From c84177fd09fe7f2e31dea60aab8dca09cb919e2c Mon Sep 17 00:00:00 2001 From: awstools Date: Mon, 12 Jun 2023 18:16:04 +0000 Subject: [PATCH] feat(client-sagemaker): Sagemaker Neo now supports compilation for inferentia2 (ML_INF2) and Trainium1 (ML_TRN1) as available targets. With these devices, you can run your workloads at highest performance with lowest cost. inferentia2 (ML_INF2) is available in CMH and Trainium1 (ML_TRN1) is available in IAD currently --- .../src/commands/AddTagsCommand.ts | 3 +- .../commands/CreateCompilationJobCommand.ts | 2 +- .../src/commands/CreateEndpointCommand.ts | 16 +- .../commands/CreateEndpointConfigCommand.ts | 12 +- ...eatePresignedNotebookInstanceUrlCommand.ts | 5 +- .../commands/DescribeCompilationJobCommand.ts | 2 +- .../commands/ListCompilationJobsCommand.ts | 2 +- ...ngJobsForHyperParameterTuningJobCommand.ts | 4 +- .../src/commands/UpdateEndpointCommand.ts | 2 +- .../client-sagemaker/src/models/models_0.ts | 101 +++++----- .../client-sagemaker/src/models/models_1.ts | 137 ++++++------- .../client-sagemaker/src/models/models_2.ts | 130 ++++++------ .../client-sagemaker/src/models/models_3.ts | 44 ++-- .../client-sagemaker/src/models/models_4.ts | 30 +-- codegen/sdk-codegen/aws-models/sagemaker.json | 188 ++++++++++-------- 15 files changed, 357 insertions(+), 321 deletions(-) diff --git a/clients/client-sagemaker/src/commands/AddTagsCommand.ts b/clients/client-sagemaker/src/commands/AddTagsCommand.ts index fbe25edd07ac..f2cfc0ad1a3a 100644 --- a/clients/client-sagemaker/src/commands/AddTagsCommand.ts +++ b/clients/client-sagemaker/src/commands/AddTagsCommand.ts @@ -59,7 +59,8 @@ export interface AddTagsCommandOutput extends AddTagsOutput, __MetadataBearer {} * this API. To make sure that the tags associated with a Domain or User Profile are * also added to all Apps that the Domain or User Profile launches, add the tags when * you first create the Domain or User Profile by specifying them in the - * Tags parameter of CreateDomain or CreateUserProfile.

+ * Tags parameter of CreateDomain + * or CreateUserProfile.

* * @example * Use a bare-bones client and the command you need to make an API call. diff --git a/clients/client-sagemaker/src/commands/CreateCompilationJobCommand.ts b/clients/client-sagemaker/src/commands/CreateCompilationJobCommand.ts index 45d11aaab7cc..04e712ec4d75 100644 --- a/clients/client-sagemaker/src/commands/CreateCompilationJobCommand.ts +++ b/clients/client-sagemaker/src/commands/CreateCompilationJobCommand.ts @@ -86,7 +86,7 @@ export interface CreateCompilationJobCommandOutput extends CreateCompilationJobR * }, * OutputConfig: { // OutputConfig * S3OutputLocation: "STRING_VALUE", // required - * TargetDevice: "lambda" || "ml_m4" || "ml_m5" || "ml_c4" || "ml_c5" || "ml_p2" || "ml_p3" || "ml_g4dn" || "ml_inf1" || "ml_eia2" || "jetson_tx1" || "jetson_tx2" || "jetson_nano" || "jetson_xavier" || "rasp3b" || "imx8qm" || "deeplens" || "rk3399" || "rk3288" || "aisage" || "sbe_c" || "qcs605" || "qcs603" || "sitara_am57x" || "amba_cv2" || "amba_cv22" || "amba_cv25" || "x86_win32" || "x86_win64" || "coreml" || "jacinto_tda4vm" || "imx8mplus", + * TargetDevice: "lambda" || "ml_m4" || "ml_m5" || "ml_c4" || "ml_c5" || "ml_p2" || "ml_p3" || "ml_g4dn" || "ml_inf1" || "ml_inf2" || "ml_trn1" || "ml_eia2" || "jetson_tx1" || "jetson_tx2" || "jetson_nano" || "jetson_xavier" || "rasp3b" || "imx8qm" || "deeplens" || "rk3399" || "rk3288" || "aisage" || "sbe_c" || "qcs605" || "qcs603" || "sitara_am57x" || "amba_cv2" || "amba_cv22" || "amba_cv25" || "x86_win32" || "x86_win64" || "coreml" || "jacinto_tda4vm" || "imx8mplus", * TargetPlatform: { // TargetPlatform * Os: "ANDROID" || "LINUX", // required * Arch: "X86_64" || "X86" || "ARM64" || "ARM_EABI" || "ARM_EABIHF", // required diff --git a/clients/client-sagemaker/src/commands/CreateEndpointCommand.ts b/clients/client-sagemaker/src/commands/CreateEndpointCommand.ts index 25315d3f2f39..71f901a553de 100644 --- a/clients/client-sagemaker/src/commands/CreateEndpointCommand.ts +++ b/clients/client-sagemaker/src/commands/CreateEndpointCommand.ts @@ -54,22 +54,22 @@ export interface CreateEndpointCommandOutput extends CreateEndpointOutput, __Met *

When it receives the request, SageMaker creates the endpoint, launches the resources (ML * compute instances), and deploys the model(s) on them.

* - *

When you call CreateEndpoint, a load call is made to DynamoDB to - * verify that your endpoint configuration exists. When you read data from a DynamoDB - * table supporting + *

When you call CreateEndpoint, a load call is made to DynamoDB to verify that your + * endpoint configuration exists. When you read data from a DynamoDB table supporting + * * Eventually Consistent Reads * , the response might not * reflect the results of a recently completed write operation. The response might * include some stale data. If the dependent entities are not yet in DynamoDB, this * causes a validation error. If you repeat your read request after a short time, the * response should return the latest data. So retry logic is recommended to handle - * these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

+ * these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB + * eventually consistent read.

* *

When SageMaker receives the request, it sets the endpoint status to * Creating. After it creates the endpoint, it sets the status to * InService. SageMaker can then process incoming requests for inferences. To - * check the status of an endpoint, use the DescribeEndpoint - * API.

+ * check the status of an endpoint, use the DescribeEndpoint API.

*

If any of the models hosted at this endpoint get model data from an Amazon S3 location, * SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the * S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services @@ -82,8 +82,8 @@ export interface CreateEndpointCommandOutput extends CreateEndpointOutput, __Met * *

To add the IAM role policies for using this API operation, go to the IAM console, and choose * Roles in the left navigation pane. Search the IAM role that you want to grant - * access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to - * the role.

+ * access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the + * role.

* - *

Your input bucket must be located in same Amazon Web Services region as your training job.

+ *

Your input bucket must be located in same Amazon Web Services region as your + * training job.

*/ S3Uri: string | undefined; @@ -1263,12 +1263,11 @@ export interface Channel { *

(Optional) The input mode to use for the data channel in a training job. If you don't * set a value for InputMode, SageMaker uses the value set for * TrainingInputMode. Use this parameter to override the - * TrainingInputMode setting in a AlgorithmSpecification - * request when you have a channel that needs a different input mode from the training - * job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML - * storage volume, and mount the directory to a Docker volume, use File input - * mode. To stream data directly from Amazon S3 to the container, choose Pipe input - * mode.

+ * TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a + * different input mode from the training job's general setting. To download the data from + * Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a + * Docker volume, use File input mode. To stream data directly from Amazon S3 to + * the container, choose Pipe input mode.

*

To use a model for incremental training, choose File input model.

*/ InputMode?: TrainingInputMode | string; @@ -2044,8 +2043,8 @@ export interface TransformJobDefinition { TransformInput: TransformInput | undefined; /** - *

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results - * from the transform job.

+ *

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the + * transform job.

*/ TransformOutput: TransformOutput | undefined; @@ -4160,7 +4159,8 @@ export interface AsyncInferenceNotificationConfig { /** *

The Amazon SNS topics where you want the inference response to be included.

* - *

The inference response is included only if the response size is less than or equal to 128 KB.

+ *

The inference response is included only if the response size is less than or equal + * to 128 KB.

* */ IncludeInferenceResponseIn?: (AsyncNotificationTopicTypes | string)[]; @@ -5028,13 +5028,12 @@ export interface AutoMLJobChannel { * content types for different problems:

* */ @@ -5487,9 +5486,9 @@ export type AutoMLSortOrder = (typeof AutoMLSortOrder)[keyof typeof AutoMLSortOr /** * @public - *

The name and an example value of the hyperparameter that - * you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible - * for Autotune, an optimal hyperparameter range is selected for you.

+ *

The name and an example value of the hyperparameter that you want to use in Autotune. + * If Automatic model tuning (AMT) determines that your hyperparameter is eligible for + * Autotune, an optimal hyperparameter range is selected for you.

*/ export interface AutoParameter { /** @@ -5532,24 +5531,26 @@ export type AutotuneMode = (typeof AutotuneMode)[keyof typeof AutotuneMode]; /** * @public - *

A flag to indicate if you want to use Autotune to automatically find optimal values for - * the following fields:

+ *

A flag to indicate if you want to use Autotune to automatically find optimal values + * for the following fields:

* */ @@ -6415,11 +6417,11 @@ export interface ChannelSpecification { /** *

The allowed input mode, either FILE or PIPE.

- *

In FILE mode, Amazon SageMaker copies the data from the input source onto the local - * Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. - * This is the most commonly used input mode.

- *

In PIPE mode, Amazon SageMaker streams input data from the source directly to your - * algorithm without using the EBS volume.

+ *

In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon + * Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This + * is the most commonly used input mode.

+ *

In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm + * without using the EBS volume.

*/ SupportedInputModes: (TrainingInputMode | string)[] | undefined; } @@ -7073,10 +7075,12 @@ export const TargetDevice = { ML_EIA2: "ml_eia2", ML_G4DN: "ml_g4dn", ML_INF1: "ml_inf1", + ML_INF2: "ml_inf2", ML_M4: "ml_m4", ML_M5: "ml_m5", ML_P2: "ml_p2", ML_P3: "ml_p3", + ML_TRN1: "ml_trn1", QCS603: "qcs603", QCS605: "qcs605", RASP3B: "rasp3b", @@ -7407,8 +7411,7 @@ export interface ContainerDefinition { * SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both * registry/repository[:tag] and registry/repository[@digest] * image path formats. For more information, see Using Your Own Algorithms with - * Amazon SageMaker. - *

+ * Amazon SageMaker.

* *

The model artifacts in an Amazon S3 bucket and the Docker image for inference container * in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are @@ -7421,8 +7424,7 @@ export interface ContainerDefinition { *

Specifies whether the model container is in Amazon ECR or a private Docker registry * accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a * private Docker registry, see Use a - * Private Docker Registry for Real-Time Inference Containers. - *

+ * Private Docker Registry for Real-Time Inference Containers.

* *

The model artifacts in an Amazon S3 bucket and the Docker image for inference container * in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are @@ -10353,7 +10355,8 @@ export interface CreateDomainRequest { * communication in VPCOnly mode. Required when * CreateDomain.AppNetworkAccessType is VPCOnly and * DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is - * provided. If setting up the domain for use with RStudio, this value must be set to Service.

+ * provided. If setting up the domain for use with RStudio, this value must be set to + * Service.

*/ AppSecurityGroupManagement?: AppSecurityGroupManagement | string; diff --git a/clients/client-sagemaker/src/models/models_1.ts b/clients/client-sagemaker/src/models/models_1.ts index 1f204ae1df88..eadf1300cc36 100644 --- a/clients/client-sagemaker/src/models/models_1.ts +++ b/clients/client-sagemaker/src/models/models_1.ts @@ -203,7 +203,7 @@ export interface CreateEndpointInput { /** *

The name of the endpoint.The name must be unique within an Amazon Web Services * Region in your Amazon Web Services account. The name is case-insensitive in - * CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.

+ * CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.

*/ EndpointName: string | undefined; @@ -411,12 +411,10 @@ export interface ProductionVariantServerlessConfig { /** * @public - *

- * Identifies a model that you want to host and the resources chosen to deploy for + *

Identifies a model that you want to host and the resources chosen to deploy for * hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic * among the models by specifying variant weights. For more information on production - * variants, check - * Production variants. + * variants, check Production variants. *

*/ export interface ProductionVariant { @@ -453,8 +451,8 @@ export interface ProductionVariant { /** *

The size of the Elastic Inference (EI) instance to use for the production variant. EI * instances provide on-demand GPU computing for inference. For more information, see - * Using Elastic - * Inference in Amazon SageMaker.

+ * Using Elastic + * Inference in Amazon SageMaker.

*/ AcceleratorType?: ProductionVariantAcceleratorType | string; @@ -490,13 +488,11 @@ export interface ProductionVariant { ContainerStartupHealthCheckTimeoutInSeconds?: number; /** - *

- * You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) + *

You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) * access for a production variant behind an endpoint. By default, SSM access is disabled * for all production variants behind an endpoint. You can turn on or turn off SSM access * for a production variant behind an existing endpoint by creating a new endpoint - * configuration and calling UpdateEndpoint. - *

+ * configuration and calling UpdateEndpoint.

*/ EnableSSMAccess?: boolean; } @@ -2005,25 +2001,26 @@ export interface IntegerParameterRange { */ export interface ParameterRanges { /** - *

The array of IntegerParameterRange objects that specify ranges of - * integer hyperparameters that a hyperparameter tuning job searches.

+ *

The array of IntegerParameterRange objects that specify ranges of integer + * hyperparameters that a hyperparameter tuning job searches.

*/ IntegerParameterRanges?: IntegerParameterRange[]; /** - *

The array of ContinuousParameterRange objects that specify ranges of - * continuous hyperparameters that a hyperparameter tuning job searches.

+ *

The array of ContinuousParameterRange objects that specify ranges of continuous + * hyperparameters that a hyperparameter tuning job searches.

*/ ContinuousParameterRanges?: ContinuousParameterRange[]; /** - *

The array of CategoricalParameterRange objects that specify ranges - * of categorical hyperparameters that a hyperparameter tuning job searches.

+ *

The array of CategoricalParameterRange objects that specify ranges of categorical + * hyperparameters that a hyperparameter tuning job searches.

*/ CategoricalParameterRanges?: CategoricalParameterRange[]; /** - *

A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.

+ *

A list containing hyperparameter names and example values to be used by Autotune to + * determine optimal ranges for your tuning job.

*/ AutoParameters?: AutoParameter[]; } @@ -2209,22 +2206,23 @@ export interface HyperParameterTuningJobConfig { StrategyConfig?: HyperParameterTuningJobStrategyConfig; /** - *

The HyperParameterTuningJobObjective specifies the objective metric - * used to evaluate the performance of training jobs launched by this tuning job.

+ *

The HyperParameterTuningJobObjective specifies the objective metric used to + * evaluate the performance of training jobs launched by this tuning job.

*/ HyperParameterTuningJobObjective?: HyperParameterTuningJobObjective; /** - *

The ResourceLimits object that specifies the maximum number of - * training and parallel training jobs that can be used for this hyperparameter tuning - * job.

+ *

The ResourceLimits + * object that specifies the maximum number of training and parallel training jobs that can + * be used for this hyperparameter tuning job.

