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Nvidia released their new RTX 30 series and the top of the line consumer graphics cards sold out in a matter of seconds. Nvidia stock is at an all time high. Demand for Graphic Processing Units (GPU) keeps growing for specialized computations in gaming and AI development. The latest wave of AI has been spurred on by the rapid development of GPU compute capability. A100/V100s with High Bandwidth Memory (HBM) stuffed into a Cray, DGX, and systems like it, power many of the foremost Deep Learning research groups and labs. Does this mean that to practice Deep Learning that you need the latest and greatest GPU? What should you do if you want to break into Deep Learning and not the bank?

This article will detail various places to access GPU compute and how I made an old linux machine into a decent system for learning Deep Learning.

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Recently, a friend sent me a Wired article entitled “The Power and Paradox of Bad Software”. The short piece, written by Paul Ford, discusses the idea that the software industry might be too obsessed with creating better and better tools for itself while neglecting mundane software such as resource scheduling systems or online library catalogs. The author claims that the winners of the bad software lottery are the computational scientists that develop our climate models. Since climate change might be one of the biggest problems for the next generation, some might find it a bit worrying if one of our best tools for examining climate change was written with “bad software”.

In this post, I discuss the question of wether climate scientists lost the “bad software sweepstakes”. I’ll cover the basics of climate models, what software is commonly used in climate modeling and why, and what alternative software exists.

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