31–40 of 57 posts
Is ARM, and in particular the NVIDIA TK1/TX1/TX2 supported? Ease of use on these platforms with Theano vs Tensorflow/PyTorch is the main thing keeping me on Theano.
Yep, if you look under the scripts/ folder we are putting on example scripts that you can use to build on specific platforms. Let me know what you think and feel free to send issues/PRs!
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Yangqing, thank you. This is awesome. Quick question: why did you develop and release this as a completely separate codebase from the original Caffe you created at UC Berkeley?
Ah, it is because we do not want to create too much chaos for people to migrate between codebases. Evan and I had played both scenarios and decided that it is cleaner to put the code in separate codebases. A lot of the runtime code are shared, and we are working on the migration tools - such as model converters - that helps migration. If there are bugs in these tools, shoot us an issue on github and we'll fix it.
Hey 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.
How difficult is it to set up custom recurrent networks? The only system I've ever seen that handles this well is CNTK - you can just say `x = PreviousValue(y)`. I've tried to work out how to do similar things in Torch and Tensorflow but all they really offer is pre-packaged layers like LSTM. If you want to make your own it's difficult, undocumented and not at all ergonomic. How does Caffe2 compare?
function (someinput):
output = do_stuff
return output
One quick question: will nivida tensorRT be supporting caffe2?
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http://caffe2.ai/docs/caffe-migration.html#null__how-is-caff... > 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 ex…
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
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.
I would have preferred them to chose another name.
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How difficult is it to set up custom recurrent networks? The only system I've ever seen that handles this well is CNTK - you can just say `x = PreviousValue(y)`. I've tried to work out how to do similar things in Torch and Tensorflow but all they really offer is pre-packaged layers like LSTM. If you want to make your own it's difficult, undocumented and not at all ergonomic. How does Caffe2 compare?
I am a complete machine learning noob, so this could just be my lack of skill. Having looked at most of the popular ML frameworks, none of them seem to provide an easy, functional way to implement a custom 'neuron'. That is: function (someinput): output = do_stuff return output Which then gets passed on to the next layer. Again, it's quite possible I've just missed something, or there is some inherent ML limitation t…
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A super inexperienced observer here, so just a very basic question - how does Caffe(2) differ from TF/Theano/Torch etc.? What are the obvious upsides and potential downsides? A tweet-sized answer will do, I'm just curious as to what the high level differences are. Cheers and congrats on the release!
http://caffe2.ai/docs/caffe-migration.html#null__how-is-caff... > 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 ex…