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.

What is the CNTK?

CNTK, the Microsoft Cognitive 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.

Why did Microsoft develop CNTK?

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.

Training deep learning models can be time intensive, can CNTK help with this?

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.

Is CNTK flexible enough for my own network?

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.

What are the key training algorithms supported by CNTK?

Today CNTK supports the following algorithms:

• Feed Forward
• CNN
• RNN
• LSTM
• Sequence-to-Sequence.

Who are the people behind the CNTK?

CNTK is developed by Microsoft's Technology and Research division. Additionally, CNTK gets major contributions from nearly all of Microsoft production teams.

When did work begin on the CNTK?

The development of CNTK has been underway since late 2014.

Is CNTK only optimized for Speech Recognition Training?

No. CNTK is used in production for the Speech Recognition as well as for Image and Text training.

How can I use CNTK?

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

Do eval libs get produced for GPU?

Unlike training, eval (as distributed through the Nuget package) is currently CPU only (e.g. on Windows it is built using the Release_CpuOnly target). If you need full GPU support, use the evaluation libraries built with the release target (e.g. Release on Windows) from the main distribution.

Why does CNTK randomize the mini-batches after each epoch?

Doing so prevents the same samples from always appearing in a mini-batch together. This leads to improvements in the validation accuracy.

Can the built-in readers be used train a network model using multiple input files?

Yes. See the description at Understanding and Extending Readers and look for the section describing how to "compose several data deserializers"