*/ ResourceLimits: ResourceLimits | undefined; /** - *

The ParameterRanges object that specifies the ranges of - * hyperparameters that this tuning job searches over to find the optimal configuration for - * the highest model performance against your chosen objective metric.

+ *

The ParameterRanges + * object that specifies the ranges of hyperparameters that this tuning job searches over + * to find the optimal configuration for the highest model performance against your chosen + * objective metric.

*/ ParameterRanges?: ParameterRanges; @@ -2277,7 +2275,7 @@ export interface HyperParameterAlgorithmSpecification { * Provided by Amazon SageMaker: Common Parameters. SageMaker supports both * registry/repository[:tag] and registry/repository[@digest] * image path formats. For more information, see Using Your Own Algorithms with - * Amazon SageMaker.

+ * Amazon SageMaker.

*/ TrainingImage?: string; @@ -2366,8 +2364,8 @@ export interface HyperParameterTuningInstanceConfig { /** *

The number of instances of the type specified by InstanceType. Choose an - * instance count larger than 1 for distributed training algorithms. See SageMaker distributed training - * jobs for more information.

+ * instance count larger than 1 for distributed training algorithms. See Step 2: + * Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.

*/ InstanceCount: number | undefined; @@ -2397,7 +2395,7 @@ export interface HyperParameterTuningInstanceConfig { */ export interface HyperParameterTuningResourceConfig { /** - *

The instance type used to run hyperparameter optimization tuning jobs. See descriptions of + *

The instance type used to run hyperparameter optimization tuning jobs. See descriptions of * instance types for more information.

*/ InstanceType?: TrainingInstanceType | string; @@ -2545,18 +2543,18 @@ export interface HyperParameterTrainingJobDefinition { RoleArn: string | undefined; /** - *

An array of Channel objects that specify + *

An array of Channel objects that + * specify * the * input for the training jobs that the tuning job launches.

*/ InputDataConfig?: Channel[]; /** - *

The VpcConfig 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 - * Protect - * Training Jobs by Using an Amazon Virtual Private Cloud.

+ *

The VpcConfig 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 Protect Training Jobs + * by Using an Amazon Virtual Private Cloud.

*/ VpcConfig?: VpcConfig; @@ -2643,8 +2641,7 @@ export interface HyperParameterTrainingJobDefinition { /** *

An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See - * Define metrics - * and variables for more information.

+ * Define metrics and variables for more information.

* *

The maximum number of items specified for Map Entries refers to the * maximum number of environment variables for each TrainingJobDefinition @@ -2759,8 +2756,8 @@ export interface CreateHyperParameterTuningJobRequest { HyperParameterTuningJobName: string | undefined; /** - *

The HyperParameterTuningJobConfig object that describes the tuning - * job, including the search strategy, the objective metric used to evaluate training jobs, + *

The HyperParameterTuningJobConfig object that describes the tuning job, + * including the search strategy, the objective metric used to evaluate training jobs, * ranges of parameters to search, and resource limits for the tuning job. For more * information, see How * Hyperparameter Tuning Works.

@@ -2768,16 +2765,16 @@ export interface CreateHyperParameterTuningJobRequest { HyperParameterTuningJobConfig: HyperParameterTuningJobConfig | undefined; /** - *

The HyperParameterTrainingJobDefinition object that describes the - * training jobs that this tuning job launches, including static hyperparameters, input - * data configuration, output data configuration, resource configuration, and stopping + *

The HyperParameterTrainingJobDefinition 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.

*/ TrainingJobDefinition?: HyperParameterTrainingJobDefinition; /** - *

A list of the HyperParameterTrainingJobDefinition objects launched - * for this tuning job.

+ *

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning + * job.

*/ TrainingJobDefinitions?: HyperParameterTrainingJobDefinition[]; @@ -2810,24 +2807,26 @@ export interface CreateHyperParameterTuningJobRequest { Tags?: Tag[]; /** - *

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters - * for the following fields:

+ *

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal + * parameters for the following fields:

*
    *
  • *

    - * ParameterRanges: The names and ranges of parameters that a hyperparameter - * tuning job can optimize.

    + * ParameterRanges: The names and ranges of parameters that a + * hyperparameter tuning job can optimize.

    *
    • *
  • *

    - * ResourceLimits: The maximum resources that can be used for a training job. - * These resources include the maximum number of training jobs, the maximum runtime of a - * tuning job, and the maximum number of training jobs to run at the same time.

    + * ResourceLimits: The maximum resources that can be used for a + * training job. These resources include the maximum number of training jobs, the + * maximum runtime of a tuning job, and the maximum number of training jobs to run + * at the same time.

    *
    • *
  • *

    - * TrainingJobEarlyStoppingType: A flag that specifies whether or not to use - * early stopping for training jobs launched by a hyperparameter tuning job.

    + * TrainingJobEarlyStoppingType: A flag that specifies whether or not + * to use early stopping for training jobs launched by a hyperparameter tuning + * job.

    *
    • *
  • *

    @@ -2835,13 +2834,14 @@ export interface CreateHyperParameterTuningJobRequest { *

    • *
  • *

    - * Strategy: Specifies how hyperparameter tuning chooses the combinations of - * hyperparameter values to use for the training jobs that it launches.

    + * Strategy: Specifies how hyperparameter tuning chooses the + * combinations of hyperparameter values to use for the training jobs that it + * launches.

    *
    • *
  • *

    - * ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model - * convergence.

    + * ConvergenceDetected: A flag to indicate that Automatic model tuning + * (AMT) has detected model convergence.

    *
    • *
*/ @@ -6141,8 +6141,8 @@ export interface CreateModelInput { Tags?: Tag[]; /** - *

A VpcConfig 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. + *

A VpcConfig 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. * VpcConfig is used in hosting services and in batch transform. For more * information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch * Transform Jobs by Using an Amazon Virtual Private Cloud.

@@ -6770,8 +6770,8 @@ export interface SourceAlgorithm { * This path must point to a single gzip compressed tar archive * (.tar.gz suffix).

* - *

The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the - * algorithm.

+ *

The model artifacts must be in an S3 bucket that is in the same Amazon Web Services + * region as the algorithm.

* */ ModelDataUrl?: string; @@ -8851,8 +8851,8 @@ export interface CreateTrainingJobRequest { *

The registry path of the Docker image that contains the training algorithm and * algorithm-specific metadata, including the input mode. For more information about * algorithms provided by SageMaker, see Algorithms. For information about - * providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker. - *

+ * providing your own algorithms, see Using Your Own Algorithms with + * Amazon SageMaker.

*/ AlgorithmSpecification: AlgorithmSpecification | undefined; @@ -8884,7 +8884,8 @@ export interface CreateTrainingJobRequest { * data files from an S3 bucket to a local directory in the Docker container, or makes it * available as input streams. For example, if you specify an EFS location, input data * files are available as input streams. They do not need to be downloaded.

- *

Your input must be in the same Amazon Web Services region as your training job.

+ *

Your input must be in the same Amazon Web Services region as your training + * job.

*/ InputDataConfig?: Channel[]; @@ -8906,9 +8907,9 @@ export interface CreateTrainingJobRequest { ResourceConfig: ResourceConfig | undefined; /** - *

A VpcConfig 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 Protect Training Jobs by Using an Amazon + *

A VpcConfig 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 Protect Training Jobs by Using an Amazon * Virtual Private Cloud.

*/ VpcConfig?: VpcConfig; diff --git a/clients/client-sagemaker/src/models/models_2.ts b/clients/client-sagemaker/src/models/models_2.ts index 693381638a1c..107c3fd3b3ed 100644 --- a/clients/client-sagemaker/src/models/models_2.ts +++ b/clients/client-sagemaker/src/models/models_2.ts @@ -2131,9 +2131,10 @@ export interface ProductionVariantStatus { /** * @public *

The production variant summary for a deployment when an endpoint is creating or - * updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus - * , weight and capacity for a production variant associated with an endpoint. - *

+ * updating with the CreateEndpoint + * or UpdateEndpoint + * operations. Describes the VariantStatus , weight and capacity for a + * production variant associated with an endpoint.

*/ export interface PendingProductionVariantSummary { /** @@ -2212,8 +2213,8 @@ export interface PendingDeploymentSummary { EndpointConfigName: string | undefined; /** - *

An array of PendingProductionVariantSummary objects, one for each - * model hosted behind this endpoint for the in-progress deployment.

+ *

An array of PendingProductionVariantSummary objects, one for each model hosted behind + * this endpoint for the in-progress deployment.

*/ ProductionVariants?: PendingProductionVariantSummary[]; @@ -2223,10 +2224,9 @@ export interface PendingDeploymentSummary { StartTime?: Date; /** - *

An array of PendingProductionVariantSummary objects, one for each - * model hosted behind this endpoint in shadow mode with production traffic replicated from - * the model specified on ProductionVariants for the in-progress - * deployment.

+ *

An array of PendingProductionVariantSummary objects, one for each model hosted behind + * this endpoint in shadow mode with production traffic replicated from the model specified + * on ProductionVariants for the in-progress deployment.

*/ ShadowProductionVariants?: PendingProductionVariantSummary[]; } @@ -2309,8 +2309,8 @@ export interface DescribeEndpointOutput { EndpointConfigName: string | undefined; /** - *

An array of ProductionVariantSummary objects, one for each model - * hosted behind this endpoint.

+ *

An array of ProductionVariantSummary objects, one for each model hosted behind this + * endpoint.

*/ ProductionVariants?: ProductionVariantSummary[]; @@ -2363,10 +2363,9 @@ export interface DescribeEndpointOutput { * *
  • *

    - * Failed: Endpoint could not be created, updated, or re-scaled. Use the - * FailureReason value returned by DescribeEndpoint for information about - * the failure. DeleteEndpoint is the only operation that can be - * performed on a failed endpoint.

    + * Failed: Endpoint could not be created, updated, or re-scaled. Use + * the FailureReason value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a + * failed endpoint.

    *
    • * */ @@ -2412,9 +2411,9 @@ export interface DescribeEndpointOutput { ExplainerConfig?: ExplainerConfig; /** - *

    An array of ProductionVariantSummary objects, one for each model - * that you want to host at this endpoint in shadow mode with production traffic replicated - * from the model specified on ProductionVariants.

    + *

    An array of ProductionVariantSummary objects, one for each model that you want to host + * at this endpoint in shadow mode with production traffic replicated from the model + * specified on ProductionVariants.

    */ ShadowProductionVariants?: ProductionVariantSummary[]; } @@ -2455,8 +2454,8 @@ export interface DescribeEndpointConfigOutput { DataCaptureConfig?: DataCaptureConfig; /** - *

    Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on - * the ML storage volume attached to the instance.

    + *

    Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML + * storage volume attached to the instance.

    */ KmsKeyId?: string; @@ -3257,7 +3256,7 @@ export interface DescribeHyperParameterTuningJobRequest { * @public *

    Shows the latest objective metric emitted by a training job that was launched by a * hyperparameter tuning job. You define the objective metric in the - * HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

    + * HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

    */ export interface FinalHyperParameterTuningJobObjectiveMetric { /** @@ -3362,8 +3361,7 @@ export interface HyperParameterTrainingJobSummary { FailureReason?: string; /** - *

    The FinalHyperParameterTuningJobObjectiveMetric object that - * specifies the + *

    The FinalHyperParameterTuningJobObjectiveMetric object that specifies the * value * of the * objective @@ -3534,20 +3532,20 @@ export interface DescribeHyperParameterTuningJobResponse { HyperParameterTuningJobArn: string | undefined; /** - *

    The HyperParameterTuningJobConfig object that specifies the - * configuration of the tuning job.

    + *

    The HyperParameterTuningJobConfig object that specifies the configuration of + * the tuning job.

    */ HyperParameterTuningJobConfig: HyperParameterTuningJobConfig | undefined; /** - *

    The HyperParameterTrainingJobDefinition object that specifies the - * definition of the training jobs that this tuning job launches.

    + *

    The HyperParameterTrainingJobDefinition object that specifies the definition of + * the training jobs that this tuning job launches.

    */ TrainingJobDefinition?: HyperParameterTrainingJobDefinition; /** - *

    A list of the HyperParameterTrainingJobDefinition objects launched - * for this tuning job.

    + *

    A list of the HyperParameterTrainingJobDefinition objects launched for this tuning + * job.

    */ TrainingJobDefinitions?: HyperParameterTrainingJobDefinition[]; @@ -3573,30 +3571,30 @@ export interface DescribeHyperParameterTuningJobResponse { LastModifiedTime?: Date; /** - *

    The TrainingJobStatusCounters object that specifies the number of - * training jobs, categorized by status, that this tuning job launched.

    + *

    The TrainingJobStatusCounters object that specifies the number of training + * jobs, categorized by status, that this tuning job launched.

    */ TrainingJobStatusCounters: TrainingJobStatusCounters | undefined; /** - *

    The ObjectiveStatusCounters object that specifies the number of - * training jobs, categorized by the status of their final objective metric, that this - * tuning job launched.

    + *

    The ObjectiveStatusCounters object that specifies the number of training jobs, + * categorized by the status of their final objective metric, that this tuning job + * launched.

    */ ObjectiveStatusCounters: ObjectiveStatusCounters | undefined; /** - *

    A TrainingJobSummary object that describes the training job that - * completed with the best current HyperParameterTuningJobObjective.

    + *

    A TrainingJobSummary object that describes the training job that completed + * with the best current HyperParameterTuningJobObjective.

    */ BestTrainingJob?: HyperParameterTrainingJobSummary; /** *

    If the hyperparameter tuning job is an warm start tuning job with a * WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the - * TrainingJobSummary for the training job with the best objective - * metric value of all training jobs launched by this tuning job and all parent jobs - * specified for the warm start tuning job.

    + * TrainingJobSummary for the training job with the best objective metric + * value of all training jobs launched by this tuning job and all parent jobs specified for + * the warm start tuning job.

    */ OverallBestTrainingJob?: HyperParameterTrainingJobSummary; @@ -4778,8 +4776,8 @@ export interface DescribeModelOutput { ExecutionRoleArn: string | undefined; /** - *

    A VpcConfig object that specifies the VPC that this model has access - * to. For more information, see Protect Endpoints by Using an Amazon Virtual + *

    A VpcConfig object that specifies the VPC that this model has access to. For + * more information, see Protect Endpoints by Using an Amazon Virtual * Private Cloud *

    */ @@ -5855,7 +5853,8 @@ export interface DescribeNotebookInstanceOutput { /** *

    A list of the Elastic Inference (EI) instance types associated with this notebook * instance. Currently only one EI instance type can be associated with a notebook - * instance. For more information, see Using Elastic Inference in Amazon SageMaker.

    + * instance. For more information, see Using Elastic Inference in + * Amazon SageMaker.

    */ AcceleratorTypes?: (NotebookInstanceAcceleratorType | string)[]; @@ -5952,7 +5951,7 @@ export interface DescribeNotebookInstanceLifecycleConfigOutput { */ export interface DescribePipelineRequest { /** - *

    The name of the pipeline to describe.

    + *

    The name or Amazon Resource Name (ARN) of the pipeline to describe.

