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Tensors and Dynamic neural networks in Python with strong GPU acceleration
Summary: Faster implementation of UniqueOp using google::dense_hash_map, as suggested by dzhulgakov. I haven't benchmarked it precisely but early measurements with my workflow show a significant speed bump (this operation went from using 20% of overall CPU time down to 7%). I gated the implementation using the "engine" feature, to avoid adding sparsehash as a dependency to caffe2. Reviewed By: dzhulgakov Differential Revision: D4219768 fbshipit-source-id: 2f142981e772105b42fffa24afb199ef816f8e0c |
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| caffe/proto | ||
| caffe2 | ||
| docs | ||
| third_party | ||
| .Doxyfile | ||
| .gitignore | ||
| .gitmodules | ||
| build.py | ||
| build_android.py | ||
| build_android_prepare.py | ||
| LICENSE | ||
| Makefile | ||
| README.md | ||
Caffe2
Caffe2 is a deep learning framework made with expression, speed, and modularity in mind. It is an experimental refactoring of Caffe, and allows a more flexible way to organize computation.
Read the installation instructions for installation details.
License and Citation
Caffe2 is released under the BSD 2-Clause license.