The State of Machine Learning Frameworks
thegradient.pub
The State of Machine Learning Frameworks
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Re: The State of Machine Learning Frameworks
#2Re: The State of Machine Learning Frameworks
#3Why?
industry: tensorRT.
Re: The State of Machine Learning Frameworks
#4Re: The State of Machine Learning Frameworks
#5Why?
As a summary, though:
PyTorch has become dominant in research because of its API (both its stability + having eager mode).
TF has become dominant in industry because A. it came out several years before PyTorch and industry is slow to move, B. It supported a lot of production use cases (mobile, serving, removing Python overhead) that PyTorch didn't for a long time.
Re: The State of Machine Learning Frameworks
#6Why?
Well, you could read the article :^) As a summary, though: PyTorch has become dominant in research because of its API (both its stability + having eager mode). TF has become dominant in industry because A. it came out several years before PyTorch and industry is slow to move, B. It supported a lot of production use cases (mobile, serving, removing Python overhead) that PyTorch didn't for a long time.
Re: The State of Machine Learning Frameworks
#7Re: The State of Machine Learning Frameworks
#8Re: The State of Machine Learning Frameworks
#9With fastai module that's built on Pytorch learning and developing Deep Learning solutions have become a lot easier. So there's a real game on now
Re: The State of Machine Learning Frameworks
#10If I were to start a theoretical computer vision company today, which would I be better off using?