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Tip revision: 2882f1c23ed317ca27920c2d58dc4170423f929c authored by Nikos Karampatziakis on 27 June 2017, 00:55:33 UTC
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Tip revision: 2882f1c
README.md
**The [CNTK Documentation](https://docs.microsoft.com/en-us/cognitive-toolkit/) has all information on CNTK including [setup](https://docs.microsoft.com/en-us/cognitive-toolkit/Setup-CNTK-on-your-machine), [examples](https://docs.microsoft.com/en-us/cognitive-toolkit/Examples), etc.**
Give us feedback through these [channels](https://docs.microsoft.com/en-us/cognitive-toolkit/Feedback-Channels).
# Latest news
***2017-06-26.*** A great class for getting started with both Deep Learning and CNTK, [Deep Learning Explained](https://www.edx.org/course/deep-learning-explained-microsoft-dat236x) is now available on edX.
***2017-06-01.* CNTK 2.0 is released.**
The first production release of Cognitive Toolkit v.2. See more in the [Release Notes](https://docs.microsoft.com/en-us/cognitive-toolkit/ReleaseNotes/CNTK_2_0_Release_Notes), and get the Release from the [CNTK Releases page](https://github.com/Microsoft/CNTK/releases).
See [all news](https://docs.microsoft.com/en-us/cognitive-toolkit/news)
# What is The Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit (https://www.microsoft.com/en-us/research/product/cognitive-toolkit/), is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK has been available under an open-source license since April 2015. It is our hope that the community will take advantage of CNTK to share ideas more quickly through the exchange of open source working code.
Go to the [CNTK Documentation](https://docs.microsoft.com/en-us/cognitive-toolkit/) for all information on CNTK including [setup](https://docs.microsoft.com/en-us/cognitive-toolkit/Setup-CNTK-on-your-machine), [examples](https://docs.microsoft.com/en-us/cognitive-toolkit/Examples), etc.
License: See [LICENSE.md](./LICENSE.md) in the root of this repository for the full license information.
Tutorial: [Microsoft Computational Network Toolkit (CNTK) @ NIPS 2015 Workshops](https://research.microsoft.com/en-us/um/people/dongyu/CNTK-Tutorial-NIPS2015.pdf)
Blogs:
* [Microsoft Computational Network Toolkit offers most efficient distributed deep learning computational performance](https://blogs.technet.com/b/inside_microsoft_research/archive/2015/12/07/microsoft-computational-network-toolkit-offers-most-efficient-distributed-deep-learning-computational-performance.aspx)
* [Microsoft researchers win ImageNet computer vision challenge (December 2015)](https://blogs.microsoft.com/next/2015/12/10/microsoft-researchers-win-imagenet-computer-vision-challenge/)
## Performance
Cognitive Toolkit (CNTK) provides significant performance gains compared to other toolkits [click here for details](https://arxiv.org/pdf/1608.07249.pdf). Here is a summary of findings by researchers at HKBU.
> * CNTK’s LSTM performance is 5-10x faster than the other toolkits.
> * For convolution (image tasks), CNTK is comparable, but note the authors were using CNTK 1.7.2, and current CNTK 2.0 beta 10 is over 30% faster than 1.7.2.
> * For all networks, CNTK's performance was superior to TensorFlow performance.
Historically, CNTK has been a pioneer in optimizing performance on multi-GPU systems. We continue to maintain the edge ([NVidia news at SuperComputing 2016](http://nvidianews.nvidia.com/news/nvidia-and-microsoft-accelerate-ai-together) and [CRAY at NIPS 2016](https://www.onmsft.com/news/microsoft-and-cray-announce-partnership-to-speed-up-deep-learning-on-supercomputers)).
CNTK was a pioneer in introducing scalability across multi-server multi-GPU systems. The figure below compares processing speed (frames processed per second) of CNTK to that of four other well-known toolkits. The configuration uses a fully connected 4-layer neural network (see our benchmark [scripts](https://github.com/Alexey-Kamenev/Benchmarks)) and an effective mini batch size (8192). All results were obtained on the same hardware with the respective latest public software versions as of Dec 3, 2015.
![Performance chart](Documentation/Documents/PerformanceChart.png)
## Citation
If you used this toolkit or part of it to do your research, please cite the work as:
Amit Agarwal, Eldar Akchurin, Chris Basoglu, Guoguo Chen, Scott Cyphers, Jasha Droppo, Adam Eversole, Brian Guenter, Mark Hillebrand, T. Ryan Hoens, Xuedong Huang, Zhiheng Huang, Vladimir Ivanov, Alexey Kamenev, Philipp Kranen, Oleksii Kuchaiev, Wolfgang Manousek, Avner May, Bhaskar Mitra, Olivier Nano, Gaizka Navarro, Alexey Orlov, Hari Parthasarathi, Baolin Peng, Marko Radmilac, Alexey Reznichenko, Frank Seide, Michael L. Seltzer, Malcolm Slaney, Andreas Stolcke, Huaming Wang, Yongqiang Wang, Kaisheng Yao, Dong Yu, Yu Zhang, Geoffrey Zweig (in alphabetical order), ["An Introduction to Computational Networks and the Computational Network Toolkit"](https://research.microsoft.com/apps/pubs/?id=226641), Microsoft Technical Report MSR-TR-2014-112, 2014.
## Disclaimer
CNTK is in active use at Microsoft and constantly evolving. There will be bugs.
## Microsoft Open Source Code of Conduct
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.