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change github.com/aws/aws-sdk-go to be the same revision in kubernetes

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Jingfang Liu
2018-10-16 09:34:22 -07:00
parent e0958159f3
commit c6a78cee92
462 changed files with 20605 additions and 85780 deletions
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362
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/api-2.json generated vendored
View File

@@ -2,12 +2,11 @@
"version":"2.0",
"metadata":{
"apiVersion":"2017-07-24",
"endpointPrefix":"api.sagemaker",
"endpointPrefix":"sagemaker",
"jsonVersion":"1.1",
"protocol":"json",
"serviceAbbreviation":"SageMaker",
"serviceFullName":"Amazon SageMaker Service",
"serviceId":"SageMaker",
"signatureVersion":"v4",
"signingName":"sagemaker",
"targetPrefix":"SageMaker",
@@ -118,19 +117,6 @@
{"shape":"ResourceLimitExceeded"}
]
},
"CreateTransformJob":{
"name":"CreateTransformJob",
"http":{
"method":"POST",
"requestUri":"/"
},
"input":{"shape":"CreateTransformJobRequest"},
"output":{"shape":"CreateTransformJobResponse"},
"errors":[
{"shape":"ResourceInUse"},
{"shape":"ResourceLimitExceeded"}
]
},
"DeleteEndpoint":{
"name":"DeleteEndpoint",
"http":{
@@ -249,18 +235,6 @@
{"shape":"ResourceNotFound"}
]
},
"DescribeTransformJob":{
"name":"DescribeTransformJob",
"http":{
"method":"POST",
"requestUri":"/"
},
"input":{"shape":"DescribeTransformJobRequest"},
"output":{"shape":"DescribeTransformJobResponse"},
"errors":[
{"shape":"ResourceNotFound"}
]
},
"ListEndpointConfigs":{
"name":"ListEndpointConfigs",
"http":{
@@ -345,15 +319,6 @@
{"shape":"ResourceNotFound"}
]
},
"ListTransformJobs":{
"name":"ListTransformJobs",
"http":{
"method":"POST",
"requestUri":"/"
},
"input":{"shape":"ListTransformJobsRequest"},
"output":{"shape":"ListTransformJobsResponse"}
},
"StartNotebookInstance":{
"name":"StartNotebookInstance",
"http":{
@@ -395,17 +360,6 @@
{"shape":"ResourceNotFound"}
]
},
"StopTransformJob":{
"name":"StopTransformJob",
"http":{
"method":"POST",
"requestUri":"/"
},
"input":{"shape":"StopTransformJobRequest"},
"errors":[
{"shape":"ResourceNotFound"}
]
},
"UpdateEndpoint":{
"name":"UpdateEndpoint",
"http":{
@@ -456,10 +410,6 @@
}
},
"shapes":{
"Accept":{
"type":"string",
"max":256
},
"AddTagsInput":{
"type":"structure",
"required":[
@@ -492,20 +442,6 @@
"TrainingInputMode":{"shape":"TrainingInputMode"}
}
},
"AssemblyType":{
"type":"string",
"enum":[
"None",
"Line"
]
},
"BatchStrategy":{
"type":"string",
"enum":[
"MultiRecord",
"SingleRecord"
]
},
"CategoricalParameterRange":{
"type":"structure",
"required":[
@@ -755,35 +691,6 @@
"TrainingJobArn":{"shape":"TrainingJobArn"}
}
},
"CreateTransformJobRequest":{
"type":"structure",
"required":[
"TransformJobName",
"ModelName",
"TransformInput",
"TransformOutput",
"TransformResources"
],
"members":{
"TransformJobName":{"shape":"TransformJobName"},
"ModelName":{"shape":"ModelName"},
"MaxConcurrentTransforms":{"shape":"MaxConcurrentTransforms"},
"MaxPayloadInMB":{"shape":"MaxPayloadInMB"},
"BatchStrategy":{"shape":"BatchStrategy"},
"Environment":{"shape":"TransformEnvironmentMap"},
"TransformInput":{"shape":"TransformInput"},
"TransformOutput":{"shape":"TransformOutput"},
"TransformResources":{"shape":"TransformResources"},
"Tags":{"shape":"TagList"}
}
},
"CreateTransformJobResponse":{
"type":"structure",
"required":["TransformJobArn"],
"members":{
"TransformJobArn":{"shape":"TransformJobArn"}
}
},
"CreationTime":{"type":"timestamp"},
"DataSource":{
"type":"structure",
@@ -843,18 +750,6 @@
"members":{
}
},
"DeployedImage":{
"type":"structure",
"members":{
"SpecifiedImage":{"shape":"Image"},
"ResolvedImage":{"shape":"Image"},
"ResolutionTime":{"shape":"Timestamp"}
}
},
"DeployedImages":{
"type":"list",
"member":{"shape":"DeployedImage"}
},
"DescribeEndpointConfigInput":{
"type":"structure",
"required":["EndpointConfigName"],
@@ -1050,44 +945,7 @@
"CreationTime":{"shape":"Timestamp"},
"TrainingStartTime":{"shape":"Timestamp"},
"TrainingEndTime":{"shape":"Timestamp"},
"LastModifiedTime":{"shape":"Timestamp"},
"SecondaryStatusTransitions":{"shape":"SecondaryStatusTransitions"}
}
},
"DescribeTransformJobRequest":{
"type":"structure",
"required":["TransformJobName"],
"members":{
"TransformJobName":{"shape":"TransformJobName"}
}
},
"DescribeTransformJobResponse":{
"type":"structure",
"required":[
"TransformJobName",
"TransformJobArn",
"TransformJobStatus",
"ModelName",
"TransformInput",
"TransformResources",
"CreationTime"
],
"members":{
"TransformJobName":{"shape":"TransformJobName"},
"TransformJobArn":{"shape":"TransformJobArn"},
"TransformJobStatus":{"shape":"TransformJobStatus"},
"FailureReason":{"shape":"FailureReason"},
"ModelName":{"shape":"ModelName"},
"MaxConcurrentTransforms":{"shape":"MaxConcurrentTransforms"},
"MaxPayloadInMB":{"shape":"MaxPayloadInMB"},
"BatchStrategy":{"shape":"BatchStrategy"},
"Environment":{"shape":"TransformEnvironmentMap"},
"TransformInput":{"shape":"TransformInput"},
"TransformOutput":{"shape":"TransformOutput"},
"TransformResources":{"shape":"TransformResources"},
"CreationTime":{"shape":"Timestamp"},
"TransformStartTime":{"shape":"Timestamp"},
"TransformEndTime":{"shape":"Timestamp"}
"LastModifiedTime":{"shape":"Timestamp"}
}
},
"DesiredWeightAndCapacity":{
@@ -1649,36 +1507,6 @@
"NextToken":{"shape":"NextToken"}
}
},
"ListTransformJobsRequest":{
"type":"structure",
"members":{
"CreationTimeAfter":{"shape":"Timestamp"},
"CreationTimeBefore":{"shape":"Timestamp"},
"LastModifiedTimeAfter":{"shape":"Timestamp"},
"LastModifiedTimeBefore":{"shape":"Timestamp"},
"NameContains":{"shape":"NameContains"},
"StatusEquals":{"shape":"TransformJobStatus"},
"SortBy":{"shape":"SortBy"},
"SortOrder":{"shape":"SortOrder"},
"NextToken":{"shape":"NextToken"},
"MaxResults":{
"shape":"MaxResults",
"box":true
}
}
},
"ListTransformJobsResponse":{
"type":"structure",
"required":["TransformJobSummaries"],
"members":{
"TransformJobSummaries":{"shape":"TransformJobSummaries"},
"NextToken":{"shape":"NextToken"}
}
},
"MaxConcurrentTransforms":{
"type":"integer",
"min":0
},
"MaxNumberOfTrainingJobs":{
"type":"integer",
"min":1
@@ -1687,10 +1515,6 @@
"type":"integer",
"min":1
},
"MaxPayloadInMB":{
"type":"integer",
"min":0
},
"MaxResults":{
"type":"integer",
"max":100,
@@ -1880,8 +1704,7 @@
"Stopping",
"Stopped",
"Failed",
"Deleting",
"Updating"
"Deleting"
]
},
"NotebookInstanceSummary":{
@@ -2030,7 +1853,6 @@
"required":["VariantName"],
"members":{
"VariantName":{"shape":"VariantName"},
"DeployedImages":{"shape":"DeployedImages"},
"CurrentWeight":{"shape":"VariantWeight"},
"DesiredWeight":{"shape":"VariantWeight"},
"CurrentInstanceCount":{"shape":"TaskCount"},
@@ -2140,10 +1962,7 @@
"type":"string",
"enum":[
"Starting",
"LaunchingMLInstances",
"PreparingTrainingStack",
"Downloading",
"DownloadingTrainingImage",
"Training",
"Uploading",
"Stopping",
@@ -2153,23 +1972,6 @@
"Failed"
]
},
"SecondaryStatusTransition":{
"type":"structure",
"required":[
"Status",
"StartTime"
],
"members":{
"Status":{"shape":"SecondaryStatus"},
"StartTime":{"shape":"Timestamp"},
"EndTime":{"shape":"Timestamp"},
"StatusMessage":{"shape":"StatusMessage"}
}
},
"SecondaryStatusTransitions":{
"type":"list",
"member":{"shape":"SecondaryStatusTransition"}
},
"SecurityGroupId":{
"type":"string",
"max":32
@@ -2199,14 +2001,6 @@
"Descending"
]
},
"SplitType":{
"type":"string",
"enum":[
"None",
"Line",
"RecordIO"
]
},
"StartNotebookInstanceInput":{
"type":"structure",
"required":["NotebookInstanceName"],
@@ -2214,7 +2008,6 @@
"NotebookInstanceName":{"shape":"NotebookInstanceName"}
}
},
"StatusMessage":{"type":"string"},
"StopHyperParameterTuningJobRequest":{
"type":"structure",
"required":["HyperParameterTuningJobName"],
@@ -2236,13 +2029,6 @@
"TrainingJobName":{"shape":"TrainingJobName"}
}
},
"StopTransformJobRequest":{
"type":"structure",
"required":["TransformJobName"],
"members":{
"TransformJobName":{"shape":"TransformJobName"}
}
},
"StoppingCondition":{
"type":"structure",
"members":{
@@ -2406,148 +2192,6 @@
"TrainingJobStatus":{"shape":"TrainingJobStatus"}
}
},
"TransformDataSource":{
"type":"structure",
"required":["S3DataSource"],
"members":{
"S3DataSource":{"shape":"TransformS3DataSource"}
}
},
"TransformEnvironmentKey":{
"type":"string",
"max":1024,
"pattern":"[a-zA-Z_][a-zA-Z0-9_]*"
},
"TransformEnvironmentMap":{
"type":"map",
"key":{"shape":"TransformEnvironmentKey"},
"value":{"shape":"TransformEnvironmentValue"},
"max":16
},
"TransformEnvironmentValue":{
"type":"string",
"max":10240
},
"TransformInput":{
"type":"structure",
"required":["DataSource"],
"members":{
"DataSource":{"shape":"TransformDataSource"},
"ContentType":{"shape":"ContentType"},
"CompressionType":{"shape":"CompressionType"},
"SplitType":{"shape":"SplitType"}
}
},
"TransformInstanceCount":{
"type":"integer",
"min":1
},
"TransformInstanceType":{
"type":"string",
"enum":[
"ml.m4.xlarge",
"ml.m4.2xlarge",
"ml.m4.4xlarge",
"ml.m4.10xlarge",
"ml.m4.16xlarge",
"ml.c4.xlarge",
"ml.c4.2xlarge",
"ml.c4.4xlarge",
"ml.c4.8xlarge",
"ml.p2.xlarge",
"ml.p2.8xlarge",
"ml.p2.16xlarge",
"ml.p3.2xlarge",
"ml.p3.8xlarge",
"ml.p3.16xlarge",
"ml.c5.xlarge",
"ml.c5.2xlarge",
"ml.c5.4xlarge",
"ml.c5.9xlarge",
"ml.c5.18xlarge",
"ml.m5.large",
"ml.m5.xlarge",
"ml.m5.2xlarge",
"ml.m5.4xlarge",
