Microsoft Cognitive Toolkit (CNTK) is a deep learning framework that defines neural networks as computational graphs. It supports various network architectures like DNNs, CNNs, and RNNs, and enables distributed training across multiple GPUs and servers. CNTK is suitable for researchers and developers working on large-scale deep learning tasks, particularly in areas like speech recognition.

How It Works

CNTK represents neural networks as directed graphs where nodes are operations and edges represent data flow. It uses automatic differentiation and stochastic gradient descent for training. Its design emphasizes flexibility in combining different network components and efficient parallelization for high-performance computing.

Quick Start & Requirements

• Installation: Python-only, script-driven, or manual installation available for Windows and Linux. Docker images are also provided.
• Prerequisites: CUDA 10 is required for GPU support. Visual Studio 2017 (v15.9) is needed for Windows builds. Python 3.4 support has been removed.
• Resources: Official documentation, tutorials, and examples are available.

Highlighted Details

• Supports CUDA 10, Volta GPUs, and FP16 precision.
• Enhanced ONNX export/import capabilities, including support for larger than 2GB models and advanced RNN loops.
• Optimized group convolution and sequential convolution implementations leveraging cuDNN7 and MKL2017.
• .NET Standard support for C#/.NET Core applications on Windows and Linux.

Maintenance & Community

CNTK's last main release was 2.7.0, with no plans for new feature development. The team encourages users to transition to ONNX and ONNX Runtime for operationalizing models. Further information can be found on the official project feed.

Licensing & Compatibility

CNTK is released under the MIT license, allowing for commercial use and integration with closed-source projects.

Limitations & Caveats

CNTK is no longer under active feature development, with a focus shifted to ONNX and ONNX Runtime. Users seeking the latest advancements in deep learning frameworks may need to consider alternatives.