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vendor: update all dependencies

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Nick Craig-Wood 2018-03-19 15:51:38 +00:00
parent 940df88eb2
commit d64789528d
4309 changed files with 1327278 additions and 1001118 deletions
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217
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/api-2.json generated vendored
View file

@ -70,6 +70,18 @@
{"shape":"ResourceLimitExceeded"}
]
},
"CreateNotebookInstanceLifecycleConfig":{
"name":"CreateNotebookInstanceLifecycleConfig",
"http":{
"method":"POST",
"requestUri":"/"
},
"input":{"shape":"CreateNotebookInstanceLifecycleConfigInput"},
"output":{"shape":"CreateNotebookInstanceLifecycleConfigOutput"},
"errors":[
{"shape":"ResourceLimitExceeded"}
]
},
"CreatePresignedNotebookInstanceUrl":{
"name":"CreatePresignedNotebookInstanceUrl",
"http":{
@ -124,6 +136,14 @@
},
"input":{"shape":"DeleteNotebookInstanceInput"}
},
"DeleteNotebookInstanceLifecycleConfig":{
"name":"DeleteNotebookInstanceLifecycleConfig",
"http":{
"method":"POST",
"requestUri":"/"
},
"input":{"shape":"DeleteNotebookInstanceLifecycleConfigInput"}
},
"DeleteTags":{
"name":"DeleteTags",
"http":{
@ -169,6 +189,15 @@
"input":{"shape":"DescribeNotebookInstanceInput"},
"output":{"shape":"DescribeNotebookInstanceOutput"}
},
"DescribeNotebookInstanceLifecycleConfig":{
"name":"DescribeNotebookInstanceLifecycleConfig",
"http":{
"method":"POST",
"requestUri":"/"
},
"input":{"shape":"DescribeNotebookInstanceLifecycleConfigInput"},
"output":{"shape":"DescribeNotebookInstanceLifecycleConfigOutput"}
},
"DescribeTrainingJob":{
"name":"DescribeTrainingJob",
"http":{
@ -208,6 +237,15 @@
"input":{"shape":"ListModelsInput"},
"output":{"shape":"ListModelsOutput"}
},
"ListNotebookInstanceLifecycleConfigs":{
"name":"ListNotebookInstanceLifecycleConfigs",
"http":{
"method":"POST",
"requestUri":"/"
},
"input":{"shape":"ListNotebookInstanceLifecycleConfigsInput"},
"output":{"shape":"ListNotebookInstanceLifecycleConfigsOutput"}
},
"ListNotebookInstances":{
"name":"ListNotebookInstances",
"http":{
@ -300,6 +338,18 @@
"errors":[
{"shape":"ResourceLimitExceeded"}
]
},
"UpdateNotebookInstanceLifecycleConfig":{
"name":"UpdateNotebookInstanceLifecycleConfig",
"http":{
"method":"POST",
"requestUri":"/"
},
"input":{"shape":"UpdateNotebookInstanceLifecycleConfigInput"},
"output":{"shape":"UpdateNotebookInstanceLifecycleConfigOutput"},
"errors":[
{"shape":"ResourceLimitExceeded"}
]
}
},
"shapes":{
@ -390,7 +440,8 @@
"members":{
"EndpointConfigName":{"shape":"EndpointConfigName"},
"ProductionVariants":{"shape":"ProductionVariantList"},
"Tags":{"shape":"TagList"}
"Tags":{"shape":"TagList"},
"KmsKeyId":{"shape":"KmsKeyId"}
}
},
"CreateEndpointConfigOutput":{
@ -454,7 +505,24 @@
"SecurityGroupIds":{"shape":"SecurityGroupIds"},
"RoleArn":{"shape":"RoleArn"},
"KmsKeyId":{"shape":"KmsKeyId"},
"Tags":{"shape":"TagList"}
"Tags":{"shape":"TagList"},
"LifecycleConfigName":{"shape":"NotebookInstanceLifecycleConfigName"},
"DirectInternetAccess":{"shape":"DirectInternetAccess"}
}
},
"CreateNotebookInstanceLifecycleConfigInput":{
"type":"structure",
"required":["NotebookInstanceLifecycleConfigName"],
"members":{
"NotebookInstanceLifecycleConfigName":{"shape":"NotebookInstanceLifecycleConfigName"},
"OnCreate":{"shape":"NotebookInstanceLifecycleConfigList"},
"OnStart":{"shape":"NotebookInstanceLifecycleConfigList"}
}
},
"CreateNotebookInstanceLifecycleConfigOutput":{
"type":"structure",
"members":{
"NotebookInstanceLifecycleConfigArn":{"shape":"NotebookInstanceLifecycleConfigArn"}
}
},
"CreateNotebookInstanceOutput":{
@ -543,6 +611,13 @@
"NotebookInstanceName":{"shape":"NotebookInstanceName"}
}
},
"DeleteNotebookInstanceLifecycleConfigInput":{
"type":"structure",
"required":["NotebookInstanceLifecycleConfigName"],
"members":{
"NotebookInstanceLifecycleConfigName":{"shape":"NotebookInstanceLifecycleConfigName"}
