feat(client-sagemaker): Heterogeneous clusters: the ability to launch training jobs with multiple instance types. This enables running component of the training job on the instance type that is most suitable for it. e.g. doing data processing and augmentation on CPU instances and neural network training on GPU instances

Author: awstools

clients/client-sagemaker/src/commands/CreateImageCommand.ts

@@ -12,8 +12,7 @@ import {
   SerdeContext as __SerdeContext,
 } from "@aws-sdk/types";
 
-import { CreateImageRequest } from "../models/models_0";
-import { CreateImageResponse } from "../models/models_1";
+import { CreateImageRequest, CreateImageResponse } from "../models/models_1";
 import {
   deserializeAws_json1_1CreateImageCommand,
   serializeAws_json1_1CreateImageCommand,

clients/client-sagemaker/src/models/models_0.ts

@@ -1029,6 +1029,12 @@ export interface S3DataSource {
    *             manifest file.</p>
    */
   AttributeNames?: string[];
+
+  /**
+   * <p>A list of names of instance groups that get data
+   *             from the S3 data source.</p>
+   */
+  InstanceGroupNames?: string[];
 }
 
 export namespace S3DataSource {
@@ -1301,6 +1307,37 @@ export enum TrainingInstanceType {
   ML_P4D_24XLARGE = "ml.p4d.24xlarge",
 }
 
+/**
+ * <p>Defines an instance group for heterogeneous cluster training.
+ *             When requesting a training job using the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html">CreateTrainingJob</a> API,
+ *             you can configure up to 5 different ML training instance groups.</p>
+ */
+export interface InstanceGroup {
+  /**
+   * <p>Specifies the instance type of the instance group.</p>
+   */
+  InstanceType: TrainingInstanceType | string | undefined;
+
+  /**
+   * <p>Specifies the number of instances of the instance group.</p>
+   */
+  InstanceCount: number | undefined;
+
+  /**
+   * <p>Specifies the name of the instance group.</p>
+   */
+  InstanceGroupName: string | undefined;
+}
+
+export namespace InstanceGroup {
+  /**
+   * @internal
+   */
+  export const filterSensitiveLog = (obj: InstanceGroup): any => ({
+    ...obj,
+  });
+}
+
 /**
  * <p>Describes the resources, including ML compute instances and ML storage volumes, to
  *             use for model training. </p>
@@ -1309,13 +1346,13 @@ export interface ResourceConfig {
   /**
    * <p>The ML compute instance type. </p>
    */
-  InstanceType: TrainingInstanceType | string | undefined;
+  InstanceType?: TrainingInstanceType | string;
 
   /**
    * <p>The number of ML compute instances to use. For distributed training, provide a
    *             value greater than 1. </p>
    */
-  InstanceCount: number | undefined;
+  InstanceCount?: number;
 
   /**
    * <p>The size of the ML storage volume that you want to provision. </p>
@@ -1369,6 +1406,11 @@ export interface ResourceConfig {
    *          </ul>
    */
   VolumeKmsKeyId?: string;
+
+  /**
+   * <p>The configuration of a heterogeneous cluster in JSON format.</p>
+   */
+  InstanceGroups?: InstanceGroup[];
 }
 
 export namespace ResourceConfig {
@@ -12000,39 +12042,3 @@ export namespace CreateHyperParameterTuningJobResponse {
     ...obj,
   });
 }
-
-export interface CreateImageRequest {
-  /**
-   * <p>The description of the image.</p>
-   */
-  Description?: string;
-
-  /**
-   * <p>The display name of the image. If not provided, <code>ImageName</code> is displayed.</p>
-   */
-  DisplayName?: string;
-
-  /**
-   * <p>The name of the image. Must be unique to your account.</p>
-   */
-  ImageName: string | undefined;
-
-  /**
-   * <p>The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.</p>
-   */
-  RoleArn: string | undefined;
-
-  /**
-   * <p>A list of tags to apply to the image.</p>
-   */
-  Tags?: Tag[];
-}
-
-export namespace CreateImageRequest {
-  /**
-   * @internal
-   */
-  export const filterSensitiveLog = (obj: CreateImageRequest): any => ({
-    ...obj,
-  });
-}

clients/client-sagemaker/src/models/models_1.ts

@@ -106,6 +106,42 @@ import {
   VpcConfig,
 } from "./models_0";
 
