Department of Computing GPU Cluster Guide

Getting Started

Please note: this service is intended primarily for supporting taught programmes in the Department of Computing. Researchers and members of other departments may want to consult the Research Computing Services (RCS) instead.

Slurm logo

 

 

 

 

 

 

Introduction

What is Slurm and the GPU Cluster?

Slurm is a Linux open-source task scheduling system for managing compute resources, in this case, the department's GPU resources.

The GPU cluster is a pool of NVIDIA GPU Linux servers for CUDA-based parallel computing such as deep-learning, machine-learning and large language models (LLMs), using frameworks such as PyTorch and Tensorflow, or Jax, among others.

Read this guide to learn how to:

  • connect to the submission host server and submit a test script
  • start an interactive job (connect directly to a GPU exclusively for a time limit)
  • compose a shell script that uses shared storage, a python environment, CUDA and your python scripts

Before you start

 

Some familiarity with the Linux command line interface (CLI) and scripting knowledge is desirable before using the GPU cluster:

  • logging in to DoC Lab PCs, especially Nvidia GPU-equipped PCs (Doc Lab PCs)
  • remotely connecting to lab PCs and Doc Shell servers from a Linux/Mac/Windows Terminal (Shell server guide)
  • composing bash scripts (examples are provided below - it is beyond the scope of this guide to explain shell scripting)
  • python environments (Python environments guide)
Tip: make sure you can successfully run your python scripts on your own device or a Doc Lab PC with GPU before using the GPU cluster
Tip: Read Nuri's remote working guide for an introduction to Doc Lab concepts and commands

 

Step by step

General Comments

Fair Usage Policy

The following policies are in effect on the GPU Cluster:

      • User can have two running jobs only (taught students), all other jobs will be queued until one of the two jobs completes running
      • A job that runs for more than four days will be automatically terminated - this is a walltime restriction for taught students - configure checkpoints with your python framework to resume training.
      • As with all departmental resources, any non-academic use of the GPU cluster is strictly prohibited.
      • Any users who violate this policy will be banned from further usage of the cluster and will be reported to the appropriate departmental and college authorities.

ICT GPGPU resources

ICT, the central college IT services provider, has approximately one-hundred CX1 cluster nodes which have GPUs installed. It is possible to select and use these computational resources through PBS Pro job specifications.

Students cannot request access to this resource but project supervisors can apply – on behalf of their students - for access to ICT GPU resources run by the Research Computing Service team:

Research Computing Service

Other resources

If you do not need a GPU for your computation then please do not use the GPU Cluster. You could end up inconveniencing users who do need a GPU. Please instead consider: