Amazon SageMaker Service Update: Enable ml.g4dn instance types for SageMaker Batch Transform and SageMaker Processing

Author: AWS

.changes/next-release/feature-AmazonSageMakerService-5a96e0c.json

@@ -0,0 +1,6 @@
+{
+    "type": "feature",
+    "category": "Amazon SageMaker Service",
+    "contributor": "",
+    "description": "Enable ml.g4dn instance types for SageMaker Batch Transform and SageMaker Processing"
+}

services/sagemaker/src/main/resources/codegen-resources/service-2.json

@@ -16916,7 +16916,7 @@
           "documentation":"<p>A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics.html\">Defining Objective Metrics</a>.</p>"
         }
       },
-      "documentation":"<p>Specifies a metric that the training algorithm writes to <code>stderr</code> or <code>stdout</code> . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.</p>"
+      "documentation":"<p>Specifies a metric that the training algorithm writes to <code>stderr</code> or <code>stdout</code>. Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.</p>"
     },
     "MetricDefinitionList":{
       "type":"list",
@@ -19262,11 +19262,11 @@
         },
         "VolumeSizeInGB":{
           "shape":"ProcessingVolumeSizeInGB",
-          "documentation":"<p>The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.</p>"
+          "documentation":"<p>The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.</p> <note> <p>Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a <code>VolumeSizeInGB</code> greater than the total size of the local instance storage.</p> <p>For a list of instance types that support local instance storage, including the total size per instance type, see <a href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes\">Instance Store Volumes</a>.</p> </note>"
         },
         "VolumeKmsKeyId":{
           "shape":"KmsKeyId",
-          "documentation":"<p>The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job. </p>"
+          "documentation":"<p>The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job. </p> <note> <p>Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a <code>VolumeKmsKeyId</code> when using an instance type with local storage.</p> <p>For a list of instance types that support local instance storage, see <a href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes\">Instance Store Volumes</a>.</p> <p>For more information about local instance storage encryption, see <a href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html\">SSD Instance Store Volumes</a>.</p> </note>"
         }
       },
       "documentation":"<p>Configuration for the cluster used to run a processing job.</p>"
@@ -19372,7 +19372,13 @@
         "ml.r5.8xlarge",
         "ml.r5.12xlarge",
         "ml.r5.16xlarge",
-        "ml.r5.24xlarge"
+        "ml.r5.24xlarge",
+        "ml.g4dn.xlarge",
+        "ml.g4dn.2xlarge",
+        "ml.g4dn.4xlarge",
+        "ml.g4dn.8xlarge",
+        "ml.g4dn.12xlarge",
+        "ml.g4dn.16xlarge"
       ]
     },
     "ProcessingJob":{
@@ -22263,7 +22269,13 @@
         "ml.m5.2xlarge",
         "ml.m5.4xlarge",
         "ml.m5.12xlarge",
-        "ml.m5.24xlarge"
+        "ml.m5.24xlarge",
+        "ml.g4dn.xlarge",
+        "ml.g4dn.2xlarge",
+        "ml.g4dn.4xlarge",
+        "ml.g4dn.8xlarge",
+        "ml.g4dn.12xlarge",
+        "ml.g4dn.16xlarge"
       ]
     },
     "TransformInstanceTypes":{
@@ -22489,15 +22501,15 @@
       "members":{
         "InstanceType":{
           "shape":"TransformInstanceType",
-          "documentation":"<p>The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or <code>ml.m5.large</code> instance types.</p>"
+          "documentation":"<p>The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or <code>ml.m5.large</code>instance types.</p>"
         },
         "InstanceCount":{
           "shape":"TransformInstanceCount",
           "documentation":"<p>The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is <code>1</code>.</p>"
         },
         "VolumeKmsKeyId":{
           "shape":"KmsKeyId",
-          "documentation":"<p>The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The <code>VolumeKmsKeyId</code> can be any of the following formats:</p> <ul> <li> <p>Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Alias name: <code>alias/ExampleAlias</code> </p> </li> <li> <p>Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code> </p> </li> </ul>"
+          "documentation":"<p>The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.</p> <note> <p>Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a <code>VolumeKmsKeyId</code> when using an instance type with local storage.</p> <p>For a list of instance types that support local instance storage, see <a href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes\">Instance Store Volumes</a>.</p> <p>For more information about local instance storage encryption, see <a href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html\">SSD Instance Store Volumes</a>.</p> </note> <p> The <code>VolumeKmsKeyId</code> can be any of the following formats:</p> <ul> <li> <p>Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Alias name: <code>alias/ExampleAlias</code> </p> </li> <li> <p>Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code> </p> </li> </ul>"
         }
       },
       "documentation":"<p>Describes the resources, including ML instance types and ML instance count, to use for transform job.</p>"