}
},
"DeleteTagsInput":{
"type":"structure",
"required":[
@ -578,6 +653,7 @@
"EndpointConfigName":{"shape":"EndpointConfigName"},
"EndpointConfigArn":{"shape":"EndpointConfigArn"},
"ProductionVariants":{"shape":"ProductionVariantList"},
"KmsKeyId":{"shape":"KmsKeyId"},
"CreationTime":{"shape":"Timestamp"}
}
},
@ -640,6 +716,24 @@
"NotebookInstanceName":{"shape":"NotebookInstanceName"}
}
},
"DescribeNotebookInstanceLifecycleConfigInput":{
"type":"structure",
"required":["NotebookInstanceLifecycleConfigName"],
"members":{
"NotebookInstanceLifecycleConfigName":{"shape":"NotebookInstanceLifecycleConfigName"}
}
},
"DescribeNotebookInstanceLifecycleConfigOutput":{
"type":"structure",
"members":{
"NotebookInstanceLifecycleConfigArn":{"shape":"NotebookInstanceLifecycleConfigArn"},
"NotebookInstanceLifecycleConfigName":{"shape":"NotebookInstanceLifecycleConfigName"},
"OnCreate":{"shape":"NotebookInstanceLifecycleConfigList"},
"OnStart":{"shape":"NotebookInstanceLifecycleConfigList"},
"LastModifiedTime":{"shape":"LastModifiedTime"},
"CreationTime":{"shape":"CreationTime"}
}
},
"DescribeNotebookInstanceOutput":{
"type":"structure",
"members":{
@ -655,7 +749,9 @@
"KmsKeyId":{"shape":"KmsKeyId"},
"NetworkInterfaceId":{"shape":"NetworkInterfaceId"},
"LastModifiedTime":{"shape":"LastModifiedTime"},
"CreationTime":{"shape":"CreationTime"}
"CreationTime":{"shape":"CreationTime"},
"NotebookInstanceLifecycleConfigName":{"shape":"NotebookInstanceLifecycleConfigName"},
"DirectInternetAccess":{"shape":"DirectInternetAccess"}
}
},
"DescribeTrainingJobRequest":{
@ -713,6 +809,13 @@
"member":{"shape":"DesiredWeightAndCapacity"},
"min":1
},
"DirectInternetAccess":{
"type":"string",
"enum":[
"Enabled",
"Disabled"
]
},
"EndpointArn":{
"type":"string",
"max":2048,
@ -848,7 +951,8 @@
"enum":[
"ml.t2.medium",
"ml.m4.xlarge",
"ml.p2.xlarge"
"ml.p2.xlarge",
"ml.p3.2xlarge"
]
},
"KmsKeyId":{
@ -919,6 +1023,27 @@
"NextToken":{"shape":"PaginationToken"}
}
},
"ListNotebookInstanceLifecycleConfigsInput":{
"type":"structure",
"members":{
"NextToken":{"shape":"NextToken"},
"MaxResults":{"shape":"MaxResults"},
"SortBy":{"shape":"NotebookInstanceLifecycleConfigSortKey"},
"SortOrder":{"shape":"NotebookInstanceLifecycleConfigSortOrder"},
"NameContains":{"shape":"NotebookInstanceLifecycleConfigNameContains"},
"CreationTimeBefore":{"shape":"CreationTime"},
"CreationTimeAfter":{"shape":"CreationTime"},
"LastModifiedTimeBefore":{"shape":"LastModifiedTime"},
"LastModifiedTimeAfter":{"shape":"LastModifiedTime"}
}
},
"ListNotebookInstanceLifecycleConfigsOutput":{
"type":"structure",
"members":{
"NextToken":{"shape":"NextToken"},
"NotebookInstanceLifecycleConfigs":{"shape":"NotebookInstanceLifecycleConfigSummaryList"}
}
},
"ListNotebookInstancesInput":{
"type":"structure",
"members":{
@ -931,7 +1056,8 @@
"CreationTimeAfter":{"shape":"CreationTime"},
"LastModifiedTimeBefore":{"shape":"LastModifiedTime"},
"LastModifiedTimeAfter":{"shape":"LastModifiedTime"},
"StatusEquals":{"shape":"NotebookInstanceStatus"}
"StatusEquals":{"shape":"NotebookInstanceStatus"},
"NotebookInstanceLifecycleConfigNameContains":{"shape":"NotebookInstanceLifecycleConfigName"}
}
},
"ListNotebookInstancesOutput":{
@ -1055,6 +1181,67 @@
"type":"string",
"max":256
},
"NotebookInstanceLifecycleConfigArn":{
"type":"string",
"max":256
},
"NotebookInstanceLifecycleConfigContent":{
"type":"string",
"max":16384,
"min":1
},
"NotebookInstanceLifecycleConfigList":{
"type":"list",
"member":{"shape":"NotebookInstanceLifecycleHook"},
"max":1
},
"NotebookInstanceLifecycleConfigName":{
"type":"string",
"max":63,
"pattern":"^[a-zA-Z0-9](-*[a-zA-Z0-9])*"
},
"NotebookInstanceLifecycleConfigNameContains":{
"type":"string",
"pattern":"[a-zA-Z0-9-]+"
},
"NotebookInstanceLifecycleConfigSortKey":{
"type":"string",
"enum":[
"Name",
"CreationTime",
"LastModifiedTime"
]
},
"NotebookInstanceLifecycleConfigSortOrder":{
"type":"string",