    */ PipelineName: string | undefined; } @@ -6778,13 +6777,12 @@ export type SecondaryStatus = (typeof SecondaryStatus)[keyof typeof SecondarySta /** * @public - *

    An array element of SecondaryStatusTransitions for DescribeTrainingJob. It provides - * additional details about a status that the training job has transitioned through. A - * training job can be in one of several states, for example, starting, downloading, - * training, or uploading. Within each state, there are a number of intermediate states. - * For example, within the starting state, SageMaker could be starting the training job or - * launching the ML instances. These transitional states are referred to as the job's - * secondary + *

    An array element of SecondaryStatusTransitions for DescribeTrainingJob. It provides additional details about a status that the + * training job has transitioned through. A training job can be in one of several states, + * for example, starting, downloading, training, or uploading. Within each state, there are + * a number of intermediate states. For example, within the starting state, SageMaker could be + * starting the training job or launching the ML instances. These transitional states are + * referred to as the job's secondary * status. *

    *

    @@ -6947,8 +6945,8 @@ export interface SecondaryStatusTransition { * messages in if statements.

    * *

    To have an overview of your training job's progress, view - * TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For - * example, at the start of a training job, you might see the following:

    + * TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example, + * at the start of a training job, you might see the following:

    *
      *
    • *

      @@ -7251,8 +7249,8 @@ export interface DescribeTrainingJobResponse { ResourceConfig: ResourceConfig | undefined; /** - *

      A VpcConfig object that specifies the VPC that this training job has - * access to. For more information, see Protect Training Jobs by Using an Amazon + *

      A VpcConfig object that specifies the VPC that this training job has access + * to. For more information, see Protect Training Jobs by Using an Amazon * Virtual Private Cloud.

      */ VpcConfig?: VpcConfig; @@ -8645,8 +8643,8 @@ export interface RStudioServerProDomainSettingsForUpdate { */ export interface DomainSettingsForUpdate { /** - *

      A collection of RStudioServerPro Domain-level app settings to - * update. A single RStudioServerPro application is created for a domain.

      + *

      A collection of RStudioServerPro Domain-level app settings to update. A + * single RStudioServerPro application is created for a domain.

      */ RStudioServerProDomainSettingsForUpdate?: RStudioServerProDomainSettingsForUpdate; @@ -9123,9 +9121,9 @@ export interface EndpointSummary { *
    • *

      * Failed: Endpoint could not be created, updated, or re-scaled. Use - * DescribeEndpointOutput$FailureReason for information about - * the failure. DeleteEndpoint is the only operation that can be - * performed on a failed endpoint.

      + * DescribeEndpointOutput$FailureReason for information about the + * failure. DeleteEndpoint is the only operation that can be performed on a + * failed endpoint.

      *
      • *
    *

    To get a list of endpoints with a specified status, use the StatusEquals @@ -10301,21 +10299,21 @@ export interface HyperParameterTuningJobSummary { LastModifiedTime?: Date; /** - *

    The TrainingJobStatusCounters object that specifies the numbers of - * training jobs, categorized by status, that this tuning job launched.

    + *

    The TrainingJobStatusCounters object that specifies the numbers of training + * jobs, categorized by status, that this tuning job launched.

    */ TrainingJobStatusCounters: TrainingJobStatusCounters | undefined; /** - *

    The ObjectiveStatusCounters object that specifies the numbers of - * training jobs, categorized by objective metric status, that this tuning job - * launched.

    + *

    The ObjectiveStatusCounters object that specifies the numbers of training jobs, + * categorized by objective metric status, that this tuning job launched.

    */ ObjectiveStatusCounters: ObjectiveStatusCounters | undefined; /** - *

    The ResourceLimits object that specifies the maximum number of - * training jobs and parallel training jobs allowed for this tuning job.

    + *

    The ResourceLimits + * object that specifies the maximum number of training jobs and parallel training jobs + * allowed for this tuning job.

    */ ResourceLimits?: ResourceLimits; } diff --git a/clients/client-sagemaker/src/models/models_3.ts b/clients/client-sagemaker/src/models/models_3.ts index 1924c2445347..68afd8c75df4 100644 --- a/clients/client-sagemaker/src/models/models_3.ts +++ b/clients/client-sagemaker/src/models/models_3.ts @@ -882,8 +882,10 @@ export interface ListAliasesResponse { */ export interface ListAppImageConfigsRequest { /** - *

    The maximum number of AppImageConfigs to return in the response. The default value is - * 10.

    + *

    The total number of items to return in the response. If the total + * number of items available is more than the value specified, a NextToken + * is provided in the response. To resume pagination, provide the NextToken + * value in the as part of a subsequent call. The default value is 10.

    */ MaxResults?: number; @@ -955,7 +957,10 @@ export interface ListAppsRequest { NextToken?: string; /** - *

    Returns a list up to a specified limit.

    + *

    The total number of items to return in the response. If the total + * number of items available is more than the value specified, a NextToken + * is provided in the response. To resume pagination, provide the NextToken + * value in the as part of a subsequent call. The default value is 10.

    */ MaxResults?: number; @@ -1807,7 +1812,10 @@ export interface ListDomainsRequest { NextToken?: string; /** - *

    Returns a list up to a specified limit.

    + *

    The total number of items to return in the response. If the total + * number of items available is more than the value specified, a NextToken + * is provided in the response. To resume pagination, provide the NextToken + * value in the as part of a subsequent call. The default value is 10.

    */ MaxResults?: number; } @@ -5272,7 +5280,7 @@ export type SortPipelineExecutionsBy = (typeof SortPipelineExecutionsBy)[keyof t */ export interface ListPipelineExecutionsRequest { /** - *

    The name of the pipeline.

    + *

    The name or Amazon Resource Name (ARN) of the pipeline.

    */ PipelineName: string | undefined; @@ -6212,7 +6220,10 @@ export interface ListSpacesRequest { NextToken?: string; /** - *

    Returns a list up to a specified limit.

    + *

    The total number of items to return in the response. If the total + * number of items available is more than the value specified, a NextToken + * is provided in the response. To resume pagination, provide the NextToken + * value in the as part of a subsequent call. The default value is 10.

    */ MaxResults?: number; @@ -6350,7 +6361,10 @@ export type StudioLifecycleConfigSortKey = */ export interface ListStudioLifecycleConfigsRequest { /** - *

    The maximum number of Studio Lifecycle Configurations to return in the response. The default value is 10.

    + *

    The total number of items to return in the response. If the total + * number of items available is more than the value specified, a NextToken + * is provided in the response. To resume pagination, provide the NextToken + * value in the as part of a subsequent call. The default value is 10.

    */ MaxResults?: number; @@ -6436,7 +6450,8 @@ export interface StudioLifecycleConfigDetails { */ export interface ListStudioLifecycleConfigsResponse { /** - *

    A token for getting the next set of actions, if there are any.

    + *

    If the previous response was truncated, you will receive this token. + * Use it in your next request to receive the next set of results.

    */ NextToken?: string; @@ -7155,7 +7170,10 @@ export interface ListUserProfilesRequest { NextToken?: string; /** - *

    Returns a list up to a specified limit.

    + *

    The total number of items to return in the response. If the total + * number of items available is more than the value specified, a NextToken + * is provided in the response. To resume pagination, provide the NextToken + * value in the as part of a subsequent call. The default value is 10.

    */ MaxResults?: number; @@ -9007,7 +9025,7 @@ export interface TrainingJob { /** *

    Provides detailed information about the state of the training job. For detailed * information about the secondary status of the training job, see - * StatusMessage under SecondaryStatusTransition.

    + * StatusMessage under SecondaryStatusTransition.

    *

    SageMaker provides primary statuses and secondary statuses that apply to each of * them:

    *
    @@ -9145,8 +9163,8 @@ export interface TrainingJob { ResourceConfig?: ResourceConfig; /** - *

    A VpcConfig object that specifies the VPC that this training job has - * access to. For more information, see Protect Training Jobs by Using an Amazon + *

    A VpcConfig object that specifies the VPC that this training job has access + * to. For more information, see Protect Training Jobs by Using an Amazon * Virtual Private Cloud.

    */ VpcConfig?: VpcConfig; @@ -9770,7 +9788,7 @@ export interface StartNotebookInstanceInput { */ export interface StartPipelineExecutionRequest { /** - *

    The name of the pipeline.

    + *

    The name or Amazon Resource Name (ARN) of the pipeline.

    */ PipelineName: string | undefined; diff --git a/clients/client-sagemaker/src/models/models_4.ts b/clients/client-sagemaker/src/models/models_4.ts index 2d885fa363ac..f89489350e1f 100644 --- a/clients/client-sagemaker/src/models/models_4.ts +++ b/clients/client-sagemaker/src/models/models_4.ts @@ -433,10 +433,10 @@ export type VariantPropertyType = (typeof VariantPropertyType)[keyof typeof Vari * @public *

    Specifies a production variant property type for an Endpoint.

    *

    If you are updating an endpoint with the RetainAllVariantProperties - * option of UpdateEndpointInput set to - * true, the VariantProperty objects listed in the ExcludeRetainedVariantProperties parameter of UpdateEndpointInput - * override the - * existing variant properties of the endpoint.

    + * option of UpdateEndpointInput set to true, the + * VariantProperty objects listed in the + * ExcludeRetainedVariantProperties parameter of UpdateEndpointInput override the existing variant properties of the + * endpoint.

    */ export interface VariantProperty { /** @@ -445,13 +445,14 @@ export interface VariantProperty { *
  • *

    * DesiredInstanceCount: Overrides the existing variant instance - * counts using the InitialInstanceCount values - * in the ProductionVariants of CreateEndpointConfig.

    + * counts using the InitialInstanceCount values in the + * ProductionVariants of CreateEndpointConfig.

    *
    • *
  • *

    * DesiredWeight: Overrides the existing variant weights using the - * InitialVariantWeight values in the ProductionVariants of CreateEndpointConfig.

    + * InitialVariantWeight values in the + * ProductionVariants of CreateEndpointConfig.

    *
    • *
  • *

    @@ -487,12 +488,12 @@ export interface UpdateEndpointInput { RetainAllVariantProperties?: boolean; /** - *

    When you are updating endpoint resources with RetainAllVariantProperties, whose value is set to - * true, ExcludeRetainedVariantProperties specifies the list - * of type VariantProperty to override with the values provided by - * EndpointConfig. If you don't specify a value for - * ExcludeRetainedVariantProperties, no variant properties are overridden. - *

    + *

    When you are updating endpoint resources with RetainAllVariantProperties, + * whose value is set to true, ExcludeRetainedVariantProperties + * specifies the list of type VariantProperty + * to override with the values provided by EndpointConfig. If you don't + * specify a value for ExcludeRetainedVariantProperties, no variant properties + * are overridden.

    */ ExcludeRetainedVariantProperties?: VariantProperty[]; @@ -1122,7 +1123,8 @@ export interface UpdateNotebookInstanceInput { /** *

    A list of the Elastic Inference (EI) instance types to associate with this notebook * instance. Currently only one EI instance type can be associated with a notebook - * instance. For more information, see Using Elastic Inference in Amazon SageMaker.

    + * instance. For more information, see Using Elastic Inference in + * Amazon SageMaker.

    */ AcceleratorTypes?: (NotebookInstanceAcceleratorType | string)[]; diff --git a/codegen/sdk-codegen/aws-models/sagemaker.json b/codegen/sdk-codegen/aws-models/sagemaker.json index b9794f4688f2..cbaec2f28794 100644 --- a/codegen/sdk-codegen/aws-models/sagemaker.json +++ b/codegen/sdk-codegen/aws-models/sagemaker.json @@ -256,7 +256,7 @@ "target": "com.amazonaws.sagemaker#AddTagsOutput" }, "traits": { - "smithy.api#documentation": "

    Adds or overwrites one or more tags for the specified SageMaker resource. You can add\n tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform\n jobs, models, labeling jobs, work teams, endpoint configurations, and\n endpoints.

    \n

    Each tag consists of a key and an optional value. Tag keys must be unique per\n resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.

    \n \n

    Tags that you add to a hyperparameter tuning job by calling this API are also\n added to any training jobs that the hyperparameter tuning job launches after you\n call this API, but not to training jobs that the hyperparameter tuning job launched\n before you called this API. To make sure that the tags associated with a\n hyperparameter tuning job are also added to all training jobs that the\n hyperparameter tuning job launches, add the tags when you first create the tuning\n job by specifying them in the Tags parameter of CreateHyperParameterTuningJob\n

    \n     \n \n

    Tags that you add to a SageMaker Studio Domain or User Profile by calling this API\n are also added to any Apps that the Domain or User Profile launches after you call\n this API, but not to Apps that the Domain or User Profile launched before you called\n this API. To make sure that the tags associated with a Domain or User Profile are\n also added to all Apps that the Domain or User Profile launches, add the tags when\n you first create the Domain or User Profile by specifying them in the\n Tags parameter of CreateDomain or CreateUserProfile.

    \n     " + "smithy.api#documentation": "

    Adds or overwrites one or more tags for the specified SageMaker resource. You can add\n tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform\n jobs, models, labeling jobs, work teams, endpoint configurations, and\n endpoints.

    \n

    Each tag consists of a key and an optional value. Tag keys must be unique per\n resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.

    \n \n

    Tags that you add to a hyperparameter tuning job by calling this API are also\n added to any training jobs that the hyperparameter tuning job launches after you\n call this API, but not to training jobs that the hyperparameter tuning job launched\n before you called this API. To make sure that the tags associated with a\n hyperparameter tuning job are also added to all training jobs that the\n hyperparameter tuning job launches, add the tags when you first create the tuning\n job by specifying them in the Tags parameter of CreateHyperParameterTuningJob\n

    \n     \n \n

    Tags that you add to a SageMaker Studio Domain or User Profile by calling this API\n are also added to any Apps that the Domain or User Profile launches after you call\n this API, but not to Apps that the Domain or User Profile launched before you called\n this API. To make sure that the tags associated with a Domain or User Profile are\n also added to all Apps that the Domain or User Profile launches, add the tags when\n you first create the Domain or User Profile by specifying them in the\n Tags parameter of CreateDomain\n or CreateUserProfile.

    \n     " } }, "com.amazonaws.sagemaker#AddTagsInput": { @@ -527,7 +527,7 @@ } }, "traits": { - "smithy.api#documentation": "

    Specifies the training algorithm to use in a CreateTrainingJob\n request.

    \n

    For more information about algorithms provided by SageMaker, see Algorithms. For\n information about using your own algorithms, see Using Your Own Algorithms with\n Amazon SageMaker.

    " + "smithy.api#documentation": "

    Specifies the training algorithm to use in a CreateTrainingJob request.

    \n

    For more information about algorithms provided by SageMaker, see Algorithms. For\n information about using your own algorithms, see Using Your Own Algorithms with\n Amazon SageMaker.

    " } }, "com.amazonaws.sagemaker#AlgorithmStatus": { @@ -1820,7 +1820,7 @@ "IncludeInferenceResponseIn": { "target": "com.amazonaws.sagemaker#AsyncNotificationTopicTypeList", "traits": { - "smithy.api#documentation": "

    The Amazon SNS topics where you want the inference response to be included.

    \n \n

    The inference response is included only if the response size is less than or equal to 128 KB.

    \n     " + "smithy.api#documentation": "

    The Amazon SNS topics where you want the inference response to be included.

    \n \n

    The inference response is included only if the response size is less than or equal\n to 128 KB.