"ml.m5.12xlarge",
"ml.m5.24xlarge"
]
},
"TransformJobArn":{
"type":"string",
"max":256,
"pattern":"arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:transform-job/.*"
},
"TransformJobName":{
"type":"string",
"max":63,
"min":1,
"pattern":"^[a-zA-Z0-9](-*[a-zA-Z0-9])*"
},
"TransformJobStatus":{
"type":"string",
"enum":[
"InProgress",
"Completed",
"Failed",
"Stopping",
"Stopped"
]
},
"TransformJobSummaries":{
"type":"list",
"member":{"shape":"TransformJobSummary"}
},
"TransformJobSummary":{
"type":"structure",
"required":[
"TransformJobName",
"TransformJobArn",
"CreationTime",
"TransformJobStatus"
],
"members":{
"TransformJobName":{"shape":"TransformJobName"},
"TransformJobArn":{"shape":"TransformJobArn"},
"CreationTime":{"shape":"Timestamp"},
"TransformEndTime":{"shape":"Timestamp"},
"LastModifiedTime":{"shape":"Timestamp"},
"TransformJobStatus":{"shape":"TransformJobStatus"},
"FailureReason":{"shape":"FailureReason"}
}
},
"TransformOutput":{
"type":"structure",
"required":["S3OutputPath"],
"members":{
"S3OutputPath":{"shape":"S3Uri"},
"Accept":{"shape":"Accept"},
"AssembleWith":{"shape":"AssemblyType"},
"KmsKeyId":{"shape":"KmsKeyId"}
}
},
"TransformResources":{
"type":"structure",
"required":[
"InstanceType",
"InstanceCount"
],
"members":{
"InstanceType":{"shape":"TransformInstanceType"},
"InstanceCount":{"shape":"TransformInstanceCount"}
}
},
"TransformS3DataSource":{
"type":"structure",
"required":[
"S3DataType",
"S3Uri"
],
"members":{
"S3DataType":{"shape":"S3DataType"},
"S3Uri":{"shape":"S3Uri"}
}
},
"UpdateEndpointInput":{
"type":"structure",
"required":[

353
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/docs-2.json generated vendored
View File

@@ -3,7 +3,7 @@
"service": "Definition of the public APIs exposed by SageMaker",
"operations": {
"AddTags": "<p>Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, models, endpoint configurations, and endpoints. </p> <p>Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see <a href=\"http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>. </p>",
"CreateEndpoint": "<p>Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpointConfig.html\">CreateEndpointConfig</a> API. </p> <note> <p> Use this API only for hosting models using Amazon SageMaker hosting services. </p> </note> <p>The endpoint name must be unique within an AWS Region in your AWS account. </p> <p>When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them. </p> <p>When Amazon SageMaker receives the request, it sets the endpoint status to <code>Creating</code>. After it creates the endpoint, it sets the status to <code>InService</code>. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html\">DescribeEndpoint</a> API.</p> <p>For an example, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/ex1.html\">Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker</a>. </p> <p>If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see <a href=\"http://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html\">Activating and Deactivating AWS STS i an AWS Region</a> in the <i>AWS Identity and Access Management User Guide</i>.</p>",
"CreateEndpoint": "<p>Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpointConfig.html\">CreateEndpointConfig</a> API. </p> <note> <p> Use this API only for hosting models using Amazon SageMaker hosting services. </p> </note> <p>The endpoint name must be unique within an AWS Region in your AWS account. </p> <p>When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them. </p> <p>When Amazon SageMaker receives the request, it sets the endpoint status to <code>Creating</code>. After it creates the endpoint, it sets the status to <code>InService</code>. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html\">DescribeEndpoint</a> API.</p> <p>For an example, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/ex1.html\">Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker</a>. </p>",
"CreateEndpointConfig": "<p>Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the <code>CreateModel</code> API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html\">CreateEndpoint</a> API.</p> <note> <p> Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production. </p> </note> <p>In the request, you define one or more <code>ProductionVariant</code>s, each of which identifies a model. Each <code>ProductionVariant</code> parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy. </p> <p>If you are hosting multiple models, you also assign a <code>VariantWeight</code> to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B. </p>",
"CreateHyperParameterTuningJob": "<p>Starts a hyperparameter tuning job.</p>",
"CreateModel": "<p>Creates a model in Amazon SageMaker. In the request, you name the model and describe one or more containers. For each container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model into production. </p> <p>Use this API to create a model only if you want to use Amazon SageMaker hosting services. To host your model, you create an endpoint configuration with the <code>CreateEndpointConfig</code> API, and then create an endpoint with the <code>CreateEndpoint</code> API. </p> <p>Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment. </p> <p>In the <code>CreateModel</code> request, you must define a container with the <code>PrimaryContainer</code> parameter. </p> <p>In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.</p>",
@@ -11,8 +11,7 @@
"CreateNotebookInstanceLifecycleConfig": "<p>Creates a lifecycle configuration that you can associate with a notebook instance. A <i>lifecycle configuration</i> is a collection of shell scripts that run when you create or start a notebook instance.</p> <p>Each lifecycle configuration script has a limit of 16384 characters.</p> <p>The value of the <code>$PATH</code> environment variable that is available to both scripts is <code>/sbin:bin:/usr/sbin:/usr/bin</code>.</p> <p>View CloudWatch Logs for notebook instance lifecycle configurations in log group <code>/aws/sagemaker/NotebookInstances</code> in log stream <code>[notebook-instance-name]/[LifecycleConfigHook]</code>.</p> <p>Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.</p> <p>For information about notebook instance lifestyle configurations, see <a>notebook-lifecycle-config</a>.</p>",
"CreatePresignedNotebookInstanceUrl": "<p>Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose <code>Open</code> next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page. </p>",
"CreateTrainingJob": "<p>Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify. </p> <p>If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences. </p> <p>In the request body, you provide the following: </p> <ul> <li> <p> <code>AlgorithmSpecification</code> - Identifies the training algorithm to use. </p> </li> <li> <p> <code>HyperParameters</code> - Specify these algorithm-specific parameters to influence the quality of the final model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. </p> </li> <li> <p> <code>InputDataConfig</code> - Describes the training dataset and the Amazon S3 location where it is stored.</p> </li> <li> <p> <code>OutputDataConfig</code> - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training. </p> <p/> </li> <li> <p> <code>ResourceConfig</code> - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. </p> </li> <li> <p> <code>RoleARN</code> - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training. </p> </li> <li> <p> <code>StoppingCondition</code> - Sets a duration for training. Use this parameter to cap model training costs. </p> </li> </ul> <p> For more information about Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html\">How It Works</a>. </p>",
"CreateTransformJob": "<p>Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.</p> <p>To perform batch transformations, you create a transform job and use the data that you have readily available.</p> <p>In the request body, you provide the following:</p> <ul> <li> <p> <code>TransformJobName</code> - Identifies the transform job. The name must be unique within an AWS Region in an AWS account.</p> </li> <li> <p> <code>ModelName</code> - Identifies the model to use. <code>ModelName</code> must be the name of an existing Amazon SageMaker model within an AWS Region in an AWS account.</p> </li> <li> <p> <code>TransformInput</code> - Describes the dataset to be transformed and the Amazon S3 location where it is stored.</p> </li> <li> <p> <code>TransformOutput</code> - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.</p> </li> <li> <p> <code>TransformResources</code> - Identifies the ML compute instances for the transform job.</p> </li> </ul> <p> For more information about how batch transformation works Amazon SageMaker, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html\">How It Works</a>. </p>",
"DeleteEndpoint": "<p>Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created. </p> <p>Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the <a href=\"http://docs.aws.amazon.com/kms/latest/APIReference/API_RevokeGrant.html\">RevokeGrant</a> API call.</p>",
"DeleteEndpoint": "<p>Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created. </p>",
"DeleteEndpointConfig": "<p>Deletes an endpoint configuration. The <code>DeleteEndpointConfig</code> API deletes only the specified configuration. It does not delete endpoints created using the configuration. </p>",
