A quick question:last year when caffe2 first came out. You suggested to stick to caffe because caffe2 at that time is not mature. Now that caffe2 is officially release, is there any reason we should still use caffe?
Caffe2: Open Source Cross-Platform Machine Learning Tools
41–50 of 57 posts
Re: Caffe2: Open Source Cross-Platform Machine Learning Tools
#42Looking forward to the 137 mutually incompatible forks of this. May caffe2 continue in the style of caffe!
Re: Caffe2: Open Source Cross-Platform Machine Learning Tools
#43Hey guys - Yangqing here, worked on Caffe and Caffe2 over the years, super excited to have continuous contribution to the OSS community. I am more than happy to answer questions if you are curious.
Will there be any project management or stewardship of this project,l? unlike caffe1 where every paper, project, or hypothesis yielded a mutually incompatible fork on github?
Re: Caffe2: Open Source Cross-Platform Machine Learning Tools
#44Looking forward to the 137 mutually incompatible forks of this. May caffe2 continue in the style of caffe!
Haha yeah, I definitely feel your pain - as a caffe developer it really makes me cry when things get so incompatible. I've made some improvements in caffe2 to make it more modular - checkout http://GitHub.com/caffe2/caffe2_bhtsne/, things like such will potentially make things more maintainable than the old Caffe solution.
Re: Caffe2: Open Source Cross-Platform Machine Learning Tools
#45Looking forward to the 137 mutually incompatible forks of this. May caffe2 continue in the style of caffe!
I think Caffe 1.0 made a fundamentally flawed design choice in using protocol buffers to represent network without allowing users to provide a standardized way of adding new operations. Since network was represented as protobuf, to add new layers you had to recompile entire library or use python layers. Thus as a result each project had a new Caffe fork which had to be compiled and maintained separately. Caffe 1.0 was ahead of its time in several aspects compared to Theano or Torch but the dependency hell combined with per paper fork made it difficult to use. It should be case study in unintended consequences of design choices.
Re: Caffe2: Open Source Cross-Platform Machine Learning Tools
#46A quick question:last year when caffe2 first came out. You suggested to stick to caffe because caffe2 at that time is not mature. Now that caffe2 is officially release, is there any reason we should still use caffe?
There is no push to migrate from Caffe to Caffe2 for sure. After Facebook "dogfooding" our own implementation, I think it is safe to say that C2 is now much stable. I would encourage you to try migrating it, letting us know if you run into problems, and stay tuned for the nice additional features that you may be able to enjoy from C2 - like optimized computation with MKLDNN, etc.
Re: Caffe2: Open Source Cross-Platform Machine Learning Tools
#47Is bringing the OpenCL caffe backend[1] to Caffe2 on the roadmap?
Re: Caffe2: Open Source Cross-Platform Machine Learning Tools
#48Is bringing the OpenCL caffe backend[1] to Caffe2 on the roadmap?
Re: Caffe2: Open Source Cross-Platform Machine Learning Tools
#49A quick question:last year when caffe2 first came out. You suggested to stick to caffe because caffe2 at that time is not mature. Now that caffe2 is officially release, is there any reason we should still use caffe?
There is no push to migrate from Caffe to Caffe2 for sure. After Facebook "dogfooding" our own implementation, I think it is safe to say that C2 is now much stable. I would encourage you to try migrating it, letting us know if you run into problems, and stay tuned for the nice additional features that you may be able to enjoy from C2 - like optimized computation with MKLDNN, etc.
Another issue I am concerned about is the visualization tools. As you know, caffe lags behind tensorflow on the nice visualization tool such as tensorboard. Would caffe2 have some nice visualization tool like this?
Re: Caffe2: Open Source Cross-Platform Machine Learning Tools
#50Earlier quoted context omitted.
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
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…
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.