    \n     " } } }, @@ -2546,7 +2546,7 @@ "ContentType": { "target": "com.amazonaws.sagemaker#ContentType", "traits": { - "smithy.api#documentation": "

    The content type of the data from the input source. The following are the allowed\n content types for different problems:

    \n
      \n
    • \n

      ImageClassification: image/png, image/jpeg,\n image/*\n

      \n
      • \n
    • \n

      TextClassification: text/csv;header=present\n

      \n
      • \n
    " + "smithy.api#documentation": "

    The content type of the data from the input source. The following are the allowed\n content types for different problems:

    \n
      \n
    • \n

      ImageClassification: image/png, image/jpeg, or\n image/*. The default value is image/*.

      \n
      • \n
    • \n

      TextClassification: text/csv;header=present or x-application/vnd.amazon+parquet. \n The default value is text/csv;header=present.

      \n
      • \n
    " } }, "CompressionType": { @@ -3356,7 +3356,7 @@ } }, "traits": { - "smithy.api#documentation": "

    The name and an example value of the hyperparameter that\n you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible\n for Autotune, an optimal hyperparameter range is selected for you.

    " + "smithy.api#documentation": "

    The name and an example value of the hyperparameter that you want to use in Autotune.\n If Automatic model tuning (AMT) determines that your hyperparameter is eligible for\n Autotune, an optimal hyperparameter range is selected for you.

    " } }, "com.amazonaws.sagemaker#AutoParameters": { @@ -3397,7 +3397,7 @@ } }, "traits": { - "smithy.api#documentation": "

    A flag to indicate if you want to use Autotune to automatically find optimal values for\n the following fields:

    \n
      \n
    • \n

      \n ParameterRanges: The names and ranges of parameters that a hyperparameter\n tuning job can optimize.

      \n
      • \n
    • \n

      \n ResourceLimits: The maximum resources that can be used for a training job.\n These resources include the maximum number of training jobs, the maximum runtime of a\n tuning job, and the maximum number of training jobs to run at the same time.

      \n
      • \n
    • \n

      \n TrainingJobEarlyStoppingType: A flag that specifies whether or not to use\n early stopping for training jobs launched by a hyperparameter tuning job.

      \n
      • \n
    • \n

      \n RetryStrategy: The number of times to retry a training job.

      \n
      • \n
    • \n

      \n Strategy: Specifies how hyperparameter tuning chooses the combinations of\n hyperparameter values to use for the training jobs that it launches.

      \n
      • \n
    • \n

      \n ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model\n convergence.

      \n
      • \n
    " + "smithy.api#documentation": "

    A flag to indicate if you want to use Autotune to automatically find optimal values\n for the following fields:

    \n
      \n
    • \n

      \n ParameterRanges: The names and ranges of parameters that a\n hyperparameter tuning job can optimize.

      \n
      • \n
    • \n

      \n ResourceLimits: The maximum resources that can be used for a\n training job. These resources include the maximum number of training jobs, the\n maximum runtime of a tuning job, and the maximum number of training jobs to run\n at the same time.

      \n
      • \n
    • \n

      \n TrainingJobEarlyStoppingType: A flag that specifies whether or not\n to use early stopping for training jobs launched by a hyperparameter tuning\n job.

      \n
      • \n
    • \n

      \n RetryStrategy: The number of times to retry a training job.

      \n
      • \n
    • \n

      \n Strategy: Specifies how hyperparameter tuning chooses the\n combinations of hyperparameter values to use for the training jobs that it\n launches.

      \n
      • \n
    • \n

      \n ConvergenceDetected: A flag to indicate that Automatic model tuning\n (AMT) has detected model convergence.

      \n
      • \n
    " } }, "com.amazonaws.sagemaker#AutotuneMode": { @@ -4336,7 +4336,7 @@ "InputMode": { "target": "com.amazonaws.sagemaker#TrainingInputMode", "traits": { - "smithy.api#documentation": "

    (Optional) The input mode to use for the data channel in a training job. If you don't\n set a value for InputMode, SageMaker uses the value set for\n TrainingInputMode. Use this parameter to override the\n TrainingInputMode setting in a AlgorithmSpecification \n request when you have a channel that needs a different input mode from the training\n job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML\n storage volume, and mount the directory to a Docker volume, use File input\n mode. To stream data directly from Amazon S3 to the container, choose Pipe input\n mode.

    \n

    To use a model for incremental training, choose File input model.

    " + "smithy.api#documentation": "

    (Optional) The input mode to use for the data channel in a training job. If you don't\n set a value for InputMode, SageMaker uses the value set for\n TrainingInputMode. Use this parameter to override the\n TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a\n different input mode from the training job's general setting. To download the data from\n Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a\n Docker volume, use File input mode. To stream data directly from Amazon S3 to\n the container, choose Pipe input mode.

    \n

    To use a model for incremental training, choose File input model.

    " } }, "ShuffleConfig": { @@ -4399,7 +4399,7 @@ "SupportedInputModes": { "target": "com.amazonaws.sagemaker#InputModes", "traits": { - "smithy.api#documentation": "

    The allowed input mode, either FILE or PIPE.

    \n

    In FILE mode, Amazon SageMaker copies the data from the input source onto the local\n Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm.\n This is the most commonly used input mode.

    \n

    In PIPE mode, Amazon SageMaker streams input data from the source directly to your\n algorithm without using the EBS volume.

    ", + "smithy.api#documentation": "

    The allowed input mode, either FILE or PIPE.

    \n

    In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon\n Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This\n is the most commonly used input mode.

    \n

    In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm\n without using the EBS volume.

    ", "smithy.api#required": {} } } @@ -5873,13 +5873,13 @@ "Image": { "target": "com.amazonaws.sagemaker#ContainerImage", "traits": { - "smithy.api#documentation": "

    The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a\n Docker registry that is accessible from the same VPC that you configure for your\n endpoint. If you are using your own custom algorithm instead of an algorithm provided by\n SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with\n Amazon SageMaker.\n

    \n \n

    The model artifacts in an Amazon S3 bucket and the Docker image for inference container\n in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are\n creating.

    \n     " + "smithy.api#documentation": "

    The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a\n Docker registry that is accessible from the same VPC that you configure for your\n endpoint. If you are using your own custom algorithm instead of an algorithm provided by\n SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with\n Amazon SageMaker.

    \n \n

    The model artifacts in an Amazon S3 bucket and the Docker image for inference container\n in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are\n creating.

    \n     " } }, "ImageConfig": { "target": "com.amazonaws.sagemaker#ImageConfig", "traits": { - "smithy.api#documentation": "

    Specifies whether the model container is in Amazon ECR or a private Docker registry\n accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a\n private Docker registry, see Use a\n Private Docker Registry for Real-Time Inference Containers.\n

    \n \n

    The model artifacts in an Amazon S3 bucket and the Docker image for inference container\n in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are\n creating.

    \n     " + "smithy.api#documentation": "

    Specifies whether the model container is in Amazon ECR or a private Docker registry\n accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a\n private Docker registry, see Use a\n Private Docker Registry for Real-Time Inference Containers.

    \n \n

    The model artifacts in an Amazon S3 bucket and the Docker image for inference container\n in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are\n creating.

    \n     " } }, "Mode": { @@ -7316,7 +7316,7 @@ "AppSecurityGroupManagement": { "target": "com.amazonaws.sagemaker#AppSecurityGroupManagement", "traits": { - "smithy.api#documentation": "

    The entity that creates and manages the required security groups for inter-app\n communication in VPCOnly mode. Required when\n CreateDomain.AppNetworkAccessType is VPCOnly and\n DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is\n provided. If setting up the domain for use with RStudio, this value must be set to Service.

    " + "smithy.api#documentation": "

    The entity that creates and manages the required security groups for inter-app\n communication in VPCOnly mode. Required when\n CreateDomain.AppNetworkAccessType is VPCOnly and\n DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is\n provided. If setting up the domain for use with RStudio, this value must be set to\n Service.

    " } }, "DomainSettings": { @@ -7561,7 +7561,7 @@ } ], "traits": { - "smithy.api#documentation": "

    Creates an endpoint using the endpoint configuration specified in the request. SageMaker\n uses the endpoint to provision resources and deploy models. You create the endpoint\n configuration with the CreateEndpointConfig API.

    \n

    Use this API to deploy models using SageMaker hosting services.

    \n

    For an example that calls this method when deploying a model to SageMaker hosting services,\n see the Create Endpoint example notebook.\n

    \n \n

    You must not delete an EndpointConfig that is in use by an endpoint\n that is live or while the UpdateEndpoint or CreateEndpoint\n operations are being performed on the endpoint. To update an endpoint, you must\n create a new EndpointConfig.

    \n     \n

    The endpoint name must be unique within an Amazon Web Services Region in your\n Amazon Web Services account.

    \n

    When it receives the request, SageMaker creates the endpoint, launches the resources (ML\n compute instances), and deploys the model(s) on them.

    \n \n

    When you call CreateEndpoint, a load call is made to DynamoDB to\n verify that your endpoint configuration exists. When you read data from a DynamoDB\n table supporting \n Eventually Consistent Reads\n , the response might not\n reflect the results of a recently completed write operation. The response might\n include some stale data. If the dependent entities are not yet in DynamoDB, this\n causes a validation error. If you repeat your read request after a short time, the\n response should return the latest data. So retry logic is recommended to handle\n these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

    \n     \n

    When SageMaker receives the request, it sets the endpoint status to\n Creating. After it creates the endpoint, it sets the status to\n InService. SageMaker can then process incoming requests for inferences. To\n check the status of an endpoint, use the DescribeEndpoint\n API.

    \n

    If any of the models hosted at this endpoint get model data from an Amazon S3 location,\n SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the\n S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services\n account by default. If you previously deactivated Amazon Web Services STS for a region,\n you need to reactivate Amazon Web Services STS for that region. For more information, see\n Activating and\n Deactivating Amazon Web Services STS in an Amazon Web Services Region in the\n Amazon Web Services Identity and Access Management User\n Guide.

    \n \n

    To add the IAM role policies for using this API operation, go to the IAM console, and choose\n Roles in the left navigation pane. Search the IAM role that you want to grant\n access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to\n the role.

    \n
      \n
    • \n

      Option 1: For a full SageMaker access, search and attach the\n AmazonSageMakerFullAccess policy.

      \n
      • \n
    • \n

      Option 2: For granting a limited access to an IAM role, paste the\n following Action elements manually into the JSON file of the IAM role:

      \n

      \n \"Action\": [\"sagemaker:CreateEndpoint\",\n \"sagemaker:CreateEndpointConfig\"]\n

      \n

      \n \"Resource\": [\n

      \n

      \n \"arn:aws:sagemaker:region:account-id:endpoint/endpointName\"\n

      \n

      \n \"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName\"\n

      \n

      \n ]\n

      \n

      For more information, see SageMaker API\n Permissions: Actions, Permissions, and Resources\n Reference.

      \n
      • \n
    \n     " + "smithy.api#documentation": "

    Creates an endpoint using the endpoint configuration specified in the request. SageMaker\n uses the endpoint to provision resources and deploy models. You create the endpoint\n configuration with the CreateEndpointConfig API.

    \n

    Use this API to deploy models using SageMaker hosting services.

    \n

    For an example that calls this method when deploying a model to SageMaker hosting services,\n see the Create Endpoint example notebook.\n

    \n \n

    You must not delete an EndpointConfig that is in use by an endpoint\n that is live or while the UpdateEndpoint or CreateEndpoint\n operations are being performed on the endpoint. To update an endpoint, you must\n create a new EndpointConfig.

    \n     \n

    The endpoint name must be unique within an Amazon Web Services Region in your\n Amazon Web Services account.

    \n

    When it receives the request, SageMaker creates the endpoint, launches the resources (ML\n compute instances), and deploys the model(s) on them.

    \n \n

    When you call CreateEndpoint, a load call is made to DynamoDB to verify that your\n endpoint configuration exists. When you read data from a DynamoDB table supporting\n \n Eventually Consistent Reads\n , the response might not\n reflect the results of a recently completed write operation. The response might\n include some stale data. If the dependent entities are not yet in DynamoDB, this\n causes a validation error. If you repeat your read request after a short time, the\n response should return the latest data. So retry logic is recommended to handle\n these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB\n eventually consistent read.

    \n     \n

    When SageMaker receives the request, it sets the endpoint status to\n Creating. After it creates the endpoint, it sets the status to\n InService. SageMaker can then process incoming requests for inferences. To\n check the status of an endpoint, use the DescribeEndpoint API.

    \n

    If any of the models hosted at this endpoint get model data from an Amazon S3 location,\n SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the\n S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services\n account by default. If you previously deactivated Amazon Web Services STS for a region,\n you need to reactivate Amazon Web Services STS for that region. For more information, see\n Activating and\n Deactivating Amazon Web Services STS in an Amazon Web Services Region in the\n Amazon Web Services Identity and Access Management User\n Guide.

    \n \n

    To add the IAM role policies for using this API operation, go to the IAM console, and choose\n Roles in the left navigation pane. Search the IAM role that you want to grant\n access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the\n role.

    \n
      \n
    • \n

      Option 1: For a full SageMaker access, search and attach the\n AmazonSageMakerFullAccess policy.

      \n
      • \n
    • \n

      Option 2: For granting a limited access to an IAM role, paste the\n following Action elements manually into the JSON file of the IAM role:

      \n

      \n \"Action\": [\"sagemaker:CreateEndpoint\",\n \"sagemaker:CreateEndpointConfig\"]\n

      \n

      \n \"Resource\": [\n

      \n

      \n \"arn:aws:sagemaker:region:account-id:endpoint/endpointName\"\n

      \n

      \n \"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName\"\n

      \n

      \n ]\n

      \n

      For more information, see SageMaker API\n Permissions: Actions, Permissions, and Resources\n Reference.

      \n
      • \n
    \n     " } }, "com.amazonaws.sagemaker#CreateEndpointConfig": { @@ -7578,7 +7578,7 @@ } ], "traits": { - "smithy.api#documentation": "

    Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In\n the configuration, you identify one or more models, created using the\n CreateModel API, to deploy and the resources that you want SageMaker to\n provision. Then you call the CreateEndpoint API.

    \n \n

    Use this API if you want to use SageMaker hosting services to deploy models into\n production.

    \n     \n

    In the request, you define a ProductionVariant, for each model that you\n want to deploy. Each ProductionVariant parameter also describes the\n resources that you want SageMaker to provision. This includes the number and type of ML\n compute instances to deploy.

    \n

    If you are hosting multiple models, you also assign a VariantWeight to\n specify how much traffic you want to allocate to each model. For example, suppose that\n you want to host two models, A and B, and you assign traffic weight 2 for model A and 1\n for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to\n model B.

    \n \n

    When you call CreateEndpoint, a load call is made to DynamoDB to\n verify that your endpoint configuration exists. When you read data from a DynamoDB\n table supporting \n Eventually Consistent Reads\n , the response might not\n reflect the results of a recently completed write operation. The response might\n include some stale data. If the dependent entities are not yet in DynamoDB, this\n causes a validation error. If you repeat your read request after a short time, the\n response should return the latest data. So retry logic is recommended to handle\n these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

    \n     " + "smithy.api#documentation": "

    Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In\n the configuration, you identify one or more models, created using the\n CreateModel API, to deploy and the resources that you want SageMaker to\n provision. Then you call the CreateEndpoint\n API.

    \n \n

    Use this API if you want to use SageMaker hosting services to deploy models into\n production.

    \n     \n

    In the request, you define a ProductionVariant, for each model that you\n want to deploy. Each ProductionVariant parameter also describes the\n resources that you want SageMaker to provision. This includes the number and type of ML\n compute instances to deploy.

