[thecity2]

> It would probably be cheaper to just buy my own Xeon computers.

What does Xeon have to do with training? You need a fast GPU (or apparently TPU). The CPU is relatively unimportant. Furthermore, you can roll your own algorithms (i.e. architecture) in TensorFlow. You don't have to use "theirs".

[felipemnoa]

Well, I'm designing my own AI engine from scratch, not using any of the current machine learning techniques, i.e. Convolutional Neural Networks, etc. Right now the way my code works is that it uses cores to make the whole training faster, ans xeon's have lots of cores. If I could use GPU's I would, maybe I can and I just need to figure it out. For now the simplest thing to do to make it faster is to add more cores.

Now, you may think I'm crazy for designing something from scratch, I may be indeed, but how else can we discover/invent something totally new. I think I'm actually onto something given that the early results look quite promising.

I actually created a video demonstration of my AI Engine which you can find a link to it in my profile but I have done a crappy job of explaining the strengths of it [1]. Too much work, so little time.

[1] https://www.youtube.com/watch?v=WHNdIuBJHTo

[bluetwo]

Neat. I also have built my own system from scratch., so I don't think it's crazy at all.

Where I get stuck is figuring out new challenges to throw at the system. I find it funny that there is lots of discussion here about performance and tuning, and not so much about practice applications and valuable problems to solve.

[thecity2]

Unless you have like 64 cores, I don't think you will be a GPU. And yes, you can use multiple GPUs in parallel to train.