+export interface CreateImageRequest {
+  /**
+   * <p>The description of the image.</p>
+   */
+  Description?: string;
+
+  /**
+   * <p>The display name of the image. If not provided, <code>ImageName</code> is displayed.</p>
+   */
+  DisplayName?: string;
+
+  /**
+   * <p>The name of the image. Must be unique to your account.</p>
+   */
+  ImageName: string | undefined;
+
+  /**
+   * <p>The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.</p>
+   */
+  RoleArn: string | undefined;
+
+  /**
+   * <p>A list of tags to apply to the image.</p>
+   */
+  Tags?: Tag[];
+}
+
+export namespace CreateImageRequest {
+  /**
+   * @internal
+   */
+  export const filterSensitiveLog = (obj: CreateImageRequest): any => ({
+    ...obj,
+  });
+}
+
 export interface CreateImageResponse {
   /**
    * <p>The Amazon Resource Name (ARN) of the image.</p>
@@ -11306,37 +11342,3 @@ export namespace EndpointOutputConfiguration {
     ...obj,
   });
 }
-
-/**
- * <p>The metrics of recommendations.</p>
- */
-export interface RecommendationMetrics {
-  /**
-   * <p>Defines the cost per hour for the instance. </p>
-   */
-  CostPerHour: number | undefined;
-
-  /**
-   * <p>Defines the cost per inference for the instance .</p>
-   */
-  CostPerInference: number | undefined;
-
-  /**
-   * <p>The expected maximum number of requests per minute for the instance.</p>
-   */
-  MaxInvocations: number | undefined;
-
-  /**
-   * <p>The expected model latency at maximum invocation per minute for the instance.</p>
-   */
-  ModelLatency: number | undefined;
-}
-
-export namespace RecommendationMetrics {
-  /**
-   * @internal
-   */
-  export const filterSensitiveLog = (obj: RecommendationMetrics): any => ({
-    ...obj,
-  });
-}

clients/client-sagemaker/src/models/models_2.ts

@@ -128,7 +128,6 @@ import {
   RecommendationJobInputConfig,
   RecommendationJobStoppingConditions,
   RecommendationJobType,
-  RecommendationMetrics,
   RootAccess,
   RuleEvaluationStatus,
   ServiceCatalogProvisioningDetails,
@@ -143,6 +142,40 @@ import {
   TrialComponentStatus,
 } from "./models_1";
 
+/**
+ * <p>The metrics of recommendations.</p>
+ */
+export interface RecommendationMetrics {
+  /**
+   * <p>Defines the cost per hour for the instance. </p>
+   */
+  CostPerHour: number | undefined;
+
+  /**
+   * <p>Defines the cost per inference for the instance .</p>
+   */
+  CostPerInference: number | undefined;
+
+  /**
+   * <p>The expected maximum number of requests per minute for the instance.</p>
+   */
+  MaxInvocations: number | undefined;
+
+  /**
+   * <p>The expected model latency at maximum invocation per minute for the instance.</p>
+   */
+  ModelLatency: number | undefined;
+}
+
+export namespace RecommendationMetrics {
+  /**
+   * @internal
+   */
+  export const filterSensitiveLog = (obj: RecommendationMetrics): any => ({
+    ...obj,
+  });
+}
+
 /**
  * <p>A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.</p>
  */
@@ -10973,8 +11006,3 @@ export namespace ListProcessingJobsResponse {
     ...obj,
   });
 }
-
-export enum ProjectSortBy {
-  CREATION_TIME = "CreationTime",
-  NAME = "Name",
-}

clients/client-sagemaker/src/models/models_3.ts

@@ -93,7 +93,6 @@ import {
   PipelineExperimentConfig,
   PipelineStatus,
   ProcessingJobStatus,
-  ProjectSortBy,
   ProjectStatus,
   ResourceType,
   SecondaryStatus,
@@ -111,6 +110,11 @@ import {
   Workteam,
 } from "./models_2";
 
+export enum ProjectSortBy {
+  CREATION_TIME = "CreationTime",
+  NAME = "Name",
+}
+
 export enum ProjectSortOrder {
   ASCENDING = "Ascending",
   DESCENDING = "Descending",