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CNTK FAQ
At this page we present the answers to some of the most common questions we get about CNTK and related Subjects. For questions and answers about problems you are seeing setting up or running CNTK, see the Common Pitfalls page.
CNTK, the Computational Network Toolkit, is a framework for deep learning. A Computational Network defines the function to be learned as a directed graph where each leaf node consists of an input value or parameter, and each non-leaf node represents a matrix or tensor operation upon its children. The beauty of CNTK is that once a computational network has been described, all the computation required to learn the network parameters is taken care of automatically. There is no need to derive gradients analytically or to code the interactions between variables for backpropagation.
We first created CNTK for ourselves. CNTK was developed for the fastest training on the biggest data sets. Many of Microsoft's critical services run on models trained with CNTK. The results were so positive, we wanted to share our toolkit with the world.
For mission critical AI research, we believe efficiency and performance are important criteria. CNTK was designed for peak performance for not only CPUs but also single-GPU, multi-GPU, and multi-machine-multi-GPU scenarios. Additionally, Microsoft’s 1-bit compression technique or Block momentum technique dramatically reduced communication costs -- enabling highly scalable parallel training on a large number of GPUs spanning multiple machines.
In addition to a wide variety of built-in computation nodes, CNTK provides a plug-in architecture allowing users to define their own computation nodes. So if your workload requires special customization, CNTK makes that easy to do. Readers are also fully customizable allowing support for arbitrary input formats.
Today CNTK supports the following algorithms:
- Feed Forward
- CNN
- RNN
- LSTM
- Sequence-to-Sequence.
CNTK is developed by Microsoft's Technology and Research division. Additionally, CNTK gets major contributions from nearly all of Microsoft production teams.
The development of CNTK has been underway since late 2014.
September 2016, we plan to release CNTK as a library with the Reinforcement Learning pipelines enabled.
CNTK config language (NDL) is great, but I want more freedom in creating my network, creating new nodes, or streaming in my unique data format. Will CNTK support lower level programming, (via Python or other languages)?
September 2016 we plan to release the new CNTK APIs supporting both Python, and C++ bindings, so you will be able to configure your networks, create new nodes, as well as programmatically feed the data from your own Python, and C++ programs.
No. CNTK is used in production for the Speech Recognition as well as for Image and Text training.
Using CNTK is easy and straightforward. Here are some ways to get started.
- Setting up CNTK on your machine
- Tutorial
- Tutorial II
- Examples
- CNTK usage overview
- How to contribute to CNTK
Unlike training, eval is currently CPU only.
I enabled Image Reader with zip support and get "Plugin not found: 'ImageReader.dll'" error when running Image Reader unit tests or trying to use the reader. What might be wrong?
Check that you have correctly installed zlib and libzip, especially that you have not forgotten to rename zlib.dll to zlib1.dll.
Doing so prevents the same samples from always appearing in a mini-batch together. This leads to improvements in the validation accuracy.
Yes. See the description at Understanding and Extending Readers and look for the section describing how to "compose several data deserializers"