GKE Managed Lustre CSI Driver for AI/ML and HPC workloads

Why it’s a best practice: Defaulting to the highest tier isn’t always the most cost-effective strategy. By analyzing your workload’s I/O profile, you can significantly optimize your total cost of ownership. 

3. Master your networking foundation

A parallel file system is a network-attached resource. Getting the networking right up front will save you days of troubleshooting. Before provisioning, ensure your VPC is correctly configured by following the setup steps in our documentation. This involves three key steps detailed in our documentation:

1. Enable Service Networking.

2. Create an IP range for VPC peering.

3. Create a firewall rule to allow traffic from that range on the Lustre network port (TCP 988 or 6988).

Why it’s a best practice: This is a one-time setup per VPC that establishes the secure peering connection that allows your GKE nodes to communicate with the Managed Lustre service. 

4. Use dynamic provisioning for simplicity, static for long-lived shared data

The Managed Lustre CSI driver supports two modes for connecting storage to your GKE workloads.

• Dynamic provisioning: Use when your storage is tightly coupled to the lifecycle of a specific workload or application. By defining a StorageClass and PersistentVolumeClaim (PVC), GKE will automatically manage the Lustre instance lifecycle for you. This is the simplest, most automated approach.

• Static provisioning: Use when you have a long-lived Lustre instance that needs to be shared across multiple GKE clusters and jobs. You create the Lustre instance once, then create a PersistentVolume (PV) and PVC in your cluster to mount it. This decouples the storage lifecycle from any single workload.

Why it’s a best practice: Thinking about your data’s lifecycle helps you choose the right pattern. Use dynamic provisioning as your default because of simplicity, and opt for static provisioning when you need to treat your file system as a persistent, shared resource across your organization.

5. Architecting for parallelism with Kubernetes Jobs

Many AI and HPC tasks, like data preprocessing or batch inference, are suited for parallel execution. Instead of running a single, large pod, use the Kubernetes Job resource to divide the work across many smaller pods.

Consider this pattern:

1. Create a single PersistentVolumeClaim for your Managed Lustre instance, making it available to your cluster.

2. Define a Kubernetes job with parallelism set to a high number (e.g., 100).

3. Each pod created by the Job mounts the same Lustre PVC.

4. Design your application so that each pod works on a different subset of the data (e.g., processing a different range of files or data chunks).

Why it’s a best practice: In this pattern, you create a single PVC for your Lustre instance and have each pod created by the Job mount that same PVC. By designing your application so that each pod works on a different subset of the data, you turn your GKE cluster into a powerful, distributed data processing engine. The GKE Job controller acts as the parallel task orchestrator, while Managed Lustre serves as the high-speed data backbone, allowing you to achieve massive aggregate throughput.

Get started today

By combining the orchestration power of GKE with the performance of Managed Lustre, you can build a truly scalable and efficient platform for AI and HPC. Following these best practices will help you create a solution that is not only powerful, but also efficient, cost-effective, and easy to manage.

Ready to get started? Explore the Managed Lustre documentation, and provision your first instance today.