"DeleteModel": "<p>Deletes a model. The <code>DeleteModel</code> API deletes only the model entry that was created in Amazon SageMaker when you called the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateModel.html\">CreateModel</a> API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model. </p>",
"DeleteNotebookInstance": "<p> Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the <code>StopNotebookInstance</code> API. </p> <important> <p>When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance. </p> </important>",
@@ -25,34 +24,25 @@
"DescribeNotebookInstance": "<p>Returns information about a notebook instance.</p>",
"DescribeNotebookInstanceLifecycleConfig": "<p>Returns a description of a notebook instance lifecycle configuration.</p> <p>For information about notebook instance lifestyle configurations, see <a>notebook-lifecycle-config</a>.</p>",
"DescribeTrainingJob": "<p>Returns information about a training job.</p>",
"DescribeTransformJob": "<p>Returns information about a transform job.</p>",
"ListEndpointConfigs": "<p>Lists endpoint configurations.</p>",
"ListEndpoints": "<p>Lists endpoints.</p>",
"ListHyperParameterTuningJobs": "<p>Gets a list of <a>HyperParameterTuningJobSummary</a> objects that describe the hyperparameter tuning jobs launched in your account.</p>",
"ListHyperParameterTuningJobs": "<p>Gets a list of objects that describe the hyperparameter tuning jobs launched in your account.</p>",
"ListModels": "<p>Lists models created with the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateModel.html\">CreateModel</a> API.</p>",
"ListNotebookInstanceLifecycleConfigs": "<p>Lists notebook instance lifestyle configurations created with the <a>CreateNotebookInstanceLifecycleConfig</a> API.</p>",
"ListNotebookInstanceLifecycleConfigs": "<p>Lists notebook instance lifestyle configurations created with the API.</p>",
"ListNotebookInstances": "<p>Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region. </p>",
"ListTags": "<p>Returns the tags for the specified Amazon SageMaker resource.</p>",
"ListTrainingJobs": "<p>Lists training jobs.</p>",
"ListTrainingJobsForHyperParameterTuningJob": "<p>Gets a list of <a>TrainingJobSummary</a> objects that describe the training jobs that a hyperparameter tuning job launched.</p>",
"ListTransformJobs": "<p>Lists transform jobs.</p>",
"ListTrainingJobsForHyperParameterTuningJob": "<p>Gets a list of objects that describe the training jobs that a hyperparameter tuning job launched.</p>",
"StartNotebookInstance": "<p>Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to <code>InService</code>. A notebook instance's status must be <code>InService</code> before you can connect to your Jupyter notebook. </p>",
"StopHyperParameterTuningJob": "<p>Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.</p> <p>All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the <code>Stopped</code> state, it releases all reserved resources for the tuning job.</p>",
"StopHyperParameterTuningJob": "<p>Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.</p> <p>All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write toAmazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the <code>Stopped</code> state, it releases all reserved resources for the tuning job.</p>",
"StopNotebookInstance": "<p>Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. </p> <p>To access data on the ML storage volume for a notebook instance that has been terminated, call the <code>StartNotebookInstance</code> API. <code>StartNotebookInstance</code> launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work. </p>",
"StopTrainingJob": "<p>Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the <code>SIGTERM</code> signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost. </p> <p>Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model. </p> <p>When it receives a <code>StopTrainingJob</code> request, Amazon SageMaker changes the status of the job to <code>Stopping</code>. After Amazon SageMaker stops the job, it sets the status to <code>Stopped</code>.</p>",
"StopTransformJob": "<p>Stops a transform job.</p> <p>When Amazon SageMaker receives a <code>StopTransformJob</code> request, the status of the job changes to <code>Stopping</code>. After Amazon SageMaker stops the job, the status is set to <code>Stopped</code>. When you stop a transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.</p>",
"UpdateEndpoint": "<p> Deploys the new <code>EndpointConfig</code> specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous <code>EndpointConfig</code> (there is no availability loss). </p> <p>When Amazon SageMaker receives the request, it sets the endpoint status to <code>Updating</code>. After updating the endpoint, it sets the status to <code>InService</code>. To check the status of an endpoint, use the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html\">DescribeEndpoint</a> API. </p> <note> <p>You cannot update an endpoint with the current <code>EndpointConfig</code>. To update an endpoint, you must create a new <code>EndpointConfig</code>.</p> </note>",
"UpdateEndpoint": "<p> Deploys the new <code>EndpointConfig</code> specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous <code>EndpointConfig</code> (there is no availability loss). </p> <p>When Amazon SageMaker receives the request, it sets the endpoint status to <code>Updating</code>. After updating the endpoint, it sets the status to <code>InService</code>. To check the status of an endpoint, use the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html\">DescribeEndpoint</a> API. </p>",
"UpdateEndpointWeightsAndCapacities": "<p>Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker sets the endpoint status to <code>Updating</code>. After updating the endpoint, it sets the status to <code>InService</code>. To check the status of an endpoint, use the <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html\">DescribeEndpoint</a> API. </p>",
"UpdateNotebookInstance": "<p>Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements. You can also update the VPC security groups.</p>",
"UpdateNotebookInstanceLifecycleConfig": "<p>Updates a notebook instance lifecycle configuration created with the <a>CreateNotebookInstanceLifecycleConfig</a> API.</p>"
"UpdateNotebookInstanceLifecycleConfig": "<p>Updates a notebook instance lifecycle configuration created with the API.</p>"
},
"shapes": {
"Accept": {
"base": null,
"refs": {
"TransformOutput$Accept": "<p>The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.</p>"
}
},
"AddTagsInput": {
"base": null,
"refs": {
@@ -77,19 +67,6 @@
"DescribeTrainingJobResponse$AlgorithmSpecification": "<p>Information about the algorithm used for training, and algorithm metadata. </p>"
}
},
"AssemblyType": {
"base": null,
"refs": {
"TransformOutput$AssembleWith": "<p>Defines how to assemble the results of the transform job as a single S3 object. You should select a format that is most convenient to you. To concatenate the results in binary format, specify <code>None</code>. To add a newline character at the end of every transformed record, specify <code>Line</code>. To assemble the output in RecordIO format, specify <code>RecordIO</code>. The default value is <code>None</code>.</p> <p>For information about the <code>RecordIO</code> format, see <a href=\"http://mxnet.io/architecture/note_data_loading.html#data-format\">Data Format</a>.</p>"
}
},
"BatchStrategy": {
"base": null,
"refs": {
"CreateTransformJobRequest$BatchStrategy": "<p>Determines the number of records included in a single mini-batch. <code>SingleRecord</code> means only one record is used per mini-batch. <code>MultiRecord</code> means a mini-batch is set to contain as many records that can fit within the <code>MaxPayloadInMB</code> limit.</p>",
"DescribeTransformJobResponse$BatchStrategy": "<p>SingleRecord means only one record was used per a batch. <code>MultiRecord</code> means batches contained as many records that could possibly fit within the <code>MaxPayloadInMB</code> limit.</p>"
}
},
"CategoricalParameterRange": {
"base": "<p>A list of categorical hyperparameters to tune.</p>",
"refs": {
@@ -117,8 +94,7 @@
"CompressionType": {
"base": null,
"refs": {
"Channel$CompressionType": "<p>If training data is compressed, the compression type. The default value is <code>None</code>. <code>CompressionType</code> is used only in Pipe input mode. In File mode, leave this field unset or set it to None.</p>",
"TransformInput$CompressionType": "<p>Compressing data helps save on storage space. If your transform data is compressed, specify the compression type.and Amazon SageMaker will automatically decompress the data for the transform job accordingly. The default value is <code>None</code>.</p>"
"Channel$CompressionType": "<p>If training data is compressed, the compression type. The default value is <code>None</code>. <code>CompressionType</code> is used only in Pipe input mode. In File mode, leave this field unset or set it to None.</p>"
}
},
"ContainerDefinition": {
@@ -137,8 +113,7 @@
"ContentType": {
"base": null,
"refs": {
"Channel$ContentType": "<p>The MIME type of the data.</p>",
