMaker returns this token. To retrieve the next set of\n training jobs, use it in the subsequent request.
" } } } @@ -27083,7 +27124,7 @@ } }, "traits": { - "smithy.api#documentation": "Specifies a metric that the training algorithm\n writes\n to stderr or stdout. Amazon SageMakerhyperparameter\n tuning captures\n all\n defined metrics.\n You\n specify one metric that a hyperparameter tuning job uses as its\n objective metric to choose the best training job.
Specifies a metric that the training algorithm\n writes\n to stderr or stdout. SageMakerhyperparameter\n tuning captures\n all\n defined metrics.\n You\n specify one metric that a hyperparameter tuning job uses as its\n objective metric to choose the best training job.
The timeout value in seconds for an invocation request.
" + "smithy.api#documentation": "The timeout value in seconds for an invocation request. The default value is 600.
" } }, "InvocationsMaxRetries": { "target": "com.amazonaws.sagemaker#InvocationsMaxRetries", "traits": { - "smithy.api#documentation": "The maximum number of retries when invocation requests are failing.
" + "smithy.api#documentation": "The maximum number of retries when invocation requests are failing. The default value is 3.
" } } }, @@ -27864,7 +27905,7 @@ "Image": { "target": "com.amazonaws.sagemaker#ContainerImage", "traits": { - "smithy.api#documentation": "The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
\nIf you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker,\n the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with Amazon\n SageMaker.
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
\nIf you are using your own custom algorithm instead of an algorithm provided by SageMaker,\n the inference code must meet SageMaker requirements. SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with Amazon\n SageMaker.
The Amazon S3 path where the model artifacts, which result from model training, are stored.\n This path must point to a single gzip compressed tar archive\n (.tar.gz suffix).
The model artifacts must be in an S3 bucket that is in the same region as the\n model package.
\nThe Amazon S3 path where the model artifacts, which result from model training, are stored.\n This path must point to a single gzip compressed tar archive\n (.tar.gz suffix).
The model artifacts must be in an S3 bucket that is in the same region as the\n model package.
\nThe approval status of the model. This can be one of the following values.
\n\n APPROVED - The model is approved
\n REJECTED - The model is rejected.
\n PENDING_MANUAL_APPROVAL - The model is waiting for manual\n approval.
The approval status of the model. This can be one of the following values.
\n\n APPROVED - The model is approved
\n REJECTED - The model is rejected.
\n PENDING_MANUAL_APPROVAL - The model is waiting for manual\n approval.
An array of ModelPackageValidationProfile objects, each of which\n specifies a batch transform job that Amazon SageMaker runs to validate your model package.
An array of ModelPackageValidationProfile objects, each of which\n specifies a batch transform job that SageMaker runs to validate your model package.
Specifies batch transform jobs that Amazon SageMaker runs to validate your model package.
" + "smithy.api#documentation": "Specifies batch transform jobs that SageMaker runs to validate your model package.
" } }, "com.amazonaws.sagemaker#ModelPackageVersion": { @@ -29833,7 +29874,7 @@ "Url": { "target": "com.amazonaws.sagemaker#NotebookInstanceUrl", "traits": { - "smithy.api#documentation": "The\n URL that you use to connect to the Jupyter instance running in your notebook instance.\n
" + "smithy.api#documentation": "The URL that you use to connect to the Jupyter notebook running in your notebook\n instance.
" } }, "InstanceType": { @@ -29863,18 +29904,18 @@ "DefaultCodeRepository": { "target": "com.amazonaws.sagemaker#CodeRepositoryNameOrUrl", "traits": { - "smithy.api#documentation": "The Git repository associated with the notebook instance as its default code\n repository. This can be either the name of a Git repository stored as a resource in your\n account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any\n other Git repository. When you open a notebook instance, it opens in the directory that\n contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker\n Notebook Instances.
" + "smithy.api#documentation": "The Git repository associated with the notebook instance as its default code\n repository. This can be either the name of a Git repository stored as a resource in your\n account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any\n other Git repository. When you open a notebook instance, it opens in the directory that\n contains this repository. For more information, see Associating Git Repositories with SageMaker\n Notebook Instances.
" } }, "AdditionalCodeRepositories": { "target": "com.amazonaws.sagemaker#AdditionalCodeRepositoryNamesOrUrls", "traits": { - "smithy.api#documentation": "An array of up to three Git repositories associated with the notebook instance. These\n can be either the names of Git repositories stored as resources in your account, or the\n URL of Git repositories in Amazon Web Services CodeCommit or in any\n other Git repository. These repositories are cloned at the same level as the default\n repository of your notebook instance. For more information, see Associating Git\n Repositories with Amazon SageMaker Notebook Instances.
" + "smithy.api#documentation": "An array of up to three Git repositories associated with the notebook instance. These\n can be either the names of Git repositories stored as resources in your account, or the\n URL of Git repositories in Amazon Web Services CodeCommit or in any\n other Git repository. These repositories are cloned at the same level as the default\n repository of your notebook instance. For more information, see Associating Git\n Repositories with SageMaker Notebook Instances.
" } } }, "traits": { - "smithy.api#documentation": "Provides summary information for an Amazon SageMaker notebook instance.
