Using FSx for Lustre for machine learning model training datasets

Question

Pulumi · Accepted Answer

Amazon FSx for Lustre is a fully managed file system that is optimized for compute-intensive workloads, like machine learning, high performance computing (HPC), video processing, and financial modeling. A Lustre file system is designed for fast processing of large datasets, which is particularly useful for speeding up machine learning training processes where data needs to be consumed by compute instances at high throughput.

In the context of using Pulumi to create an AWS FSx for Lustre file system, we'll utilize the `aws.fsx.LustreFileSystem` resource. This will set up an FSx for Lustre file system that can be used for machine learning model training datasets. Below is a Pulumi program in TypeScript that demonstrates how to provision such a file system.

Before the program, let me provide you with a brief explanation of the key components:

- **LustreFileSystem**: The core resource we're using to provision the file system.
- **tags**: Optional metadata that can help you organize and identify your Lustre file system resource.
- **subnetIds**: The IDs of the subnets that the file system will be accessible from.
- **storageCapacity**: The storage capacity of the file system in gibibytes (GiB).
- **deploymentType**: The type of deployment of the file system - for high availability or scratch purposes.
- **perUnitStorageThroughput**: Specifies the KB/s that you can drive per TiB of storage of the file system.

Please, ensure you have Pulumi installed, and you have set up your AWS provider credentials before running the program. Now, let's proceed with the Pulumi TypeScript program to create an FSx for Lustre file system:

```typescript
import * as pulumi from '@pulumi/pulumi';
import * as aws from '@pulumi/aws';

// Create a new FSx for Lustre file system
const lustreFileSystem = new aws.fsx.LustreFileSystem("myLustreFileSystem", {
    // Tags are key-value pairs that can help you manage, identify,
    // search for, and filter resources.
    tags: { 
        "Name": "my-machine-learning-fs",
    },
    // Subnet ID that the file system will be accessible from.
    // Replace with the actual subnet ID.
    subnetIds: ["subnet-074e2b698dEXAMPLE"],
    // Storage capacity, in GiB. Set according to your needs.
    storageCapacity: 1200,
    // The deployment type of the file system. "SCRATCH_1" is optimized
    // for temporary storage and shorter-term processing of data.
    // Other options include "SCRATCH_2" and "PERSISTENT_1".
    deploymentType: "SCRATCH_1",
    // The throughput per TiB of storage, measured in MB/s/TiB. Higher values
    // increase the speed at which the file system can read and write data.
    perUnitStorageThroughput: 200,
    // Weekly maintenance start time - use appropriate cron expression.
    weeklyMaintenanceStartTime: "2:00:00",
    // Security group IDs that will be attached to the file system's network interface.
    securityGroupIds: ["sg-086a66e7f953EXAMPLE"],
    // If set to true, Amazon FSx will copy all tags from the file system to
    // backups of the file system. Backups are not demonstrated in this program.
    copyTagsToBackups: true,
});

// Export the DNS name of the file system to be used by clients
export const fileSystemDns = lustreFileSystem.dnsName;
```

This program initializes an FSx for Lustre file system with some sensible defaults and parameters that are common in machine learning workflows. It's important to replace placeholder values like the subnet and security group IDs with actual values from your AWS environment. After running this code using Pulumi, it will deploy the specified resources and output the file system DNS name which can be used to mount the file system on your compute instances.

Remember, some parameters used in this program are highly dependent on your specific needs, and you should adjust them accordingly, such as `storageCapacity`, `subnetIds`, and `securityGroupIds`. These need to be specifically tailored to match your AWS VPC configuration and storage requirements.

Also take into account the `deploymentType` and `perUnitStorageThroughput` parameters. They could be refined based on whether you prioritize storage persistence (use `PERSISTENT_1`) or throughput (use a higher `perUnitStorageThroughput` value) for your workload.

Lastly, remember to check for the latest information on FSx for Lustre in AWS documentation as some details might have changed since my last knowledge update. Here's the [LustreFileSystem documentation](https://www.pulumi.com/registry/packages/aws/api-docs/fsx/lustrefilesystem/) for additional details and configurations that you can apply to the Lustre file system.