"TransformInput$ContentType": "<p>The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.</p>"
"Channel$ContentType": "<p>The MIME type of the data.</p>"
}
},
"ContinuousParameterRange": {
@@ -233,16 +208,6 @@
"refs": {
}
},
"CreateTransformJobRequest": {
"base": null,
"refs": {
}
},
"CreateTransformJobResponse": {
"base": null,
"refs": {
}
},
"CreationTime": {
"base": null,
"refs": {
@@ -297,18 +262,6 @@
"refs": {
}
},
"DeployedImage": {
"base": "<p>Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this <a>ProductionVariant</a>.</p> <p>If you used the <code>registry/repository[:tag]</code> form to to specify the image path of the primary container when you created the model hosted in this <code>ProductionVariant</code>, the path resolves to a path of the form <code>registry/repository[@digest]</code>. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see <a href=\"http://docs.aws.amazon.com//AmazonECR/latest/userguide/docker-pull-ecr-image.html\">Pulling an Image</a> in the <i>Amazon ECR User Guide</i>.</p>",
"refs": {
"DeployedImages$member": null
}
},
"DeployedImages": {
"base": null,
"refs": {
"ProductionVariantSummary$DeployedImages": "<p>An array of <code>DeployedImage</code> objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this <code>ProductionVariant</code>.</p>"
}
},
"DescribeEndpointConfigInput": {
"base": null,
"refs": {
@@ -379,16 +332,6 @@
"refs": {
}
},
"DescribeTransformJobRequest": {
"base": null,
"refs": {
}
},
"DescribeTransformJobResponse": {
"base": null,
"refs": {
}
},
"DesiredWeightAndCapacity": {
"base": "<p>Specifies weight and capacity values for a production variant.</p>",
"refs": {
@@ -532,43 +475,41 @@
"DescribeHyperParameterTuningJobResponse$FailureReason": "<p>If the tuning job failed, the reason it failed.</p>",
"DescribeNotebookInstanceOutput$FailureReason": "<p>If status is failed, the reason it failed.</p>",
"DescribeTrainingJobResponse$FailureReason": "<p>If the training job failed, the reason it failed. </p>",
"DescribeTransformJobResponse$FailureReason": "<p>If the transform job failed, the reason that it failed.</p>",
"HyperParameterTrainingJobSummary$FailureReason": "<p>The reason that the training job failed. </p>",
"HyperParameterTrainingJobSummary$FailureReason": "<p>The reason that the </p>",
"ResourceInUse$Message": null,
"ResourceLimitExceeded$Message": null,
"ResourceNotFound$Message": null,
"TransformJobSummary$FailureReason": "<p>If the transform job failed, the reason it failed.</p>"
"ResourceNotFound$Message": null
}
},
"FinalHyperParameterTuningJobObjectiveMetric": {
"base": "<p>Shows the final value for the objective metric for a training job that was launched by a hyperparameter tuning job. You define the objective metric in the <code>HyperParameterTuningJobObjective</code> parameter of <a>HyperParameterTuningJobConfig</a>.</p>",
"refs": {
"HyperParameterTrainingJobSummary$FinalHyperParameterTuningJobObjectiveMetric": "<p>The <a>FinalHyperParameterTuningJobObjectiveMetric</a> object that specifies the value of the objective metric of the tuning job that launched this training job.</p>"
"HyperParameterTrainingJobSummary$FinalHyperParameterTuningJobObjectiveMetric": "<p>The object that specifies the value of the objective metric of the tuning job that launched this training job.</p>"
}
},
"HyperParameterAlgorithmSpecification": {
"base": "<p>Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.</p>",
"refs": {
"HyperParameterTrainingJobDefinition$AlgorithmSpecification": "<p>The <a>HyperParameterAlgorithmSpecification</a> object that specifies the algorithm to use for the training jobs that the tuning job launches.</p>"
"HyperParameterTrainingJobDefinition$AlgorithmSpecification": "<p>The object that specifies the algorithm to use for the training jobs that the tuning job launches.</p>"
}
},
"HyperParameterTrainingJobDefinition": {
"base": "<p>Defines the training jobs launched by a hyperparameter tuning job.</p>",
"refs": {
"CreateHyperParameterTuningJobRequest$TrainingJobDefinition": "<p>The <a>HyperParameterTrainingJobDefinition</a> object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.</p>",
"DescribeHyperParameterTuningJobResponse$TrainingJobDefinition": "<p>The <a>HyperParameterTrainingJobDefinition</a> object that specifies the definition of the training jobs that this tuning job launches.</p>"
"CreateHyperParameterTuningJobRequest$TrainingJobDefinition": "<p>The object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.</p>",
"DescribeHyperParameterTuningJobResponse$TrainingJobDefinition": "<p>The object that specifies the definition of the training jobs that this tuning job launches.</p>"
}
},
"HyperParameterTrainingJobSummaries": {
"base": null,
"refs": {
"ListTrainingJobsForHyperParameterTuningJobResponse$TrainingJobSummaries": "<p>A list of <a>TrainingJobSummary</a> objects that describe the training jobs that the <code>ListTrainingJobsForHyperParameterTuningJob</code> request returned.</p>"
"ListTrainingJobsForHyperParameterTuningJobResponse$TrainingJobSummaries": "<p>A list of objects that describe the training jobs that the <code>ListTrainingJobsForHyperParameterTuningJob</code> request returned.</p>"
}
},
"HyperParameterTrainingJobSummary": {
"base": "<p>Specifies summary information about a training job.</p>",
"refs": {
"DescribeHyperParameterTuningJobResponse$BestTrainingJob": "<p>A <a>TrainingJobSummary</a> object that describes the training job that completed with the best current <a>HyperParameterTuningJobObjective</a>.</p>",
"DescribeHyperParameterTuningJobResponse$BestTrainingJob": "<p>A object that describes the training job that completed with the best current .</p>",
"HyperParameterTrainingJobSummaries$member": null
}
},
@@ -584,8 +525,8 @@
"HyperParameterTuningJobConfig": {
"base": "<p>Configures a hyperparameter tuning job.</p>",
"refs": {
"CreateHyperParameterTuningJobRequest$HyperParameterTuningJobConfig": "<p>The <a>HyperParameterTuningJobConfig</a> object that describes the tuning job, including the search strategy, metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job.</p>",
"DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobConfig": "<p>The <a>HyperParameterTuningJobConfig</a> object that specifies the configuration of the tuning job.</p>"
"CreateHyperParameterTuningJobRequest$HyperParameterTuningJobConfig": "<p>The object that describes the tuning job, including the search strategy, metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job.</p>",
"DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobConfig": "<p>The object that specifies the configuration of the tuning job.</p>"
}
},
"HyperParameterTuningJobName": {
@@ -602,7 +543,7 @@
"HyperParameterTuningJobObjective": {
"base": "<p>Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the <code>Type</code> parameter.</p>",
"refs": {
"HyperParameterTuningJobConfig$HyperParameterTuningJobObjective": "<p>The <a>HyperParameterTuningJobObjective</a> object that specifies the objective metric for this tuning job.</p>"
"HyperParameterTuningJobConfig$HyperParameterTuningJobObjective": "<p>The object that specifies the objective metric for this tuning job.</p>"
}
},
"HyperParameterTuningJobObjectiveType": {
@@ -636,7 +577,7 @@
"HyperParameterTuningJobSummaries": {
"base": null,
"refs": {
"ListHyperParameterTuningJobsResponse$HyperParameterTuningJobSummaries": "<p>A list of <a>HyperParameterTuningJobSummary</a> objects that describe the tuning jobs that the <code>ListHyperParameterTuningJobs</code> request returned.</p>"
"ListHyperParameterTuningJobsResponse$HyperParameterTuningJobSummaries": "<p>A list of objects that describe the tuning jobs that the <code>ListHyperParameterTuningJobs</code> request returned.</p>"
}
},
"HyperParameterTuningJobSummary": {
@@ -657,9 +598,7 @@
"Image": {
"base": null,
"refs": {
"ContainerDefinition$Image": "<p>The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both <code>registry/repository[:tag]</code> and <code>registry/repository[@digest]</code> image path formats. For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html\">Using Your Own Algorithms with Amazon SageMaker</a> </p>",
"DeployedImage$SpecifiedImage": "<p>The image path you specified when you created the model.</p>",
"DeployedImage$ResolvedImage": "<p>The specific digest path of the image hosted in this <code>ProductionVariant</code>.</p>"
"ContainerDefinition$Image": "<p>The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html\">Using Your Own Algorithms with Amazon SageMaker</a> </p>"
}
},
"InputDataConfig": {
@@ -667,7 +606,7 @@
"refs": {
"CreateTrainingJobRequest$InputDataConfig": "<p>An array of <code>Channel</code> objects. Each channel is a named input source. <code>InputDataConfig</code> describes the input data and its location. </p> <p>Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, <code>training_data</code> and <code>validation_data</code>. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format. </p> <p>Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. </p>",
"DescribeTrainingJobResponse$InputDataConfig": "<p>An array of <code>Channel</code> objects that describes each data input channel. </p>",
"HyperParameterTrainingJobDefinition$InputDataConfig": "<p>An array of <a>Channel</a> objects that specify the input for the training jobs that the tuning job launches.</p>"
