aws-lambda-sagemakerendpoint - AWS Solutions Constructs
OverviewPattern Construct PropsPattern PropertiesDefault settingsArchitectureExample Lambda Function ImplementationGithub

aws-lambda-sagemakerendpoint

Stability:Experimental

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

Overview

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

Here is a minimal deployable pattern definition:

Example
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_14,
        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_22_X)
                        .code(Code.fromAsset("lambda"))
                        .handler("index.handler")
                        .timeout(Duration.minutes(5))
                        .build())
                .build());

Pattern Construct Props

Name Type Description

existingLambdaObj?

lambda.Function

Optional - instance of an existing Lambda Function object, providing both this and lambdaFunctionProps will cause an error.

lambdaFunctionProps?

lambda.FunctionProps

Optional - user provided props to override the default props for the Lambda function. Providing both this and existingLambdaObj causes an error.

existingSagemakerEndpointObj?

sagemaker.CfnEndpoint

An optional, existing SageMaker Endpoint 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 overridden. 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:

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

Diagram showing the Lambda function, SageMaker endpoint, CloudWatch log group and IAM roles created by the construct

Example Lambda Function Implementation

While Solutions Constructs does not publish code for the Lambda function to interact with SageMaker, this repo has several SageMaker examples: examples. (these examples are in JavaScript, but examples in other languages can also be found at this site)

Github

Go to the Github repo for this pattern to view the code, read/create issues and pull requests and more.