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https://github.com/dmlc/cxxnet
move forward to https://github.com/dmlc/mxnet
https://github.com/dmlc/cxxnet
Last synced: about 1 month ago
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move forward to https://github.com/dmlc/mxnet
- Host: GitHub
- URL: https://github.com/dmlc/cxxnet
- Owner: dmlc
- License: other
- Archived: true
- Created: 2014-02-13T18:26:55.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2015-09-29T03:57:26.000Z (about 9 years ago)
- Last Synced: 2024-05-29T04:43:53.101Z (5 months ago)
- Language: C++
- Homepage:
- Size: 5.05 MB
- Stars: 1,026
- Watchers: 178
- Forks: 423
- Open Issues: 65
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# We move forward to [MXNet](https://github.com/dmlc/mxnet) !
----
Dear users,
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](https://github.com/dmlc/mxnet).**
Please check the feature [highlights](https://github.com/dmlc/mxnet#features), [speed/memory comparation](https://github.com/dmlc/mxnet/tree/master/example/imagenet) and [examples](https://github.com/dmlc/mxnet/tree/master/example) in MXNet.
cxxnet developers,
28th, Sep, 2015
-----
Note: We provide a very simple converter to MXNet. Check [guide](simple_mxnet_converter) to see whether your model is able to be converted.
------
#cxxnet
CXXNET is a fast, concise, distributed deep learning framework.
Contributors: https://github.com/antinucleon/cxxnet/graphs/contributors
* [Documentation](doc)
* [Learning to use cxxnet by examples](example)
* [Note on Code](src)
* User Group(TODO)
###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](#backbone-library)
- 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
### News
* 24-May, 2015: Pretrained [Inception model](example/ImageNet/Inception-BN.conf) with 89.9% Top-5 Correctness is ready to use.
* 09-Apr, 2015: Matlab Interface is ready to use
### Backbone Library
CXXNET is built on [MShadow: Lightweight CPU/GPU Tensor Template Library](https://github.com/tqchen/mshadow)
* 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
###Build
* Copy ```make/config.mk``` to root foler of the project
* Modify the config to adjust your enviroment settings
* Type ```./build.sh``` to build cxxnet