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feat(sagemaker): add support uncompressed model #30949

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5af304a
feat(sagemaker): add support uncompressed model
WinterYukky Jul 25, 2024
90cf701
fix: un-expect change
WinterYukky Jul 25, 2024
518bfbc
Merge branch 'main' into feat/sagemaker/support-uncompressed-model
WinterYukky Jul 25, 2024
69265e1
fix: missing import
WinterYukky Jul 25, 2024
a4882a0
Merge branch 'feat/sagemaker/support-uncompressed-model' of https://g…
WinterYukky Jul 25, 2024
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80 changes: 44 additions & 36 deletions packages/@aws-cdk/aws-sagemaker-alpha/README.md
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Expand Up @@ -35,13 +35,12 @@ In the event that a single container is sufficient for your inference use-case,
single-container model:

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

const image = sagemaker.ContainerImage.fromAsset(path.join('path', 'to', 'Dockerfile', 'directory'));
const modelData = sagemaker.ModelData.fromAsset(path.join('path', 'to', 'artifact', 'file.tar.gz'));
const image = ContainerImage.fromAsset(path.join('path', 'to', 'Dockerfile', 'directory'));
const modelData = ModelData.fromAsset(path.join('path', 'to', 'artifact', 'file.tar.gz'));

const model = new sagemaker.Model(this, 'PrimaryContainerModel', {
const model = new Model(this, 'PrimaryContainerModel', {
containers: [
{
image: image,
Expand All @@ -60,16 +59,15 @@ more about SageMaker inference pipelines. To define an inference pipeline, you c
additional containers for your model:

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

declare const image1: sagemaker.ContainerImage;
declare const modelData1: sagemaker.ModelData;
declare const image2: sagemaker.ContainerImage;
declare const modelData2: sagemaker.ModelData;
declare const image3: sagemaker.ContainerImage;
declare const modelData3: sagemaker.ModelData;
declare const image1: ContainerImage;
declare const modelData1: ModelData;
declare const image2: ContainerImage;
declare const modelData2: ModelData;
declare const image3: ContainerImage;
declare const modelData3: ModelData;

const model = new sagemaker.Model(this, 'InferencePipelineModel', {
const model = new Model(this, 'InferencePipelineModel', {
containers: [
{ image: image1, modelData: modelData1 },
{ image: image2, modelData: modelData2 },
Expand All @@ -89,10 +87,9 @@ abstract base class.
Reference a local directory containing a Dockerfile:

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

const image = sagemaker.ContainerImage.fromAsset(path.join('path', 'to', 'Dockerfile', 'directory'));
const image = ContainerImage.fromAsset(path.join('path', 'to', 'Dockerfile', 'directory'));
```

#### ECR Image
Expand All @@ -101,23 +98,21 @@ Reference an image available within ECR:

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

const repository = ecr.Repository.fromRepositoryName(this, 'Repository', 'repo');
const image = sagemaker.ContainerImage.fromEcrRepository(repository, 'tag');
const image = ContainerImage.fromEcrRepository(repository, 'tag');
```

#### DLC Image

Reference a deep learning container image:

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

const repositoryName = 'huggingface-pytorch-training';
const tag = '1.13.1-transformers4.26.0-gpu-py39-cu117-ubuntu20.04';

const image = sagemaker.ContainerImage.fromDlc(repositoryName, tag);
const image = ContainerImage.fromDlc(repositoryName, tag);
```

### Model Artifacts
Expand All @@ -132,10 +127,9 @@ base class. The default is to have no model artifacts associated with a model.
Reference local model data:

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

const modelData = sagemaker.ModelData.fromAsset(path.join('path', 'to', 'artifact', 'file.tar.gz'));
const modelData = ModelData.fromAsset(path.join('path', 'to', 'artifact', 'file.tar.gz'));
```

#### S3 Model Data
Expand All @@ -144,10 +138,28 @@ Reference an S3 bucket and object key as the artifacts for a model:

```typescript
import * as s3 from 'aws-cdk-lib/aws-s3';
import * as sagemaker from '@aws-cdk/aws-sagemaker-alpha';

const bucket = new s3.Bucket(this, 'MyBucket');
const modelData = sagemaker.ModelData.fromBucket(bucket, 'path/to/artifact/file.tar.gz');
const modelData = ModelData.fromBucket(bucket, 'path/to/artifact/file.tar.gz');
```

When deploying ML models, one option is to archive andcompress the model artifacts into a tar.gz format.
Although this method works well for small models,
compressing a large model artifact with hundreds of billions of parameters and
then decompressing it on an endpoint can take a significant amount of time.
For large model inference, we recommend that you deploy uncompressed ML model.

