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@aws-solutions-constructs/aws-lambda-sagemakerendpoint

v2.8.0

Published

CDK constructs for defining an interaction between an AWS Lambda function and an Amazon SageMaker inference endpoint.

Downloads

791

Readme

aws-lambda-sagemakerendpoint module


Stability: Experimental

All classes are under active development and subject to non-backward compatible changes or removal in any future version. These are not subject to the Semantic Versioning model. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.


| Reference Documentation: | https://docs.aws.amazon.com/solutions/latest/constructs/ | | :--------------------------- | :------------------------------------------------------------------------------------------------ |

| Language | Package | | :--------------------------------------------------------------------------------------------- | ---------------------------------------------------------------- | | Python Logo Python | aws_solutions_constructs.aws_lambda_sagemakerendpoint | | Typescript Logo Typescript | @aws-solutions-constructs/aws-lambda-sagemakerendpoint | | Java Logo Java | software.amazon.awsconstructs.services.lambdasagemakerendpoint |

This AWS Solutions Construct implements an AWS Lambda function connected to an Amazon Sagemaker Endpoint.

Here is a minimal deployable pattern definition:

Typescript

import { Construct } from 'constructs';
import { Stack, StackProps, Duration } from 'aws-cdk-lib';
import * as lambda from 'aws-cdk-lib/aws-lambda';
import { LambdaToSagemakerEndpoint, LambdaToSagemakerEndpointProps } from '@aws-solutions-constructs/aws-lambda-sagemakerendpoint';

const constructProps: LambdaToSagemakerEndpointProps = {
  modelProps: {
    primaryContainer: {
      image: '<AccountId>.dkr.ecr.<region>.amazonaws.com/linear-learner:latest',
      modelDataUrl: "s3://<bucket-name>/<prefix>/model.tar.gz",
    },
  },
  lambdaFunctionProps: {
    runtime: lambda.Runtime.PYTHON_3_8,
    code: lambda.Code.fromAsset(`lambda`),
    handler: 'index.handler',
    timeout: Duration.minutes(5),
    memorySize: 128,
  },
};

new LambdaToSagemakerEndpoint(this, 'LambdaToSagemakerEndpointPattern', constructProps);

Python

from constructs import Construct
from aws_solutions_constructs.aws_lambda_sagemakerendpoint import LambdaToSagemakerEndpoint, LambdaToSagemakerEndpointProps
from aws_cdk import (
    aws_lambda as _lambda,
    aws_sagemaker as sagemaker,
    Duration,
    Stack
)
from constructs import Construct

LambdaToSagemakerEndpoint(
    self, 'LambdaToSagemakerEndpointPattern',
    model_props=sagemaker.CfnModelProps(
        primary_container=sagemaker.CfnModel.ContainerDefinitionProperty(
            image='<AccountId>.dkr.ecr.<region>.amazonaws.com/linear-learner:latest',
            model_data_url='s3://<bucket-name>/<prefix>/model.tar.gz',
        ),
        execution_role_arn="executionRoleArn"
    ),
    lambda_function_props=_lambda.FunctionProps(
        code=_lambda.Code.from_asset('lambda'),
        runtime=_lambda.Runtime.PYTHON_3_9,
        handler='index.handler',
        timeout=Duration.minutes(5),
        memory_size=128
    ))

Java

import software.constructs.Construct;

import software.amazon.awscdk.Stack;
import software.amazon.awscdk.StackProps;
import software.amazon.awscdk.Duration;
import software.amazon.awscdk.services.lambda.*;
import software.amazon.awscdk.services.lambda.Runtime;
import software.amazon.awscdk.services.sagemaker.*;
import software.amazon.awsconstructs.services.lambdasagemakerendpoint.*;

new LambdaToSagemakerEndpoint(this, "LambdaToSagemakerEndpointPattern",
        new LambdaToSagemakerEndpointProps.Builder()
                .modelProps(new CfnModelProps.Builder()
                        .primaryContainer(new CfnModel.ContainerDefinitionProperty.Builder()
                                .image("<AccountId>.dkr.ecr.<region>.amazonaws.com/linear_learner:latest")
                                .modelDataUrl("s3://<bucket_name>/<prefix>/model.tar.gz")
                                .build())
                        .executionRoleArn("executionRoleArn")
                        .build())
                .lambdaFunctionProps(new FunctionProps.Builder()
                        .runtime(Runtime.NODEJS_14_X)
                        .code(Code.fromAsset("lambda"))
                        .handler("index.handler")
                        .timeout(Duration.minutes(5))
                        .build())
                .build());