" + "smithy.api#documentation": "Provides summary information for an SageMaker notebook instance.
" } }, "com.amazonaws.sagemaker#NotebookInstanceSummaryList": { @@ -30361,13 +30402,13 @@ "KmsKeyId": { "target": "com.amazonaws.sagemaker#KmsKeyId", "traits": { - "smithy.api#documentation": "The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using\n Amazon S3 server-side encryption. The KmsKeyId can be any of the following\n formats:
// KMS Key ID
\n\n \"1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// Amazon Resource Name (ARN) of a KMS Key
\n\n \"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// KMS Key Alias
\n\n \"alias/ExampleAlias\"\n
// Amazon Resource Name (ARN) of a KMS Key Alias
\n\n \"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias\"\n
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must\n include permissions to call kms:Encrypt. If you don't provide a KMS key ID,\n Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side\n encryption with KMS-managed keys for OutputDataConfig. If you use a bucket\n policy with an s3:PutObject permission that only allows objects with\n server-side encryption, set the condition key of\n s3:x-amz-server-side-encryption to \"aws:kms\". For more\n information, see KMS-Managed Encryption\n Keys in the Amazon Simple Storage Service Developer Guide.\n
The KMS key policy must grant permission to the IAM role that you specify in your\n CreateTrainingJob, CreateTransformJob, or\n CreateHyperParameterTuningJob requests. For more information, see\n Using\n Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer\n Guide.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using\n Amazon S3 server-side encryption. The KmsKeyId can be any of the following\n formats:
// KMS Key ID
\n\n \"1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// Amazon Resource Name (ARN) of a KMS Key
\n\n \"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// KMS Key Alias
\n\n \"alias/ExampleAlias\"\n
// Amazon Resource Name (ARN) of a KMS Key Alias
\n\n \"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias\"\n
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must\n include permissions to call kms:Encrypt. If you don't provide a KMS key ID,\n SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side\n encryption with KMS-managed keys for OutputDataConfig. If you use a bucket\n policy with an s3:PutObject permission that only allows objects with\n server-side encryption, set the condition key of\n s3:x-amz-server-side-encryption to \"aws:kms\". For more\n information, see KMS-Managed Encryption\n Keys in the Amazon Simple Storage Service Developer Guide.\n
The KMS key policy must grant permission to the IAM role that you specify in your\n CreateTrainingJob, CreateTransformJob, or\n CreateHyperParameterTuningJob requests. For more information, see\n Using\n Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer\n Guide.
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For\n example, s3://bucket-name/key-name-prefix.
Identifies the S3 path where you want SageMaker to store the model artifacts. For\n example, s3://bucket-name/key-name-prefix.
The serverless configuration for the endpoint.
\nServerless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
\nThe serverless configuration for the endpoint.
" } }, "DesiredServerlessConfig": { "target": "com.amazonaws.sagemaker#ProductionVariantServerlessConfig", "traits": { - "smithy.api#documentation": "The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
\nServerless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
\nThe serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
" } } }, @@ -31216,7 +31257,7 @@ "ClarifyCheck": { "target": "com.amazonaws.sagemaker#ClarifyCheckStepMetadata", "traits": { - "smithy.api#documentation": "Container for the metadata for a Clarify check step. The configurations \n and outcomes of the check step execution. This includes:
\nThe type of the check conducted,
\nThe Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check.
\nThe Amazon S3 URIs of newly calculated baseline constraints and statistics.
\nThe model package group name provided.
\nThe Amazon S3 URI of the violation report if violations detected.
\nThe Amazon Resource Name (ARN) of check processing job initiated by the step execution.
\nThe boolean flags indicating if the drift check is skipped.
\nIf step property BaselineUsedForDriftCheck is set the same as \n CalculatedBaseline.
Container for the metadata for a Clarify check step. The configurations \n and outcomes of the check step execution. This includes:
\nThe type of the check conducted,
\nThe Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check.
\nThe Amazon S3 URIs of newly calculated baseline constraints and statistics.
\nThe model package group name provided.
\nThe Amazon S3 URI of the violation report if violations detected.
\nThe Amazon Resource Name (ARN) of check processing job initiated by the step execution.
\nThe boolean flags indicating if the drift check is skipped.
\nIf step property BaselineUsedForDriftCheck is set the same as \n CalculatedBaseline.
The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
\nServerless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
\nThe serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
" } } }, "traits": { - "smithy.api#documentation": "Identifies a model that you want to host and the resources chosen to deploy for\n hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic\n among the models by specifying variant weights.
" + "smithy.api#documentation": "Identifies a model that you want to host and the resources chosen to deploy for\n hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic\n among the models by specifying variant weights.