    \n

    If you are hosting multiple models, you also assign a VariantWeight to\n specify how much traffic you want to allocate to each model. For example, suppose that\n you want to host two models, A and B, and you assign traffic weight 2 for model A and 1\n for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to\n model B.

    \n \n

    When you call CreateEndpoint, a load call is made to DynamoDB to verify that your\n endpoint configuration exists. When you read data from a DynamoDB table supporting\n \n Eventually Consistent Reads\n , the response might not\n reflect the results of a recently completed write operation. The response might\n include some stale data. If the dependent entities are not yet in DynamoDB, this\n causes a validation error. If you repeat your read request after a short time, the\n response should return the latest data. So retry logic is recommended to handle\n these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB\n eventually consistent read.

    \n     " } }, "com.amazonaws.sagemaker#CreateEndpointConfigInput": { @@ -7657,7 +7657,7 @@ "EndpointName": { "target": "com.amazonaws.sagemaker#EndpointName", "traits": { - "smithy.api#documentation": "

    The name of the endpoint.The name must be unique within an Amazon Web Services\n Region in your Amazon Web Services account. The name is case-insensitive in\n CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.

    ", + "smithy.api#documentation": "

    The name of the endpoint.The name must be unique within an Amazon Web Services\n Region in your Amazon Web Services account. The name is case-insensitive in\n CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.

    ", "smithy.api#required": {} } }, @@ -8127,20 +8127,20 @@ "HyperParameterTuningJobConfig": { "target": "com.amazonaws.sagemaker#HyperParameterTuningJobConfig", "traits": { - "smithy.api#documentation": "

    The HyperParameterTuningJobConfig object that describes the tuning\n job, including the search strategy, the objective metric used to evaluate training jobs,\n ranges of parameters to search, and resource limits for the tuning job. For more\n information, see How\n Hyperparameter Tuning Works.

    ", + "smithy.api#documentation": "

    The HyperParameterTuningJobConfig object that describes the tuning job,\n including the search strategy, the objective metric used to evaluate training jobs,\n ranges of parameters to search, and resource limits for the tuning job. For more\n information, see How\n Hyperparameter Tuning Works.

    ", "smithy.api#required": {} } }, "TrainingJobDefinition": { "target": "com.amazonaws.sagemaker#HyperParameterTrainingJobDefinition", "traits": { - "smithy.api#documentation": "

    The HyperParameterTrainingJobDefinition object that describes the\n training jobs that this tuning job launches, including static hyperparameters, input\n data configuration, output data configuration, resource configuration, and stopping\n condition.

    " + "smithy.api#documentation": "

    The HyperParameterTrainingJobDefinition object that describes the training jobs\n that this tuning job launches, including static hyperparameters, input data\n configuration, output data configuration, resource configuration, and stopping\n condition.

    " } }, "TrainingJobDefinitions": { "target": "com.amazonaws.sagemaker#HyperParameterTrainingJobDefinitions", "traits": { - "smithy.api#documentation": "

    A list of the HyperParameterTrainingJobDefinition objects launched\n for this tuning job.

    " + "smithy.api#documentation": "

    A list of the HyperParameterTrainingJobDefinition objects launched for this tuning\n job.

    " } }, "WarmStartConfig": { @@ -8158,7 +8158,7 @@ "Autotune": { "target": "com.amazonaws.sagemaker#Autotune", "traits": { - "smithy.api#documentation": "

    Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters\n for the following fields:

    \n
      \n
    • \n

      \n ParameterRanges: The names and ranges of parameters that a hyperparameter\n tuning job can optimize.

      \n
      • \n
    • \n

      \n ResourceLimits: The maximum resources that can be used for a training job.\n These resources include the maximum number of training jobs, the maximum runtime of a\n tuning job, and the maximum number of training jobs to run at the same time.

      \n
      • \n
    • \n

      \n TrainingJobEarlyStoppingType: A flag that specifies whether or not to use\n early stopping for training jobs launched by a hyperparameter tuning job.

      \n
      • \n
    • \n

      \n RetryStrategy: The number of times to retry a training job.

      \n
      • \n
    • \n

      \n Strategy: Specifies how hyperparameter tuning chooses the combinations of\n hyperparameter values to use for the training jobs that it launches.

      \n
      • \n
    • \n

      \n ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model\n convergence.

      \n
      • \n
    " + "smithy.api#documentation": "

    Configures SageMaker Automatic model tuning (AMT) to automatically find optimal\n parameters for the following fields:

    \n
      \n
    • \n

      \n ParameterRanges: The names and ranges of parameters that a\n hyperparameter tuning job can optimize.

      \n
      • \n
    • \n

      \n ResourceLimits: The maximum resources that can be used for a\n training job. These resources include the maximum number of training jobs, the\n maximum runtime of a tuning job, and the maximum number of training jobs to run\n at the same time.

      \n
      • \n
    • \n

      \n TrainingJobEarlyStoppingType: A flag that specifies whether or not\n to use early stopping for training jobs launched by a hyperparameter tuning\n job.

      \n
      • \n
    • \n

      \n RetryStrategy: The number of times to retry a training job.

      \n
      • \n
    • \n

      \n Strategy: Specifies how hyperparameter tuning chooses the\n combinations of hyperparameter values to use for the training jobs that it\n launches.

      \n
      • \n
    • \n

      \n ConvergenceDetected: A flag to indicate that Automatic model tuning\n (AMT) has detected model convergence.

      \n
      • \n
    " } } }, @@ -9109,7 +9109,7 @@ "VpcConfig": { "target": "com.amazonaws.sagemaker#VpcConfig", "traits": { - "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that you want your model\n to connect to. Control access to and from your model container by configuring the VPC.\n VpcConfig is used in hosting services and in batch transform. For more\n information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch\n Transform Jobs by Using an Amazon Virtual Private Cloud.

    " + "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that you want your model to connect\n to. Control access to and from your model container by configuring the VPC.\n VpcConfig is used in hosting services and in batch transform. For more\n information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch\n Transform Jobs by Using an Amazon Virtual Private Cloud.

    " } }, "EnableNetworkIsolation": { @@ -9891,7 +9891,7 @@ "target": "com.amazonaws.sagemaker#CreatePresignedNotebookInstanceUrlOutput" }, "traits": { - "smithy.api#documentation": "

    Returns a URL that you can use to connect to the Jupyter server from a notebook\n instance. In the SageMaker console, when you choose Open next to a notebook\n instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook\n instance. The console uses this API to get the URL and show the page.

    \n

    The IAM role or user used to call this API defines the permissions to access the\n notebook instance. Once the presigned URL is created, no additional permission is\n required to access this URL. IAM authorization policies for this API are also enforced\n for every HTTP request and WebSocket frame that attempts to connect to the notebook\n instance.

    \n

    You can restrict access to this API and to the URL that it returns to a list of IP\n addresses that you specify. Use the NotIpAddress condition operator and the\n aws:SourceIP condition context key to specify the list of IP addresses\n that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.

    \n \n

    The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If\n you try to use the URL after the 5-minute limit expires, you are directed to the\n Amazon Web Services console sign-in page.

    \n     " + "smithy.api#documentation": "

    Returns a URL that you can use to connect to the Jupyter server from a notebook\n instance. In the SageMaker console, when you choose Open next to a notebook\n instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook\n instance. The console uses this API to get the URL and show the page.

    \n

    The IAM role or user used to call this API defines the permissions to access the\n notebook instance. Once the presigned URL is created, no additional permission is\n required to access this URL. IAM authorization policies for this API are also enforced\n for every HTTP request and WebSocket frame that attempts to connect to the notebook\n instance.

    \n

    You can restrict access to this API and to the URL that it returns to a list of IP\n addresses that you specify. Use the NotIpAddress condition operator and the\n aws:SourceIP condition context key to specify the list of IP addresses\n that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.

    \n \n

    The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you\n try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.

    \n     " } }, "com.amazonaws.sagemaker#CreatePresignedNotebookInstanceUrlInput": { @@ -10291,7 +10291,7 @@ "AlgorithmSpecification": { "target": "com.amazonaws.sagemaker#AlgorithmSpecification", "traits": { - "smithy.api#documentation": "

    The registry path of the Docker image that contains the training algorithm and\n algorithm-specific metadata, including the input mode. For more information about\n algorithms provided by SageMaker, see Algorithms. For information about\n providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.\n

    ", + "smithy.api#documentation": "

    The registry path of the Docker image that contains the training algorithm and\n algorithm-specific metadata, including the input mode. For more information about\n algorithms provided by SageMaker, see Algorithms. For information about\n providing your own algorithms, see Using Your Own Algorithms with\n Amazon SageMaker.

    ", "smithy.api#required": {} } }, @@ -10305,7 +10305,7 @@ "InputDataConfig": { "target": "com.amazonaws.sagemaker#InputDataConfig", "traits": { - "smithy.api#documentation": "

    An array of Channel objects. Each channel is a named input source.\n InputDataConfig describes the input data and its location.

    \n

    Algorithms can accept input data from one or more channels. For example, an\n algorithm might have two channels of input data, training_data and\n validation_data. The configuration for each channel provides the S3,\n EFS, or FSx location where the input data is stored. It also provides information about\n the stored data: the MIME type, compression method, and whether the data is wrapped in\n RecordIO format.

    \n

    Depending on the input mode that the algorithm supports, SageMaker either copies input\n data files from an S3 bucket to a local directory in the Docker container, or makes it\n available as input streams. For example, if you specify an EFS location, input data\n files are available as input streams. They do not need to be downloaded.

    \n

    Your input must be in the same Amazon Web Services region as your training job.

    " + "smithy.api#documentation": "

    An array of Channel objects. Each channel is a named input source.\n InputDataConfig describes the input data and its location.

    \n

    Algorithms can accept input data from one or more channels. For example, an\n algorithm might have two channels of input data, training_data and\n validation_data. The configuration for each channel provides the S3,\n EFS, or FSx location where the input data is stored. It also provides information about\n the stored data: the MIME type, compression method, and whether the data is wrapped in\n RecordIO format.

    \n

    Depending on the input mode that the algorithm supports, SageMaker either copies input\n data files from an S3 bucket to a local directory in the Docker container, or makes it\n available as input streams. For example, if you specify an EFS location, input data\n files are available as input streams. They do not need to be downloaded.

    \n

    Your input must be in the same Amazon Web Services region as your training\n job.

    " } }, "OutputDataConfig": { @@ -10325,7 +10325,7 @@ "VpcConfig": { "target": "com.amazonaws.sagemaker#VpcConfig", "traits": { - "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that you want your\n training job to connect to. Control access to and from your training container by\n configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon\n Virtual Private Cloud.

    " + "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that you want your training job to\n connect to. Control access to and from your training container by configuring the VPC.\n For more information, see Protect Training Jobs by Using an Amazon\n Virtual Private Cloud.

    " } }, "StoppingCondition": { @@ -15584,7 +15584,7 @@ "KmsKeyId": { "target": "com.amazonaws.sagemaker#KmsKeyId", "traits": { - "smithy.api#documentation": "

    Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on\n the ML storage volume attached to the instance.

    " + "smithy.api#documentation": "

    Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML\n storage volume attached to the instance.

    " } }, "CreationTime": { @@ -15659,7 +15659,7 @@ "ProductionVariants": { "target": "com.amazonaws.sagemaker#ProductionVariantSummaryList", "traits": { - "smithy.api#documentation": "

    An array of ProductionVariantSummary objects, one for each model\n hosted behind this endpoint.

    " + "smithy.api#documentation": "

    An array of ProductionVariantSummary objects, one for each model hosted behind this\n endpoint.

    " } }, "DataCaptureConfig": { @@ -15668,7 +15668,7 @@ "EndpointStatus": { "target": "com.amazonaws.sagemaker#EndpointStatus", "traits": { - "smithy.api#documentation": "

    The status of the endpoint.

    \n
      \n
    • \n

      \n OutOfService: Endpoint is not available to take incoming\n requests.

      \n
      • \n
    • \n

      \n Creating: CreateEndpoint is executing.

      \n
      • \n
    • \n

      \n Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

      \n
      • \n
    • \n

      \n SystemUpdating: Endpoint is undergoing maintenance and cannot be\n updated or deleted or re-scaled until it has completed. This maintenance\n operation does not change any customer-specified values such as VPC config, KMS\n encryption, model, instance type, or instance count.

      \n
      • \n
    • \n

      \n RollingBack: Endpoint fails to scale up or down or change its\n variant weight and is in the process of rolling back to its previous\n configuration. Once the rollback completes, endpoint returns to an\n InService status. This transitional status only applies to an\n endpoint that has autoscaling enabled and is undergoing variant weight or\n capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called\n explicitly.

      \n
      • \n
    • \n

      \n InService: Endpoint is available to process incoming\n requests.

      \n
      • \n
    • \n

      \n Deleting: DeleteEndpoint is executing.

      \n
      • \n
    • \n

      \n Failed: Endpoint could not be created, updated, or re-scaled. Use the \n FailureReason value returned by DescribeEndpoint for information about\n the failure. DeleteEndpoint is the only operation that can be\n performed on a failed endpoint.

      \n
      • \n
    ", + "smithy.api#documentation": "

    The status of the endpoint.

    \n
      \n
    • \n

      \n OutOfService: Endpoint is not available to take incoming\n requests.

      \n
      • \n
    • \n

      \n Creating: CreateEndpoint is executing.

      \n
      • \n
    • \n

      \n Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

      \n
      • \n
    • \n

      \n SystemUpdating: Endpoint is undergoing maintenance and cannot be\n updated or deleted or re-scaled until it has completed. This maintenance\n operation does not change any customer-specified values such as VPC config, KMS\n encryption, model, instance type, or instance count.

      \n
      • \n
    • \n

      \n RollingBack: Endpoint fails to scale up or down or change its\n variant weight and is in the process of rolling back to its previous\n configuration. Once the rollback completes, endpoint returns to an\n InService status. This transitional status only applies to an\n endpoint that has autoscaling enabled and is undergoing variant weight or\n capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called\n explicitly.

      \n
      • \n
    • \n

      \n InService: Endpoint is available to process incoming\n requests.

      \n
      • \n
    • \n

      \n Deleting: DeleteEndpoint is executing.

      \n
      • \n
    • \n

      \n Failed: Endpoint could not be created, updated, or re-scaled. Use\n the FailureReason value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a\n failed endpoint.

      \n
      • \n
    ", "smithy.api#required": {} } }, @@ -15719,7 +15719,7 @@ "ShadowProductionVariants": { "target": "com.amazonaws.sagemaker#ProductionVariantSummaryList", "traits": { - "smithy.api#documentation": "

    An array of ProductionVariantSummary objects, one for each model\n that you want to host at this endpoint in shadow mode with production traffic replicated\n from the model specified on ProductionVariants.

    " + "smithy.api#documentation": "

    An array of ProductionVariantSummary objects, one for each model that you want to host\n at this endpoint in shadow mode with production traffic replicated from the model\n specified on ProductionVariants.

    " } } }, @@ -16580,20 +16580,20 @@ "HyperParameterTuningJobConfig": { "target": "com.amazonaws.sagemaker#HyperParameterTuningJobConfig", "traits": { - "smithy.api#documentation": "

    The HyperParameterTuningJobConfig object that specifies the\n configuration of the tuning job.

    ", + "smithy.api#documentation": "

    The HyperParameterTuningJobConfig object that specifies the configuration of\n the tuning job.

