Join me on an exhilarating journey as I dive into the world of Machine Learning by building my very own ML rig from scratch! This video is your all-in-one guide to assembling a powerful machine learning computer, explaining every component's role in ML tasks, and setting up the essential software to get you up and running in the world of artificial intelligence.
🔧 What's Inside:
Component Selection: Dive deep into the heart of my machine as I explain why I chose each piece of hardware for my ML rig, covering the CPU, GPU, RAM, and more. Understand the critical role these components play in machine learning projects and how they can accelerate your computations.
Parts list w/ cost breakdown: [ Ссылка ]
Building Process: Watch as I put together all the pieces, sharing tips and tricks for assembling your machine learning computer. Whether you're a seasoned builder or a first-timer, there's something for everyone to learn.
Software Installation: The right tools can make or break your ML projects. I'll walk you through installing and configuring key machine learning software and libraries such as PyTorch, TensorFlow, CUDA, local Large Language Models (LLMs), Stable Diffusion, and more. Get insights into each tool's unique strengths and find out how to leverage them for your projects.
📚 Why This Matters:
Building a dedicated machine learning rig can be a game-changer for hobbyists, students, and professionals alike. By customizing your setup, you unlock new potentials in processing speed, efficiency, and the ability to handle complex models and datasets. This video aims to demystify the process and empower you to take your first step into a larger world of AI and ML.
💡 Perfect For:
AI/ML enthusiasts eager to build their specialized hardware.
Anyone interested in the hardware aspect of machine learning.
Viewers looking to install and use major ML software and libraries.
📌 Timestamps:
0:00 - Intro
0:25 - Parts
3:04 - The Build
4:48 - Ubuntu Server 22.04 LTS
7:25 - Tailscale
8:10 - NVIDIA Drivers
8:54 - Ollama
10:01 - Code Server
11:30 - Anaconda
12:07 - CodeLlama
13:47 - PyTorch
14:37 - Stable Diffusion
15:41 - Docker w/ GPUs
16:37 - Isaac Gym
18:23 - Tensorflow & CUDA Toolkit
21:10 - Conclusion
🔗Links & Resources:
Graphics Drivers Issue: [ Ссылка ]
Docker Issue: [ Ссылка ]
Isaac Gym:
[ Ссылка ] [ Ссылка ]
#MachineLearning #TechBuild #AISoftwareSetup #DIYComputerAssembly #MLRig #ArtificialIntelligence
