Amazon SageMaker is a powerful machine learning platform that allows developers to build, train, and deploy machine learning models at scale. One of the key features of SageMaker is its Role Manager, which allows users to define customized permissions for different roles within their organization. With the help of AWS CDK, it is now possible to quickly and easily define these permissions, making it easier than ever to manage access to your SageMaker resources.
AWS CDK (Cloud Development Kit) is a software development framework that allows developers to define infrastructure as code using familiar programming languages such as TypeScript, Python, and Java. With CDK, you can define your infrastructure in code, which makes it easier to manage and maintain over time. In addition, CDK provides a number of pre-built constructs that make it easy to define common AWS resources such as EC2 instances, S3 buckets, and Lambda functions.
To get started with defining customized permissions using SageMaker Role Manager through AWS CDK, you will first need to install the AWS CDK CLI (Command Line Interface) on your local machine. Once you have done this, you can create a new CDK project using the following command:
“`
cdk init app –language=typescript
“`
This will create a new CDK project in TypeScript. You can then add the necessary dependencies for working with SageMaker and Role Manager by running the following commands:
“`
npm install @aws-cdk/aws-sagemaker
npm install @aws-cdk/aws-iam
“`
Once you have installed these dependencies, you can define your SageMaker resources and roles using the following code:
“`typescript
import * as cdk from ‘@aws-cdk/core’;
import * as sagemaker from ‘@aws-cdk/aws-sagemaker’;
import * as iam from ‘@aws-cdk/aws-iam’;
export class SageMakerRoleManagerStack extends cdk.Stack {
constructor(scope: cdk.Construct, id: string, props?: cdk.StackProps) {
super(scope, id, props);
// Define a SageMaker notebook instance
const notebookInstance = new sagemaker.CfnNotebookInstance(this, ‘NotebookInstance’, {
instanceType: ‘ml.t2.medium’,
roleArn: ‘arn:aws:iam::123456789012:role/SageMakerRole’,
// Other notebook instance properties…
});
// Define a SageMaker execution role
const executionRole = new iam.Role(this, ‘ExecutionRole’, {
assumedBy: new iam.ServicePrincipal(‘sagemaker.amazonaws.com’),
// Other execution role properties…
});
// Define a SageMaker model training job
const trainingJob = new sagemaker.CfnTrainingJob(this, ‘TrainingJob’, {
algorithmSpecification: {
algorithmName: ‘my-algorithm’,
trainingImage: ‘123456789012.dkr.ecr.us-west-2.amazonaws.com/my-algorithm’,
// Other algorithm specification properties…
},
roleArn: ‘arn:aws:iam::123456789012:role/SageMakerRole’,
// Other training job properties…
});
// Define a SageMaker model hosting role
const hostingRole = new iam.Role(this, ‘HostingRole’, {
assumedBy: new iam.ServicePrincipal(‘sagemaker.amazonaws.com’),
// Other hosting role properties…
});
// Define a SageMaker model endpoint configuration
const endpointConfig = new sagemaker.CfnEndpointConfig(this, ‘EndpointConfig’, {
productionVariants: [{
initialInstanceCount: 1,
instanceType: ‘ml.t2.medium’,
modelName: ‘my-model’,
variantName: ‘my-variant’,
}],
roleArn: ‘arn:aws:iam::123456789012:role/SageMakerRole’,
// Other endpoint configuration properties…
});
// Define a SageMaker model endpoint
const endpoint = new sagemaker.CfnEndpoint(this, ‘Endpoint’, {
endpointConfigName: endpointConfig.ref,
// Other endpoint properties…
});
}
}
“`
In this code, we define a SageMaker notebook instance, a SageMaker execution role, a SageMaker model training job, a SageMaker model hosting role, a SageMaker model endpoint configuration, and a SageMaker model endpoint. We also define a custom IAM role called “SageMakerRole” that is used by all of these resources.
To customize the permissions for each of these roles, you can add additional IAM policies to the role definition. For example, to allow the SageMaker execution role to access an S3 bucket, you could add the following policy:
“`typescript
executionRole.addToPolicy(new iam.PolicyStatement({
actions: [‘s3:GetObject’],
resources: [‘arn:aws:s3:::my-bucket/*’],
}));
“`
This policy statement grants the execution role
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