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feat(sagemaker): add EndpointConfig L2 construct
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This is the second of three PRs to complete the implementation of RFC
431:
aws/aws-cdk-rfcs#431

related to aws#2809

Co-authored-by: Matt McClean <[email protected]>
Co-authored-by: Long Yao <[email protected]>
Co-authored-by: Drew Jetter <[email protected]>
Co-authored-by: Murali Ganesh <[email protected]>
Co-authored-by: Abilash Rangoju <[email protected]>
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6 people committed Nov 10, 2022
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39 changes: 39 additions & 0 deletions packages/@aws-cdk/aws-sagemaker/README.md
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Expand Up @@ -156,3 +156,42 @@ import * as sagemaker from '@aws-cdk/aws-sagemaker';
const bucket = new s3.Bucket(this, 'MyBucket');
const modelData = sagemaker.ModelData.fromBucket(bucket, 'path/to/artifact/file.tar.gz');
```

## Model Hosting

Amazon SageMaker provides model hosting services for model deployment. Amazon SageMaker provides an
HTTPS endpoint where your machine learning model is available to provide inferences.

### Endpoint Configuration

By using the `EndpointConfig` construct, you can define a set of endpoint configuration which can be
used to provision one or more endpoints. In this configuration, you identify one or more models to
deploy and the resources that you want Amazon SageMaker to provision. You define one or more
production variants, each of which identifies a model. Each production variant also describes the
resources that you want Amazon SageMaker to provision. If you are hosting multiple models, you also
assign a variant weight to specify how much traffic you want to allocate to each model. For example,
suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1
for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to
model B:

```typescript
import * as sagemaker from '@aws-cdk/aws-sagemaker';

declare const modelA: sagemaker.Model;
declare const modelB: sagemaker.Model;

const endpointConfig = new sagemaker.EndpointConfig(this, 'EndpointConfig', {
instanceProductionVariants: [
{
model: modelA,
variantName: 'modelA',
initialVariantWeight: 2.0,
},
{
model: modelB,
variantName: 'variantB',
initialVariantWeight: 1.0,
},
]
});
```
64 changes: 64 additions & 0 deletions packages/@aws-cdk/aws-sagemaker/lib/accelerator-type.ts
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@@ -0,0 +1,64 @@
import * as cdk from '@aws-cdk/core';

/**
* Supported Elastic Inference (EI) instance types for SageMaker instance-based production variants.
* EI instances provide on-demand GPU computing for inference.
*/
export class AcceleratorType {
/**
* ml.eia1.large
*/
public static readonly EIA1_LARGE = AcceleratorType.of('ml.eia1.large');

/**
* ml.eia1.medium
*/
public static readonly EIA1_MEDIUM = AcceleratorType.of('ml.eia1.medium');

/**
* ml.eia1.xlarge
*/
public static readonly EIA1_XLARGE = AcceleratorType.of('ml.eia1.xlarge');

/**
* ml.eia2.large
*/
public static readonly EIA2_LARGE = AcceleratorType.of('ml.eia2.large');

/**
* ml.eia2.medium
*/
public static readonly EIA2_MEDIUM = AcceleratorType.of('ml.eia2.medium');

/**
* ml.eia2.xlarge
*/
public static readonly EIA2_XLARGE = AcceleratorType.of('ml.eia2.xlarge');

/**
* Builds an AcceleratorType from a given string or token (such as a CfnParameter).
* @param acceleratorType An accelerator type as string
* @returns A strongly typed AcceleratorType
*/
public static of(acceleratorType: string): AcceleratorType {
return new AcceleratorType(acceleratorType);
}

private readonly acceleratorTypeIdentifier: string;

constructor(acceleratorType: string) {
if (cdk.Token.isUnresolved(acceleratorType) || acceleratorType.startsWith('ml.')) {
this.acceleratorTypeIdentifier = acceleratorType;
} else {
throw new Error(`instance type must start with 'ml.'; (got ${acceleratorType})`);
}
}

/**
* Return the accelerator type as a string
* @returns The accelerator type as a string
*/
public toString(): string {
return this.acceleratorTypeIdentifier;
}
}
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