feat(client-sagemaker): This change allows customers to provide a custom entrypoint script for the docker container to be run while executing training jobs, and provide custom arguments to the entrypoint script.

Author: awstools

clients/client-sagemaker/src/SageMaker.ts

@@ -2313,10 +2313,10 @@ export class SageMaker extends SageMakerClient {
    *             Amazon SageMaker Studio. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/experiments-view-compare.html#experiments-view">View
    *                 Experiments, Trials, and Trial Components</a>.</p>
    *         <important>
-   *             <p>Do not include any security-sensitive information including account access
-   *                 IDs, secrets or tokens in any hyperparameter field. If the use of
-   *                 security-sensitive credentials are detected, SageMaker will reject your training
-   *                 job request and return an exception error.</p>
+   *             <p>Do not include any security-sensitive information including account access IDs,
+   *                 secrets or tokens in any hyperparameter field. If the use of security-sensitive
+   *                 credentials are detected, SageMaker will reject your training job request and return an
+   *                 exception error.</p>
    *         </important>
    */
   public createHyperParameterTuningJob(
@@ -3143,7 +3143,7 @@ export class SageMaker extends SageMakerClient {
    *             </li>
    *             <li>
    *                 <p>
-   *                   <code>InputDataConfig</code> - Describes the training dataset and the Amazon S3,
+   *                   <code>InputDataConfig</code> - Describes the input required by the training job and the Amazon S3,
    *                     EFS, or FSx location where it is stored.</p>
    *             </li>
    *             <li>

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

@@ -41,10 +41,10 @@ export interface CreateHyperParameterTuningJobCommandOutput
  *             Amazon SageMaker Studio. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/experiments-view-compare.html#experiments-view">View
  *                 Experiments, Trials, and Trial Components</a>.</p>
  *         <important>
- *             <p>Do not include any security-sensitive information including account access
- *                 IDs, secrets or tokens in any hyperparameter field. If the use of
- *                 security-sensitive credentials are detected, SageMaker will reject your training
- *                 job request and return an exception error.</p>
+ *             <p>Do not include any security-sensitive information including account access IDs,
+ *                 secrets or tokens in any hyperparameter field. If the use of security-sensitive
+ *                 credentials are detected, SageMaker will reject your training job request and return an
+ *                 exception error.</p>
  *         </important>
  * @example
  * Use a bare-bones client and the command you need to make an API call.

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

@@ -59,7 +59,7 @@ export interface CreateTrainingJobCommandOutput extends CreateTrainingJobRespons
  *             </li>
  *             <li>
  *                 <p>
- *                   <code>InputDataConfig</code> - Describes the training dataset and the Amazon S3,
+ *                   <code>InputDataConfig</code> - Describes the input required by the training job and the Amazon S3,
  *                     EFS, or FSx location where it is stored.</p>
  *             </li>
  *             <li>

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

@@ -705,6 +705,20 @@ export interface AlgorithmSpecification {
    *          </ul>
    */
   EnableSageMakerMetricsTimeSeries?: boolean;
+
+  /**
+   * <p>The <a href="https://docs.docker.com/engine/reference/builder/">entrypoint script
+   *                 for a Docker container</a> used to run a training job. This script takes
+   *             precedence over the default train processing instructions. See <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo-dockerfile.html">How Amazon SageMaker
+   *                 Runs Your Training Image</a> for more information.</p>
+   */
+  ContainerEntrypoint?: string[];
+
+  /**
+   * <p>The arguments for a container used to run a training job. See <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo-dockerfile.html">How Amazon SageMaker
+   *                 Runs Your Training Image</a> for additional information.</p>
+   */
+  ContainerArguments?: string[];
 }
 
 export enum AlgorithmStatus {

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

@@ -6855,8 +6855,8 @@ export interface CreateTrainingJobRequest {
    *         <important>
    *             <p>Do not include any security-sensitive information including account access IDs,
    *                 secrets or tokens in any hyperparameter field. If the use of security-sensitive
-   *                 credentials are detected, SageMaker will reject your training job request and return
-   *                 an exception error.</p>
+   *                 credentials are detected, SageMaker will reject your training job request and return an
+   *                 exception error.</p>
    *         </important>
    */
   HyperParameters?: Record<string, string>;