If you want to use uncompressed ML model,
you can provide options to `ModelData.fromBucket` like a following the code.

```typescript
import * as s3 from 'aws-cdk-lib/aws-s3';

const bucket = new s3.Bucket(this, 'MyBucket');
const modelData = ModelData.fromBucket(bucket, 'path/to/artifact', {
compressionType: CompressionType.NONE,
s3DataType: S3DataType.S3_PREFIX,
});
```

## Model Hosting
Expand All @@ -168,12 +180,11 @@ for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A,
model B:

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

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

const endpointConfig = new sagemaker.EndpointConfig(this, 'EndpointConfig', {
const endpointConfig = new EndpointConfig(this, 'EndpointConfig', {
instanceProductionVariants: [
{
model: modelA,
Expand All @@ -199,24 +210,22 @@ more information about the API, see the
API. Defining an endpoint requires at minimum the associated endpoint configuration:

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

declare const endpointConfig: sagemaker.EndpointConfig;
declare const endpointConfig: EndpointConfig;

const endpoint = new sagemaker.Endpoint(this, 'Endpoint', { endpointConfig });
const endpoint = new Endpoint(this, 'Endpoint', { endpointConfig });
```

### AutoScaling

To enable autoscaling on the production variant, use the `autoScaleInstanceCount` method:

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

declare const model: sagemaker.Model;
declare const model: Model;

const variantName = 'my-variant';
const endpointConfig = new sagemaker.EndpointConfig(this, 'EndpointConfig', {
const endpointConfig = new EndpointConfig(this, 'EndpointConfig', {
instanceProductionVariants: [
{
model: model,
Expand All @@ -225,7 +234,7 @@ const endpointConfig = new sagemaker.EndpointConfig(this, 'EndpointConfig', {
]
});

const endpoint = new sagemaker.Endpoint(this, 'Endpoint', { endpointConfig });
const endpoint = new Endpoint(this, 'Endpoint', { endpointConfig });
const productionVariant = endpoint.findInstanceProductionVariant(variantName);
const instanceCount = productionVariant.autoScaleInstanceCount({
maxCapacity: 3
Expand All @@ -244,11 +253,10 @@ To monitor CloudWatch metrics for a production variant, use one or more of the m
methods:

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

declare const endpointConfig: sagemaker.EndpointConfig;
declare const endpointConfig: EndpointConfig;

const endpoint = new sagemaker.Endpoint(this, 'Endpoint', { endpointConfig });
const endpoint = new Endpoint(this, 'Endpoint', { endpointConfig });
const productionVariant = endpoint.findInstanceProductionVariant('my-variant');
productionVariant.metricModelLatency().createAlarm(this, 'ModelLatencyAlarm', {
threshold: 100000,
Expand Down
108 changes: 100 additions & 8 deletions packages/@aws-cdk/aws-sagemaker-alpha/lib/model-data.ts
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Expand Up @@ -6,7 +6,58 @@ import { hashcode } from './private/util';

// The only supported extension for local asset model data
// https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-model-containerdefinition.html#cfn-sagemaker-model-containerdefinition-modeldataurl
const ARTIFACT_EXTENSION = '.tar.gz';
const COMPRESSED_ARTIFACT_EXTENSION = '.tar.gz';

/**
* Specifies how the ML model data is prepared.
*/
export enum CompressionType {
/**
* If you choose `CompressionType.GZIP` and choose `S3DataType.S3_OBJECT` as the value of `s3DataType`,
* S3 URI identifies an object that is a gzip-compressed TAR archive.
* SageMaker will attempt to decompress and untar the object during model deployment.
*/
GZIP = 'Gzip',
/**
* If you choose `CompressionType.NONE` and choose `S3DataType.S3_PREFIX` as the value of `s3DataType`,
* S3 URI identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
*
* If you choose `CompressionType.NONE`, then SageMaker will follow rules below when creating model data files
