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.

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 Caffe2ChainerKerasMATLABMxNetPyTorch, and TensorFlow. For access to NVIDIA optimized deep learning framework containers that have cuDNN integrated into frameworks, visit NVIDIA GPU CLOUD to learn more and get started.


Key Features

  • Tensor Core acceleration for all popular convolutions including 2D, 3D, Grouped, Depth-wise separable, and Dilated with NHWC and NCHW inputs and outputs
  • Optimized kernels for computer vision and speech models including ResNet, ResNext, SSD, MaskRCNN, Unet, VNet, BERT, GPT-2, Tacotron2 and WaveGlow
  • Supports FP32, FP16, and TF32 floating point formats and INT8, and UINT8 integer formats
  • Arbitrary dimension ordering, striding, and sub-regions for 4d tensors means easy integration into any neural net implementation
  • Speed up fused operations on any CNN architecture

cuDNN is supported on Windows and Linux with Ampere, Turing, Volta, Pascal, Maxwell, and Kepler GPU architectures in data center and mobile GPUs.



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