@aws-solutions-constructs/aws-lambda-sagemakerendpoint
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    2.8.0 • Public • Published

    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 This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.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 program
    • enableDnsHostnames and enableDnsSupport will both be set to true
    If 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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    Install

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

    DownloadsWeekly Downloads

    104

    Version

    2.8.0

    License

    Apache-2.0

    Unpacked Size

    331 kB

    Total Files

    28

    Last publish

    Collaborators

    • aws-solutions-constructs-team