"enum":[
"Ascending",
"Descending"
]
},
"NotebookInstanceLifecycleConfigSummary":{
"type":"structure",
"required":[
"NotebookInstanceLifecycleConfigName",
"NotebookInstanceLifecycleConfigArn"
],
"members":{
"NotebookInstanceLifecycleConfigName":{"shape":"NotebookInstanceLifecycleConfigName"},
"NotebookInstanceLifecycleConfigArn":{"shape":"NotebookInstanceLifecycleConfigArn"},
"CreationTime":{"shape":"CreationTime"},
"LastModifiedTime":{"shape":"LastModifiedTime"}
}
},
"NotebookInstanceLifecycleConfigSummaryList":{
"type":"list",
"member":{"shape":"NotebookInstanceLifecycleConfigSummary"}
},
"NotebookInstanceLifecycleHook":{
"type":"structure",
"members":{
"Content":{"shape":"NotebookInstanceLifecycleConfigContent"}
}
},
"NotebookInstanceName":{
"type":"string",
"max":63,
@ -1103,7 +1290,8 @@
"Url":{"shape":"NotebookInstanceUrl"},
"InstanceType":{"shape":"InstanceType"},
"CreationTime":{"shape":"CreationTime"},
"LastModifiedTime":{"shape":"LastModifiedTime"}
"LastModifiedTime":{"shape":"LastModifiedTime"},
"NotebookInstanceLifecycleConfigName":{"shape":"NotebookInstanceLifecycleConfigName"}
}
},
"NotebookInstanceSummaryList":{
@ -1211,7 +1399,8 @@
"members":{
"InstanceType":{"shape":"TrainingInstanceType"},
"InstanceCount":{"shape":"TrainingInstanceCount"},
"VolumeSizeInGB":{"shape":"VolumeSizeInGB"}
"VolumeSizeInGB":{"shape":"VolumeSizeInGB"},
"VolumeKmsKeyId":{"shape":"KmsKeyId"}
}
},
"ResourceInUse":{
@ -1506,6 +1695,20 @@
"RoleArn":{"shape":"RoleArn"}
}
},
"UpdateNotebookInstanceLifecycleConfigInput":{
"type":"structure",
"required":["NotebookInstanceLifecycleConfigName"],
"members":{
"NotebookInstanceLifecycleConfigName":{"shape":"NotebookInstanceLifecycleConfigName"},
"OnCreate":{"shape":"NotebookInstanceLifecycleConfigList"},
"OnStart":{"shape":"NotebookInstanceLifecycleConfigList"}
}
},
"UpdateNotebookInstanceLifecycleConfigOutput":{
"type":"structure",
"members":{
}
},
"UpdateNotebookInstanceOutput":{
"type":"structure",
"members":{

169
vendor/github.com/aws/aws-sdk-go/models/apis/sagemaker/2017-07-24/docs-2.json generated vendored
View file

@ -6,22 +6,26 @@
"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>",
"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>",
"CreateNotebookInstance": "<p>Creates an Amazon SageMaker notebook instance. A notebook instance is an ML compute instance running on a Jupyter notebook. </p> <p>In a <code>CreateNotebookInstance</code> request, you specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance. </p> <p>Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific an algorithm or with a machine learning framework. </p> <p>After receiving the request, Amazon SageMaker does the following:</p> <ol> <li> <p>Creates a network interface in the Amazon SageMaker VPC.</p> </li> <li> <p>(Option) If you specified <code>SubnetId</code>, creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.</p> </li> <li> <p>Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified <code>SubnetId</code> of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.</p> </li> </ol> <p>After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).</p> <p>After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models. </p> <p>For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html\">How It Works</a>. </p>",
"CreateNotebookInstance": "<p>Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook. </p> <p>In a <code>CreateNotebookInstance</code> request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance. </p> <p>Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework. </p> <p>After receiving the request, Amazon SageMaker does the following:</p> <ol> <li> <p>Creates a network interface in the Amazon SageMaker VPC.</p> </li> <li> <p>(Option) If you specified <code>SubnetId</code>, Amazon SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.</p> </li> <li> <p>Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified <code>SubnetId</code> of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.</p> </li> </ol> <p>After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).</p> <p>After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models. </p> <p>For more information, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html\">How It Works</a>. </p>",
