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Tensors and Dynamic neural networks in Python with strong GPU acceleration
Summary: This adds support for automatic aggregation of sparse gradients. We simply concatenate indices and values (no attempt to deduplicate, since this is already done before feeding into the optimizer). This should support various cases (indices and/or values can be generated by one or more gradient ops, or gradient outputs can be directly passed from inputs). I tried to minimize the code footprint, but I introduced SparseGradGenMeta because GradGenMeta didn't lend itself very well to be used with sparse gradients. Reviewed By: dzhulgakov Differential Revision: D4219788 fbshipit-source-id: 1d074664cffd82a8764e4b1473ada6bc46e6c51a |
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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.