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Caffe2: Open Source Cross-Platform Machine Learning Tools

caffe2.ai

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Re: Caffe2: Open Source Cross-Platform Machine Learning Tools

#21

Post by mtw »

smhx wrote: post #8
drej wrote: post #7

Earlier quoted context omitted.

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…

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

#22

Post by mtw »

Congrats ! I find the site really well-made and I see a great deal of efforts were made on making the library usable

Q: I've never used Caffe - based on the examples provided, I would say it's best for images and videos? I'm interested in NLP (eg seeing patterns in science papers) or in studying wearables data (gps, heart rate etc.) to predict user activity.

Re: Caffe2: Open Source Cross-Platform Machine Learning Tools

#25

Post by jiayq84 »

cs702 wrote: post #20
jiayq84 wrote: post #2

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.

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.

Re: Caffe2: Open Source Cross-Platform Machine Learning Tools

#26

Post by jiayq84 »

mtw wrote: post #22

Congrats ! I find the site really well-made and I see a great deal of efforts were made on making the library usable Q: I've never used Caffe - based on the examples provided, I would say it's best for images and videos? I'm interested in NLP (eg seeing patterns in science papers) or in studying wearables data (gps, heart rate etc.) to predict user activity.

Yeah, Caffe2 has the same root as Caffe, so you'll see the image side really polished - for object recognition, AR, etc.

Over the next weeks/months we'll share more examples on other applications such as RNNs.

Re: Caffe2: Open Source Cross-Platform Machine Learning Tools

#27

Post by jiayq84 »

ceyhunkazel wrote: post #23

Great work! Hope you will support Python 3. "Required Dependencies# Anaconda. Python 2.7 version is needed for Caffe2, and Anaconda is recommended."

Thanks - Python 3 support is coming soon, as you may expect, there are a few issues regarding string manipulation but we are figuring out the details.

Re: Caffe2: Open Source Cross-Platform Machine Learning Tools

#28

Post by jiayq84 »

danielmorozoff wrote: post #14
jiayq84 wrote: post #13

Earlier quoted context omitted.

RE the performance - many frameworks nowadays enjoy the high-performance libraries such as CuDNN for optimized runtime on different platforms. For example, we've been collaborating with NVidia on optimized distributed training on GPUs, and you can check out more details here: https://blogs.nvidia.com/blog/2017/04/18/caffe2/

Thanks for the link. Are there any public specifics comparing distributed tf to Caffe2's distributed implementation?

I am not aware of that right now. Internally we measure the performance by looking at the theoretical peak possible (like the scaling efficiency when you use distributed training). C2 has been doing pretty well; if you find performance degradations, send an issue to us and we are usually very good to figure out the perf.
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