"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>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 Juypter 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>",
"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>DeleteEndpoingConfig</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>",
"DeleteNotebookInstanceLifecycleConfig": "<p>Deletes a notebook instance lifecycle configuration.</p>",
"DeleteTags": "<p>Deletes the specified tags from an Amazon SageMaker resource.</p> <p>To list a resource's tags, use the <code>ListTags</code> API. </p>",
"DescribeEndpoint": "<p>Returns the description of an endpoint.</p>",
"DescribeEndpointConfig": "<p>Returns the description of an endpoint configuration created using the <code>CreateEndpointConfig</code> API.</p>",
"DescribeModel": "<p>Describes a model that you created using the <code>CreateModel</code> API.</p>",
"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>",
"ListEndpointConfigs": "<p>Lists endpoint configurations.</p>",
"ListEndpoints": "<p>Lists endpoints.</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 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>",
@ -29,8 +33,9 @@
"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>",
"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, capacity, or both of one or more variants associated with an endpoint. This operation updates weight, capacity, or both for the previously provisioned 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>"
"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 API.</p>"
},
"shapes": {
"AddTagsInput": {
@ -128,6 +133,16 @@
"refs": {
}
},
"CreateNotebookInstanceLifecycleConfigInput": {
"base": null,
"refs": {
}
},
"CreateNotebookInstanceLifecycleConfigOutput": {
"base": null,
"refs": {
}
},
"CreateNotebookInstanceOutput": {
"base": null,
"refs": {
@ -156,9 +171,13 @@
"CreationTime": {
"base": null,
"refs": {
"DescribeNotebookInstanceLifecycleConfigOutput$CreationTime": "<p>A timestamp that tells when the lifecycle configuration was created.</p>",
"DescribeNotebookInstanceOutput$CreationTime": "<p>A timestamp. Use this parameter to return the time when the notebook instance was created</p>",
"ListNotebookInstanceLifecycleConfigsInput$CreationTimeBefore": "<p>A filter that returns only lifecycle configurations that were created before the specified time (timestamp).</p>",
"ListNotebookInstanceLifecycleConfigsInput$CreationTimeAfter": "<p>A filter that returns only lifecycle configurations that were created after the specified time (timestamp).</p>",
"ListNotebookInstancesInput$CreationTimeBefore": "<p>A filter that returns only notebook instances that were created before the specified time (timestamp). </p>",
"ListNotebookInstancesInput$CreationTimeAfter": "<p>A filter that returns only notebook instances that were created after the specified time (timestamp).</p>",
"NotebookInstanceLifecycleConfigSummary$CreationTime": "<p>A timestamp that tells when the lifecycle configuration was created.</p>",
"NotebookInstanceSummary$CreationTime": "<p>A timestamp that shows when the notebook instance was created.</p>"
}
},
@ -188,6 +207,11 @@
"refs": {
}
},
"DeleteNotebookInstanceLifecycleConfigInput": {
"base": null,
"refs": {
}
},
"DeleteTagsInput": {
"base": null,
"refs": {
@ -233,6 +257,16 @@
"refs": {
}
},
"DescribeNotebookInstanceLifecycleConfigInput": {
"base": null,
"refs": {
}
},
"DescribeNotebookInstanceLifecycleConfigOutput": {
"base": null,
"refs": {
}
},
"DescribeNotebookInstanceOutput": {
"base": null,
"refs": {
@ -260,6 +294,13 @@