clients/client-sagemaker/src/protocols/Aws_json1_1.ts

@@ -15499,6 +15499,12 @@ const serializeAws_json1_1AlarmList = (input: Alarm[], context: __SerdeContext):
 const serializeAws_json1_1AlgorithmSpecification = (input: AlgorithmSpecification, context: __SerdeContext): any => {
   return {
     ...(input.AlgorithmName != null && { AlgorithmName: input.AlgorithmName }),
+    ...(input.ContainerArguments != null && {
+      ContainerArguments: serializeAws_json1_1TrainingContainerArguments(input.ContainerArguments, context),
+    }),
+    ...(input.ContainerEntrypoint != null && {
+      ContainerEntrypoint: serializeAws_json1_1TrainingContainerEntrypoint(input.ContainerEntrypoint, context),
+    }),
     ...(input.EnableSageMakerMetricsTimeSeries != null && {
       EnableSageMakerMetricsTimeSeries: input.EnableSageMakerMetricsTimeSeries,
     }),
@@ -22432,6 +22438,22 @@ const serializeAws_json1_1TrafficRoutingConfig = (input: TrafficRoutingConfig, c
   };
 };
 
+const serializeAws_json1_1TrainingContainerArguments = (input: string[], context: __SerdeContext): any => {
+  return input
+    .filter((e: any) => e != null)
+    .map((entry) => {
+      return entry;
+    });
+};
+
+const serializeAws_json1_1TrainingContainerEntrypoint = (input: string[], context: __SerdeContext): any => {
+  return input
+    .filter((e: any) => e != null)
+    .map((entry) => {
+      return entry;
+    });
+};
+
 const serializeAws_json1_1TrainingEnvironmentMap = (input: Record<string, string>, context: __SerdeContext): any => {
   return Object.entries(input).reduce((acc: Record<string, any>, [key, value]: [string, any]) => {
     if (value === null) {
@@ -23344,6 +23366,14 @@ const deserializeAws_json1_1AlarmList = (output: any, context: __SerdeContext):
 const deserializeAws_json1_1AlgorithmSpecification = (output: any, context: __SerdeContext): AlgorithmSpecification => {
   return {
     AlgorithmName: __expectString(output.AlgorithmName),
+    ContainerArguments:
+      output.ContainerArguments != null
+        ? deserializeAws_json1_1TrainingContainerArguments(output.ContainerArguments, context)
+        : undefined,
+    ContainerEntrypoint:
+      output.ContainerEntrypoint != null
+        ? deserializeAws_json1_1TrainingContainerEntrypoint(output.ContainerEntrypoint, context)
+        : undefined,
     EnableSageMakerMetricsTimeSeries: __expectBoolean(output.EnableSageMakerMetricsTimeSeries),
     MetricDefinitions:
       output.MetricDefinitions != null
@@ -33427,6 +33457,30 @@ const deserializeAws_json1_1TrafficRoutingConfig = (output: any, context: __Serd
   } as any;
 };
 
+const deserializeAws_json1_1TrainingContainerArguments = (output: any, context: __SerdeContext): string[] => {
+  const retVal = (output || [])
+    .filter((e: any) => e != null)
+    .map((entry: any) => {
+      if (entry === null) {
+        return null as any;
+      }
+      return __expectString(entry) as any;
+    });
+  return retVal;
+};
+
+const deserializeAws_json1_1TrainingContainerEntrypoint = (output: any, context: __SerdeContext): string[] => {
+  const retVal = (output || [])
+    .filter((e: any) => e != null)
+    .map((entry: any) => {
+      if (entry === null) {
+        return null as any;
+      }
+      return __expectString(entry) as any;
+    });
+  return retVal;
+};
+
 const deserializeAws_json1_1TrainingEnvironmentMap = (output: any, context: __SerdeContext): Record<string, string> => {
   return Object.entries(output).reduce((acc: Record<string, string>, [key, value]: [string, any]) => {
     if (value === null) {