" } }, "com.amazonaws.sagemaker#ProductionVariantAcceleratorType": { @@ -32424,7 +32465,7 @@ "KmsKeyId": { "target": "com.amazonaws.sagemaker#KmsKeyId", "traits": { - "smithy.api#documentation": "The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the core dump data at rest using\n Amazon S3 server-side encryption. The KmsKeyId can be any of the following\n formats:
// KMS Key ID
\n\n \"1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// Amazon Resource Name (ARN) of a KMS Key
\n\n \"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// KMS Key Alias
\n\n \"alias/ExampleAlias\"\n
// Amazon Resource Name (ARN) of a KMS Key Alias
\n\n \"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias\"\n
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must\n include permissions to call kms:Encrypt. If you don't provide a KMS key ID,\n Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side\n encryption with KMS-managed keys for OutputDataConfig. If you use a bucket\n policy with an s3:PutObject permission that only allows objects with\n server-side encryption, set the condition key of\n s3:x-amz-server-side-encryption to \"aws:kms\". For more\n information, see KMS-Managed Encryption\n Keys in the Amazon Simple Storage Service Developer Guide.\n
The KMS key policy must grant permission to the IAM role that you specify in your\n CreateEndpoint and UpdateEndpoint requests. For more\n information, see Using Key Policies in Amazon Web Services\n KMS in the Amazon Web Services Key Management Service Developer Guide.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using\n Amazon S3 server-side encryption. The KmsKeyId can be any of the following\n formats:
// KMS Key ID
\n\n \"1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// Amazon Resource Name (ARN) of a KMS Key
\n\n \"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// KMS Key Alias
\n\n \"alias/ExampleAlias\"\n
// Amazon Resource Name (ARN) of a KMS Key Alias
\n\n \"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias\"\n
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must\n include permissions to call kms:Encrypt. If you don't provide a KMS key ID,\n SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side\n encryption with KMS-managed keys for OutputDataConfig. If you use a bucket\n policy with an s3:PutObject permission that only allows objects with\n server-side encryption, set the condition key of\n s3:x-amz-server-side-encryption to \"aws:kms\". For more\n information, see KMS-Managed Encryption\n Keys in the Amazon Simple Storage Service Developer Guide.\n
The KMS key policy must grant permission to the IAM role that you specify in your\n CreateEndpoint and UpdateEndpoint requests. For more\n information, see Using Key Policies in Amazon Web Services\n KMS in the Amazon Web Services Key Management Service Developer Guide.
Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
\nSpecifies the serverless configuration for an endpoint variant.
" + "smithy.api#documentation": "Specifies the serverless configuration for an endpoint variant.
" } }, "com.amazonaws.sagemaker#ProductionVariantStatus": { @@ -32825,13 +32866,13 @@ "CurrentServerlessConfig": { "target": "com.amazonaws.sagemaker#ProductionVariantServerlessConfig", "traits": { - "smithy.api#documentation": "The serverless configuration for the endpoint.
\nServerless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
\nThe serverless configuration for the endpoint.
" } }, "DesiredServerlessConfig": { "target": "com.amazonaws.sagemaker#ProductionVariantServerlessConfig", "traits": { - "smithy.api#documentation": "The serverless configuration requested for the endpoint update.
\nServerless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
\nThe serverless configuration requested for the endpoint update.
" } } }, @@ -33556,12 +33597,12 @@ "Properties": { "target": "com.amazonaws.sagemaker#QueryProperties", "traits": { - "smithy.api#documentation": "Filter the lineage entities connected to the StartArn(s) by a set if property key value pairs. \n If multiple pairs are provided, an entity will be included in the results if it matches any of the provided pairs.
Filter the lineage entities connected to the StartArn(s) by a set if property key value pairs. \n If multiple pairs are provided, an entity is included in the results if it matches any of the provided pairs.
A set of filters to narrow the set of lineage entities connected to the StartArn(s) returned by the \n QueryLineage API action.
A set of filters to narrow the set of lineage entities connected to the StartArn(s) returned by the \n QueryLineage API action.
Associations between lineage entities are directed. This parameter determines the direction from the \n StartArn(s) the query will look.
" + "smithy.api#documentation": "Associations between lineage entities have a direction. This parameter determines the direction from the \n StartArn(s) that the query traverses.
" } }, "IncludeEdges": { "target": "com.amazonaws.sagemaker#Boolean", "traits": { - "smithy.api#documentation": " Setting this value to True will retrieve not only the entities of interest but also the \n Associations and \n lineage entities on the path. Set to False to only return lineage entities that match your query.
Setting this value to True retrieves not only the entities of interest but also the \n Associations and \n lineage entities on the path. Set to False to only return lineage entities that match your query.
The maximum depth in lineage relationships from the StartArns that will be traversed. Depth is a measure of the number \n of Associations from the StartArn entity to the matched results.
The maximum depth in lineage relationships from the StartArns that are traversed. Depth is a measure of the number \n of Associations from the StartArn entity to the matched results.
The size of the ML storage volume that you want to provision.
\nML storage volumes store model artifacts and incremental states. Training\n algorithms might also use the ML storage volume for scratch space. If you want to store\n the training data in the ML storage volume, choose File as the\n TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
\nAmazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.\n
\nCertain Nitro-based instances include local storage with a fixed total size,\n dependent on the instance type. When using these instances for training, Amazon SageMaker mounts\n the local instance storage instead of Amazon EBS gp2 storage. You can't request a\n VolumeSizeInGB greater than the total size of the local instance\n storage.
For a list of instance types that support local instance storage, including the\n total size per instance type, see Instance Store Volumes.