"UpdateEndpointWeightsAndCapacitiesInput$DesiredWeightsAndCapacities": "<p>An object that provides new capacity and weight values for a variant.</p>"
}
},
"DirectInternetAccess": {
"base": null,
"refs": {
"CreateNotebookInstanceInput$DirectInternetAccess": "<p>Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to <code>Disabled</code> this notebook instance will be able to access resources only in your VPC, and will not be able to connect to Amazon SageMaker training and endpoint services unless your configure a NAT Gateway in your VPC.</p> <p>For more information, see <a>appendix-notebook-and-internet-access</a>. You can set the value of this parameter to <code>Disabled</code> only if you set a value for the <code>SubnetId</code> parameter.</p>",
"DescribeNotebookInstanceOutput$DirectInternetAccess": "<p>Describes whether the notebook instance has internet access.</p> <p>For more information, see <a>appendix-notebook-and-internet-access</a>.</p>"
}
},
"EndpointArn": {
"base": null,
"refs": {
@ -381,7 +422,7 @@
"base": null,
"refs": {
"DescribeEndpointOutput$FailureReason": "<p>If the status of the endpoint is <code>Failed</code>, the reason why it failed. </p>",
"DescribeNotebookInstanceOutput$FailureReason": "<p>If staus is failed, the reason it failed.</p>",
"DescribeNotebookInstanceOutput$FailureReason": "<p>If status is failed, the reason it failed.</p>",
"DescribeTrainingJobResponse$FailureReason": "<p>If the training job failed, the reason it failed. </p>",
"ResourceInUse$Message": null,
"ResourceLimitExceeded$Message": null,
@ -420,17 +461,24 @@
"KmsKeyId": {
"base": null,
"refs": {
"CreateEndpointConfigInput$KmsKeyId": "<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 that hosts the endpoint.</p>",
"CreateNotebookInstanceInput$KmsKeyId": "<p> If you provide a AWS KMS key ID, Amazon SageMaker uses it to encrypt data at rest on the ML storage volume that is attached to your notebook instance. </p>",
"DescribeEndpointConfigOutput$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 the configuration of the output S3 bucket requires server-side encryption for objects, and you don't provide the KMS key ID, Amazon SageMaker uses the default service key. 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>"
"OutputDataConfig$KmsKeyId": "<p>The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. </p> <note> <p>If the configuration of the output S3 bucket requires server-side encryption for objects, and you don't provide the KMS key ID, Amazon SageMaker uses the default service key. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html\">KMS-Managed Encryption Keys</a> in Amazon Simple Storage Service developer guide.</p> </note> <note> <p> The KMS key policy must grant permission to the IAM role you specify in your <code>CreateTrainingJob</code> request. <a href=\"http://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html\">Using Key Policies in AWS KMS</a> in the AWS Key Management Service Developer Guide. </p> </note>",
"ResourceConfig$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>"
}
},
"LastModifiedTime": {
"base": null,
"refs": {
"DescribeNotebookInstanceLifecycleConfigOutput$LastModifiedTime": "<p>A timestamp that tells when the lifecycle configuration was last modified.</p>",
"DescribeNotebookInstanceOutput$LastModifiedTime": "<p>A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified. </p>",
"ListNotebookInstanceLifecycleConfigsInput$LastModifiedTimeBefore": "<p>A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).</p>",
"ListNotebookInstanceLifecycleConfigsInput$LastModifiedTimeAfter": "<p>A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).</p>",
"ListNotebookInstancesInput$LastModifiedTimeBefore": "<p>A filter that returns only notebook instances that were modified before the specified time (timestamp).</p>",
"ListNotebookInstancesInput$LastModifiedTimeAfter": "<p>A filter that returns only notebook instances that were modified after the specified time (timestamp).</p>",
