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feat(client-sagemaker): Amazon SageMaker Autopilot adds support for m…
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…anually selecting features from the input dataset using the CreateAutoMLJob API.
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awstools committed May 25, 2022
1 parent 9cb0805 commit 212b3e3
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Showing 10 changed files with 266 additions and 147 deletions.
2 changes: 1 addition & 1 deletion clients/client-sagemaker/src/SageMaker.ts
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Expand Up @@ -3097,7 +3097,7 @@ export class SageMaker extends SageMakerClient {
* <p>
* <code>ModelName</code> - Identifies the model to use. <code>ModelName</code>
* must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services
* account. For information on creating a model, see <a>CreateModel</a>.</p>
* account. For information on creating a model, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html">CreateModel</a>.</p>
* </li>
* <li>
* <p>
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3 changes: 2 additions & 1 deletion clients/client-sagemaker/src/commands/CreateImageCommand.ts
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Expand Up @@ -12,7 +12,8 @@ import {
SerdeContext as __SerdeContext,
} from "@aws-sdk/types";

import { CreateImageRequest, CreateImageResponse } from "../models/models_0";
import { CreateImageRequest } from "../models/models_0";
import { CreateImageResponse } from "../models/models_1";
import {
deserializeAws_json1_1CreateImageCommand,
serializeAws_json1_1CreateImageCommand,
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2 changes: 1 addition & 1 deletion clients/client-sagemaker/src/commands/CreateTransformJobCommand.ts
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Expand Up @@ -38,7 +38,7 @@ export interface CreateTransformJobCommandOutput extends CreateTransformJobRespo
* <p>
* <code>ModelName</code> - Identifies the model to use. <code>ModelName</code>
* must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services
* account. For information on creating a model, see <a>CreateModel</a>.</p>
* account. For information on creating a model, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html">CreateModel</a>.</p>
* </li>
* <li>
* <p>
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3 changes: 1 addition & 2 deletions clients/client-sagemaker/src/commands/ListTagsCommand.ts
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Expand Up @@ -12,8 +12,7 @@ import {
SerdeContext as __SerdeContext,
} from "@aws-sdk/types";

import { ListTagsInput } from "../models/models_2";
import { ListTagsOutput } from "../models/models_3";
import { ListTagsInput, ListTagsOutput } from "../models/models_3";
import { deserializeAws_json1_1ListTagsCommand, serializeAws_json1_1ListTagsCommand } from "../protocols/Aws_json1_1";
import { SageMakerClientResolvedConfig, ServiceInputTypes, ServiceOutputTypes } from "../SageMakerClient";

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139 changes: 94 additions & 45 deletions clients/client-sagemaker/src/models/models_0.ts
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Expand Up @@ -1772,7 +1772,8 @@ export interface TransformOutput {
* Developer Guide.</i>
* </p>
* <p>The KMS key policy must grant permission to the IAM role that you specify in your
* <a>CreateModel</a> request. For more information, see <a href="http://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html">Using
* <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html">CreateModel</a>
* request. For more information, see <a href="https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html">Using
* Key Policies in Amazon Web Services KMS</a> in the <i>Amazon Web Services Key Management Service Developer
* Guide</i>.</p>
*/
Expand Down Expand Up @@ -4254,7 +4255,12 @@ export interface MetricDatum {
Set?: MetricSetSource | string;

/**
* <p>The name of the standard metric.</p>
* <p>The name of the standard metric. </p>
* <note>
* <p>For definitions of the standard metrics, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-metrics">
* <code>Autopilot candidate metrics</code>
* </a>.</p>
* </note>
*/
StandardMetricName?: AutoMLMetricExtendedEnum | string;
}
Expand Down Expand Up @@ -4477,6 +4483,32 @@ export namespace AutoMLCandidate {
});
}

