CNTK_1_7_Release_Notes

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CNTK v.1.7 Release Notes

This is a summary on what's new in CNTK 1.7 Binary Release. Apart from many bug fixes we have the following new features.

BrainScript

We have many improvements in BrainScript.

• New library of predefined common layer types. Using function objects, this enables very succinct definition of models by sequential composition. E.g., a simple ATIS slot tagger can be written as:

model = Sequential (
EmbeddingLayer {150} :
RecurrentLSTMLayer {300} :
DenseLayer {129}
)

Read more in the Wiki on Layers and Sequential.

• Support of CuDNN5 RNN which significantly improves performance.
• Support of Common random-initialization types, e.g.

W = ParameterTensor {(1024:42), init="glorotNormal"}

• Dimensions inference for model parameters. E.g.

W = ParameterTensor {(1024:Inferred)}
z = W * x

will infer the dimension marked as Inferred from the input.
(See the complete description of ParameterTensor in the Wiki.)

• Curly braces in configuration and BrainScript making it more similar to other familiar languages. See more in the Wiki Article.
• We have significantly simplified Handling of Gated Recurrent Units (GRU) was significantly improved by adding the convenience functions to Brainscript library. Read more in the corresponding article.

Support of NVIDIA cuDNN 5.1

CNTK now relies on version 5.1 of NVIDIA cuDNN Library.

This in turns allows utilizing NVIDIA GPU Cards based on Pascal architecture like GeForce GTX 1080. However note, that NVIDIA Pascal architecture requires CUDA 8.0 while CNTK 1.7 is built with CUDA 7.5. Today CUDA 8.0 is available as a Release Candidate version, and if you would like to use CNTK with NVIDIA Pascal architecture cards you need to download and install the current Release Candidate of CUDA 8.0 and build CNTK from Sources for Windows or Linux using CUDA 8.0 RC instead of CUDA 7.5. CNTK will fully support CUDA 8.0 when it is released to production.

Readers/Deserializers

We have the following improvements in Readers and Deserializers.

• Switch to Mersenne Twister randomization for the new readers (Image, CNTKTextFormat, HTKDeserializers) This fixes a rand bug in a distributed environment when the same samples/sequences could be picked up by several workers
• Support of IO prefetching for data
• Default input for labels (Image and HTKMLF) was switched to sparse format

The following changes were implemented for ImageReader:

• Access to zip index was restructured to enable faster initialization
• Enabling Retry logic for image reading

CNTK Model Evaluation library

V.1.7 features CNTK Evaluator both for Windows and Linux.

For Windows you can get CNTK Eval is a NuGet Package or directly from the Binary Distribution.

Linux CNTK Evaluator is included in Linux binary distribution.

The following changes and improvements are introduced in V.1.7:

• Eval client samples (both C++ and C#) are included in the CNTK binary drop for Windows. The samples demonstrate how to use the CNTK evaluation library in C++ and C#. Instructions for using samples are available in the Wiki.
• EvalWrapper.dll now has a Strong Name. Implementation details available in the description in the header of EvalWrapperAssemblyInfo.cpp.

Unit Tests

Unit tests have been enabled on Linux. The wiki How-To-Test article contains the instructions on unit tests for both Windows and Linux.

We now use Mersenne Twister random engine and Boost random distribution functions in unit tests for both Windows and Linux.

Python support

Starting from v.1.5 you can use the preview of Python API. See CNTK v.1.5 Release Notes for further instructions.