Re:Caffe2: Open Source Cross-Platform Machine Learning Tools
by mtw on Tuesday April 18, 2017 @04:22PM
(#14142541)
does it mean PyTorch or Tensorflow are better for research, experimentation and Caffe2 was more designed for production?
Or it actually means Caffe2 was designed for both research and production
Re:Caffe2: Open Source Cross-Platform Machine Learning Tools
by jiayq84 on Tuesday April 18, 2017 @08:28PM
(#14144394)
PyTorch definitely makes experimentation much better. For example, if you want to train some system that is highly dynamic (reinforcement learning, for example), you might want to use a real scripting language which is Python, and PyTorch makes that really sweet.
Sometimes the line gets a bit blurred - for research that are focusing on relatively fixed patterns, such as Mask RCNN, both PyTorch and caffe2 are working great. In fact, Mask RCNN is trained in Caffe2, and that also makes things much easy when we put it on mobile - what our CTO Mike Schroepfer showed in his keynote is a Mask RCNN model trained and then deployed onto mobile with Caffe2.
Re:Caffe2: Open Source Cross-Platform Machine Learning Tools
by binaryzeitgeist on Wednesday April 19, 2017 @05:43AM
(#14146575)
When are you planning to release the Mask-RCNN code?
I'm trying to implement the RoIAlign layer in Tensorflow and I've a few doubts and having the author's code would definitely help in implementing it.
Re:Caffe2: Open Source Cross-Platform Machine Learning Tools
by anand_MSFT on Tuesday April 18, 2017 @11:28PM
(#14145242)
We launched a Data Science VM supporting caffe2 - https://blogs.technet.microsoft.com/machinelearning/2017/04/...
> Caffe2 is built to excel at mobile and at large scale deployments. While it is new in Caffe2 to support multi-GPU, bringing Torch and Caffe2 together with the same level of GPU support, Caffe2 is built to excel at utilizing both multiple GPUs on a single-host and multiple hosts with GPUs. PyTorch is great for research, experimentation and trying out exotic neural networks, while Caffe2 is headed towards supporting more industrial-strength applications with a heavy focus on mobile. This is not to say that PyTorch doesn’t do mobile or doesn’t scale or that you can’t use Caffe2 with some awesome new paradigm of neural network, we’re just highlighting some of the current characteristics and directions for these two projects. We plan to have plenty of interoperability and methods of converting back and forth so you can experience the best of both worlds.