    ", "smithy.api#required": {} } }, "TrainingJobDefinition": { "target": "com.amazonaws.sagemaker#HyperParameterTrainingJobDefinition", "traits": { - "smithy.api#documentation": "

    The HyperParameterTrainingJobDefinition object that specifies the\n definition of the training jobs that this tuning job launches.

    " + "smithy.api#documentation": "

    The HyperParameterTrainingJobDefinition object that specifies the definition of\n the training jobs that this tuning job launches.

    " } }, "TrainingJobDefinitions": { "target": "com.amazonaws.sagemaker#HyperParameterTrainingJobDefinitions", "traits": { - "smithy.api#documentation": "

    A list of the HyperParameterTrainingJobDefinition objects launched\n for this tuning job.

    " + "smithy.api#documentation": "

    A list of the HyperParameterTrainingJobDefinition objects launched for this tuning\n job.

    " } }, "HyperParameterTuningJobStatus": { @@ -16625,27 +16625,27 @@ "TrainingJobStatusCounters": { "target": "com.amazonaws.sagemaker#TrainingJobStatusCounters", "traits": { - "smithy.api#documentation": "

    The TrainingJobStatusCounters object that specifies the number of\n training jobs, categorized by status, that this tuning job launched.

    ", + "smithy.api#documentation": "

    The TrainingJobStatusCounters object that specifies the number of training\n jobs, categorized by status, that this tuning job launched.

    ", "smithy.api#required": {} } }, "ObjectiveStatusCounters": { "target": "com.amazonaws.sagemaker#ObjectiveStatusCounters", "traits": { - "smithy.api#documentation": "

    The ObjectiveStatusCounters object that specifies the number of\n training jobs, categorized by the status of their final objective metric, that this\n tuning job launched.

    ", + "smithy.api#documentation": "

    The ObjectiveStatusCounters object that specifies the number of training jobs,\n categorized by the status of their final objective metric, that this tuning job\n launched.

    ", "smithy.api#required": {} } }, "BestTrainingJob": { "target": "com.amazonaws.sagemaker#HyperParameterTrainingJobSummary", "traits": { - "smithy.api#documentation": "

    A TrainingJobSummary object that describes the training job that\n completed with the best current HyperParameterTuningJobObjective.

    " + "smithy.api#documentation": "

    A TrainingJobSummary object that describes the training job that completed\n with the best current HyperParameterTuningJobObjective.

    " } }, "OverallBestTrainingJob": { "target": "com.amazonaws.sagemaker#HyperParameterTrainingJobSummary", "traits": { - "smithy.api#documentation": "

    If the hyperparameter tuning job is an warm start tuning job with a\n WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the\n TrainingJobSummary for the training job with the best objective\n metric value of all training jobs launched by this tuning job and all parent jobs\n specified for the warm start tuning job.

    " + "smithy.api#documentation": "

    If the hyperparameter tuning job is an warm start tuning job with a\n WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the\n TrainingJobSummary for the training job with the best objective metric\n value of all training jobs launched by this tuning job and all parent jobs specified for\n the warm start tuning job.

    " } }, "WarmStartConfig": { @@ -18100,7 +18100,7 @@ "VpcConfig": { "target": "com.amazonaws.sagemaker#VpcConfig", "traits": { - "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that this model has access\n to. For more information, see Protect Endpoints by Using an Amazon Virtual\n Private Cloud\n

    " + "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that this model has access to. For\n more information, see Protect Endpoints by Using an Amazon Virtual\n Private Cloud\n

    " } }, "CreationTime": { @@ -18881,7 +18881,7 @@ "AcceleratorTypes": { "target": "com.amazonaws.sagemaker#NotebookInstanceAcceleratorTypes", "traits": { - "smithy.api#documentation": "

    A list of the Elastic Inference (EI) instance types associated with this notebook\n instance. Currently only one EI instance type can be associated with a notebook\n instance. For more information, see Using Elastic Inference in Amazon SageMaker.

    " + "smithy.api#documentation": "

    A list of the Elastic Inference (EI) instance types associated with this notebook\n instance. Currently only one EI instance type can be associated with a notebook\n instance. For more information, see Using Elastic Inference in\n Amazon SageMaker.

    " } }, "DefaultCodeRepository": { @@ -19103,7 +19103,7 @@ "PipelineName": { "target": "com.amazonaws.sagemaker#PipelineNameOrArn", "traits": { - "smithy.api#documentation": "

    The name of the pipeline to describe.

    ", + "smithy.api#documentation": "

    The name or Amazon Resource Name (ARN) of the pipeline to describe.

    ", "smithy.api#required": {} } } @@ -19907,7 +19907,7 @@ "VpcConfig": { "target": "com.amazonaws.sagemaker#VpcConfig", "traits": { - "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that this training job has\n access to. For more information, see Protect Training Jobs by Using an Amazon\n Virtual Private Cloud.

    " + "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that this training job has access\n to. For more information, see Protect Training Jobs by Using an Amazon\n Virtual Private Cloud.

    " } }, "StoppingCondition": { @@ -21542,7 +21542,7 @@ "RStudioServerProDomainSettingsForUpdate": { "target": "com.amazonaws.sagemaker#RStudioServerProDomainSettingsForUpdate", "traits": { - "smithy.api#documentation": "

    A collection of RStudioServerPro Domain-level app settings to\n update. A single RStudioServerPro application is created for a domain.

    " + "smithy.api#documentation": "

    A collection of RStudioServerPro Domain-level app settings to update. A\n single RStudioServerPro application is created for a domain.

    " } }, "ExecutionRoleIdentityConfig": { @@ -22903,7 +22903,7 @@ "EndpointStatus": { "target": "com.amazonaws.sagemaker#EndpointStatus", "traits": { - "smithy.api#documentation": "

    The status of the endpoint.

    \n
      \n
    • \n

      \n OutOfService: Endpoint is not available to take incoming\n requests.

      \n
      • \n
    • \n

      \n Creating: CreateEndpoint is executing.

      \n
      • \n
    • \n

      \n Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

      \n
      • \n
    • \n

      \n SystemUpdating: Endpoint is undergoing maintenance and cannot be\n updated or deleted or re-scaled until it has completed. This maintenance\n operation does not change any customer-specified values such as VPC config, KMS\n encryption, model, instance type, or instance count.

      \n
      • \n
    • \n

      \n RollingBack: Endpoint fails to scale up or down or change its\n variant weight and is in the process of rolling back to its previous\n configuration. Once the rollback completes, endpoint returns to an\n InService status. This transitional status only applies to an\n endpoint that has autoscaling enabled and is undergoing variant weight or\n capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called\n explicitly.

      \n
      • \n
    • \n

      \n InService: Endpoint is available to process incoming\n requests.

      \n
      • \n
    • \n

      \n Deleting: DeleteEndpoint is executing.

      \n
      • \n
    • \n

      \n Failed: Endpoint could not be created, updated, or re-scaled. Use\n DescribeEndpointOutput$FailureReason for information about\n the failure. DeleteEndpoint is the only operation that can be\n performed on a failed endpoint.

      \n
      • \n
    \n

    To get a list of endpoints with a specified status, use the StatusEquals\n filter with a call to ListEndpoints.

    ", + "smithy.api#documentation": "

    The status of the endpoint.

    \n
      \n
    • \n

      \n OutOfService: Endpoint is not available to take incoming\n requests.

      \n
      • \n
    • \n

      \n Creating: CreateEndpoint is executing.

      \n
      • \n
    • \n

      \n Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

      \n
      • \n
    • \n

      \n SystemUpdating: Endpoint is undergoing maintenance and cannot be\n updated or deleted or re-scaled until it has completed. This maintenance\n operation does not change any customer-specified values such as VPC config, KMS\n encryption, model, instance type, or instance count.

      \n
      • \n
    • \n

      \n RollingBack: Endpoint fails to scale up or down or change its\n variant weight and is in the process of rolling back to its previous\n configuration. Once the rollback completes, endpoint returns to an\n InService status. This transitional status only applies to an\n endpoint that has autoscaling enabled and is undergoing variant weight or\n capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called\n explicitly.

      \n
      • \n
    • \n

      \n InService: Endpoint is available to process incoming\n requests.

      \n
      • \n
    • \n

      \n Deleting: DeleteEndpoint is executing.

      \n
      • \n
    • \n

      \n Failed: Endpoint could not be created, updated, or re-scaled. Use\n DescribeEndpointOutput$FailureReason for information about the\n failure. DeleteEndpoint is the only operation that can be performed on a\n failed endpoint.

      \n
      • \n
    \n

    To get a list of endpoints with a specified status, use the StatusEquals\n filter with a call to ListEndpoints.

    ", "smithy.api#required": {} } } @@ -24152,7 +24152,7 @@ } }, "traits": { - "smithy.api#documentation": "

    Shows the latest objective metric emitted by a training job that was launched by a\n hyperparameter tuning job. You define the objective metric in the\n HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

    " + "smithy.api#documentation": "

    Shows the latest objective metric emitted by a training job that was launched by a\n hyperparameter tuning job. You define the objective metric in the\n HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

    " } }, "com.amazonaws.sagemaker#FinalMetricDataList": { @@ -25548,7 +25548,7 @@ "TrainingImage": { "target": "com.amazonaws.sagemaker#AlgorithmImage", "traits": { - "smithy.api#documentation": "

    The registry path of the Docker image that contains the training algorithm. For\n information about Docker registry paths for built-in algorithms, see Algorithms\n Provided by Amazon SageMaker: Common Parameters. SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with\n Amazon SageMaker.

    " + "smithy.api#documentation": "

    The registry path of the Docker image that contains the training algorithm. For\n information about Docker registry paths for built-in algorithms, see Algorithms\n Provided by Amazon SageMaker: Common Parameters. SageMaker supports both\n registry/repository[:tag] and registry/repository[@digest]\n image path formats. For more information, see Using Your Own Algorithms with\n Amazon SageMaker.

    " } }, "TrainingInputMode": { @@ -25717,13 +25717,13 @@ "InputDataConfig": { "target": "com.amazonaws.sagemaker#InputDataConfig", "traits": { - "smithy.api#documentation": "

    An array of Channel objects that specify\n the\n input for the training jobs that the tuning job launches.

    " + "smithy.api#documentation": "

    An array of Channel objects that\n specify\n the\n input for the training jobs that the tuning job launches.

    " } }, "VpcConfig": { "target": "com.amazonaws.sagemaker#VpcConfig", "traits": { - "smithy.api#documentation": "

    The VpcConfig object that specifies the VPC that you want the\n training jobs that this hyperparameter tuning job launches to connect to. Control access\n to and from your training container by configuring the VPC. For more information, see\n Protect\n Training Jobs by Using an Amazon Virtual Private Cloud.

    " + "smithy.api#documentation": "

    The VpcConfig object that specifies the VPC that you want the training jobs\n that this hyperparameter tuning job launches to connect to. Control access to and from\n your training container by configuring the VPC. For more information, see Protect Training Jobs\n by Using an Amazon Virtual Private Cloud.

    " } }, "OutputDataConfig": { @@ -25785,7 +25785,7 @@ "Environment": { "target": "com.amazonaws.sagemaker#HyperParameterTrainingJobEnvironmentMap", "traits": { - "smithy.api#documentation": "

    An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See\n Define metrics\n and variables for more information.

    \n \n

    The maximum number of items specified for Map Entries refers to the\n maximum number of environment variables for each TrainingJobDefinition\n and also the maximum for the hyperparameter tuning job itself. That is, the sum of\n the number of environment variables for all the training job definitions can't\n exceed the maximum number specified.

    \n     " + "smithy.api#documentation": "

    An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See\n Define metrics and variables for more information.

    \n \n

    The maximum number of items specified for Map Entries refers to the\n maximum number of environment variables for each TrainingJobDefinition\n and also the maximum for the hyperparameter tuning job itself. That is, the sum of\n the number of environment variables for all the training job definitions can't\n exceed the maximum number specified.

    \n     " } } }, @@ -25927,7 +25927,7 @@ "FinalHyperParameterTuningJobObjectiveMetric": { "target": "com.amazonaws.sagemaker#FinalHyperParameterTuningJobObjectiveMetric", "traits": { - "smithy.api#documentation": "

    The FinalHyperParameterTuningJobObjectiveMetric object that\n specifies the\n value\n of the\n objective\n metric of the tuning job that launched this training job.

    " + "smithy.api#documentation": "

    The FinalHyperParameterTuningJobObjectiveMetric object that specifies the\n value\n of the\n objective\n metric of the tuning job that launched this training job.

    " } }, "ObjectiveStatus": { @@ -25966,7 +25966,7 @@ "target": "com.amazonaws.sagemaker#TrainingInstanceCount", "traits": { "smithy.api#default": 0, - "smithy.api#documentation": "

    The number of instances of the type specified by InstanceType. Choose an\n instance count larger than 1 for distributed training algorithms. See SageMaker distributed training\n jobs for more information.

    ", + "smithy.api#documentation": "

    The number of instances of the type specified by InstanceType. Choose an\n instance count larger than 1 for distributed training algorithms. See Step 2:\n Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.

    ", "smithy.api#required": {} } }, @@ -26045,20 +26045,20 @@ "HyperParameterTuningJobObjective": { "target": "com.amazonaws.sagemaker#HyperParameterTuningJobObjective", "traits": { - "smithy.api#documentation": "

    The HyperParameterTuningJobObjective specifies the objective metric\n used to evaluate the performance of training jobs launched by this tuning job.

    " + "smithy.api#documentation": "

    The HyperParameterTuningJobObjective specifies the objective metric used to\n evaluate the performance of training jobs launched by this tuning job.

    " } }, "ResourceLimits": { "target": "com.amazonaws.sagemaker#ResourceLimits", "traits": { - "smithy.api#documentation": "

    The ResourceLimits object that specifies the maximum number of\n training and parallel training jobs that can be used for this hyperparameter tuning\n job.

    ", + "smithy.api#documentation": "

    The ResourceLimits\n object that specifies the maximum number of training and parallel training jobs that can\n be used for this hyperparameter tuning job.

    ", "smithy.api#required": {} } }, "ParameterRanges": { "target": "com.amazonaws.sagemaker#ParameterRanges", "traits": { - "smithy.api#documentation": "

    The ParameterRanges object that specifies the ranges of\n hyperparameters that this tuning job searches over to find the optimal configuration for\n the highest model performance against your chosen objective metric.

    " + "smithy.api#documentation": "

    The ParameterRanges\n object that specifies the ranges of hyperparameters that this tuning job searches over\n to find the optimal configuration for the highest model performance against your chosen\n objective metric.

    " } }, "TrainingJobEarlyStoppingType": { @@ -26412,21 +26412,21 @@ "TrainingJobStatusCounters": { "target": "com.amazonaws.sagemaker#TrainingJobStatusCounters", "traits": { - "smithy.api#documentation": "

    The TrainingJobStatusCounters object that specifies the numbers of\n training jobs, categorized by status, that this tuning job launched.

    ", + "smithy.api#documentation": "

    The TrainingJobStatusCounters object that specifies the numbers of training\n jobs, categorized by status, that this tuning job launched.

    ", "smithy.api#required": {} } }, "ObjectiveStatusCounters": { "target": "com.amazonaws.sagemaker#ObjectiveStatusCounters", "traits": { - "smithy.api#documentation": "

    The ObjectiveStatusCounters object that specifies the numbers of\n training jobs, categorized by objective metric status, that this tuning job\n launched.