"HyperParameterTrainingJobDefinition$InputDataConfig": "<p>An array of objects that specify the input for the training jobs that the tuning job launches.</p>"
}
},
"InstanceType": {
@@ -699,8 +638,7 @@
"DescribeEndpointConfigOutput$KmsKeyId": "<p>AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.</p>",
"DescribeNotebookInstanceOutput$KmsKeyId": "<p> AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance. </p>",
"OutputDataConfig$KmsKeyId": "<p>The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. </p> <note> <p>If you don't provide the KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html\">KMS-Managed Encryption Keys</a> in Amazon Simple Storage Service developer guide.</p> </note> <note> <p> The KMS key policy must grant permission to the IAM role you specify in your <code>CreateTrainingJob</code> request. <a href=\"http://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html\">Using Key Policies in AWS KMS</a> in the AWS Key Management Service Developer Guide. </p> </note>",
"ResourceConfig$VolumeKmsKeyId": "<p>The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.</p>",
"TransformOutput$KmsKeyId": "<p>The AWS Key Management Service (AWS KMS) key for Amazon S3 server-side encryption that Amazon SageMaker uses to encrypt the transformed data.</p> <p>If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html\">KMS-Managed Encryption Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i> </p> <p>The KMS key policy must grant permission to the IAM role that you specify in your <code>CreateTramsformJob</code> request. For more information, see <a href=\"http://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html\">Using Key Policies in AWS KMS</a> in the <i>AWS Key Management Service Developer Guide</i>.</p>"
"ResourceConfig$VolumeKmsKeyId": "<p>The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.</p>"
}
},
"LastModifiedTime": {
@@ -812,23 +750,6 @@
"refs": {
}
},
"ListTransformJobsRequest": {
"base": null,
"refs": {
}
},
"ListTransformJobsResponse": {
"base": null,
"refs": {
}
},
"MaxConcurrentTransforms": {
"base": null,
"refs": {
"CreateTransformJobRequest$MaxConcurrentTransforms": "<p>The maximum number of parallel requests that can be sent to each instance in a transform job. This is good for algorithms that implement multiple workers on larger instances . The default value is <code>1</code>. To allow Amazon SageMaker to determine the appropriate number for <code>MaxConcurrentTransforms</code>, set the value to <code>0</code>.</p>",
"DescribeTransformJobResponse$MaxConcurrentTransforms": "<p>The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.</p>"
}
},
"MaxNumberOfTrainingJobs": {
"base": null,
"refs": {
@@ -841,25 +762,17 @@
"ResourceLimits$MaxParallelTrainingJobs": "<p>The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.</p>"
}
},
"MaxPayloadInMB": {
"base": null,
"refs": {
"CreateTransformJobRequest$MaxPayloadInMB": "<p>The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata). The value in <code>MaxPayloadInMB</code> must be greater or equal to the size of a single record. You can approximate the size of a record by dividing the size of your dataset by the number of records. Then multiply this value by the number of records you want in a mini-batch. It is recommended to enter a value slightly larger than this to ensure the records fit within the maximum payload size. The default value is <code>6</code> MB. For an unlimited payload size, set the value to <code>0</code>.</p>",
"DescribeTransformJobResponse$MaxPayloadInMB": "<p>The maximum payload size , in MB used in the transform job.</p>"
}
},
"MaxResults": {
"base": null,
"refs": {
"ListEndpointConfigsInput$MaxResults": "<p>The maximum number of training jobs to return in the response.</p>",
"ListEndpointsInput$MaxResults": "<p>The maximum number of endpoints to return in the response.</p>",
"ListHyperParameterTuningJobsRequest$MaxResults": "<p>The maximum number of tuning jobs to return. The default value is 10.</p>",
"ListHyperParameterTuningJobsRequest$MaxResults": "<p>The maximum number of tuning jobs to return.</p>",
"ListModelsInput$MaxResults": "<p>The maximum number of models to return in the response.</p>",
"ListNotebookInstanceLifecycleConfigsInput$MaxResults": "<p>The maximum number of lifecycle configurations to return in the response.</p>",
"ListNotebookInstancesInput$MaxResults": "<p>The maximum number of notebook instances to return.</p>",
"ListTrainingJobsForHyperParameterTuningJobRequest$MaxResults": "<p>The maximum number of training jobs to return. The default value is 10.</p>",
"ListTrainingJobsRequest$MaxResults": "<p>The maximum number of training jobs to return in the response.</p>",
"ListTransformJobsRequest$MaxResults": "<p>The maximum number of transform jobs to return in the response. The default value is <code>10</code>.</p>"
"ListTrainingJobsForHyperParameterTuningJobRequest$MaxResults": "<p>The maximum number of training jobs to return.</p>",
"ListTrainingJobsRequest$MaxResults": "<p>The maximum number of training jobs to return in the response.</p>"
}
},
"MaxRuntimeInSeconds": {
@@ -877,7 +790,7 @@
"MetricDefinitionList": {
"base": null,
"refs": {
"HyperParameterAlgorithmSpecification$MetricDefinitions": "<p>An array of <a>MetricDefinition</a> objects that specify the metrics that the algorithm emits.</p>"
"HyperParameterAlgorithmSpecification$MetricDefinitions": "<p>An array of objects that specify the metrics that the algorithm emits.</p>"
}
},
"MetricName": {
@@ -891,7 +804,7 @@
"MetricRegex": {
"base": null,
"refs": {
"MetricDefinition$Regex": "<p>A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see <a>automatic-model-tuning-define-metrics</a>.</p>"
"MetricDefinition$Regex": "<p>A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see <a>hpo-define-metrics</a>.</p>"
}
},
"MetricValue": {
@@ -918,11 +831,9 @@
"base": null,
"refs": {
"CreateModelInput$ModelName": "<p>The name of the new model.</p>",
"CreateTransformJobRequest$ModelName": "<p>The name of the model that you want to use for the transform job. <code>ModelName</code> must be the name of an existing Amazon SageMaker model within an AWS Region in an AWS account.</p>",
"DeleteModelInput$ModelName": "<p>The name of the model to delete.</p>",
"DescribeModelInput$ModelName": "<p>The name of the model.</p>",
"DescribeModelOutput$ModelName": "<p>Name of the Amazon SageMaker model.</p>",
"DescribeTransformJobResponse$ModelName": "<p>The name of the model used in the transform job.</p>",
"ModelSummary$ModelName": "<p>The name of the model that you want a summary for.</p>",
"ProductionVariant$ModelName": "<p>The name of the model that you want to host. This is the name that you specified when creating the model.</p>"
}
@@ -955,8 +866,7 @@
"base": null,
"refs": {
"ListHyperParameterTuningJobsRequest$NameContains": "<p>A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.</p>",
"ListTrainingJobsRequest$NameContains": "<p>A string in the training job name. This filter returns only training jobs whose name contains the specified string.</p>",
"ListTransformJobsRequest$NameContains": "<p>A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.</p>"
"ListTrainingJobsRequest$NameContains": "<p>A string in the training job name. This filter returns only training jobs whose name contains the specified string.</p>"
}
},
"NetworkInterfaceId": {
@@ -979,9 +889,7 @@
"ListTrainingJobsForHyperParameterTuningJobRequest$NextToken": "<p>If the result of the previous <code>ListTrainingJobsForHyperParameterTuningJob</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of training jobs, use the token in the next request.</p>",
"ListTrainingJobsForHyperParameterTuningJobResponse$NextToken": "<p>If the result of this <code>ListTrainingJobsForHyperParameterTuningJob</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of training jobs, use the token in the next request.</p>",
"ListTrainingJobsRequest$NextToken": "<p>If the result of the previous <code>ListTrainingJobs</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of training jobs, use the token in the next request. </p>",
"ListTrainingJobsResponse$NextToken": "<p>If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.</p>",
"ListTransformJobsRequest$NextToken": "<p>If the result of the previous <code>ListTransformJobs</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of transform jobs, use the token in the next request.</p>",
"ListTransformJobsResponse$NextToken": "<p>If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.</p>"
"ListTrainingJobsResponse$NextToken": "<p>If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.</p>"
}
},
"NotebookInstanceArn": {
@@ -1145,8 +1053,8 @@
"ObjectiveStatusCounters": {
"base": "<p>Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.</p>",
"refs": {
"DescribeHyperParameterTuningJobResponse$ObjectiveStatusCounters": "<p>The <a>ObjectiveStatusCounters</a> object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.</p>",
"HyperParameterTuningJobSummary$ObjectiveStatusCounters": "<p>The <a>ObjectiveStatusCounters</a> object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.</p>"
"DescribeHyperParameterTuningJobResponse$ObjectiveStatusCounters": "<p>The object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.</p>",
