CNTK

The Microsoft Cognitive Toolkit ( is a unified deep learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK has been available under an open-source license since April 2015. It is our hope that the community will take advantage of CNTK to share ideas more quickly through the exchange of open source working code. General documentation Python API documentation Evaluation documentation (C++, C /.NET, Python, Java) Manual Tutorials Examples Pretrained models Blog Presentations License With our ongoing contributions to ONNX and the ONNX Runtime, we have made it easier to interoperate within the AI framework ecosystem and to access high performance, cross-platform inferencing capabilities for both traditional ML models and deep neural networks. Over the last few years we have been privileged to develop such key open-source machine learning projects, including the Microsoft Cognitive Toolkit, which has enabled its users to leverage industry-wide advancements in deep learning at scale. Today’s 2.7 release will be the last main release of CNTK. We may have some subsequent minor releases for bug fixes, but these will be evaluated on a case-by-case basis. There are no plans for new feature development post this release. The CNTK 2.7 release has full support for ONNX 1.4.1, and we encourage those seeking to operationalize their CNTK models to take advantage of ONNX and the ONNX Runtime. Moving forward, users can continue to leverage evolving ONNX innovations via the number of frameworks that support it. For example, users can natively export ONNX models from PyTorch or convert TensorFlow models to ONNX with the TensorFlow-ONNX converter. This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments. 2019-03-29. CNTK 2.7.0 Highlights of this release Moved to CUDA 10 for both Windows and Linux. Support advance RNN loop in ONNX export. Export larger than 2GB models in ONNX format. Support FP16 in Brain Script train action. CNTK now supports CUDA 10. This requires an update to build environment to Visual Studio 2017 v15.9 for Windows. To setup build and runtime environment on Windows: Install Visual Studio 2017. Note: going forward for CUDA 10 and beyond, it is no longer required to install and run with the specific VC Tools version 14.11. Install Nvidia CUDA 10 From PowerShell, run: DevInstall.ps1 Start Visual Studio 2017 and open CNTK.sln. To setup build and runtime environment on Linux using docker, please build Unbuntu 16.04 docker image using Dockerfiles here. For other Linux systems, please refer to the Dockerfiles to setup dependent libraries for CNTK. Support advance RNN loop in ONNX export CNTK models with recursive loops can be exported to ONNX models with scan ops. Export larger than 2GB models in ONNX format To export models larger than 2GB in ONNX format, use cntk.Function API: save(self, filename, format=ModelFormat.CNTKv2, use external files to store parameters=False) with 'format' set to ModelFormat.ONNX and use external files to store parameters set to True. In this case, model parameters are saved in external files. Exported models shall be used with external parameter files when doing model evaluation with onnxruntime. First Header GPU exec. time (in millisec., 1000 run avg.) CPU exec. time (in millisec., 1000 run avg.) Model Size (in KB, CNTK format) Sequential Convol