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Managing multiple CUDA versions using environment modules in Ubuntu
Steps to manage multiple CUDA environments
Latest Update: May 19th, 2024
This gist contains all the steps required to:
Install multiple CUDA versions (e.g., CUDA 11.8 and CUDA 12.1
Manage multiple CUDA environments on Ubuntu using the utility called environment modules.
Use this approach to avoid CUDA environment conflicts.
Environment Modules is a package that provides for the dynamic modification of a user's environment via modulefiles. You can find more on it at https://modules.readthedocs.io/en/latest/
Step-by-Step Guide to setup your own personal GPU server
Setting Up Your Personal GPU Server: A Step-by-Step Guide
I've been using a GPU workstation with an RTX 4090 for almost a year now, and it's been one of the best decisions I've made. With a personal GPU server, you no longer need to rely on cloud-based GPU instances from services like RunPod or Vast.ai every time you want to run a job or try new models. The best part? No stress about recurring GPU instance costs! :-)
However, I rarely work directly on my workstation. Instead, I prefer the flexibility of accessing the GPU remotely using my MacBook, whether I'm working from different locations within my home, from a co-working space, or a cozy cafe in another part of town.
In this blog, I will walk you through the steps to configure a personal GPU Ubuntu server.
For this guide, I assume you already have a workstation running Ubuntu with a GPU and it is connected to your local network