    ", + "smithy.api#documentation": "

    The ObjectiveStatusCounters object that specifies the numbers of training jobs,\n categorized by objective metric status, that this tuning job launched.

    ", "smithy.api#required": {} } }, "ResourceLimits": { "target": "com.amazonaws.sagemaker#ResourceLimits", "traits": { - "smithy.api#documentation": "

    The ResourceLimits object that specifies the maximum number of\n training jobs and parallel training jobs allowed for this tuning job.

    " + "smithy.api#documentation": "

    The ResourceLimits\n object that specifies the maximum number of training jobs and parallel training jobs\n allowed for this tuning job.

    " } } }, @@ -26488,7 +26488,7 @@ "InstanceType": { "target": "com.amazonaws.sagemaker#TrainingInstanceType", "traits": { - "smithy.api#documentation": "

    The instance type used to run hyperparameter optimization tuning jobs. See descriptions of\n instance types for more information.

    " + "smithy.api#documentation": "

    The instance type used to run hyperparameter optimization tuning jobs. See descriptions of\n instance types for more information.

    " } }, "InstanceCount": { @@ -29741,7 +29741,7 @@ "MaxResults": { "target": "com.amazonaws.sagemaker#MaxResults", "traits": { - "smithy.api#documentation": "

    The maximum number of AppImageConfigs to return in the response. The default value is\n 10.

    " + "smithy.api#documentation": "

    The total number of items to return in the response. If the total\n number of items available is more than the value specified, a NextToken\n is provided in the response. To resume pagination, provide the NextToken\n value in the as part of a subsequent call. The default value is 10.

    " } }, "NextToken": { @@ -29847,7 +29847,7 @@ "MaxResults": { "target": "com.amazonaws.sagemaker#MaxResults", "traits": { - "smithy.api#documentation": "

    Returns a list up to a specified limit.

    " + "smithy.api#documentation": "

    The total number of items to return in the response. If the total\n number of items available is more than the value specified, a NextToken\n is provided in the response. To resume pagination, provide the NextToken\n value in the as part of a subsequent call. The default value is 10.

    " } }, "SortOrder": { @@ -30983,7 +30983,7 @@ "MaxResults": { "target": "com.amazonaws.sagemaker#MaxResults", "traits": { - "smithy.api#documentation": "

    Returns a list up to a specified limit.

    " + "smithy.api#documentation": "

    The total number of items to return in the response. If the total\n number of items available is more than the value specified, a NextToken\n is provided in the response. To resume pagination, provide the NextToken\n value in the as part of a subsequent call. The default value is 10.

    " } } }, @@ -34812,7 +34812,7 @@ "PipelineName": { "target": "com.amazonaws.sagemaker#PipelineNameOrArn", "traits": { - "smithy.api#documentation": "

    The name of the pipeline.

    ", + "smithy.api#documentation": "

    The name or Amazon Resource Name (ARN) of the pipeline.

    ", "smithy.api#required": {} } }, @@ -35260,7 +35260,7 @@ "MaxResults": { "target": "com.amazonaws.sagemaker#MaxResults", "traits": { - "smithy.api#documentation": "

    Returns a list up to a specified limit.

    " + "smithy.api#documentation": "

    The total number of items to return in the response. If the total\n number of items available is more than the value specified, a NextToken\n is provided in the response. To resume pagination, provide the NextToken\n value in the as part of a subsequent call. The default value is 10.

    " } }, "SortOrder": { @@ -35422,7 +35422,7 @@ "MaxResults": { "target": "com.amazonaws.sagemaker#MaxResults", "traits": { - "smithy.api#documentation": "

    The maximum number of Studio Lifecycle Configurations to return in the response. The default value is 10.

    " + "smithy.api#documentation": "

    The total number of items to return in the response. If the total\n number of items available is more than the value specified, a NextToken\n is provided in the response. To resume pagination, provide the NextToken\n value in the as part of a subsequent call. The default value is 10.

    " } }, "NextToken": { @@ -35490,7 +35490,7 @@ "NextToken": { "target": "com.amazonaws.sagemaker#NextToken", "traits": { - "smithy.api#documentation": "

    A token for getting the next set of actions, if there are any.

    " + "smithy.api#documentation": "

    If the previous response was truncated, you will receive this token.\n Use it in your next request to receive the next set of results.

    " } }, "StudioLifecycleConfigs": { @@ -35674,7 +35674,7 @@ } ], "traits": { - "smithy.api#documentation": "

    Gets a list of TrainingJobSummary objects that describe the training\n jobs that a hyperparameter tuning job launched.

    ", + "smithy.api#documentation": "

    Gets a list of TrainingJobSummary objects that describe the training jobs that a\n hyperparameter tuning job launched.

    ", "smithy.api#paginated": { "inputToken": "NextToken", "outputToken": "NextToken", @@ -36191,7 +36191,7 @@ "MaxResults": { "target": "com.amazonaws.sagemaker#MaxResults", "traits": { - "smithy.api#documentation": "

    Returns a list up to a specified limit.

    " + "smithy.api#documentation": "

    The total number of items to return in the response. If the total\n number of items available is more than the value specified, a NextToken\n is provided in the response. To resume pagination, provide the NextToken\n value in the as part of a subsequent call. The default value is 10.

    " } }, "SortOrder": { @@ -36787,7 +36787,7 @@ "Regex": { "target": "com.amazonaws.sagemaker#MetricRegex", "traits": { - "smithy.api#documentation": "

    A regular expression that searches the output of a training job and gets the value of\n the metric. For more information about using regular expressions to define metrics, see\n Defining\n metrics and environment variables.

    ", + "smithy.api#documentation": "

    A regular expression that searches the output of a training job and gets the value of\n the metric. For more information about using regular expressions to define metrics, see\n Defining metrics and environment variables.

    ", "smithy.api#required": {} } } @@ -41728,25 +41728,25 @@ "IntegerParameterRanges": { "target": "com.amazonaws.sagemaker#IntegerParameterRanges", "traits": { - "smithy.api#documentation": "

    The array of IntegerParameterRange objects that specify ranges of\n integer hyperparameters that a hyperparameter tuning job searches.

    " + "smithy.api#documentation": "

    The array of IntegerParameterRange objects that specify ranges of integer\n hyperparameters that a hyperparameter tuning job searches.

    " } }, "ContinuousParameterRanges": { "target": "com.amazonaws.sagemaker#ContinuousParameterRanges", "traits": { - "smithy.api#documentation": "

    The array of ContinuousParameterRange objects that specify ranges of\n continuous hyperparameters that a hyperparameter tuning job searches.

    " + "smithy.api#documentation": "

    The array of ContinuousParameterRange objects that specify ranges of continuous\n hyperparameters that a hyperparameter tuning job searches.

    " } }, "CategoricalParameterRanges": { "target": "com.amazonaws.sagemaker#CategoricalParameterRanges", "traits": { - "smithy.api#documentation": "

    The array of CategoricalParameterRange objects that specify ranges\n of categorical hyperparameters that a hyperparameter tuning job searches.

    " + "smithy.api#documentation": "

    The array of CategoricalParameterRange objects that specify ranges of categorical\n hyperparameters that a hyperparameter tuning job searches.

    " } }, "AutoParameters": { "target": "com.amazonaws.sagemaker#AutoParameters", "traits": { - "smithy.api#documentation": "

    A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.

    " + "smithy.api#documentation": "

    A list containing hyperparameter names and example values to be used by Autotune to\n determine optimal ranges for your tuning job.

    " } } }, @@ -41870,7 +41870,7 @@ "ProductionVariants": { "target": "com.amazonaws.sagemaker#PendingProductionVariantSummaryList", "traits": { - "smithy.api#documentation": "

    An array of PendingProductionVariantSummary objects, one for each\n model hosted behind this endpoint for the in-progress deployment.

    " + "smithy.api#documentation": "

    An array of PendingProductionVariantSummary objects, one for each model hosted behind\n this endpoint for the in-progress deployment.

    " } }, "StartTime": { @@ -41882,7 +41882,7 @@ "ShadowProductionVariants": { "target": "com.amazonaws.sagemaker#PendingProductionVariantSummaryList", "traits": { - "smithy.api#documentation": "

    An array of PendingProductionVariantSummary objects, one for each\n model hosted behind this endpoint in shadow mode with production traffic replicated from\n the model specified on ProductionVariants for the in-progress\n deployment.

    " + "smithy.api#documentation": "

    An array of PendingProductionVariantSummary objects, one for each model hosted behind\n this endpoint in shadow mode with production traffic replicated from the model specified\n on ProductionVariants for the in-progress deployment.

    " } } }, @@ -41962,7 +41962,7 @@ } }, "traits": { - "smithy.api#documentation": "

    The production variant summary for a deployment when an endpoint is creating or\n updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus\n , weight and capacity for a production variant associated with an endpoint.\n

    " + "smithy.api#documentation": "

    The production variant summary for a deployment when an endpoint is creating or\n updating with the CreateEndpoint\n or UpdateEndpoint\n operations. Describes the VariantStatus , weight and capacity for a\n production variant associated with an endpoint.

    " } }, "com.amazonaws.sagemaker#PendingProductionVariantSummaryList": { @@ -43755,7 +43755,7 @@ "AcceleratorType": { "target": "com.amazonaws.sagemaker#ProductionVariantAcceleratorType", "traits": { - "smithy.api#documentation": "

    The size of the Elastic Inference (EI) instance to use for the production variant. EI\n instances provide on-demand GPU computing for inference. For more information, see\n Using Elastic\n Inference in Amazon SageMaker.

    " + "smithy.api#documentation": "

    The size of the Elastic Inference (EI) instance to use for the production variant. EI\n instances provide on-demand GPU computing for inference. For more information, see\n Using Elastic\n Inference in Amazon SageMaker.

    " } }, "CoreDumpConfig": { @@ -43791,12 +43791,12 @@ "EnableSSMAccess": { "target": "com.amazonaws.sagemaker#ProductionVariantSSMAccess", "traits": { - "smithy.api#documentation": "

    \n You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM)\n access for a production variant behind an endpoint. By default, SSM access is disabled\n for all production variants behind an endpoint. You can turn on or turn off SSM access\n for a production variant behind an existing endpoint by creating a new endpoint\n configuration and calling UpdateEndpoint.\n

    " + "smithy.api#documentation": "

    You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM)\n access for a production variant behind an endpoint. By default, SSM access is disabled\n for all production variants behind an endpoint. You can turn on or turn off SSM access\n for a production variant behind an existing endpoint by creating a new endpoint\n configuration and calling UpdateEndpoint.

    " } } }, "traits": { - "smithy.api#documentation": "

    \n Identifies a model that you want to host and the resources chosen to deploy for\n hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic\n among the models by specifying variant weights. For more information on production\n variants, check \n Production variants.\n

    " + "smithy.api#documentation": "

    Identifies a model that you want to host and the resources chosen to deploy for\n hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic\n among the models by specifying variant weights. For more information on production\n variants, check Production variants.\n

    " } }, "com.amazonaws.sagemaker#ProductionVariantAcceleratorType": { @@ -47614,7 +47614,7 @@ "S3Uri": { "target": "com.amazonaws.sagemaker#S3Uri", "traits": { - "smithy.api#documentation": "

    Depending on the value specified for the S3DataType, identifies either\n a key name prefix or a manifest. For example:

    \n
      \n
    • \n

      A key name prefix might look like this:\n s3://bucketname/exampleprefix\n

      \n
      • \n
    • \n

      A manifest might look like this:\n s3://bucketname/example.manifest\n

      \n

      A manifest is an S3 object which is a JSON file consisting of an array of\n elements. The first element is a prefix which is followed by one or more\n suffixes. SageMaker appends the suffix elements to the prefix to get a full set\n of S3Uri. Note that the prefix must be a valid non-empty\n S3Uri that precludes users from specifying a manifest whose\n individual S3Uri is sourced from different S3 buckets.

      \n

      The following code example shows a valid manifest format:

      \n

      \n [ {\"prefix\": \"s3://customer_bucket/some/prefix/\"},\n

      \n

      \n \"relative/path/to/custdata-1\",\n

      \n

      \n \"relative/path/custdata-2\",\n

      \n

      \n ...\n

      \n

      \n \"relative/path/custdata-N\"\n

      \n

      \n ]\n

      \n

      This JSON is equivalent to the following S3Uri\n list:

      \n

      \n s3://customer_bucket/some/prefix/relative/path/to/custdata-1\n

      \n

      \n s3://customer_bucket/some/prefix/relative/path/custdata-2\n

      \n

      \n ...\n

      \n

      \n s3://customer_bucket/some/prefix/relative/path/custdata-N\n

      \n

      The complete set of S3Uri in this manifest is the input data\n for the channel for this data source. The object that each S3Uri\n points to must be readable by the IAM role that SageMaker uses to perform tasks on\n your behalf.

      \n
      • \n
    \n

    Your input bucket must be located in same Amazon Web Services region as your training job.

    ", + "smithy.api#documentation": "

    Depending on the value specified for the S3DataType, identifies either\n a key name prefix or a manifest. For example:

    \n
      \n
    • \n

      A key name prefix might look like this:\n s3://bucketname/exampleprefix\n

      \n
      • \n
    • \n

      A manifest might look like this:\n s3://bucketname/example.manifest\n

      \n

      A manifest is an S3 object which is a JSON file consisting of an array of\n elements. The first element is a prefix which is followed by one or more\n suffixes. SageMaker appends the suffix elements to the prefix to get a full set of\n S3Uri. Note that the prefix must be a valid non-empty\n S3Uri that precludes users from specifying a manifest whose\n individual S3Uri is sourced from different S3 buckets.

      \n

      The following code example shows a valid manifest format:

      \n

      \n [ {\"prefix\": \"s3://customer_bucket/some/prefix/\"},\n

      \n

      \n \"relative/path/to/custdata-1\",\n

      \n

      \n \"relative/path/custdata-2\",\n

      \n

      \n ...\n

      \n

      \n \"relative/path/custdata-N\"\n

      \n

      \n ]\n

      \n

      This JSON is equivalent to the following S3Uri\n list:

      \n

      \n s3://customer_bucket/some/prefix/relative/path/to/custdata-1\n

      \n

      \n s3://customer_bucket/some/prefix/relative/path/custdata-2\n

      \n

      \n ...\n

      \n

      \n s3://customer_bucket/some/prefix/relative/path/custdata-N\n

      \n

      The complete set of S3Uri in this manifest is the input data\n for the channel for this data source. The object that each S3Uri\n points to must be readable by the IAM role that SageMaker uses to perform tasks on\n your behalf.

      \n
      • \n
    \n

    Your input bucket must be located in same Amazon Web Services region as your\n training job.

    ", "smithy.api#required": {} } }, @@ -47638,7 +47638,7 @@ } }, "traits": { - "smithy.api#documentation": "

    Describes the S3 data source.

    \n

    Your input bucket must be in the same Amazon Web Services region as your training job.

    " + "smithy.api#documentation": "

    Describes the S3 data source.

    \n

    Your input bucket must be in the same Amazon Web Services region as your training\n job.

    " } }, "com.amazonaws.sagemaker#S3DataType": { @@ -50152,12 +50152,12 @@ "StatusMessage": { "target": "com.amazonaws.sagemaker#StatusMessage", "traits": { - "smithy.api#documentation": "

    A detailed description of the progress within a secondary status.\n

    \n

    SageMaker provides secondary statuses and status messages that apply to each of\n them:

    \n
    \n
    Starting
    \n
    \n
      \n
    • \n

      Starting the training job.

