MXNet !We move forward to
Thanks for using and supporting cxxnet. Today, we finally make a hard but exciting decision: we decide to deprecate cxxnet and fully move forward to next generation toolkit MXNet.
28th, Sep, 2015
Note: We provide a very simple converter to MXNet. Check guide to see whether your model is able to be converted.
CXXNET is a fast, concise, distributed deep learning framework.
- 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
- 24-May, 2015: Pretrained Inception model with 89.9% Top-5 Correctness is ready to use.
- 09-Apr, 2015: Matlab Interface is ready to use
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
make/config.mkto root foler of the project
- Modify the config to adjust your enviroment settings
./build.shto build cxxnet