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feat(client-sagemaker): This release adds support for G5, P4d, and C6…
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…i instance types in Amazon SageMaker Inference and increases the number of hyperparameters that can be searched from 20 to 30 in Amazon SageMaker Automatic Model Tuning
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awstools committed Jul 14, 2022
1 parent 58d673a commit 4d43eac
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34 changes: 27 additions & 7 deletions clients/client-sagemaker/src/models/models_0.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -329,12 +329,29 @@ export enum ProductionVariantInstanceType {
ML_C5_9XLARGE = "ml.c5.9xlarge",
ML_C5_LARGE = "ml.c5.large",
ML_C5_XLARGE = "ml.c5.xlarge",
ML_C6I_12XLARGE = "ml.c6i.12xlarge",
ML_C6I_16XLARGE = "ml.c6i.16xlarge",
ML_C6I_24XLARGE = "ml.c6i.24xlarge",
ML_C6I_2XLARGE = "ml.c6i.2xlarge",
ML_C6I_32XLARGE = "ml.c6i.32xlarge",
ML_C6I_4XLARGE = "ml.c6i.4xlarge",
ML_C6I_8XLARGE = "ml.c6i.8xlarge",
ML_C6I_LARGE = "ml.c6i.large",
ML_C6I_XLARGE = "ml.c6i.xlarge",
ML_G4DN_12XLARGE = "ml.g4dn.12xlarge",
ML_G4DN_16XLARGE = "ml.g4dn.16xlarge",
ML_G4DN_2XLARGE = "ml.g4dn.2xlarge",
ML_G4DN_4XLARGE = "ml.g4dn.4xlarge",
ML_G4DN_8XLARGE = "ml.g4dn.8xlarge",
ML_G4DN_XLARGE = "ml.g4dn.xlarge",
ML_G5_12XLARGE = "ml.g5.12xlarge",
ML_G5_16XLARGE = "ml.g5.16xlarge",
ML_G5_24XLARGE = "ml.g5.24xlarge",
ML_G5_2XLARGE = "ml.g5.2xlarge",
ML_G5_48XLARGE = "ml.g5.48xlarge",
ML_G5_4XLARGE = "ml.g5.4xlarge",
ML_G5_8XLARGE = "ml.g5.8xlarge",
ML_G5_XLARGE = "ml.g5.xlarge",
ML_INF1_24XLARGE = "ml.inf1.24xlarge",
ML_INF1_2XLARGE = "ml.inf1.2xlarge",
ML_INF1_6XLARGE = "ml.inf1.6xlarge",
Expand Down Expand Up @@ -362,6 +379,7 @@ export enum ProductionVariantInstanceType {
ML_P3_16XLARGE = "ml.p3.16xlarge",
ML_P3_2XLARGE = "ml.p3.2xlarge",
ML_P3_8XLARGE = "ml.p3.8xlarge",
ML_P4D_24XLARGE = "ml.p4d.24xlarge",
ML_R5D_12XLARGE = "ml.r5d.12xlarge",
ML_R5D_24XLARGE = "ml.r5d.24xlarge",
ML_R5D_2XLARGE = "ml.r5d.2xlarge",
Expand Down Expand Up @@ -1310,7 +1328,7 @@ export enum TrainingInstanceType {
/**
* <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>
* you can configure multiple instance groups .</p>
*/
export interface InstanceGroup {
/**
Expand Down Expand Up @@ -11435,9 +11453,10 @@ export namespace IntegerParameterRange {
* result in the training job with the best performance as measured by the objective metric
* of the hyperparameter tuning job.</p>
* <note>
* <p>You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job
* can search over. Every possible value of a categorical parameter range counts
* against this limit.</p>
* <p>The maximum number of items specified for <code>Array Members</code> refers to
* the maximum number of hyperparameters for each range and also the maximum for the
* hyperparameter tuning job itself. That is, the sum of the number of hyperparameters
* for all the ranges can't exceed the maximum number specified.</p>
* </note>
*/
export interface ParameterRanges {
Expand Down Expand Up @@ -11734,9 +11753,10 @@ export interface HyperParameterTrainingJobDefinition {
* result in the training job with the best performance as measured by the objective metric
* of the hyperparameter tuning job.</p>
* <note>
* <p>You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job
* can search over. Every possible value of a categorical parameter range counts
* against this limit.</p>
* <p>The maximum number of items specified for <code>Array Members</code> refers to
* the maximum number of hyperparameters for each range and also the maximum for the
* hyperparameter tuning job itself. That is, the sum of the number of hyperparameters
* for all the ranges can't exceed the maximum number specified.</p>
* </note>
*/
HyperParameterRanges?: ParameterRanges;
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84 changes: 78 additions & 6 deletions codegen/sdk-codegen/aws-models/sagemaker.json
