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.

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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 Caffe, Caffe2, TensorFlow, Theano, Torch, PyTorch, MXNet, and Microsoft Cognitive Toolkit. CuDNN is freely available to members of the NVIDIA Developer Program.

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Ensure you meet the following requirements before you install cuDNN. • A GPU of compute capability 3.0 or higher. To understand the compute capability of the GPU on your system, see:. • If you are using cuDNN with a Volta GPU, version 7 or later is required.

• One of the following supported Architecture - OS combinations: • On x86_64 (for installing cuDNN with debian files) - Ubuntu 14.04 or Ubuntu 16.04 • On x86_64 (for installing tgz files) - Any Linux distribution • On POWER8/POWER9 - RHEL7.4 • One of the following supported CUDA versions and NVIDIA graphics driver: • NVIDIA graphics driver R375 or newer for CUDA 8 • NVIDIA graphics driver R384 or newer for CUDA 9 • NVIDIA graphics driver R390 or newer for CUDA 9.2. The following steps describe how to build a cuDNN dependent program. In the following sections: • your CUDA directory path is referred to as /usr/local/cuda/ • your cuDNN directory path is referred to as • Navigate to your directory containing cuDNN. • Unzip the cuDNN package.

$ tar -xzvf cudnn-9.0-osx-x64-v7.tgz • Copy the following files into the CUDA Toolkit directory, and change the file permissions. $ sudo cp cuda/include/cudnn.h /usr/local/cuda/include $ sudo cp cuda/lib/libcudnn* /usr/local/cuda/lib $ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib/libcudnn* • Set the following environment variables to point to where cuDNN is located. $ export DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH.

Ensure you meet the following requirements before you install cuDNN. • A GPU of compute capability 3.0 or higher.

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To understand the compute capability of the GPU on your system, see:. • One of the following supported platforms: • Windows 7 • Windows 10 • Windows Server 2012 • One of the following supported CUDA versions and NVIDIA graphics driver: • NVIDIA graphics driver R377 or newer for CUDA 8 • NVIDIA graphics driver R384 or newer for CUDA 9 • NVIDIA graphics driver R390 or newer for CUDA 9.2. The following steps describe how to build a cuDNN dependent program. In the following sections: • your CUDA directory path is referred to as C: Program Files NVIDIA GPU Computing Toolkit CUDA v9.0 • your cuDNN directory path is referred to as • Navigate to your directory containing cuDNN. • Unzip the cuDNN package.

Cudnn-9.0-windows7-x64-v7.zip or cudnn-9.0-windows10-x64-v7.zip • Copy the following files into the CUDA Toolkit directory. • Copy cuda bin cudnn64_7.dll to C: Program Files NVIDIA GPU Computing Toolkit CUDA v9.0 bin.

• Copy cuda include cudnn.h to C: Program Files NVIDIA GPU Computing Toolkit CUDA v9.0 include. • Copy cuda lib x64 cudnn.lib to C: Program Files NVIDIA GPU Computing Toolkit CUDA v9.0 lib x64. • Set the following environment variables to point to where cuDNN is located. To access the value of the $(CUDA_PATH) environment variable, perform the following steps: • Open a command prompt from the Start menu. Huawei e303 usb modem driver for mac.