Collaborative Model Training Workflows With GitLab CI/CD

Introduction

In this guide, we will set up a collaborative model training workflow using GitLab CI/CD and Pulumi. The key services involved include GitLab CI/CD for continuous integration and deployment, and Pulumi for infrastructure as code to manage cloud resources.

Step-by-Step Explanation

Step 1: Set Up GitLab Repository

1. Create a new repository on GitLab for your project.
2. Clone the repository to your local machine.
3. Initialize a new Pulumi project in the repository.

Step 2: Define Pulumi Infrastructure

1. Create a Pulumi.yaml file to define your Pulumi project.
2. Define the necessary cloud resources in your Pulumi program (e.g., S3 buckets, EC2 instances, etc.).
3. Commit the Pulumi program files to the GitLab repository.

Step 3: Configure GitLab CI/CD

1. Create a .gitlab-ci.yml file in the root of your repository.
2. Define the stages and jobs for your CI/CD pipeline, including Pulumi commands to preview and update the infrastructure.
3. Set up environment variables in GitLab for Pulumi access tokens and cloud provider credentials.
4. Commit the .gitlab-ci.yml file to the repository.

Step 4: Run the Pipeline

1. Push your changes to GitLab to trigger the CI/CD pipeline.
2. Monitor the pipeline execution in GitLab CI/CD to ensure the infrastructure is deployed successfully.

Summary

By following these steps, you can set up a collaborative model training workflow using GitLab CI/CD and Pulumi. This setup allows you to manage your infrastructure as code and automate the deployment process, ensuring consistency and reliability in your model training workflows. For more information, refer to the Pulumi documentation and GitLab CI/CD documentation.

Full Code Example

import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";

// Create an S3 bucket
const bucket = new aws.s3.Bucket("model-training-bucket", {
    acl: "private",
});

// Create an EC2 instance
const instance = new aws.ec2.Instance("model-training-instance", {
    instanceType: "t2.micro",
    ami: "ami-0c55b159cbfafe1f0", // Amazon Linux 2 AMI
    tags: {
        Name: "model-training-instance",
    },
});

// Export the name of the bucket and instance ID
export const bucketName = bucket.id;
export const instanceId = instance.id;