How do I set up AWS CodeBuild for ML CI/CD?
In this guide, we will set up an AWS CodeBuild project using Pulumi to create a CI/CD pipeline for a machine learning project. AWS CodeBuild is a fully managed build service in the cloud that compiles source code, runs tests, and produces software packages that are ready to deploy. We will define the necessary resources, including the CodeBuild project, IAM roles, and S3 bucket for storing build artifacts.
Key Points
- Define an S3 bucket to store build artifacts.
- Create an IAM role with the necessary permissions for CodeBuild.
- Define a CodeBuild project with source, environment, and artifacts configurations.
import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";
// Create an S3 bucket to store build artifacts
const artifactBucket = new aws.s3.Bucket("artifactBucket");
// Create an IAM role for CodeBuild with necessary policies
const codeBuildRole = new aws.iam.Role("codeBuildRole", {
assumeRolePolicy: aws.iam.assumeRolePolicyForPrincipal({ Service: "codebuild.amazonaws.com" }),
});
const codeBuildRolePolicy = new aws.iam.RolePolicy("codeBuildRolePolicy", {
role: codeBuildRole.id,
policy: pulumi.output({
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"s3:PutObject",
"s3:ListBucket"
],
"Resource": [
artifactBucket.arn,
pulumi.interpolate`${artifactBucket.arn}/*`
]
},
{
"Effect": "Allow",
"Action": [
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents"
],
"Resource": "*"
},
{
"Effect": "Allow",
"Action": [
"codebuild:StartBuild",
"codebuild:StopBuild",
"codebuild:BatchGetBuilds",
"codebuild:BatchGetProjects",
"codebuild:BatchGetReportGroups",
"codebuild:BatchGetReports"
],
"Resource": "*"
}
]
})
});
// Define the CodeBuild project
const codeBuildProject = new aws.codebuild.Project("codeBuildProject", {
name: "ML-CICD-Project",
source: {
type: "GITHUB",
location: "https://github.com/your-repo/your-ml-project",
buildspec: `
version: 0.2
phases:
install:
runtime-versions:
python: 3.8
pre_build:
commands:
- pip install -r requirements.txt
build:
commands:
- python your_ml_script.py
artifacts:
files:
- '**/*'
base-directory: 'output'
discard-paths: yes
`,
},
artifacts: {
type: "S3",
location: artifactBucket.bucket,
packaging: "ZIP",
path: "build-artifacts",
},
environment: {
computeType: "BUILD_GENERAL1_SMALL",
image: "aws/codebuild/standard:5.0",
type: "LINUX_CONTAINER",
environmentVariables: [
{
name: "ENV_VAR",
value: "value",
},
],
},
serviceRole: codeBuildRole.arn,
description: "CodeBuild project for ML CI/CD pipeline",
});
// Export the bucket name and CodeBuild project name
export const bucketName = artifactBucket.bucket;
export const codeBuildProjectName = codeBuildProject.name;
Conclusion
In this guide, we set up an AWS CodeBuild project for a machine learning CI/CD pipeline using Pulumi. We created an S3 bucket for storing build artifacts, an IAM role with the necessary permissions, and defined the CodeBuild project with the appropriate configurations. This setup allows you to automate the build and deployment process of your machine learning projects.
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