CXXNET is a fast, concise, distributed deep learning framework.
Contributors: https://github.com/antinucleon/cxxnet/graphs/contributors
- Documentation
- Learning to use cxxnet by examples
- Note on Code
- User Group(TODO)
- Lightweight: small but sharp knife
- cxxnet contains concise implementation of state-of-art deep learning models
- The project maintains a minimum dependency that makes it portable and easy to build
- Scale beyond single GPU and single machine
- The library works on multiple GPUs, with nearly linear speedup
- THe library works distributedly backed by disrtibuted parameter server
- Easy extensibility with no requirement on GPU programming
- cxxnet is build on mshadow
- developer can write numpy-style template expressions to extend the library only once
- mshadow will generate high performance CUDA and CPU code for users
- It brings concise and readable code, with performance matching hand crafted kernels
- Convenient interface for other languages
- Python interface for training from numpy array, and prediction/extraction to numpy array
- Matlab interface (TODO)
CXXNET is built on MShadow: Lightweight CPU/GPU Tensor Template Library
- MShadow is an efficient, device invariant and simple tensor library
- MShadow allows user to write expressions for machine learning while still provides
- This means developer do not need to have knowledge on CUDA kernels to extend cxxnet.
- MShadow also provides a parameter interface for Multi-GPU and distributed deep learning
- Improvements to cxxnet can naturally run on Multiple GPUs and being distributed
- Copy
make/config.mkto root foler of the project - Modify the config to adjust your enviroment settings
- Type
./build.shto build cxxnet