@aws-solutions-constructs/aws-lambda-sagemakerendpoint v2.74.0

aws-lambda-sagemakerendpoint module

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 This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

Language	Package
Python	aws_solutions_constructs.aws_lambda_sagemakerendpoint
Typescript	@aws-solutions-constructs/aws-lambda-sagemakerendpoint
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:

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_16_X)
                        .code(Code.fromAsset("lambda"))
                        .handler("index.handler")
                        .timeout(Duration.minutes(5))
                        .build())
                .build());

Pattern Construct Props

Pattern Properties

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

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