"HyperParameterTuningJobSummary$ObjectiveStatusCounters": "<p>The object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.</p>"
}
},
"OrderKey": {
@@ -1188,7 +1096,7 @@
"ParameterRanges": {
"base": "<p>Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches.</p>",
"refs": {
"HyperParameterTuningJobConfig$ParameterRanges": "<p>The <a>ParameterRanges</a> object that specifies the ranges of hyperparameters that this tuning job searches.</p>"
"HyperParameterTuningJobConfig$ParameterRanges": "<p>The object that specifies the ranges of hyperparameters that this tuning job searches.</p>"
}
},
"ParameterValue": {
@@ -1236,7 +1144,7 @@
"ProductionVariantSummaryList": {
"base": null,
"refs": {
"DescribeEndpointOutput$ProductionVariants": "<p> An array of <a>ProductionVariantSummary</a> objects, one for each model hosted behind this endpoint. </p>"
"DescribeEndpointOutput$ProductionVariants": "<p> An array of ProductionVariant objects, one for each model hosted behind this endpoint. </p>"
}
},
"RecordWrapper": {
@@ -1274,8 +1182,8 @@
"ResourceLimits": {
"base": "<p>Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.</p>",
"refs": {
"HyperParameterTuningJobConfig$ResourceLimits": "<p>The <a>ResourceLimits</a> object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.</p>",
"HyperParameterTuningJobSummary$ResourceLimits": "<p>The <a>ResourceLimits</a> object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.</p>"
"HyperParameterTuningJobConfig$ResourceLimits": "<p>The object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.</p>",
"HyperParameterTuningJobSummary$ResourceLimits": "<p>The object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.</p>"
}
},
"ResourceNotFound": {
@@ -1311,8 +1219,7 @@
"S3DataType": {
"base": null,
"refs": {
"S3DataSource$S3DataType": "<p>If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for model training. </p> <p>If you choose <code>ManifestFile</code>, <code>S3Uri</code> identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training. </p>",
"TransformS3DataSource$S3DataType": "<p>If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform. </p> <p>If you choose <code>ManifestFile</code>, <code>S3Uri</code> identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform. </p>"
"S3DataSource$S3DataType": "<p>If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for model training. </p> <p>If you choose <code>ManifestFile</code>, <code>S3Uri</code> identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training. </p>"
}
},
"S3Uri": {
@@ -1320,28 +1227,13 @@
"refs": {
"ModelArtifacts$S3ModelArtifacts": "<p>The path of the S3 object that contains the model artifacts. For example, <code>s3://bucket-name/keynameprefix/model.tar.gz</code>.</p>",
"OutputDataConfig$S3OutputPath": "<p>Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, <code>s3://bucket-name/key-name-prefix</code>. </p>",
"S3DataSource$S3Uri": "<p>Depending on the value specified for the <code>S3DataType</code>, identifies either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key name prefix might look like this: <code>s3://bucketname/exampleprefix</code>. </p> </li> <li> <p> A manifest might look like this: <code>s3://bucketname/example.manifest</code> </p> <p> The manifest is an S3 object which is a JSON file with the following format: </p> <p> <code>[</code> </p> <p> <code> {\"prefix\": \"s3://customer_bucket/some/prefix/\"},</code> </p> <p> <code> \"relative/path/to/custdata-1\",</code> </p> <p> <code> \"relative/path/custdata-2\",</code> </p> <p> <code> ...</code> </p> <p> <code> ]</code> </p> <p> The preceding JSON matches the following <code>s3Uris</code>: </p> <p> <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p> <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-1</code> </p> <p> <code>...</code> </p> <p> The complete set of <code>s3uris</code> in this manifest constitutes the input data for the channel for this datasource. The object that each <code>s3uris</code> points to must readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf. </p> </li> </ul>",
"TransformOutput$S3OutputPath": "<p>The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, <code>s3://bucket-name/key-name-prefix</code>.</p> <p>For every S3 object used as input for the transform job, the transformed data is stored in a corresponding subfolder in the location under the output prefix. For example, the input data <code>s3://bucket-name/input-name-prefix/dataset01/data.csv</code> will have the transformed data stored at <code>s3://bucket-name/key-name-prefix/dataset01/</code>, based on the original name, as a series of .part files (.part0001, part0002, etc).</p>",
"TransformS3DataSource$S3Uri": "<p>Depending on the value specified for the <code>S3DataType</code>, identifies either a key name prefix or a manifest. For example:</p> <ul> <li> <p> A key name prefix might look like this: <code>s3://bucketname/exampleprefix</code>. </p> </li> <li> <p> A manifest might look like this: <code>s3://bucketname/example.manifest</code> </p> <p> The manifest is an S3 object which is a JSON file with the following format: </p> <p> <code>[</code> </p> <p> <code> {\"prefix\": \"s3://customer_bucket/some/prefix/\"},</code> </p> <p> <code> \"relative/path/to/custdata-1\",</code> </p> <p> <code> \"relative/path/custdata-2\",</code> </p> <p> <code> ...</code> </p> <p> <code> ]</code> </p> <p> The preceding JSON matches the following <code>S3Uris</code>: </p> <p> <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p> <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-1</code> </p> <p> <code>...</code> </p> <p> The complete set of <code>S3Uris</code> in this manifest constitutes the input data for the channel for this datasource. The object that each <code>S3Uris</code> points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.</p> </li> </ul>"
"S3DataSource$S3Uri": "<p>Depending on the value specified for the <code>S3DataType</code>, identifies either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key name prefix might look like this: <code>s3://bucketname/exampleprefix</code>. </p> </li> <li> <p> A manifest might look like this: <code>s3://bucketname/example.manifest</code> </p> <p> The manifest is an S3 object which is a JSON file with the following format: </p> <p> <code>[</code> </p> <p> <code> {\"prefix\": \"s3://customer_bucket/some/prefix/\"},</code> </p> <p> <code> \"relative/path/to/custdata-1\",</code> </p> <p> <code> \"relative/path/custdata-2\",</code> </p> <p> <code> ...</code> </p> <p> <code> ]</code> </p> <p> The preceding JSON matches the following <code>s3Uris</code>: </p> <p> <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p> <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-1</code> </p> <p> <code>...</code> </p> <p> The complete set of <code>s3uris</code> in this manifest constitutes the input data for the channel for this datasource. The object that each <code>s3uris</code> points to must readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf. </p> </li> </ul>"
}
},
"SecondaryStatus": {
"base": null,
"refs": {
"DescribeTrainingJobResponse$SecondaryStatus": "<p> Provides granular information about the system state. For more information, see <code>TrainingJobStatus</code>. </p> <ul> <li> <p> <code>Starting</code> - starting the training job.</p> </li> <li> <p> <code>LaunchingMLInstances</code> - launching ML instances for the training job.</p> </li> <li> <p> <code>PreparingTrainingStack</code> - preparing the ML instances for the training job.</p> </li> <li> <p> <code>Downloading</code> - downloading the input data.</p> </li> <li> <p> <code>DownloadingTrainingImage</code> - downloading the training algorithm image.</p> </li> <li> <p> <code>Training</code> - model training is in progress.</p> </li> <li> <p> <code>Uploading</code> - uploading the trained model.</p> </li> <li> <p> <code>Stopping</code> - stopping the training job.</p> </li> <li> <p> <code>Stopped</code> - the training job has stopped.</p> </li> <li> <p> <code>MaxRuntimeExceeded</code> - the training exceed the specified the max run time, which means the training job is stopping.</p> </li> <li> <p> <code>Completed</code> - the training job has completed.</p> </li> <li> <p> <code>Failed</code> - the training job has failed. The failure reason is provided in the <code>StatusMessage</code>.</p> </li> </ul> <important> <p>The valid values for <code>SecondaryStatus</code> are subject to change. They primary provide information on the progress of the training job.</p> </important>",
"SecondaryStatusTransition$Status": "<p>Provides granular information about the system state. For more information, see <code>SecondaryStatus</code> under the <a>DescribeTrainingJob</a> response elements.</p>"
}
},
"SecondaryStatusTransition": {
"base": "<p>Specifies a secondary status the job has transitioned into. It includes a start timestamp and later an end timestamp. The end timestamp is added either after the job transitions to a different secondary status or after the job has ended.</p>",
"refs": {
"SecondaryStatusTransitions$member": null
}
},
"SecondaryStatusTransitions": {
"base": null,
"refs": {
"DescribeTrainingJobResponse$SecondaryStatusTransitions": "<p>A log of time-ordered secondary statuses that a training job has transitioned.</p>"