Pattern Construct Props

| Name | Type | Description | |:-------------|:----------------|-----------------| |existingLambdaObj?|lambda.Function|An optional, existing Lambda function to be used instead of the default function. Providing both this and lambdaFunctionProps will cause an error.| |lambdaFunctionProps?|lambda.FunctionProps|Optional user-provided properties to override the default properties for the Lambda function.| |existingSagemakerEndpointObj?|sagemaker.CfnEndpoint|An optional, existing SageMaker Enpoint to be used. Providing both this and endpointProps? will cause an error.| |modelProps?|sagemaker.CfnModelProps | any|User-provided properties to override the default properties for the SageMaker Model. At least modelProps?.primaryContainer must be provided to create a model. By default, the pattern will create a role with the minimum required permissions, but the client can provide a custom role with additional capabilities using modelProps?.executionRoleArn.| |endpointConfigProps?|sagemaker.CfnEndpointConfigProps|Optional user-provided properties to override the default properties for the SageMaker Endpoint Config. | |endpointProps?|sagemaker.CfnEndpointProps| Optional user-provided properties to override the default properties for the SageMaker Endpoint Config. | |existingVpc?|ec2.IVpc|An optional, existing VPC into which this construct should be deployed. When deployed in a VPC, the Lambda function and Sagemaker Endpoint will use ENIs in the VPC to access network resources. An Interface Endpoint will be created in the VPC for Amazon SageMaker Runtime, and Amazon S3 VPC Endpoint. If an existing VPC is provided, the deployVpc? property cannot be true.| |vpcProps?|ec2.VpcProps|Optional user-provided properties to override the default properties for the new VPC. enableDnsHostnames, enableDnsSupport, natGateways and subnetConfiguration are set by the Construct, so any values for those properties supplied here will be overrriden. If deployVpc? is not true then this property will be ignored.| |deployVpc?|boolean|Whether to create a new VPC based on vpcProps into which to deploy this pattern. Setting this to true will deploy the minimal, most private VPC to run the pattern: One isolated subnet in each Availability Zone used by the CDK programenableDnsHostnames and enableDnsSupport will both be set to trueIf this property is true then existingVpc cannot be specified. Defaults to false.| |sagemakerEnvironmentVariableName?|string|Optional Name for the Lambda function environment variable set to the name of the SageMaker endpoint. Default: SAGEMAKER_ENDPOINT_NAME |

Pattern Properties

| Name | Type | Description | | :----------------------- | :----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | | lambdaFunction | lambda.Function | Returns an instance of the Lambda function created by the pattern. | | sagemakerEndpoint | sagemaker.CfnEndpoint | Returns an instance of the SageMaker Endpoint created by the pattern. | | sagemakerEndpointConfig? | sagemaker.CfnEndpointConfig | Returns an instance of the SageMaker EndpointConfig created by the pattern, if existingSagemakerEndpointObj? is not provided. | | sagemakerModel? | sagemaker.CfnModel | Returns an instance of the SageMaker Model created by the pattern, if existingSagemakerEndpointObj? is not provided. | | vpc? | ec2.IVpc | Returns an instance of the VPC created by the pattern, if deployVpc? is true, or existingVpc? is provided. |

Default settings

Out of the box implementation of the Construct without any override will set the following defaults:

AWS Lambda Function

  • Configure limited privilege access IAM role for Lambda function
  • Enable reusing connections with Keep-Alive for NodeJs Lambda function
  • Allow the function to invoke the SageMaker endpoint for Inferences
  • Configure the function to access resources in the VPC, where the SageMaker endpoint is deployed
  • Enable X-Ray Tracing
  • Set environment variables:
    • (default) SAGEMAKER_ENDPOINT_NAME
    • AWS_NODEJS_CONNECTION_REUSE_ENABLED (for Node 10.x and higher functions).

Amazon SageMaker Endpoint

  • Configure limited privilege to create SageMaker resources
  • Deploy SageMaker model, endpointConfig, and endpoint
  • Configure the SageMaker endpoint to be deployed in a VPC
  • Deploy S3 VPC Endpoint and SageMaker Runtime VPC Interface

Architecture

Architecture Diagram


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