\nThe size of the ML storage volume that you want to provision.
\nML storage volumes store model artifacts and incremental states. Training\n algorithms might also use the ML storage volume for scratch space. If you want to store\n the training data in the ML storage volume, choose File as the\n TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
\nSageMaker supports only the General Purpose SSD (gp2) ML storage volume type.\n
\nCertain Nitro-based instances include local storage with a fixed total size,\n dependent on the instance type. When using these instances for training, SageMaker mounts\n the local instance storage instead of Amazon EBS gp2 storage. You can't request a\n VolumeSizeInGB greater than the total size of the local instance\n storage.
For a list of instance types that support local instance storage, including the\n total size per instance type, see Instance Store Volumes.
\nThe Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML\n compute instance(s) that run the training job.
\nCertain Nitro-based instances include local storage, dependent on the instance\n type. Local storage volumes are encrypted using a hardware module on the instance.\n You can't request a VolumeKmsKeyId when using an instance type with\n local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes.
\nFor more information about local instance storage encryption, see SSD\n Instance Store Volumes.
\nThe VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID
\n\n \"1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// Amazon Resource Name (ARN) of a KMS Key
\n\n \"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab\"\n
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML\n compute instance(s) that run the training job.
\nCertain Nitro-based instances include local storage, dependent on the instance\n type. Local storage volumes are encrypted using a hardware module on the instance.\n You can't request a VolumeKmsKeyId when using an instance type with\n local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes.
\nFor more information about local instance storage encryption, see SSD\n Instance Store Volumes.
\nThe VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID
\n\n \"1234abcd-12ab-34cd-56ef-1234567890ab\"\n
// Amazon Resource Name (ARN) of a KMS Key
\n\n \"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab\"\n
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many\n training jobs created.
", + "smithy.api#documentation": "You have exceeded an SageMaker resource limit. For example, you might have too many\n training jobs created.
", "smithy.api#error": "client" } }, @@ -34607,7 +34648,7 @@ "InstanceType": { "target": "com.amazonaws.sagemaker#AppInstanceType", "traits": { - "smithy.api#documentation": "The instance type that the image version runs on.
" + "smithy.api#documentation": "The instance type that the image version runs on.
\nJupyterServer Apps only support the system value. KernelGateway Apps do not support the system value, but support all other values for available instance types.
If you choose S3Prefix, S3Uri identifies a key name prefix.\n Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that\n is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model\n training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is\n an augmented manifest file in JSON lines format. This file contains the data you want to\n use for model training. AugmentedManifestFile can only be used if the\n Channel's input mode is Pipe.
If you choose S3Prefix, S3Uri identifies a key name prefix.\n SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that\n is a manifest file containing a list of object keys that you want SageMaker to use for model\n training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is\n an augmented manifest file in JSON lines format. This file contains the data you want to\n use for model training. AugmentedManifestFile can only be used if the\n Channel's input mode is Pipe.
Depending on the value specified for the S3DataType, identifies either\n a key name prefix or a manifest. For example:
A key name prefix might look like this:\n s3://bucketname/exampleprefix\n
A manifest might look like this:\n s3://bucketname/example.manifest\n
A manifest is an S3 object which is a JSON file consisting of an array of\n elements. The first element is a prefix which is followed by one or more\n suffixes. SageMaker appends the suffix elements to the prefix to get a full set\n of S3Uri. Note that the prefix must be a valid non-empty\n S3Uri that precludes users from specifying a manifest whose\n individual S3Uri is sourced from different S3 buckets.
The following code example shows a valid manifest format:
\n\n [ {\"prefix\": \"s3://customer_bucket/some/prefix/\"},\n
\n \"relative/path/to/custdata-1\",\n
\n \"relative/path/custdata-2\",\n
\n ...\n
\n \"relative/path/custdata-N\"\n
\n ]\n
This JSON is equivalent to the following S3Uri\n list:
\n s3://customer_bucket/some/prefix/relative/path/to/custdata-1\n
\n s3://customer_bucket/some/prefix/relative/path/custdata-2\n
\n ...\n
\n s3://customer_bucket/some/prefix/relative/path/custdata-N\n
The complete set of S3Uri in this manifest is the input data\n for the channel for this data source. The object that each S3Uri\n points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on\n your behalf.
Depending on the value specified for the S3DataType, identifies either\n a key name prefix or a manifest. For example:
A key name prefix might look like this:\n s3://bucketname/exampleprefix\n
A manifest might look like this:\n s3://bucketname/example.manifest\n
A manifest is an S3 object which is a JSON file consisting of an array of\n elements. The first element is a prefix which is followed by one or more\n suffixes. SageMaker appends the suffix elements to the prefix to get a full set\n of S3Uri. Note that the prefix must be a valid non-empty\n S3Uri that precludes users from specifying a manifest whose\n individual S3Uri is sourced from different S3 buckets.
