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ThirdAI Uses Ray for Parallel Training of Billion-Parameter NN on Commodity CPUs

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Re: ThirdAI Uses Ray for Parallel Training of Billion-Parameter NN on Commodity CPUs

#3
Their methods rely on heuristics to sparsify NNs. They compare their sparse and dense methods to PyTorch and Tensorflow and get worse perf on dense inferences but better on sparse. It would be useful to know if PyTorch runs faster or slower than their methods using their sparsified matrix techniques.

Re: ThirdAI Uses Ray for Parallel Training of Billion-Parameter NN on Commodity CPUs

#4

Their methods rely on heuristics to sparsify NNs. They compare their sparse and dense methods to PyTorch and Tensorflow and get worse perf on dense inferences but better on sparse. It would be useful to know if PyTorch runs faster or slower than their methods using their sparsified matrix techniques.

> It would be useful to know if PyTorch runs faster or slower than their methods using their sparsified matrix techniques.

Probably not an easy feat. Sparse support in pytorch is minimal.

Re: ThirdAI Uses Ray for Parallel Training of Billion-Parameter NN on Commodity CPUs

#5
imo this sounds like the right move, relying on a single company to produce all the gpus results in massive competition to get them, and a large barrier to entry to buy them

by skipping it you can play a little slower but probably a lot cheaper

Re: ThirdAI Uses Ray for Parallel Training of Billion-Parameter NN on Commodity CPUs

#9
post #8

Seems to require Tensorflow. That makes it basically irrelevant?

Hi, I am one of the co-authors of the blog from ThirdAI. Our software has no dependency on TensorFlow or any other deep learning framework. You can see the libraries we import for training and try it out for yourself in our public github repo: https://github.com/ThirdAILabs/Public-Benchmarks/blob/main/c...
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