* under `/opt/ml/model` directory for use by your inference code:
* - If you choose `S3DataType.S3_OBJECT` as the value of `s3DataType`, then SageMaker will split the key of the S3 object referenced by S3 URI by slash (/),
* and use the last part as the filename of the file holding the content of the S3 object.
* - If you choose `S3DataType.S3_PREFIX` as the value of `s3DataType`, then for each S3 object under the key name pefix referenced by S3 URI,
* SageMaker will trim its key by the prefix, and use the remainder as the path (relative to `/opt/ml/model`) of the file holding the content of the S3 object.
* SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
* - Do not use any of the following as file names or directory names:
* - An empty or blank string
* - A string which contains null bytes
* - A string longer than 255 bytes
* - A single dot (.)
* - A double dot (..)
* - Ambiguous file names will result in model deployment failure.
* For example, if your uncompressed ML model consists of two S3 objects `s3://mybucket/model/weights` and `s3://mybucket/model/weights/part1`
* and you specify `s3://mybucket/model/` as the value of S3 URI and `S3DataType.S3_PREFIX` as the value of `s3DataType`,
* then it will result in name clash between `/opt/ml/model/weights` (a regular file) and `/opt/ml/model/weights/` (a directory).
*/
NONE = 'None',
}

/**
* Specifies the type of ML model data to deploy.
*/
export enum S3DataType {
/**
* If you choose `S3DataType.S3_OBJECT`, S3 UTI identifies an object that is the ML model data to deploy.
*/
S3_OBJECT = 'S3Object',
/**
* If you choose `S3DataType.S3_PREFIX`, S3 URI identifies a key name prefix.
* SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy.
* A valid key name prefix identified by S3 URI always ends with a forward slash (/).
*/
S3_PREFIX = 'S3Prefix',
}

/**
* The configuration needed to reference model artifacts.
Expand All @@ -17,6 +68,16 @@ export interface ModelDataConfig {
* must point to a single gzip compressed tar archive (.tar.gz suffix).
*/
readonly uri: string;
/**
* Specifies how the ML model data is prepared.
* @default CompressionType.GZIP
*/
readonly compressionType?: CompressionType;
/**
* Specifies the type of ML model data to deploy.
* @default S3DataType.S3_OBJECT
*/
readonly s3DataType?: S3DataType;
}

/**
Expand All @@ -28,9 +89,10 @@ export abstract class ModelData {
* Constructs model data which is already available within S3.
* @param bucket The S3 bucket within which the model artifacts are stored
* @param objectKey The S3 object key at which the model artifacts are stored
* @param options The options for identifying model artifacts
*/
public static fromBucket(bucket: s3.IBucket, objectKey: string): ModelData {
return new S3ModelData(bucket, objectKey);
public static fromBucket(bucket: s3.IBucket, objectKey: string, options?: S3ModelDataOptions): ModelData {
return new S3ModelData(bucket, objectKey, options);
}

/**
Expand All @@ -51,8 +113,33 @@ export abstract class ModelData {
public abstract bind(scope: Construct, model: IModel): ModelDataConfig;
}

/**
* The options for identifying model artifacts.
* When you choose `CompressionType.GZIP` and `S3DataType.S3_OBJECT` then use `ModelDataUrl` property.
* Otherwise, use `ModelDataSource` property.
*
* Currently you cannot use ModelDataSource in conjunction with:
* - SageMaker batch transform
* - SageMaker serverless endpoints
* - SageMaker multi-model endpoints
* - SageMaker Marketplace
*/
export interface S3ModelDataOptions {
/**
* Specifies how the ML model data is prepared.
* @default CompressionType.GZIP
*/
readonly compressionType: CompressionType;
/**
* Specifies the type of ML model data to deploy.
* @default S3DataType.S3_OBJECT
*/
readonly s3DataType: S3DataType;
}

class S3ModelData extends ModelData {
constructor(private readonly bucket: s3.IBucket, private readonly objectKey: string) {
constructor(private readonly bucket: s3.IBucket,