The following code example shows a valid manifest format:
\n\n [ {\"prefix\": \"s3://customer_bucket/some/prefix/\"},\n
\n \"relative/path/to/custdata-1\",\n
\n \"relative/path/custdata-2\",\n
\n ...\n
\n \"relative/path/custdata-N\"\n
\n ]\n
This JSON is equivalent to the following S3Uri\n list:
\n s3://customer_bucket/some/prefix/relative/path/to/custdata-1\n
\n s3://customer_bucket/some/prefix/relative/path/custdata-2\n
\n ...\n
\n s3://customer_bucket/some/prefix/relative/path/custdata-N\n
The complete set of S3Uri in this manifest is the input data\n for the channel for this data source. The object that each S3Uri\n points to must be readable by the IAM role that SageMaker uses to perform tasks on\n your behalf.
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that\n is launched for model training, specify FullyReplicated.
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is\n launched for model training, specify ShardedByS3Key. If there are\n n ML compute instances launched for a training job, each\n instance gets approximately 1/n of the number of S3 objects. In\n this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If\n you do, some nodes won't get any data and you will pay for nodes that aren't getting any\n training data. This applies in both File and Pipe modes. Keep this in mind when\n developing algorithms.
\nIn distributed training, where you use multiple ML compute EC2 instances, you might\n choose ShardedByS3Key. If the algorithm requires copying training data to\n the ML storage volume (when TrainingInputMode is set to File),\n this copies 1/n of the number of objects.
If you want SageMaker to replicate the entire dataset on each ML compute instance that\n is launched for model training, specify FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is\n launched for model training, specify ShardedByS3Key. If there are\n n ML compute instances launched for a training job, each\n instance gets approximately 1/n of the number of S3 objects. In\n this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If\n you do, some nodes won't get any data and you will pay for nodes that aren't getting any\n training data. This applies in both File and Pipe modes. Keep this in mind when\n developing algorithms.
\nIn distributed training, where you use multiple ML compute EC2 instances, you might\n choose ShardedByS3Key. If the algorithm requires copying training data to\n the ML storage volume (when TrainingInputMode is set to File),\n this copies 1/n of the number of objects.
Provides APIs for creating and managing Amazon SageMaker resources.
\nOther Resources:
\nProvides APIs for creating and managing SageMaker resources.
\nOther Resources:
\nA detailed description of the progress within a secondary status.\n
\nAmazon SageMaker provides secondary statuses and status messages that apply to each of\n them:
\nStarting the training job.
\nLaunching requested ML\n instances.
\nInsufficient\n capacity error from EC2 while launching instances,\n retrying!
\nLaunched\n instance was unhealthy, replacing it!
\nPreparing the instances for training.
\nDownloading the training image.
\nTraining\n image download completed. Training in\n progress.
\nStatus messages are subject to change. Therefore, we recommend not including them\n in code that programmatically initiates actions. For examples, don't use status\n messages in if statements.
\nTo have an overview of your training job's progress, view\n TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For\n example, at the start of a training job, you might see the following:
\n TrainingJobStatus - InProgress
\n SecondaryStatus - Training
\n StatusMessage - Downloading the training image
A detailed description of the progress within a secondary status.\n
\nSageMaker provides secondary statuses and status messages that apply to each of\n them:
\nStarting the training job.
\nLaunching requested ML\n instances.
\nInsufficient\n capacity error from EC2 while launching instances,\n retrying!
\nLaunched\n instance was unhealthy, replacing it!
\nPreparing the instances for training.
\nDownloading the training image.
\nTraining\n image download completed. Training in\n progress.
\nStatus messages are subject to change. Therefore, we recommend not including them\n in code that programmatically initiates actions. For examples, don't use status\n messages in if statements.
\nTo have an overview of your training job's progress, view\n TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For\n example, at the start of a training job, you might see the following:
\n TrainingJobStatus - InProgress
\n SecondaryStatus - Training
\n StatusMessage - Downloading the training image
An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions. It provides\n additional details about a status that the training job has transitioned through. A\n training job can be in one of several states, for example, starting, downloading,\n training, or uploading. Within each state, there are a number of intermediate states.\n For example, within the starting state, Amazon SageMaker could be starting the training job or\n launching the ML instances. These transitional states are referred to as the job's\n secondary\n status.\n
\n " + "smithy.api#documentation": "An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions. It provides\n additional details about a status that the training job has transitioned through. A\n training job can be in one of several states, for example, starting, downloading,\n training, or uploading. Within each state, there are a number of intermediate states.\n For example, within the starting state, SageMaker could be starting the training job or\n launching the ML instances. These transitional states are referred to as the job's\n secondary\n status.\n
\n " } }, "com.amazonaws.sagemaker#SecondaryStatusTransitions": { @@ -36658,13 +36699,13 @@ "AlgorithmName": { "target": "com.amazonaws.sagemaker#ArnOrName", "traits": { - "smithy.api#documentation": "The name of an algorithm that was used to create the model package. The algorithm must\n be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you\n are subscribed to.
", + "smithy.api#documentation": "The name of an algorithm that was used to create the model package. The algorithm must\n be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you\n are subscribed to.
", "smithy.api#required": {} } } }, "traits": { - "smithy.api#documentation": "Specifies an algorithm that was used to create the model package. The algorithm must\n be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you\n are subscribed to.
" + "smithy.api#documentation": "Specifies an algorithm that was used to create the model package. The algorithm must\n be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you\n are subscribed to.
