How do I set up scalable storage for LLM training data?

In this guide, we will set up scalable storage on AWS using Pulumi. We will create an S3 bucket to store training data for a language model. S3 is a highly scalable and durable storage service, making it ideal for storing large datasets required for training machine learning models.

Key Points

• We will create an S3 bucket using Pulumi.
• The bucket will have versioning enabled for data protection.
• We will also set up a lifecycle policy to manage the storage of objects.

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

// Create an S3 bucket
const trainingDataBucket = new aws.s3.Bucket("trainingDataBucket", {
    versioning: {
        enabled: true, // Enable versioning to protect against accidental deletions
    },
    lifecycleRules: [{
        enabled: true,
        noncurrentVersionExpiration: {
            days: 30, // Keep noncurrent versions for 30 days
        },
        expiration: {
            days: 365, // Automatically delete objects after 365 days
        },
    }],
});

// Export the bucket name
export const bucketName = trainingDataBucket.bucket;

Summary

In this guide, we set up scalable storage for language model training data on AWS using Pulumi. We created an S3 bucket with versioning enabled to protect data and a lifecycle policy to manage object storage. This setup ensures that your training data is stored reliably and efficiently, with automated management of object versions and expiration.