/**
* <p>Stores the config information for how a candidate is generated (optional).</p>
*/
export interface AutoMLCandidateGenerationConfig {
/**
* <p>A URL to the Amazon S3 data source containing selected features from the input data source to
* run an Autopilot job (optional). This file should be in json format as shown below: </p>
* <p>
* <code>{ "FeatureAttributeNames":["col1", "col2", ...] }</code>.</p>
* <p>The key name <code>FeatureAttributeNames</code> is fixed. The values listed in
* <code>["col1", "col2", ...]</code> is case sensitive and should be a list of strings
* containing unique values that are a subset of the column names in the input data. The list
* of columns provided must not include the target column.</p>
*/
FeatureSpecificationS3Uri?: string;
}

export namespace AutoMLCandidateGenerationConfig {
/**
* @internal
*/
export const filterSensitiveLog = (obj: AutoMLCandidateGenerationConfig): any => ({
...obj,
});
}

export enum AutoMLChannelType {
TRAINING = "training",
VALIDATION = "validation",
Expand Down Expand Up @@ -4534,9 +4566,9 @@ export namespace AutoMLDataSource {
}

/**
* <p>A channel is a named input source that training algorithms can consume. The
* validation dataset size is limited to less than 2 GB. The training dataset size must be
* less than 100 GB. For more information, see .</p>
* <p>A channel is a named input source that training algorithms can consume. The validation
* dataset size is limited to less than 2 GB. The training dataset size must be less than 100
* GB. For more information, see .</p>
* <note>
* <p>A validation dataset must contain the same headers as the training dataset.</p>
* </note>
Expand Down Expand Up @@ -4568,9 +4600,12 @@ export interface AutoMLChannel {
ContentType?: string;

/**
* <p>The channel type (optional) is an enum string. The default value is
* <p>The channel type (optional) is an <code>enum</code> string. The default value is
* <code>training</code>. Channels for training and validation must share the same
* <code>ContentType</code> and <code>TargetAttributeName</code>.</p>
* <code>ContentType</code> and <code>TargetAttributeName</code>. For information on
* specifying training and validation channel types, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-data-sources-training-or-validation">
* <code>How to specify training and validation datasets</code>
* </a>.</p>
*/
ChannelType?: AutoMLChannelType | string;
}
Expand All @@ -4592,8 +4627,8 @@ export namespace AutoMLChannel {
export interface AutoMLDataSplitConfig {
/**
* <p>The validation fraction (optional) is a float that specifies the portion of the training
* dataset to be used for validation. The default value is 0.2, and values can range from 0 to
* 1. We recommend setting this value to be less than 0.5.</p>
* dataset to be used for validation. The default value is 0.2, and values must be greater
* than 0 and less than 1. We recommend setting this value to be less than 0.5.</p>
*/
ValidationFraction?: number;
}
Expand Down Expand Up @@ -4745,6 +4780,11 @@ export interface AutoMLJobConfig {
* <p>Type: AutoMLDataSplitConfig</p>
*/
DataSplitConfig?: AutoMLDataSplitConfig;

/**
* <p>The configuration for generating a candidate for an AutoML job (optional). </p>
*/
CandidateGenerationConfig?: AutoMLCandidateGenerationConfig;
}

export namespace AutoMLJobConfig {
Expand Down Expand Up @@ -5480,16 +5520,17 @@ export enum CandidateSortBy {
}

/**
* <p></p>
* <p>Configuration specifying how to treat different headers. If no headers are specified SageMaker
* will by default base64 encode when capturing the data.</p>
*/
export interface CaptureContentTypeHeader {
/**
* <p></p>
* <p>The list of all content type headers that SageMaker will treat as CSV and capture accordingly.</p>
*/
CsvContentTypes?: string[];

/**
* <p></p>
* <p>The list of all content type headers that SageMaker will treat as JSON and capture accordingly.</p>
*/
JsonContentTypes?: string[];
}
Expand All @@ -5509,11 +5550,11 @@ export enum CaptureMode {
}