"DescribeTrainingJobResponse$SecondaryStatus": "<p> Provides granular information about the system state. For more information, see <code>TrainingJobStatus</code>. </p>"
}
},
"SecurityGroupId": {
@@ -1367,8 +1259,7 @@
"SortBy": {
"base": null,
"refs": {
"ListTrainingJobsRequest$SortBy": "<p>The field to sort results by. The default is <code>CreationTime</code>.</p>",
"ListTransformJobsRequest$SortBy": "<p>The field to sort results by. The default is <code>CreationTime</code>.</p>"
"ListTrainingJobsRequest$SortBy": "<p>The field to sort results by. The default is <code>CreationTime</code>.</p>"
}
},
"SortOrder": {
@@ -1376,14 +1267,7 @@
"refs": {
"ListHyperParameterTuningJobsRequest$SortOrder": "<p>The sort order for results. The default is <code>Ascending</code>.</p>",
"ListTrainingJobsForHyperParameterTuningJobRequest$SortOrder": "<p>The sort order for results. The default is <code>Ascending</code>.</p>",
"ListTrainingJobsRequest$SortOrder": "<p>The sort order for results. The default is <code>Ascending</code>.</p>",
"ListTransformJobsRequest$SortOrder": "<p>The sort order for results. The default is <code>Descending</code>.</p>"
}
},
"SplitType": {
"base": null,
"refs": {
"TransformInput$SplitType": "<p>The method to use to split the transform job's data into smaller batches. The default value is <code>None</code>. If you don't want to split the data, specify <code>None</code>. If you want to split records on a newline character boundary, specify <code>Line</code>. To split records according to the RecordIO format, specify <code>RecordIO</code>.</p> <p>Amazon SageMaker will send maximum number of records per batch in each request up to the MaxPayloadInMB limit. For more information, see <a href=\"http://mxnet.io/architecture/note_data_loading.html#data-format\">RecordIO data format</a>.</p> <note> <p>For information about the <code>RecordIO</code> format, see <a href=\"http://mxnet.io/architecture/note_data_loading.html#data-format\">Data Format</a>.</p> </note>"
"ListTrainingJobsRequest$SortOrder": "<p>The sort order for results. The default is <code>Ascending</code>.</p>"
}
},
"StartNotebookInstanceInput": {
@@ -1391,12 +1275,6 @@
"refs": {
}
},
"StatusMessage": {
"base": null,
"refs": {
"SecondaryStatusTransition$StatusMessage": "<p>Shows a brief description and other information about the secondary status. For example, the <code>LaunchingMLInstances</code> secondary status could show a status message of \"Insufficent capacity error while launching instances\".</p>"
}
},
"StopHyperParameterTuningJobRequest": {
"base": null,
"refs": {
@@ -1412,11 +1290,6 @@
"refs": {
}
},
"StopTransformJobRequest": {
"base": null,
"refs": {
}
},
"StoppingCondition": {
"base": "<p>Specifies how long model training can run. When model training reaches the limit, Amazon SageMaker ends the training job. Use this API to cap model training cost.</p> <p>To stop a job, Amazon SageMaker sends the algorithm the <code>SIGTERM</code> signal, which delays job termination for120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of training is not lost. </p> <p>Training algorithms provided by Amazon SageMaker automatically saves the intermediate results of a model training job (it is best effort case, as model might not be ready to save as some stages, for example training just started). This intermediate data is a valid model artifact. You can use it to create a model (<code>CreateModel</code>). </p>",
"refs": {
@@ -1469,7 +1342,6 @@
"CreateModelInput$Tags": "<p>An array of key-value pairs. For more information, see <a href=\"http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>. </p>",
"CreateNotebookInstanceInput$Tags": "<p>A list of tags to associate with the notebook instance. You can add tags later by using the <code>CreateTags</code> API.</p>",
"CreateTrainingJobRequest$Tags": "<p>An array of key-value pairs. For more information, see <a href=\"http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>. </p>",
"CreateTransformJobRequest$Tags": "<p>An array of key-value pairs. Adding tags is optional. For more information, see <a href=\"http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what\">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>.</p>",
"ListTagsOutput$Tags": "<p>An array of <code>Tag</code> objects, each with a tag key and a value.</p>"
}
},
@@ -1491,7 +1363,6 @@
"Timestamp": {
"base": null,
"refs": {
"DeployedImage$ResolutionTime": "<p>The date and time when the image path for the model resolved to the <code>ResolvedImage</code> </p>",
"DescribeEndpointConfigOutput$CreationTime": "<p>A timestamp that shows when the endpoint configuration was created.</p>",
"DescribeEndpointOutput$CreationTime": "<p>A timestamp that shows when the endpoint was created.</p>",
"DescribeEndpointOutput$LastModifiedTime": "<p>A timestamp that shows when the endpoint was last modified.</p>",
@@ -1503,9 +1374,6 @@
"DescribeTrainingJobResponse$TrainingStartTime": "<p>Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of <code>TrainingEndTime</code>. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.</p>",
"DescribeTrainingJobResponse$TrainingEndTime": "<p>Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of <code>TrainingStartTime</code> and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.</p>",
"DescribeTrainingJobResponse$LastModifiedTime": "<p>A timestamp that indicates when the status of the training job was last modified.</p>",
"DescribeTransformJobResponse$CreationTime": "<p>A timestamp that shows when the transform Job was created.</p>",
"DescribeTransformJobResponse$TransformStartTime": "<p>Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of <code>TransformEndTime</code>.</p>",
"DescribeTransformJobResponse$TransformEndTime": "<p>Indicates when the transform job is <code>Completed</code>, <code>Stopped</code>, or <code>Failed</code>. You are billed for the time interval between this time and the value of <code>TransformStartTime</code>.</p>",
"EndpointConfigSummary$CreationTime": "<p>A timestamp that shows when the endpoint configuration was created.</p>",
"EndpointSummary$CreationTime": "<p>A timestamp that shows when the endpoint was created.</p>",
"EndpointSummary$LastModifiedTime": "<p>A timestamp that shows when the endpoint was last modified.</p>",
@@ -1527,23 +1395,14 @@
"ListHyperParameterTuningJobsRequest$LastModifiedTimeBefore": "<p>A filter that returns only tuning jobs that were modified before the specified time.</p>",
"ListModelsInput$CreationTimeBefore": "<p>A filter that returns only models created before the specified time (timestamp).</p>",
"ListModelsInput$CreationTimeAfter": "<p>A filter that returns only models created after the specified time (timestamp).</p>",
"ListTrainingJobsRequest$CreationTimeAfter": "<p>A filter that returns only training jobs created after the specified time (timestamp).</p>",
"ListTrainingJobsRequest$CreationTimeAfter": "<p>A filter that only training jobs created after the specified time (timestamp).</p>",
"ListTrainingJobsRequest$CreationTimeBefore": "<p>A filter that returns only training jobs created before the specified time (timestamp).</p>",
"ListTrainingJobsRequest$LastModifiedTimeAfter": "<p>A filter that returns only training jobs modified after the specified time (timestamp).</p>",
"ListTrainingJobsRequest$LastModifiedTimeBefore": "<p>A filter that returns only training jobs modified before the specified time (timestamp).</p>",
"ListTransformJobsRequest$CreationTimeAfter": "<p>A filter that returns only transform jobs created after the specified time.</p>",
"ListTransformJobsRequest$CreationTimeBefore": "<p>A filter that returns only transform jobs created before the specified time.</p>",
"ListTransformJobsRequest$LastModifiedTimeAfter": "<p>A filter that returns only transform jobs modified after the specified time.</p>",
"ListTransformJobsRequest$LastModifiedTimeBefore": "<p>A filter that returns only transform jobs modified before the specified time.</p>",
"ModelSummary$CreationTime": "<p>A timestamp that indicates when the model was created.</p>",
"SecondaryStatusTransition$StartTime": "<p>A timestamp that shows when the training job has entered this secondary status.</p>",
"SecondaryStatusTransition$EndTime": "<p>A timestamp that shows when the secondary status has ended and the job has transitioned into another secondary status. </p>",
"TrainingJobSummary$CreationTime": "<p>A timestamp that shows when the training job was created.</p>",
"TrainingJobSummary$TrainingEndTime": "<p>A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (<code>Completed</code>, <code>Failed</code>, or <code>Stopped</code>). </p>",
"TrainingJobSummary$LastModifiedTime": "<p> Timestamp when the training job was last modified. </p>",
"TransformJobSummary$CreationTime": "<p>A timestamp that shows when the transform Job was created.</p>",
"TransformJobSummary$TransformEndTime": "<p>Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.</p>",
"TransformJobSummary$LastModifiedTime": "<p>Indicates when the transform job was last modified.</p>"
"TrainingJobSummary$LastModifiedTime": "<p> Timestamp when the training job was last modified. </p>"
}
},
"TrainingInputMode": {
@@ -1577,7 +1436,7 @@