" } }, "com.amazonaws.sagemaker#SourceAlgorithmList": { @@ -36803,7 +36844,7 @@ } ], "traits": { - "smithy.api#documentation": "Launches an ML compute instance with the latest version of the libraries and\n attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the\n notebook instance status to InService. A notebook instance's status must be\n InService before you can connect to your Jupyter notebook.
Launches an ML compute instance with the latest version of the libraries and\n attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the\n notebook instance status to InService. A notebook instance's status must be\n InService before you can connect to your Jupyter notebook.
Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker\n disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. Amazon SageMaker\n stops charging you for the ML compute instance when you call\n StopNotebookInstance.
To access data on the ML storage volume for a notebook instance that has been\n terminated, call the StartNotebookInstance API.\n StartNotebookInstance launches another ML compute instance, configures\n it, and attaches the preserved ML storage volume so you can continue your work.\n
Terminates the ML compute instance. Before terminating the instance, SageMaker\n disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. SageMaker\n stops charging you for the ML compute instance when you call\n StopNotebookInstance.
To access data on the ML storage volume for a notebook instance that has been\n terminated, call the StartNotebookInstance API.\n StartNotebookInstance launches another ML compute instance, configures\n it, and attaches the preserved ML storage volume so you can continue your work.\n
Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the\n SIGTERM signal, which delays job termination for 120 seconds.\n Algorithms might use this 120-second window to save the model artifacts, so the results\n of the training is not lost.
When it receives a StopTrainingJob request, Amazon SageMaker changes the status of\n the job to Stopping. After Amazon SageMaker stops the job, it sets the status to\n Stopped.
Stops a training job. To stop a job, SageMaker sends the algorithm the\n SIGTERM signal, which delays job termination for 120 seconds.\n Algorithms might use this 120-second window to save the model artifacts, so the results\n of the training is not lost.
When it receives a StopTrainingJob request, SageMaker changes the status of\n the job to Stopping. After SageMaker stops the job, it sets the status to\n Stopped.
The maximum length of time, in seconds, that a training or compilation job can run.
\nFor compilation jobs, if the job does not complete during this time, you will \n receive a TimeOut error. We recommend starting with 900 seconds and increase as \n necessary based on your model.
For all other jobs, if the job does not complete during this time, Amazon SageMaker ends the job. When \n RetryStrategy is specified in the job request,\n MaxRuntimeInSeconds specifies the maximum time for all of the attempts\n in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a training or compilation job can run.
\nFor compilation jobs, if the job does not complete during this time, a TimeOut error\n is generated. We recommend starting with 900 seconds and increasing as \n necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When \n RetryStrategy is specified in the job request,\n MaxRuntimeInSeconds specifies the maximum time for all of the attempts\n in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to\n complete. It is the amount of time spent waiting for Spot capacity plus the amount of\n time the job can run. It must be equal to or greater than\n MaxRuntimeInSeconds. If the job does not complete during this time,\n Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request,\n MaxWaitTimeInSeconds specifies the maximum time for all of the attempts\n in total, not each individual attempt.
The maximum length of time, in seconds, that a managed Spot training job has to\n complete. It is the amount of time spent waiting for Spot capacity plus the amount of\n time the job can run. It must be equal to or greater than\n MaxRuntimeInSeconds. If the job does not complete during this time,\n SageMaker ends the job.
When RetryStrategy is specified in the job request,\n MaxWaitTimeInSeconds specifies the maximum time for all of the attempts\n in total, not each individual attempt.
Specifies a limit to how long a model training job or model compilation job \n can run. It also specifies how long a managed spot training\n job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training or\n compilation job. Use this API to cap model training costs.
\nTo stop a training job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays\n job termination for 120 seconds. Algorithms can use this 120-second window to save the\n model artifacts, so the results of training are not lost.
The training algorithms provided by Amazon SageMaker automatically save the intermediate results\n of a model training job when possible. This attempt to save artifacts is only a best\n effort case as model might not be in a state from which it can be saved. For example, if\n training has just started, the model might not be ready to save. When saved, this\n intermediate data is a valid model artifact. You can use it to create a model with\n CreateModel.
The Neural Topic Model (NTM) currently does not support saving intermediate model\n artifacts. When training NTMs, make sure that the maximum runtime is sufficient for\n the training job to complete.
\nSpecifies a limit to how long a model training job or model compilation job \n can run. It also specifies how long a managed spot training\n job has to complete. When the job reaches the time limit, SageMaker ends the training or\n compilation job. Use this API to cap model training costs.
\nTo stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays\n job termination for 120 seconds. Algorithms can use this 120-second window to save the\n model artifacts, so the results of training are not lost.
The training algorithms provided by SageMaker automatically save the intermediate results\n of a model training job when possible. This attempt to save artifacts is only a best\n effort case as model might not be in a state from which it can be saved. For example, if\n training has just started, the model might not be ready to save. When saved, this\n intermediate data is a valid model artifact. You can use it to create a model with\n CreateModel.
The Neural Topic Model (NTM) currently does not support saving intermediate model\n artifacts. When training NTMs, make sure that the maximum runtime is sufficient for\n the training job to complete.
