Skip to content

Instantly share code, notes, and snippets.

View garg-aayush's full-sized avatar
🎯
Focusing

Aayush Garg garg-aayush

🎯
Focusing
View GitHub Profile
@hofmannsven
hofmannsven / README.md
Last active July 16, 2024 01:30
Git CLI Cheatsheet
@garg-aayush
garg-aayush / Steps_multiple_cuda_environments.md
Last active July 12, 2024 13:33
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/

@garg-aayush
garg-aayush / setup-personal-gpu-server.md
Created June 30, 2024 15:35
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