cxxnet

CXXNET is a fast, concise, distributed deep learning framework.

Feature Highlights

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)

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