21–30 of 201 posts
Going from doing ML research to data ingestion and analysis to web frameworks to API design, blogging and static site generation is very powerful. The trend seems to be that python will dominate all of these .
Code: https://github.com/Chillee/pytorch-vs-tensorflow
Ablation of claims: https://thegradient.pub/p/cef6dd26-f952-4265-a2bc-f8bfb9eb1e...
JS interactive charts: https://chillee.github.io/pytorch-vs-tensorflow/
I think tensorflow dominates industry purely because of its capability of exporting the model into a coreml Android model or easy of moving it to production in a GCP environment or in whatever form. Pytorch might have to build a good production pipeline around it to catch up in this game. With fastai module that's built on Pytorch learning and developing Deep Learning solutions have become a lot easier. So there's a…
And simply TensorFlow was there earlier, so people implemented stuff in it. I think there's more inertia in industry, whereas researchers may more easily switch frameworks between two papers.
I think tensorflow dominates industry purely because of its capability of exporting the model into a coreml Android model or easy of moving it to production in a GCP environment or in whatever form. Pytorch might have to build a good production pipeline around it to catch up in this game. With fastai module that's built on Pytorch learning and developing Deep Learning solutions have become a lot easier. So there's a…
It's only a matter of time until PyTorch will also dominate industry. It's always like this. Think how Ubuntu took over the server market because amateurs were preferring it instead of Redhat/CentOS. And when they became professionals or were in a position to decide, they also put Ubuntu on the server because this is what they knew best.
It's only a matter of time until PyTorch will also dominate industry. It's always like this. Think how Ubuntu took over the server market because amateurs were preferring it instead of Redhat/CentOS. And when they became professionals or were in a position to decide, they also put Ubuntu on the server because this is what they knew best.
Perhaps it’s the nature of the game that changed with many new kinds of architectures and so on. But maybe Keras is already overengineered for someone who just wants to make thumbnail sized GAN stuff at home.
Earlier quoted context omitted.
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.
well, for one pytorch added the python overhead over an otherwise very clean lua jit