Skip to content

Home

Jump to bottom
Wolfgang Manousek edited this page Apr 21, 2017 · 124 revisions

The Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit - CNTK - is a unified deep-learning toolkit by Microsoft. This video provides a high-level view of the toolkit.

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.

The latest release of the Microsoft Cognitive Toolkit 2.0 is RC2 (release candidate 2), released on April 21st, 2017.

Here are a few pages to get started:

2017-04-21. CNTK 2.0 Release Candidate 2
With Release Candidate 2 we reacted to customer feedback and improved/added features, functionality, and performance.

Highlights:

  • New operators like pow, sequence.reduce_max, sequence.softmax.
  • New feature for Linux source builds (GPU Direct RDMA support in distributed gradients aggregation, NCCL support for Python in V2 gradients aggregation).
  • Support for Python 3.6 for source and binary installation; see here.
  • UserMinibatchSource to write custom minibatch sources; see here.
  • New APIs: class NDArrayView and methods, SetMaxNumCPUThreads(), GetMaxNumCPUThreads(), SetTraceLevel(), GetTraceLevel()
  • A new set of NuGet Packages is provided with this Release.

The release notes contain an overview. Get the release from the CNTK Releases Page.

2017-03-31. CNTK 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 2.0.

Highlights:

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 Python 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 and Argmin, improved performance of numpy 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.

New Documentation Site

Iteration Plans

Clone this wiki locally