"NotebookInstanceLifecycleConfigSummary$LastModifiedTime": "<p>A timestamp that tells when the lifecycle configuration was last modified.</p>",
"NotebookInstanceSummary$LastModifiedTime": "<p>A timestamp that shows when the notebook instance was last modified.</p>"
}
},
@ -464,6 +512,16 @@
"refs": {
}
},
"ListNotebookInstanceLifecycleConfigsInput": {
"base": null,
"refs": {
}
},
"ListNotebookInstanceLifecycleConfigsOutput": {
"base": null,
"refs": {
}
},
"ListNotebookInstancesInput": {
"base": null,
"refs": {
@ -506,6 +564,7 @@
"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>",
"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$MaxResults": "<p>The maximum number of notebook instances to return.</p>",
"ListTrainingJobsRequest$MaxResults": "<p>The maximum number of training jobs to return in the response.</p>"
}
@ -580,6 +639,8 @@
"NextToken": {
"base": null,
"refs": {
"ListNotebookInstanceLifecycleConfigsInput$NextToken": "<p>If the result of a <code>ListNotebookInstanceLifecycleConfigs</code> request was truncated, the response includes a <code>NextToken</code>. To get the next set of lifecycle configurations, use the token in the next request.</p>",
"ListNotebookInstanceLifecycleConfigsOutput$NextToken": "<p>If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request. </p>",
"ListNotebookInstancesInput$NextToken": "<p> If the previous call to the <code>ListNotebookInstances</code> is truncated, the response includes a <code>NextToken</code>. You can use this token in your subsequent <code>ListNotebookInstances</code> request to fetch the next set of notebook instances. </p> <note> <p> You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request. </p> </note>",
"ListNotebookInstancesOutput$NextToken": "<p>If the response to the previous <code>ListNotebookInstances</code> request was truncated, Amazon SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.</p>",
"ListTagsInput$NextToken": "<p> If the response to the previous <code>ListTags</code> request is truncated, Amazon SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request. </p>",
@ -596,6 +657,82 @@
"NotebookInstanceSummary$NotebookInstanceArn": "<p>The Amazon Resource Name (ARN) of the notebook instance.</p>"
}
},
"NotebookInstanceLifecycleConfigArn": {
"base": null,
"refs": {
"CreateNotebookInstanceLifecycleConfigOutput$NotebookInstanceLifecycleConfigArn": "<p>The Amazon Resource Name (ARN) of the lifecycle configuration.</p>",
"DescribeNotebookInstanceLifecycleConfigOutput$NotebookInstanceLifecycleConfigArn": "<p>The Amazon Resource Name (ARN) of the lifecycle configuration.</p>",
"NotebookInstanceLifecycleConfigSummary$NotebookInstanceLifecycleConfigArn": "<p>The Amazon Resource Name (ARN) of the lifecycle configuration.</p>"
}
},
"NotebookInstanceLifecycleConfigContent": {
"base": null,
"refs": {
"NotebookInstanceLifecycleHook$Content": "<p>A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.</p>"
}
},
"NotebookInstanceLifecycleConfigList": {
"base": null,
"refs": {
"CreateNotebookInstanceLifecycleConfigInput$OnCreate": "<p>A shell script that runs only once, when you create a notebook instance.</p>",
"CreateNotebookInstanceLifecycleConfigInput$OnStart": "<p>A shell script that runs every time you start a notebook instance, including when you create the notebook instance.</p>",
"DescribeNotebookInstanceLifecycleConfigOutput$OnCreate": "<p>The shell script that runs only once, when you create a notebook instance.</p>",
"DescribeNotebookInstanceLifecycleConfigOutput$OnStart": "<p>The shell script that runs every time you start a notebook instance, including when you create the notebook instance.</p>",
"UpdateNotebookInstanceLifecycleConfigInput$OnCreate": "<p>The shell script that runs only once, when you create a notebook instance</p>",
