Google Cloud Cluster Toolkit 2025: The Definitive Guide for Slurm-Based Workloads | by Aleksei Aleinikov | Google Cloud – Community | May, 2025

High-performance computing and generative AI are no longer reserved for giant research labs. In 2025 any engineer, scientist, or start-up can stand up a full cluster in Google Cloud — without writing hundreds of shell scripts. Cluster Toolkit is the open-source kit that turns modular building blocks into ready-to-run HPC, AI, and ML environments. Below I explain how it works, why it matters, and share real-world scenarios that already save teams weeks of effort.

• Cluster constructor. You outline your desired architecture in a single YAML “blueprint.”
• Module catalog. Need compute nodes, networking, a file system, or a Slurm scheduler? Pick the bricks you need and stack them together.
• The gcluster engine. It reads your blueprint, assembles a self-contained “deployment folder” with Terraform or Packer, and prints the exact commands to launch the cluster.
• Deployment folder. A standalone directory that can create — or tear down — a cluster at any time.

• Speed. A typical HPC cluster spins up in under ten minutes; a GPU-rich AI stack in roughly an hour.
• Open source. Adapt modules, write your own, or patch existing ones at will.
• Batteries included. Slurm, Batch, GKE, Filestore, Lustre, Parallelstore, Cloud Monitoring — all pre-integrated.
• Cost clarity. Automatic labels let you filter billing reports and see cluster spend instantly.
• Flexibility. Tweak a variable, regenerate the folder, redeploy — no vendor lock-in.

1. Prep your environment
   Use Cloud Shell or a local workstation with gcloud, Terraform, Packer, Go, and Git. Enable Compute Engine, Filestore, Cloud Storage, and Resource Manager APIs.
2. Clone the repo

git clone https://github.com/GoogleCloudPlatform/cluster-toolkit.git
cd cluster-toolkit
make              # builds the gcluster binary

3. Write a blueprint
Start from an example like hpc-slurm.yaml or draft your own: define a name, variables, and module groups.

4. Generate the deployment folder

./gcluster create my-blueprint.yaml --vars project_id=MY_PROJECT

5. Deploy

./gcluster deploy my-blueprint

• Confirm the plan; minutes later the cluster is live.

6. Run jobs & monitor
SSH to the login node, submit jobs, and watch metrics in Cloud Monitoring.

7. Clean up

./gcluster destroy my-blueprint --auto-approve

1. One-hour coastal storm simulation

• Scenario: A university group models micro-climate for a seaside city.
• Toolkit angle: The serverless-batch-mpi example deploys a Batch cluster on cost-efficient C-series VMs that auto-scale with MPI tasks.
• Outcome: Spot VMs cut compute costs by half; the cluster stood up faster than the researchers compiled the weather code.

2. Training a chat-bot on A4 GPUs

• Scenario: A start-up fine-tunes a large Russian language model across eight B200 GPUs.
• Toolkit angle: The a4-highgpu-8g blueprint wires up high-bandwidth gVNIC networking, Filestore storage, and a Slurm scheduler in a single shot.
• Outcome: Instead of juggling reservations and drivers, the team focused on optimizing the training pipeline.

3. Processing 10 000 genomic samples

• Scenario: Bioinformaticians launch thousands of short, CPU-heavy jobs.
• Toolkit angle: The htc-slurm blueprint is tuned for high throughput: it spins up many inexpensive N-series nodes and tears them down the moment they go idle.
• Outcome: Turnaround time dropped from days to hours; sequencing machines no longer sit idle.

4. Rendering an animated short film

• Scenario: An indie studio renders frames with Blender Cycles.
• Toolkit angle: A partition on H-class compute-optimized machines offers huge core counts and auto-scales overnight to save budget.
• Outcome: Peak demand hit 88 vCPU per node, then collapsed to a single controller by morning — completely unattended.

• Check quotas first, especially for specialized machine families and Filestore.
• Mind the firewall. Enabling OS Login simplifies SSH access without juggling keys.
• Store Terraform state in versioned Cloud Storage for safe rollbacks.
• Label everything. Custom tags in the blueprint make billing and logging painless.
• Prototype locally. Use gcluster create -l ERROR to validate your YAML before spending a cent.

• The deployment folder’s name becomes a billing label. One team called their cluster “potato,” resulting in a finance report where most of the spend was “Potato-Compute.”
• A minimal Slurm cluster can finish deploying faster than your IDE installs its extensions.
• Setting exclusive: false keeps nodes alive after jobs—perfect for debugging, but don’t forget to shut them down later!

Cluster Toolkit turns the complexity of HPC, AI, and ML orchestration into a straight path: draft a blueprint, generate a folder, hit “deploy.” In 2025 it’s arguably the quickest route from an idea for a compute cluster to real results in the cloud. Give it a try and an hour from now you could be running Slurm — or GKE — on infrastructure you fully control.

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