Re:New AI edits images based on text instructions (instructP...
by michaelt on Monday January 23, 2023 @04:13AM
(#34486895)
> Can you imagine only being able to cook a hamburger on one brand of grill? [...] the crypto/cuda nexus of GPU work is simply not rational. Why are we still here?
Because nvidia spent a long time chasing the market and spending resources, like they wanted it.
You wanted to learn about GPU compute in 2012? Here's a free udacity course sponsored by nvidia, complete with an online runtime backed by cloud GPUs so you can run the exercises straight from your browser.
You're building a deep learning framework? Here's a free library of accelerated primitives, and a developer who'll integrate it into your open source framework and update it as needed.
OpenCL, in contrast, behaves as if every member of the consortium is hoping some other member will bear these costs - as if they don't really want to be in the GPU compute business, except out of a begrudging need for feature parity.
And in terms of being rational - if you're skilled enough to be able to add support for a new GPU vendor into an ML library, you're probably paid enough that the price of a midrange nvidia GPU is trivial in comparison.
All is not lost, though - vendors like Intel are increasingly offering ML acceleration libraries [1] and most neural networks can be exported from one framework and imported into another.
Because nvidia spent a long time chasing the market and spending resources, like they wanted it.
You wanted to learn about GPU compute in 2012? Here's a free udacity course sponsored by nvidia, complete with an online runtime backed by cloud GPUs so you can run the exercises straight from your browser.
You're building a deep learning framework? Here's a free library of accelerated primitives, and a developer who'll integrate it into your open source framework and update it as needed.
OpenCL, in contrast, behaves as if every member of the consortium is hoping some other member will bear these costs - as if they don't really want to be in the GPU compute business, except out of a begrudging need for feature parity.
And in terms of being rational - if you're skilled enough to be able to add support for a new GPU vendor into an ML library, you're probably paid enough that the price of a midrange nvidia GPU is trivial in comparison.
All is not lost, though - vendors like Intel are increasingly offering ML acceleration libraries [1] and most neural networks can be exported from one framework and imported into another.
[1] https://www.intel.com/content/www/us/en/developer/tools/onea...