private readonly objectKey: string, private readonly options?: S3ModelDataOptions) {
super();
}

Expand All @@ -61,6 +148,8 @@ class S3ModelData extends ModelData {

return {
uri: this.bucket.urlForObject(this.objectKey),
compressionType: this.options?.compressionType,
s3DataType: this.options?.s3DataType,
};
}
}
Expand All @@ -70,9 +159,6 @@ class AssetModelData extends ModelData {

constructor(private readonly path: string, private readonly options: assets.AssetOptions) {
super();
if (!path.toLowerCase().endsWith(ARTIFACT_EXTENSION)) {
throw new Error(`Asset must be a gzipped tar file with extension ${ARTIFACT_EXTENSION} (${this.path})`);
}
Comment on lines -73 to -75
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The asset's availability determination was moved after binding it as an asset.

}

public bind(scope: Construct, model: IModel): ModelDataConfig {
Expand All @@ -83,11 +169,17 @@ class AssetModelData extends ModelData {
...this.options,
});
}

if (!this.asset.isFile) {
throw new Error(`Asset must be a file, if you want to use directory you can use 'ModelData.fromBucket()' with the 's3DataType' option to 'S3DataType.S3_PREFIX' and 'compressionType' option to 'CompressionType.NONE' (${this.path})`);
}
Comment on lines +172 to +174
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The bundled assets are now allowed if they are not directories.

this.asset.grantRead(model);

return {
uri: this.asset.httpUrl,
compressionType: this.asset.assetPath.toLowerCase().endsWith(COMPRESSED_ARTIFACT_EXTENSION)
? CompressionType.GZIP
: CompressionType.NONE,
s3DataType: S3DataType.S3_OBJECT,
Comment on lines +179 to +182
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Uncompressed single files are also supported

};
}
}
19 changes: 15 additions & 4 deletions packages/@aws-cdk/aws-sagemaker-alpha/lib/model.ts
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@@ -1,10 +1,10 @@
import * as ec2 from 'aws-cdk-lib/aws-ec2';
import * as iam from 'aws-cdk-lib/aws-iam';
import { CfnModel } from 'aws-cdk-lib/aws-sagemaker';
import * as cdk from 'aws-cdk-lib/core';
import { Construct } from 'constructs';
import { ContainerImage } from './container-image';
import { ModelData } from './model-data';
import { CfnModel } from 'aws-cdk-lib/aws-sagemaker';
import { CompressionType, ModelData, S3DataType } from './model-data';

/**
* Interface that defines a Model resource.
Expand Down Expand Up @@ -357,11 +357,22 @@ export class Model extends ModelBase {
}

private renderContainer(container: ContainerDefinition): CfnModel.ContainerDefinitionProperty {
const image = container.image.bind(this, this);
const modelDataConfig = container.modelData?.bind(this, this);
const useModelDataSource = modelDataConfig?.compressionType === CompressionType.NONE
|| modelDataConfig?.s3DataType === S3DataType.S3_PREFIX;
return {
image: container.image.bind(this, this).imageName,
image: image.imageName,
containerHostname: container.containerHostname,
environment: container.environment,
modelDataUrl: container.modelData ? container.modelData.bind(this, this).uri : undefined,
modelDataSource: useModelDataSource ? {
s3DataSource: {
s3Uri: modelDataConfig.uri,
s3DataType: modelDataConfig.s3DataType!,
compressionType: modelDataConfig.compressionType!,
},
} : undefined,
modelDataUrl: !useModelDataSource ? modelDataConfig?.uri : undefined,
};
}

Expand Down
1 change: 1 addition & 0 deletions packages/@aws-cdk/aws-sagemaker-alpha/rosetta/default.ts-fixture
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
// Fixture with packages imported, but nothing else
import * as cdk from 'aws-cdk-lib';
import { Construct } from 'constructs';
import { ContainerImage, Endpoint, EndpointConfig,Model, ModelData } from '@aws-cdk/aws-sagemaker-alpha';

class Fixture extends cdk.Stack {
constructor(scope: Construct, id: string) {
Expand Down
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