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Original file line number Diff line number Diff line change
Expand Up @@ -3320,7 +3320,7 @@
"traits": {
"smithy.api#length": {
"min": 0,
"max": 20
"max": 30
}
}
},
Expand Down Expand Up @@ -4418,7 +4418,7 @@
"traits": {
"smithy.api#length": {
"min": 0,
"max": 20
"max": 30
}
}
},
Expand Down Expand Up @@ -20782,7 +20782,7 @@
}
},
"traits": {
"smithy.api#documentation": "<p>Defines an instance group for heterogeneous cluster training. \n When requesting a training job using the <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html\">CreateTrainingJob</a> API, \n you can configure up to 5 different ML training instance groups.</p>"
"smithy.api#documentation": "<p>Defines an instance group for heterogeneous cluster training. \n When requesting a training job using the <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html\">CreateTrainingJob</a> API, \n you can configure multiple instance groups .</p>"
}
},
"com.amazonaws.sagemaker#InstanceGroupName": {
Expand Down Expand Up @@ -21185,7 +21185,7 @@
"traits": {
"smithy.api#length": {
"min": 0,
"max": 20
"max": 30
}
}
},
Expand Down Expand Up @@ -31168,7 +31168,7 @@
}
},
"traits": {
"smithy.api#documentation": "<p>Specifies ranges of integer, continuous, and categorical hyperparameters that a\n hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs\n with hyperparameter values within these ranges to find the combination of values that\n result in the training job with the best performance as measured by the objective metric\n of the hyperparameter tuning job.</p>\n <note>\n <p>You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job\n can search over. Every possible value of a categorical parameter range counts\n against this limit.</p>\n </note>"
"smithy.api#documentation": "<p>Specifies ranges of integer, continuous, and categorical hyperparameters that a\n hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs\n with hyperparameter values within these ranges to find the combination of values that\n result in the training job with the best performance as measured by the objective metric\n of the hyperparameter tuning job.</p>\n <note>\n <p>The maximum number of items specified for <code>Array Members</code> refers to\n the maximum number of hyperparameters for each range and also the maximum for the\n hyperparameter tuning job itself. That is, the sum of the number of hyperparameters\n for all the ranges can't exceed the maximum number specified.</p>\n </note>"
}
},
"com.amazonaws.sagemaker#ParameterType": {
Expand Down Expand Up @@ -31212,7 +31212,7 @@
"traits": {
"smithy.api#length": {
"min": 1,
"max": 20
"max": 30
}
}
},
Expand Down Expand Up @@ -33329,6 +33329,78 @@
{
"value": "ml.inf1.24xlarge",
"name": "ML_INF1_24XLARGE"
},
{
"value": "ml.c6i.large",
"name": "ML_C6I_LARGE"
},
{
"value": "ml.c6i.xlarge",
"name": "ML_C6I_XLARGE"
},
{
"value": "ml.c6i.2xlarge",
"name": "ML_C6I_2XLARGE"
},
{
"value": "ml.c6i.4xlarge",
"name": "ML_C6I_4XLARGE"
},
{
"value": "ml.c6i.8xlarge",
"name": "ML_C6I_8XLARGE"
},
{
"value": "ml.c6i.12xlarge",
"name": "ML_C6I_12XLARGE"
},
{
"value": "ml.c6i.16xlarge",
"name": "ML_C6I_16XLARGE"
},
{
"value": "ml.c6i.24xlarge",
"name": "ML_C6I_24XLARGE"
},
{
"value": "ml.c6i.32xlarge",
"name": "ML_C6I_32XLARGE"
},
{
"value": "ml.g5.xlarge",
"name": "ML_G5_XLARGE"
},
{
"value": "ml.g5.2xlarge",
"name": "ML_G5_2XLARGE"
},
{
"value": "ml.g5.4xlarge",
"name": "ML_G5_4XLARGE"
},
{
"value": "ml.g5.8xlarge",
"name": "ML_G5_8XLARGE"
},
{
"value": "ml.g5.12xlarge",
"name": "ML_G5_12XLARGE"
},
{
"value": "ml.g5.16xlarge",
"name": "ML_G5_16XLARGE"
},
{
"value": "ml.g5.24xlarge",
"name": "ML_G5_24XLARGE"
},
{
"value": "ml.g5.48xlarge",
"name": "ML_G5_48XLARGE"
},
{
"value": "ml.p4d.24xlarge",
"name": "ML_P4D_24XLARGE"
}
]
}
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