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Description:

awssolutionsconstructs.awslambdasagemakerendpoint 2.70.0

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This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

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

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

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 Endpoint to be used. Providing both this and endpointProps? will cause an error.

modelProps?
sagemaker.CfnModelProps
any

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: 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

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