The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN is part of the NVIDIA Deep Learning SDK.
Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning. cuDNN accelerates widely used deep learning frameworks, including Caffe2, MATLAB, Microsoft Cognitive Toolkit, TensorFlow, Theano, and PyTorch. For access to NVIDIA optimized deep learning framework containers, visit NVIDIA GPU CLOUD to learn more and get started.
Deep learning frameworks using cuDNN 7 and later, can leverage new features and performance of the Volta architecture to deliver up to 3x faster training performance compared to Pascal GPUs. cuDNN 7.1 highlights include:
Read the cuDNN 7.1.1 release notes for a detailed list of new features and enhancements.
cuDNN is supported on Windows, Linux and MacOS systems with Volta, Pascal, Kepler, Maxwell Tegra K1, Tegra X1 and Tegra X2 GPUs.
Watch the GPU-Accelerated Deep Learning with cuDNN webinar to learn more about cuDNN.