-
Notifications
You must be signed in to change notification settings - Fork 4.3k
Home
The Microsoft Cognitive Toolkit - CNTK - is a unified deep-learning toolkit by Microsoft Research. This video provides a high-level view of the toolkit.
The latest release of the Microsoft Cognitive Toolkit 2.0 is RC1 (release candidate 1). If you are a previous user of the toolkit, see this page for more information about (breaking) changes in this release.
It can be included as a library in your Python or C++ programs, or used as a standalone machine learning tool through its own model description language (BrainScript). CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the Toolkit from the source provided in Github.
Here are a few pages to get started:
- Setting up CNTK on your machine
-
Tutorials, Examples, etc..
- Try the tutorials on Azure Notebooks with pre-installed CNTK
- The CNTK Library APIs
- CNTK as a machine learning tool through BrainScript
- How to contribute to CNTK
- Give us feedback through these channels.
2017-03-31. V 2.0 Release Candidate 1
With Release Candidate 1 the Microsoft Cognitive Toolkit enters the final set of enhancements before release of the production version of CNTK v.2.0.
Highlights:
- The release candidate contains all changes and improvements introduced in CNTK 2.0 during beta phase.
- Enables Caffe-converted pretrained models on image classification including AlexNet, ResNet, VGG and BN-Inception.
- Slice now supports multiple-axis slicing.
- Improves performance and memory footprint
- Improvements in the device selection API.
- New Python model debugging functions.
- Improvements in Python and C# API. See the release notes for detailed description.
- New file names for CNTK libraries and dlls.
The release notes contain an overview. Get the release from the CNTK Releases Page.
2017-03-16. V 2.0 Beta 15 Release available at Docker Hub
CNTK V 2.0 Beta 15 Runtime packages are now available as Public Images at Docker Hub.
See more on CNTK as Docker Images in this Wiki article.
2017-03-15. V 2.0 Beta 15 Release
Highlights of this Release:
- In addition to pre-existing python support, added support for TensorBoard output in BrainScript. Read more here.
- Learners can now be implemented in pure Python by means of
UserLearners
. Read more here. - New debugging helpers:
dump_function()
,dump_signature()
. - Tensors can be indexed using advanced indexing. E.g.
x[[0,2,3]]
would return a tensor that contains the first, third and fourth element of the first axis. - Significant updates in the Layers Library of Pythin API. See Release Notes for detailed description.
- Updates and new examples in C# API.
- Various bug fixes.
See more in the Release Notes.
Get the Release from the CNTK Releases page.
2017-02-28. V 2.0 Beta 12 Release available at Docker Hub
CNTK V 2.0 Beta 12 Runtime packages are now available as Public Images at Docker Hub.
See more on CNTK as Docker Images in this Wiki article.
2017-02-23. V 2.0 Beta 12 Release
Highlights of this Release:
- New and updated features: new activation functions, support of
Argmax
andArgmin
, improved performance ofnumpy
interop, new functionality of existing operators, and more. -
CNTK for CPU on Windows can now be installed via
pip install
on Anaconda 3. Other configurations will be enabled soon. - HTK deserializers are now exposed in Python. All deserializers are exposed in C++.
- The memory pool implementation of CNTK has been updated with a new global optimization algorithm. Hyper memory compression has been removed.
- New features in C++ API.
- New Eval examples for RNN models.
- New CNTK NuGet Packages with CNTK V2 C++ Library.
See more in the Release Notes.
Get the Release from the CNTK Releases page.
2017-02-13. V 2.0 Beta 11 Release available at Docker Hub
CNTK V 2.0 Beta 11 Runtime packages are now available as Public Images at Docker Hub.
See more on CNTK as Docker Images in this Wiki article.
See all news.