\nBatch size for the first step to turn on traffic on the new endpoint fleet. Value must be less than\n or equal to 50% of the variant's total instance count.
Batch size for the first step to turn on traffic on the new endpoint fleet. Value must be less than\n or equal to 50% of the variant's total instance count.
Provides detailed information about the state of the training job. For detailed\n information about the secondary status of the training job, see\n StatusMessage under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of\n them:
\n\n Starting\n - Starting the training job.
\n Downloading - An optional stage for algorithms that\n support File training input mode. It indicates that\n data is being downloaded to the ML storage volumes.
\n Training - Training is in progress.
\n Uploading - Training is complete and the model\n artifacts are being uploaded to the S3 location.
\n Completed - The training job has completed.
\n Failed - The training job has failed. The reason for\n the failure is returned in the FailureReason field of\n DescribeTrainingJobResponse.
\n MaxRuntimeExceeded - The job stopped because it\n exceeded the maximum allowed runtime.
\n Stopped - The training job has stopped.
\n Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
\n\n LaunchingMLInstances\n
\n PreparingTrainingStack\n
\n DownloadingTrainingImage\n
Provides detailed information about the state of the training job. For detailed\n information about the secondary status of the training job, see\n StatusMessage under SecondaryStatusTransition.
SageMaker provides primary statuses and secondary statuses that apply to each of\n them:
\n\n Starting\n - Starting the training job.
\n Downloading - An optional stage for algorithms that\n support File training input mode. It indicates that\n data is being downloaded to the ML storage volumes.
\n Training - Training is in progress.
\n Uploading - Training is complete and the model\n artifacts are being uploaded to the S3 location.
\n Completed - The training job has completed.
\n Failed - The training job has failed. The reason for\n the failure is returned in the FailureReason field of\n DescribeTrainingJobResponse.
\n MaxRuntimeExceeded - The job stopped because it\n exceeded the maximum allowed runtime.
\n Stopped - The training job has stopped.
\n Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
\n\n LaunchingMLInstances\n
\n PreparingTrainingStack\n
\n DownloadingTrainingImage\n
The S3 path where model artifacts that you configured when creating the job are\n stored. Amazon SageMaker creates subfolders for model artifacts.
" + "smithy.api#documentation": "The S3 path where model artifacts that you configured when creating the job are\n stored. SageMaker creates subfolders for model artifacts.
" } }, "ResourceConfig": { @@ -38592,7 +38633,7 @@ "StoppingCondition": { "target": "com.amazonaws.sagemaker#StoppingCondition", "traits": { - "smithy.api#documentation": "Specifies a limit to how long a model training job can run. It also specifies how long\n a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker\n ends the training job. Use this API to cap model training costs.
\nTo stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays\n job termination for 120 seconds. Algorithms can use this 120-second window to save the\n model artifacts, so the results of training are not lost.
Specifies a limit to how long a model training job can run. It also specifies how long\n a managed Spot training job has to complete. When the job reaches the time limit, SageMaker\n ends the training job. Use this API to cap model training costs.
\nTo stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays\n job termination for 120 seconds. Algorithms can use this 120-second window to save the\n model artifacts, so the results of training are not lost.
Indicates the time when the training job ends on training instances. You are billed\n for the time interval between the value of TrainingStartTime and this time.\n For successful jobs and stopped jobs, this is the time after model artifacts are\n uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
Indicates the time when the training job ends on training instances. You are billed\n for the time interval between the value of TrainingStartTime and this time.\n For successful jobs and stopped jobs, this is the time after model artifacts are\n uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates\n subfolders for the artifacts.
", + "smithy.api#documentation": "the path to the S3 bucket where you want to store model artifacts. SageMaker creates\n subfolders for the artifacts.
", "smithy.api#required": {} } }, @@ -38757,7 +38798,7 @@ "StoppingCondition": { "target": "com.amazonaws.sagemaker#StoppingCondition", "traits": { - "smithy.api#documentation": "Specifies a limit to how long a model training job can run. It also specifies how long\n a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker\n ends the training job. Use this API to cap model training costs.
\nTo stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job\n termination for 120 seconds. Algorithms can use this 120-second window to save the model\n artifacts.
", + "smithy.api#documentation": "Specifies a limit to how long a model training job can run. It also specifies how long\n a managed Spot training job has to complete. When the job reaches the time limit, SageMaker\n ends the training job. Use this API to cap model training costs.
\nTo stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job\n termination for 120 seconds. Algorithms can use this 120-second window to save the model\n artifacts.
", "smithy.api#required": {} } } @@ -40870,7 +40911,7 @@ } ], "traits": { - "smithy.api#documentation": "Deploys the new EndpointConfig specified in the request, switches to\n using newly created endpoint, and then deletes resources provisioned for the endpoint\n using the previous EndpointConfig (there is no availability loss).
When Amazon SageMaker receives the request, it sets the endpoint status to\n Updating. After updating the endpoint, it sets the status to\n InService. To check the status of an endpoint, use the DescribeEndpoint API.\n \n
You must not delete an EndpointConfig in use by an endpoint that is\n live or while the UpdateEndpoint or CreateEndpoint\n operations are being performed on the endpoint. To update an endpoint, you must\n create a new EndpointConfig.
