3 ms·
I'd be interested to know how you formed this impression. My own impression is formed by: * The work I and my colleagues are doing * Recently published literat
by laingc 9y ago
I'd be interested to know how you formed this impression. My own impression is formed by:
* The work I and my colleagues are doing
* Recently published literature and arxiv pre-prints
* Conference talks
* Industry meet ups
* Blog posts
Based on my anecdotal experience from these sources, I see absolutely no evidence of Torch experiencing any kind of resurgence. If I had to make a call about the direction if the community as a whole, I would say that it is very clearly heading towards TensorFlow, with some holdouts using Theano and some using mxnet. Torch is used by some groups, certainly, but I have the impression that it's use is decreasing, rather than increasing.
- p1esk 9y agoJust to give you another anecdotal experience: I'm a researcher who used Theano for 3 years to train convnets. A couple of months ago I realized that Theano is getting too much pain to work with (main reasons being the lack of implementations for latest models, and difficulty of using multiple GPUs), so I decided to switch to a more popular framework. I looked at TF, and almost started porting my code to it, then someone suggested I look at PyTorch. After 30 minutes playing with it, I was sold. Much more intuitive. Major architectures from the last 12 months have been implemented. Dynamic graphs are probably something I will need in the future. Community is very active and helpful. The downside is that the software is not as mature as TF, and the community is smaller, but that's changing fast. p.s. What you wrote about Torch is correct though, but we are talking about PyTorch, not Torch.