You don't have to look far or wide to find guides on building the best gaming rigs. That is a tougher search, although there can be some overlap. There aren't many companies talking about the ins and outs of DIY AI computer build essentials. It is exactly for that reason that we have compiled a list of the most important components you will need for an artificial intelligence (AI) build and what we recommend. What else should you consider for an AI computer?
Power supplies are a frequently misunderstood--and overlooked--PC component. Many users choose a PC power supply based on total wattage alone, assuming that higher is always synonymous with better. Others pay no attention to their power supply unit (PSU) selection at all, and settle for whatever abomination arrived with their machine. But considering how important a good power supply is to a system's stability and long-term reliability, it's a shame that PSUs get so little attention in comparison to sexier components like graphics cards and SSDs. It doesn't help that the power supply market is awash with products from unscrupulous manufacturers that use substandard components and overstate the hardware's capabilities, especially now that booming cryptocurrency prices have created huge demand for graphics cards and PSUs.
Creating my workstation has been a dream for me, if nothing else. I knew the process involved, yet I somehow never got to it. It might have been time or money. But this time I just had to do it. I was just fed up with setting up a server on AWS for any small personal project and fiddling with all the installations.
If you're learning Data Science and Machine Learning, you definitely need a laptop. This is because you need to write and run your own code to get hands-on experience. When you also consider portability, the laptop is the best option instead of a desktop. A traditional laptop may not be perfect for your data science and machine learning tasks. You need to consider laptop specifications carefully to choose the right laptop.
In this post we are going to learn about Venus, my deep learning computer, and how I built it. Along the way, I will explain at a high-level what each hardware component of a computer does and how I navigated the landscape of selecting parts for a functional build. I'll also describe how I installed relevant software for the machine and include some benchmarks showing the superior performance of a GPU system over a pure CPU system. WARNING: this is a pretty long post that functions as a complete tutorial for building a deep learning computer literally from scratch, no assumptions made. But…since it's long I highly encourage you to peruse and skip any sections depending on your interest. While there are numerous build descriptions out there showing how people constructed their own deep learning rigs, as I went about consulting some of them, I often felt there was some crucial component missing. As you start on your build journey, it's easy to get mired in the weeds of hardware terminology. Should I pick an M.2 SSD or will SATA suffice? Can I get away with HDD? How many PCIe x16 slots do I need? Should I pick DDR4-3000 or DDR4-2400 memory? All this lingo can be very overwhelming especially for newcomers to hardware. But before we start shamelessly name-dropping so that we sound smart, let's go back to the fundamentals.