/**
* <p></p>
* <p>Specifies data Model Monitor will capture.</p>
*/
export interface CaptureOption {
/**
* <p></p>
* <p>Specify the boundary of data to capture.</p>
*/
CaptureMode: CaptureMode | string | undefined;
}
Expand Down Expand Up @@ -7177,8 +7218,9 @@ export interface CreateAutoMLJobRequest {

/**
* <p>An array of channel objects that describes the input data and its location. Each channel
* is a named input source. Similar to <code>InputDataConfig</code> supported by . Format(s) supported: CSV. Minimum
* of 500 rows.</p>
* is a named input source. Similar to <code>InputDataConfig</code> supported by . Format(s) supported: CSV, Parquet.
* A minimum of 500 rows is required for the training dataset. There is not a minimum number
* of rows required for the validation dataset.</p>
*/
InputDataConfig: AutoMLChannel[] | undefined;

Expand All @@ -7189,9 +7231,8 @@ export interface CreateAutoMLJobRequest {
OutputDataConfig: AutoMLOutputDataConfig | undefined;

/**
* <p>Defines the type of supervised learning available for the candidates. Options include:
* <code>BinaryClassification</code>, <code>MulticlassClassification</code>, and
* <code>Regression</code>. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-problem-types.html">
* <p>Defines the type of supervised learning available for the candidates. For more
* information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-problem-types.html">
* Amazon SageMaker Autopilot problem types and algorithm support</a>.</p>
*/
ProblemType?: ProblemType | string;
Expand All @@ -7204,8 +7245,7 @@ export interface CreateAutoMLJobRequest {
AutoMLJobObjective?: AutoMLJobObjective;

/**
* <p>Contains <code>CompletionCriteria</code> and <code>SecurityConfig</code> settings for
* the AutoML job.</p>
* <p>A collection of settings used to configure an AutoML job.</p>
*/
AutoMLJobConfig?: AutoMLJobConfig;

Expand Down Expand Up @@ -9503,36 +9543,61 @@ export namespace CreateEndpointOutput {
}

/**
* <p></p>
* <p>Configuration to control how SageMaker captures inference data.</p>
*/
export interface DataCaptureConfig {
/**
* <p></p>
* <p>Whether data capture should be enabled or disabled (defaults to enabled).</p>
*/
EnableCapture?: boolean;

/**
* <p></p>
* <p>The percentage of requests SageMaker will capture. A lower value is recommended for
* Endpoints with high traffic.</p>
*/
InitialSamplingPercentage: number | undefined;

/**
* <p></p>
* <p>The Amazon S3 location used to capture the data.</p>
*/
DestinationS3Uri: string | undefined;

/**
* <p></p>
* <p>The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on
* the storage volume attached to the ML compute instance that hosts the endpoint.</p>
* <p>The KmsKeyId 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>
*/
KmsKeyId?: string;

/**
* <p></p>
* <p>Specifies data Model Monitor will capture. You can configure whether to
* collect only input, only output, or both</p>
*/
CaptureOptions: CaptureOption[] | undefined;

/**
* <p></p>
* <p>Configuration specifying how to treat different headers. If no headers are specified SageMaker will
* by default base64 encode when capturing the data.</p>
*/
CaptureContentTypeHeader?: CaptureContentTypeHeader;
}
Expand Down Expand Up @@ -9725,7 +9790,7 @@ export interface CreateEndpointConfigInput {
ProductionVariants: ProductionVariant[] | undefined;

/**
* <p></p>
* <p>Configuration to control how SageMaker captures inference data.</p>
*/
DataCaptureConfig?: DataCaptureConfig;

Expand Down Expand Up @@ -11926,19 +11991,3 @@ export namespace CreateImageRequest {
...obj,
});
}

export interface CreateImageResponse {
/**
* <p>The Amazon Resource Name (ARN) of the image.</p>
*/
ImageArn?: string;
}

export namespace CreateImageResponse {
/**
* @internal
*/
export const filterSensitiveLog = (obj: CreateImageResponse): any => ({
...obj,
});
}
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