If you delete the EndpointConfig of an endpoint that is active or\n being created or updated you may lose visibility into the instance type the endpoint\n is using. The endpoint must be deleted in order to stop incurring charges.
Deploys the new EndpointConfig specified in the request, switches to\n using newly created endpoint, and then deletes resources provisioned for the endpoint\n using the previous EndpointConfig (there is no availability loss).
When SageMaker receives the request, it sets the endpoint status to\n Updating. After updating the endpoint, it sets the status to\n InService. To check the status of an endpoint, use the DescribeEndpoint API.\n \n
You must not delete an EndpointConfig in use by an endpoint that is\n live or while the UpdateEndpoint or CreateEndpoint\n operations are being performed on the endpoint. To update an endpoint, you must\n create a new EndpointConfig.
If you delete the EndpointConfig of an endpoint that is active or\n being created or updated you may lose visibility into the instance type the endpoint\n is using. The endpoint must be deleted in order to stop incurring charges.
Updates variant weight of one or more variants associated with an existing\n endpoint, or capacity of one variant associated with an existing endpoint. When it\n receives the request, Amazon SageMaker sets the endpoint status to Updating. After\n updating the endpoint, it sets the status to InService. To check the status\n of an endpoint, use the DescribeEndpoint API.
Updates variant weight of one or more variants associated with an existing\n endpoint, or capacity of one variant associated with an existing endpoint. When it\n receives the request, SageMaker sets the endpoint status to Updating. After\n updating the endpoint, it sets the status to InService. To check the status\n of an endpoint, use the DescribeEndpoint API.
The name of an existing Amazon SageMaker endpoint.
", + "smithy.api#documentation": "The name of an existing SageMaker endpoint.
", "smithy.api#required": {} } }, @@ -41251,7 +41292,7 @@ "RoleArn": { "target": "com.amazonaws.sagemaker#RoleArn", "traits": { - "smithy.api#documentation": "The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the\n notebook instance. For more information, see Amazon SageMaker Roles.
\nTo be able to pass this role to Amazon SageMaker, the caller of this API must have the\n iam:PassRole permission.
The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access the\n notebook instance. For more information, see SageMaker Roles.
\nTo be able to pass this role to SageMaker, the caller of this API must have the\n iam:PassRole permission.
The size, in GB, of the ML storage volume to attach to the notebook instance. The\n default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can't determine the\n amount of available free space on the volume. Because of this, you can increase the\n volume size when you update a notebook instance, but you can't decrease the volume size.\n If you want to decrease the size of the ML storage volume in use, create a new notebook\n instance with the desired size.
" + "smithy.api#documentation": "The size, in GB, of the ML storage volume to attach to the notebook instance. The\n default value is 5 GB. ML storage volumes are encrypted, so SageMaker can't determine the\n amount of available free space on the volume. Because of this, you can increase the\n volume size when you update a notebook instance, but you can't decrease the volume size.\n If you want to decrease the size of the ML storage volume in use, create a new notebook\n instance with the desired size.
" } }, "DefaultCodeRepository": { "target": "com.amazonaws.sagemaker#CodeRepositoryNameOrUrl", "traits": { - "smithy.api#documentation": "The Git repository to associate with the notebook instance as its default code\n repository. This can be either the name of a Git repository stored as a resource in your\n account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any\n other Git repository. When you open a notebook instance, it opens in the directory that\n contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker\n Notebook Instances.
" + "smithy.api#documentation": "The Git repository to associate with the notebook instance as its default code\n repository. This can be either the name of a Git repository stored as a resource in your\n account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any\n other Git repository. When you open a notebook instance, it opens in the directory that\n contains this repository. For more information, see Associating Git Repositories with SageMaker\n Notebook Instances.
" } }, "AdditionalCodeRepositories": { "target": "com.amazonaws.sagemaker#AdditionalCodeRepositoryNamesOrUrls", "traits": { - "smithy.api#documentation": "An array of up to three Git repositories to associate with the notebook instance.\n These can be either the names of Git repositories stored as resources in your account,\n or the URL of Git repositories in Amazon Web Services CodeCommit or in any\n other Git repository. These repositories are cloned at the same level as the default\n repository of your notebook instance. For more information, see Associating Git\n Repositories with Amazon SageMaker Notebook Instances.
" + "smithy.api#documentation": "An array of up to three Git repositories to associate with the notebook instance.\n These can be either the names of Git repositories stored as resources in your account,\n or the URL of Git repositories in Amazon Web Services CodeCommit or in any\n other Git repository. These repositories are cloned at the same level as the default\n repository of your notebook instance. For more information, see Associating Git\n Repositories with SageMaker Notebook Instances.
" } }, "AcceleratorTypes": { @@ -42130,6 +42171,16 @@ "smithy.api#documentation": "A collection of settings that apply to users of Amazon SageMaker Studio. These settings are\n specified when the CreateUserProfile API is called, and as DefaultUserSettings\n when the CreateDomain API is called.
\n SecurityGroups is aggregated when specified in both calls. For all other\n settings in UserSettings, the values specified in CreateUserProfile\n take precedence over those specified in CreateDomain.