      \n
      • \n
    • \n

      Launching requested ML\n instances.

      \n
      • \n
    • \n

      Insufficient\n capacity error from EC2 while launching instances,\n retrying!

      \n
      • \n
    • \n

      Launched\n instance was unhealthy, replacing it!

      \n
      • \n
    • \n

      Preparing the instances for training.

      \n
      • \n
    \n
    \n
    Training
    \n
    \n
      \n
    • \n

      Downloading the training image.

      \n
      • \n
    • \n

      Training\n image download completed. Training in\n progress.

      \n
      • \n
    \n
    \n
    \n \n

    Status messages are subject to change. Therefore, we recommend not including them\n in code that programmatically initiates actions. For examples, don't use status\n messages in if statements.

    \n     \n

    To have an overview of your training job's progress, view\n TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For\n example, at the start of a training job, you might see the following:

    \n
      \n
    • \n

      \n TrainingJobStatus - InProgress

      \n
      • \n
    • \n

      \n SecondaryStatus - Training

      \n
      • \n
    • \n

      \n StatusMessage - Downloading the training image

      \n
      • \n
    " + "smithy.api#documentation": "

    A detailed description of the progress within a secondary status.\n

    \n

    SageMaker provides secondary statuses and status messages that apply to each of\n them:

    \n
    \n
    Starting
    \n
    \n
      \n
    • \n

      Starting the training job.

      \n
      • \n
    • \n

      Launching requested ML\n instances.

      \n
      • \n
    • \n

      Insufficient\n capacity error from EC2 while launching instances,\n retrying!

      \n
      • \n
    • \n

      Launched\n instance was unhealthy, replacing it!

      \n
      • \n
    • \n

      Preparing the instances for training.

      \n
      • \n
    \n
    \n
    Training
    \n
    \n
      \n
    • \n

      Downloading the training image.

      \n
      • \n
    • \n

      Training\n image download completed. Training in\n progress.

      \n
      • \n
    \n
    \n
    \n \n

    Status messages are subject to change. Therefore, we recommend not including them\n in code that programmatically initiates actions. For examples, don't use status\n messages in if statements.

    \n     \n

    To have an overview of your training job's progress, view\n TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example,\n at the start of a training job, you might see the following:

    \n
      \n
    • \n

      \n TrainingJobStatus - InProgress

      \n
      • \n
    • \n

      \n SecondaryStatus - Training

      \n
      • \n
    • \n

      \n StatusMessage - Downloading the training image

      \n
      • \n
    " } } }, "traits": { - "smithy.api#documentation": "

    An array element of SecondaryStatusTransitions for DescribeTrainingJob. It provides\n additional details about a status that the training job has transitioned through. A\n training job can be in one of several states, for example, starting, downloading,\n training, or uploading. Within each state, there are a number of intermediate states.\n For example, within the starting state, SageMaker could be starting the training job or\n launching the ML instances. These transitional states are referred to as the job's\n secondary\n status.\n

    \n

    " + "smithy.api#documentation": "

    An array element of SecondaryStatusTransitions for DescribeTrainingJob. It provides additional details about a status that the\n training job has transitioned through. A training job can be in one of several states,\n for example, starting, downloading, training, or uploading. Within each state, there are\n a number of intermediate states. For example, within the starting state, SageMaker could be\n starting the training job or launching the ML instances. These transitional states are\n referred to as the job's secondary\n status.\n

    \n

    " } }, "com.amazonaws.sagemaker#SecondaryStatusTransitions": { @@ -50876,7 +50876,7 @@ "ModelDataUrl": { "target": "com.amazonaws.sagemaker#Url", "traits": { - "smithy.api#documentation": "

    The Amazon S3 path where the model artifacts, which result from model training, are stored.\n This path must point to a single gzip compressed tar archive\n (.tar.gz suffix).

    \n \n

    The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the\n algorithm.

    \n     " + "smithy.api#documentation": "

    The Amazon S3 path where the model artifacts, which result from model training, are stored.\n This path must point to a single gzip compressed tar archive\n (.tar.gz suffix).

    \n \n

    The model artifacts must be in an S3 bucket that is in the same Amazon Web Services\n region as the algorithm.

    \n     " } }, "AlgorithmName": { @@ -51358,7 +51358,7 @@ "PipelineName": { "target": "com.amazonaws.sagemaker#PipelineNameOrArn", "traits": { - "smithy.api#documentation": "

    The name of the pipeline.

    ", + "smithy.api#documentation": "

    The name or Amazon Resource Name (ARN) of the pipeline.

    ", "smithy.api#required": {} } }, @@ -52493,6 +52493,18 @@ "smithy.api#enumValue": "ml_inf1" } }, + "ML_INF2": { + "target": "smithy.api#Unit", + "traits": { + "smithy.api#enumValue": "ml_inf2" + } + }, + "ML_TRN1": { + "target": "smithy.api#Unit", + "traits": { + "smithy.api#enumValue": "ml_trn1" + } + }, "ML_EIA2": { "target": "smithy.api#Unit", "traits": { @@ -53555,7 +53567,7 @@ "SecondaryStatus": { "target": "com.amazonaws.sagemaker#SecondaryStatus", "traits": { - "smithy.api#documentation": "

    Provides detailed information about the state of the training job. For detailed\n information about the secondary status of the training job, see\n StatusMessage under SecondaryStatusTransition.

    \n

    SageMaker provides primary statuses and secondary statuses that apply to each of\n them:

    \n
    \n
    InProgress
    \n
    \n
      \n
    • \n

      \n Starting\n - Starting the training job.

      \n
      • \n
    • \n

      \n Downloading - An optional stage for algorithms that\n support File training input mode. It indicates that\n data is being downloaded to the ML storage volumes.

      \n
      • \n
    • \n

      \n Training - Training is in progress.

      \n
      • \n
    • \n

      \n Uploading - Training is complete and the model\n artifacts are being uploaded to the S3 location.

      \n
      • \n
    \n
    \n
    Completed
    \n
    \n
      \n
    • \n

      \n Completed - The training job has completed.

      \n
      • \n
    \n
    \n
    Failed
    \n
    \n
      \n
    • \n

      \n Failed - The training job has failed. The reason for\n the failure is returned in the FailureReason field of\n DescribeTrainingJobResponse.

      \n
      • \n
    \n
    \n
    Stopped
    \n
    \n
      \n
    • \n

      \n MaxRuntimeExceeded - The job stopped because it\n exceeded the maximum allowed runtime.

      \n
      • \n
    • \n

      \n Stopped - The training job has stopped.

      \n
      • \n
    \n
    \n
    Stopping
    \n
    \n
      \n
    • \n

      \n Stopping - Stopping the training job.

      \n
      • \n
    \n
    \n
    \n \n

    Valid values for SecondaryStatus are subject to change.

    \n     \n

    We no longer support the following secondary statuses:

    \n
      \n
    • \n

      \n LaunchingMLInstances\n

      \n
      • \n
    • \n

      \n PreparingTrainingStack\n

      \n
      • \n
    • \n

      \n DownloadingTrainingImage\n

      \n
      • \n
    " + "smithy.api#documentation": "

    Provides detailed information about the state of the training job. For detailed\n information about the secondary status of the training job, see\n StatusMessage under SecondaryStatusTransition.

    \n

    SageMaker provides primary statuses and secondary statuses that apply to each of\n them:

    \n
    \n
    InProgress
    \n
    \n
      \n
    • \n

      \n Starting\n - Starting the training job.

      \n
      • \n
    • \n

      \n Downloading - An optional stage for algorithms that\n support File training input mode. It indicates that\n data is being downloaded to the ML storage volumes.

      \n
      • \n
    • \n

      \n Training - Training is in progress.

      \n
      • \n
    • \n

      \n Uploading - Training is complete and the model\n artifacts are being uploaded to the S3 location.

      \n
      • \n
    \n
    \n
    Completed
    \n
    \n
      \n
    • \n

      \n Completed - The training job has completed.

      \n
      • \n
    \n
    \n
    Failed
    \n
    \n
      \n
    • \n

      \n Failed - The training job has failed. The reason for\n the failure is returned in the FailureReason field of\n DescribeTrainingJobResponse.

      \n
      • \n
    \n
    \n
    Stopped
    \n
    \n
      \n
    • \n

      \n MaxRuntimeExceeded - The job stopped because it\n exceeded the maximum allowed runtime.

      \n
      • \n
    • \n

      \n Stopped - The training job has stopped.

      \n
      • \n
    \n
    \n
    Stopping
    \n
    \n
      \n
    • \n

      \n Stopping - Stopping the training job.

      \n
      • \n
    \n
    \n
    \n \n

    Valid values for SecondaryStatus are subject to change.

    \n     \n

    We no longer support the following secondary statuses:

    \n
      \n
    • \n

      \n LaunchingMLInstances\n

      \n
      • \n
    • \n

      \n PreparingTrainingStack\n

      \n
      • \n
    • \n

      \n DownloadingTrainingImage\n

      \n
      • \n
    " } }, "FailureReason": { @@ -53603,7 +53615,7 @@ "VpcConfig": { "target": "com.amazonaws.sagemaker#VpcConfig", "traits": { - "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that this training job has\n access to. For more information, see Protect Training Jobs by Using an Amazon\n Virtual Private Cloud.

    " + "smithy.api#documentation": "

    A VpcConfig object that specifies the VPC that this training job has access\n to. For more information, see Protect Training Jobs by Using an Amazon\n Virtual Private Cloud.

    " } }, "StoppingCondition": { @@ -54575,7 +54587,7 @@ "TransformOutput": { "target": "com.amazonaws.sagemaker#TransformOutput", "traits": { - "smithy.api#documentation": "

    Identifies the Amazon S3 location where you want Amazon SageMaker to save the results\n from the transform job.

    ", + "smithy.api#documentation": "

    Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the\n transform job.

    ", "smithy.api#required": {} } }, @@ -56117,7 +56129,7 @@ } ], "traits": { - "smithy.api#documentation": "

    Deploys the new EndpointConfig specified in the request, switches to\n using newly created endpoint, and then deletes resources provisioned for the endpoint\n using the previous EndpointConfig (there is no availability loss).

    \n

    When SageMaker receives the request, it sets the endpoint status to\n Updating. After updating the endpoint, it sets the status to\n InService. To check the status of an endpoint, use the DescribeEndpoint API.\n \n

    \n \n

    You must not delete an EndpointConfig in use by an endpoint that is\n live or while the UpdateEndpoint or CreateEndpoint\n operations are being performed on the endpoint. To update an endpoint, you must\n create a new EndpointConfig.

    \n

    If you delete the EndpointConfig of an endpoint that is active or\n being created or updated you may lose visibility into the instance type the endpoint\n is using. The endpoint must be deleted in order to stop incurring charges.

    \n     " + "smithy.api#documentation": "

    Deploys the new EndpointConfig specified in the request, switches to\n using newly created endpoint, and then deletes resources provisioned for the endpoint\n using the previous EndpointConfig (there is no availability loss).

    \n

    When SageMaker receives the request, it sets the endpoint status to\n Updating. After updating the endpoint, it sets the status to\n InService. To check the status of an endpoint, use the DescribeEndpoint API.\n \n

    \n \n

    You must not delete an EndpointConfig in use by an endpoint that is\n live or while the UpdateEndpoint or CreateEndpoint\n operations are being performed on the endpoint. To update an endpoint, you must\n create a new EndpointConfig.

    \n

    If you delete the EndpointConfig of an endpoint that is active or\n being created or updated you may lose visibility into the instance type the endpoint\n is using. The endpoint must be deleted in order to stop incurring charges.

    \n     " } }, "com.amazonaws.sagemaker#UpdateEndpointInput": { @@ -56147,7 +56159,7 @@ "ExcludeRetainedVariantProperties": { "target": "com.amazonaws.sagemaker#VariantPropertyList", "traits": { - "smithy.api#documentation": "

    When you are updating endpoint resources with RetainAllVariantProperties, whose value is set to\n true, ExcludeRetainedVariantProperties specifies the list\n of type VariantProperty to override with the values provided by\n EndpointConfig. If you don't specify a value for\n ExcludeRetainedVariantProperties, no variant properties are overridden.\n

    " + "smithy.api#documentation": "

    When you are updating endpoint resources with RetainAllVariantProperties,\n whose value is set to true, ExcludeRetainedVariantProperties\n specifies the list of type VariantProperty\n to override with the values provided by EndpointConfig. If you don't\n specify a value for ExcludeRetainedVariantProperties, no variant properties\n are overridden.

    " } }, "DeploymentConfig": { @@ -57086,7 +57098,7 @@ "AcceleratorTypes": { "target": "com.amazonaws.sagemaker#NotebookInstanceAcceleratorTypes", "traits": { - "smithy.api#documentation": "

    A list of the Elastic Inference (EI) instance types to associate with this notebook\n instance. Currently only one EI instance type can be associated with a notebook\n instance. For more information, see Using Elastic Inference in Amazon SageMaker.

    " + "smithy.api#documentation": "

    A list of the Elastic Inference (EI) instance types to associate with this notebook\n instance. Currently only one EI instance type can be associated with a notebook\n instance. For more information, see Using Elastic Inference in\n Amazon SageMaker.

    " } }, "DisassociateAcceleratorTypes": { @@ -58145,13 +58157,13 @@ "VariantPropertyType": { "target": "com.amazonaws.sagemaker#VariantPropertyType", "traits": { - "smithy.api#documentation": "

    The type of variant property. The supported values are:

    \n
      \n
    • \n

      \n DesiredInstanceCount: Overrides the existing variant instance\n counts using the InitialInstanceCount values\n in the ProductionVariants of CreateEndpointConfig.

      \n
      • \n
    • \n

      \n DesiredWeight: Overrides the existing variant weights using the\n InitialVariantWeight values in the ProductionVariants of CreateEndpointConfig.

      \n
      • \n
    • \n

      \n DataCaptureConfig: (Not currently supported.)

      \n
      • \n
    ", + "smithy.api#documentation": "

    The type of variant property. The supported values are:

    \n
      \n
    • \n

      \n DesiredInstanceCount: Overrides the existing variant instance\n counts using the InitialInstanceCount values in the\n ProductionVariants of CreateEndpointConfig.

      \n
      • \n
    • \n

      \n DesiredWeight: Overrides the existing variant weights using the\n InitialVariantWeight values in the\n ProductionVariants of CreateEndpointConfig.

      \n
      • \n
    • \n

      \n DataCaptureConfig: (Not currently supported.)

      \n
      • \n
    ", "smithy.api#required": {} } } }, "traits": { - "smithy.api#documentation": "

    Specifies a production variant property type for an Endpoint.

    \n

    If you are updating an endpoint with the RetainAllVariantProperties\n option of UpdateEndpointInput set to\n true, the VariantProperty objects listed in the ExcludeRetainedVariantProperties parameter of UpdateEndpointInput\n override the\n existing variant properties of the endpoint.

    " + "smithy.api#documentation": "

    Specifies a production variant property type for an Endpoint.

    \n

    If you are updating an endpoint with the RetainAllVariantProperties\n option of UpdateEndpointInput set to true, the\n VariantProperty objects listed in the\n ExcludeRetainedVariantProperties parameter of UpdateEndpointInput override the existing variant properties of the\n endpoint.

    " } }, "com.amazonaws.sagemaker#VariantPropertyList": {