"TrainingJobName": {
"base": null,
"refs": {
"CreateTrainingJobRequest$TrainingJobName": "<p>The name of the training job. The name must be unique within an AWS Region in an AWS account. </p>",
"CreateTrainingJobRequest$TrainingJobName": "<p>The name of the training job. The name must be unique within an AWS Region in an AWS account. It appears in the Amazon SageMaker console. </p>",
"DescribeTrainingJobRequest$TrainingJobName": "<p>The name of the training job.</p>",
"DescribeTrainingJobResponse$TrainingJobName": "<p> Name of the model training job. </p>",
"HyperParameterTrainingJobSummary$TrainingJobName": "<p>The name of the training job.</p>",
@@ -1588,7 +1447,7 @@
"TrainingJobSortByOptions": {
"base": null,
"refs": {
"ListTrainingJobsForHyperParameterTuningJobRequest$SortBy": "<p>The field to sort results by. The default is <code>Name</code>.</p> <p>If the value of this field is <code>FinalObjectiveMetricValue</code>, any training jobs that did not return an objective metric are not listed.</p>"
"ListTrainingJobsForHyperParameterTuningJobRequest$SortBy": "<p>The field to sort results by. The default is <code>Name</code>.</p>"
}
},
"TrainingJobStatus": {
@@ -1614,8 +1473,8 @@
"TrainingJobStatusCounters": {
"base": "<p>The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.</p>",
"refs": {
"DescribeHyperParameterTuningJobResponse$TrainingJobStatusCounters": "<p>The <a>TrainingJobStatusCounters</a> object that specifies the number of training jobs, categorized by status, that this tuning job launched.</p>",
"HyperParameterTuningJobSummary$TrainingJobStatusCounters": "<p>The <a>TrainingJobStatusCounters</a> object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.</p>"
"DescribeHyperParameterTuningJobResponse$TrainingJobStatusCounters": "<p>The object that specifies the number of training jobs, categorized by status, that this tuning job launched.</p>",
"HyperParameterTuningJobSummary$TrainingJobStatusCounters": "<p>The object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.</p>"
}
},
"TrainingJobSummaries": {
@@ -1630,108 +1489,6 @@
"TrainingJobSummaries$member": null
}
},
"TransformDataSource": {
"base": "<p>Describes the location of the channel data.</p>",
"refs": {
"TransformInput$DataSource": "<p>Describes the location of the channel data, meaning the S3 location of the input data that the model can consume.</p>"
}
},
"TransformEnvironmentKey": {
"base": null,
"refs": {
"TransformEnvironmentMap$key": null
}
},
"TransformEnvironmentMap": {
"base": null,
"refs": {
"CreateTransformJobRequest$Environment": "<p>The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.</p>",
"DescribeTransformJobResponse$Environment": "<p/>"
}
},
"TransformEnvironmentValue": {
"base": null,
"refs": {
"TransformEnvironmentMap$value": null
}
},
"TransformInput": {
"base": "<p>Describes the input source of a transform job and the way the transform job consumes it.</p>",
"refs": {
"CreateTransformJobRequest$TransformInput": "<p>Describes the input source and the way the transform job consumes it.</p>",
"DescribeTransformJobResponse$TransformInput": "<p>Describes the dataset to be transformed and the Amazon S3 location where it is stored.</p>"
}
},
"TransformInstanceCount": {
"base": null,
"refs": {
"TransformResources$InstanceCount": "<p>The number of ML compute instances to use in the transform job. For distributed transform, provide a value greater than 1. The default value is <code>1</code>.</p>"
}
},
"TransformInstanceType": {
"base": null,
"refs": {
"TransformResources$InstanceType": "<p>The ML compute instance type for the transform job. For using built-in algorithms to transform moderately sized datasets, ml.m4.xlarge or <code>ml.m5.large</code> should suffice. There is no default value for <code>InstanceType</code>.</p>"
}
},
"TransformJobArn": {
"base": null,
"refs": {
"CreateTransformJobResponse$TransformJobArn": "<p>The Amazon Resource Name (ARN) of the transform job.</p>",
"DescribeTransformJobResponse$TransformJobArn": "<p>The Amazon Resource Name (ARN) of the transform job.</p>",
"TransformJobSummary$TransformJobArn": "<p>The Amazon Resource Name (ARN) of the transform job.</p>"
}
},
"TransformJobName": {
"base": null,
"refs": {
"CreateTransformJobRequest$TransformJobName": "<p>The name of the transform job. The name must be unique within an AWS Region in an AWS account. </p>",
"DescribeTransformJobRequest$TransformJobName": "<p>The name of the transform job that you want to view details of.</p>",
"DescribeTransformJobResponse$TransformJobName": "<p>The name of the transform job.</p>",
"StopTransformJobRequest$TransformJobName": "<p>The name of the transform job to stop.</p>",
"TransformJobSummary$TransformJobName": "<p>The name of the transform job.</p>"
}
},
"TransformJobStatus": {
"base": null,
"refs": {
"DescribeTransformJobResponse$TransformJobStatus": "<p>The status of the transform job. If the transform job failed, the reason is returned in the <code>FailureReason</code> field.</p>",
"ListTransformJobsRequest$StatusEquals": "<p>A filter that retrieves only transform jobs with a specific status.</p>",
"TransformJobSummary$TransformJobStatus": "<p>The status of the transform job.</p>"
}
},
"TransformJobSummaries": {
"base": null,
"refs": {
"ListTransformJobsResponse$TransformJobSummaries": "<p>An array of <code>TransformJobSummary</code> objects.</p>"
}
},
"TransformJobSummary": {
"base": "<p>Provides a summary information for a transform job. Multiple TransformJobSummary objects are returned as a list after calling <a>ListTransformJobs</a>.</p>",
"refs": {
"TransformJobSummaries$member": null
}
},
"TransformOutput": {
"base": "<p>Describes the results of a transform job output.</p>",
"refs": {
"CreateTransformJobRequest$TransformOutput": "<p>Describes the results of the transform job.</p>",
"DescribeTransformJobResponse$TransformOutput": "<p>Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.</p>"
}
},
"TransformResources": {
"base": "<p>Describes the resources, including ML instance types and ML instance count, to use for transform job.</p>",
"refs": {
"CreateTransformJobRequest$TransformResources": "<p>Describes the resources, including ML instance types and ML instance count, to use for the transform job.</p>",
"DescribeTransformJobResponse$TransformResources": "<p>Describes the resources, including ML instance types and ML instance count, to use for the transform job.</p>"
}
},
"TransformS3DataSource": {
"base": "<p>Describes the S3 data source.</p>",
"refs": {
"TransformDataSource$S3DataSource": "<p>The S3 location of the data source that is associated with a channel.</p>"
}
},
"UpdateEndpointInput": {
"base": null,
"refs": {
@@ -1775,7 +1532,7 @@
"Url": {
"base": null,
"refs": {
"ContainerDefinition$ModelDataUrl": "<p>The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). </p> <p>If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see <a href=\"http://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html\">Activating and Deactivating AWS STS i an AWS Region</a> in the <i>AWS Identity and Access Management User Guide</i>.</p>"
"ContainerDefinition$ModelDataUrl": "<p>The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). </p>"
}
},
"VariantName": {
@@ -1804,11 +1561,11 @@
"VpcConfig": {
"base": "<p>Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see <a>host-vpc</a> and <a>train-vpc</a>.</p>",
"refs": {
"CreateModelInput$VpcConfig": "<p>A <a>VpcConfig</a> object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. For more information, see <a>host-vpc</a>.</p>",
"CreateTrainingJobRequest$VpcConfig": "<p>A <a>VpcConfig</a> object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see <a>train-vpc</a> </p>",
"DescribeModelOutput$VpcConfig": "<p>A <a>VpcConfig</a> object that specifies the VPC that this model has access to. For more information, see <a>host-vpc</a> </p>",
"DescribeTrainingJobResponse$VpcConfig": "<p>A <a>VpcConfig</a> object that specifies the VPC that this training job has access to. For more information, see <a>train-vpc</a>.</p>",
"HyperParameterTrainingJobDefinition$VpcConfig": "<p>The <a>VpcConfig</a> object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see <a>train-vpc</a>.</p>"
"CreateModelInput$VpcConfig": "<p>A object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. For more information, see <a>host-vpc</a>.</p>",
"CreateTrainingJobRequest$VpcConfig": "<p>A object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see <a>train-vpc</a> </p>",
"DescribeModelOutput$VpcConfig": "<p>A object that specifies the VPC that this model has access to. For more information, see <a>host-vpc</a> </p>",
"DescribeTrainingJobResponse$VpcConfig": "<p>A object that specifies the VPC that this training job has access to. For more information, see <a>train-vpc</a>.</p>",
"HyperParameterTrainingJobDefinition$VpcConfig": "<p>The object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see <a>train-vpc</a>.</p>"
}
},
"VpcSecurityGroupIds": {

5
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/paginators-1.json generated vendored
View File

@@ -44,11 +44,6 @@
"input_token": "NextToken",
"output_token": "NextToken",
"limit_key": "MaxResults"
},
"ListTransformJobs": {
"input_token": "NextToken",
"output_token": "NextToken",
"limit_key": "MaxResults"
}
}
}