"UpdateNotebookInstanceLifecycleConfigInput$OnStart": "<p>The shell script that runs every time you start a notebook instance, including when you create the notebook instance.</p>"
}
},
"NotebookInstanceLifecycleConfigName": {
"base": null,
"refs": {
"CreateNotebookInstanceInput$LifecycleConfigName": "<p>The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see <a>notebook-lifecycle-config</a>.</p>",
"CreateNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName": "<p>The name of the lifecycle configuration.</p>",
"DeleteNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName": "<p>The name of the lifecycle configuration to delete.</p>",
"DescribeNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName": "<p>The name of the lifecycle configuration to describe.</p>",
"DescribeNotebookInstanceLifecycleConfigOutput$NotebookInstanceLifecycleConfigName": "<p>The name of the lifecycle configuration.</p>",
"DescribeNotebookInstanceOutput$NotebookInstanceLifecycleConfigName": "<p>Returns the name of a notebook instance lifecycle configuration.</p> <p>For information about notebook instance lifestyle configurations, see <a>notebook-lifecycle-config</a>.</p>",
"ListNotebookInstancesInput$NotebookInstanceLifecycleConfigNameContains": "<p>A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.</p>",
"NotebookInstanceLifecycleConfigSummary$NotebookInstanceLifecycleConfigName": "<p>The name of the lifecycle configuration.</p>",
"NotebookInstanceSummary$NotebookInstanceLifecycleConfigName": "<p>The name of a notebook instance lifecycle configuration associated with this notebook instance.</p> <p>For information about notebook instance lifestyle configurations, see <a>notebook-lifecycle-config</a>.</p>",
"UpdateNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName": "<p>The name of the lifecycle configuration.</p>"
}
},
"NotebookInstanceLifecycleConfigNameContains": {
"base": null,
"refs": {
"ListNotebookInstanceLifecycleConfigsInput$NameContains": "<p>A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.</p>"
}
},
"NotebookInstanceLifecycleConfigSortKey": {
"base": null,
"refs": {
"ListNotebookInstanceLifecycleConfigsInput$SortBy": "<p>Sorts the list of results. The default is <code>CreationTime</code>.</p>"
}
},
"NotebookInstanceLifecycleConfigSortOrder": {
"base": null,
"refs": {
"ListNotebookInstanceLifecycleConfigsInput$SortOrder": "<p>The sort order for results.</p>"
}
},
"NotebookInstanceLifecycleConfigSummary": {
"base": "<p>Provides a summary of a notebook instance lifecycle configuration.</p>",
"refs": {
"NotebookInstanceLifecycleConfigSummaryList$member": null
}
},
"NotebookInstanceLifecycleConfigSummaryList": {
"base": null,
"refs": {
"ListNotebookInstanceLifecycleConfigsOutput$NotebookInstanceLifecycleConfigs": "<p>An array of <code>NotebookInstanceLifecycleConfiguration</code> objects, each listing a lifecycle configuration.</p>"
}
},
"NotebookInstanceLifecycleHook": {
"base": "<p>Contains the notebook instance lifecycle configuration script.</p> <p>This script runs in the path <code>/sbin:bin:/usr/sbin:/usr/bin</code>.</p> <p>For information about notebook instance lifestyle configurations, see <a>notebook-lifecycle-config</a>.</p>",
"refs": {
"NotebookInstanceLifecycleConfigList$member": null
}
},
"NotebookInstanceName": {
"base": null,
"refs": {
@ -613,7 +750,7 @@
"NotebookInstanceNameContains": {
"base": null,
"refs": {
"ListNotebookInstancesInput$NameContains": "<p>A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string. </p>"
"ListNotebookInstancesInput$NameContains": "<p>A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.</p>"
}
},
"NotebookInstanceSortKey": {
@ -714,7 +851,7 @@
}
},
"ProductionVariantSummary": {
"base": "<p>Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the <code>UpdateWeightAndCapacities</code> API and the endpoint status is <code>Updating</code>, you get different desired and current values. </p>",
"base": "<p>Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the <code>UpdateEndpointWeightsAndCapacities</code> API and the endpoint status is <code>Updating</code>, you get different desired and current values. </p>",
"refs": {
"ProductionVariantSummaryList$member": null
}
@ -734,7 +871,7 @@
"ResourceArn": {
"base": null,
"refs": {
"AddTagsInput$ResourceArn": "<p>The Amazon Resource Name (ARN) of the resource that you want to tag. </p>",
"AddTagsInput$ResourceArn": "<p>The Amazon Resource Name (ARN) of the resource that you want to tag.</p>",
"DeleteTagsInput$ResourceArn": "<p>The Amazon Resource Name (ARN) of the resource whose tags you want to delete.</p>",
"ListTagsInput$ResourceArn": "<p>The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.</p>"
}
@ -909,7 +1046,7 @@
"DesiredWeightAndCapacity$DesiredInstanceCount": "<p>The variant's capacity.</p>",
"ProductionVariant$InitialInstanceCount": "<p>Number of instances to launch initially.</p>",
"ProductionVariantSummary$CurrentInstanceCount": "<p>The number of instances associated with the variant.</p>",
"ProductionVariantSummary$DesiredInstanceCount": "<p>The number of instances requested in the <code>UpdateWeightAndCapacities</code> request. </p>"
"ProductionVariantSummary$DesiredInstanceCount": "<p>The number of instances requested in the <code>UpdateEndpointWeightsAndCapacities</code> request. </p>"
}
},
"Timestamp": {
@ -947,7 +1084,7 @@
"TrainingInputMode": {
"base": null,
"refs": {
"AlgorithmSpecification$TrainingInputMode": "<p>The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. If an algorithm supports the <code>File</code> input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the <code>Pipe</code> input mode, Amazon SageMaker streams data directly from S3 to the container. </p> <p> In File mode, make sure you provision ML storage volume with sufficient capacity to accomodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any. </p> <p> For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training. </p>"
"AlgorithmSpecification$TrainingInputMode": "<p>The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see <a href=\"http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\">Algorithms</a>. If an algorithm supports the <code>File</code> input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the <code>Pipe</code> input mode, Amazon SageMaker streams data directly from S3 to the container. </p> <p> In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any. </p> <p> For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training. </p>"
}
},
"TrainingInstanceCount": {
@ -1025,6 +1162,16 @@
"refs": {
}
},
"UpdateNotebookInstanceLifecycleConfigInput": {
"base": null,
"refs": {
}
},
"UpdateNotebookInstanceLifecycleConfigOutput": {
"base": null,
"refs": {
}
},
"UpdateNotebookInstanceOutput": {
"base": null,
"refs": {
@ -1050,7 +1197,7 @@
"DesiredWeightAndCapacity$DesiredWeight": "<p>The variant's weight.</p>",
"ProductionVariant$InitialVariantWeight": "<p>Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the <code>VariantWeight</code> to the sum of all <code>VariantWeight</code> values across all ProductionVariants. If unspecified, it defaults to 1.0. </p>",
"ProductionVariantSummary$CurrentWeight": "<p>The weight associated with the variant.</p>",
"ProductionVariantSummary$DesiredWeight": "<p>The requested weight, as specified in the <code>UpdateWeightAndCapacities</code> request. </p>"
"ProductionVariantSummary$DesiredWeight": "<p>The requested weight, as specified in the <code>UpdateEndpointWeightsAndCapacities</code> request. </p>"
}
},
"VolumeSizeInGB": {

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

@ -15,6 +15,11 @@
"output_token": "NextToken",
"limit_key": "MaxResults"
},
"ListNotebookInstanceLifecycleConfigs": {
"input_token": "NextToken",
"output_token": "NextToken",
"limit_key": "MaxResults"
},
"ListNotebookInstances": {
"input_token": "NextToken",
"output_token": "NextToken",