nvidia cudnn

Today I installed on the computer Cuda, for small white, a naïve time is very long, a simple record, in the future to facilitate the installation of their own. You are operating according to the installation files on the official website. Http://developer.nvidia.com/cuda-downloads. The model for the official web site map: The installation steps are as follows: 1. Pre-preparation (1) Determine the GPU used by Cuda, you can use the following command to view: $ LSPCI | Grep-i

Installation InstructionsPlatform: Currently available on Ubuntu, Mac OS, WindowsVersion: GPU version, CPU version availableInstallation mode: PIP mode, Anaconda modeTips: Currently supports python3.5.x on Windows GPU version requires cuda8,cudnn5.1 Installation progress2017/3/4 Progress:Anaconda 4.3 (corresponding to python3.6) is being installed, deleted, nothing.2017/3/5 Progress:Anaconda 4.3 (corresponds to python3.6) getAnaconda in Python3.5.2getTensorflow1.0.0getIdeasIn t

Install Torch in Ubuntu and configure CUDA and cuDNNGeneral description Ubuntu is 14.04, and cuda is 7.5 cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64. Cudnn is 7.5, cudnn-7.5-linux-x64-v5.0-ga.tgz.Reference: Link: https://github.com/jcjohnson/neural-style/blob/master/INSTALL.mdNeural-styleIn fact, this article has clearly explained how to install it, but it still encountered many pitfalls during installation

/8.0CUDA Capability Major/minor version number:3.5Total amount ofGlobalmemory:2004 MBytes (2100953088bytes) ( 2) multiprocessors, (192) CUDA cores/mp:384CUDA cores GPU Max Clock rate:1032 MHz (1.03GHz) Memory Clock Rate:800Mhz Memory Bus Width:64-bit L2 Cache Size:524288bytes Maximum Texture Dimension Size (x, Y, z) 1D= (65536), 2d= (65536, 65536), 3d= (4096, 4096, 4096) Maximum layered 1D Texture Size, (num) layers 1D= (16384), 2048 layersStep three, install

. Conditions allow, or try to install and configure it under Ubuntu, for those unfamiliar with Ubuntu may be more troublesome, but I feel that in Linux installation, compilation, including the use of the back is more convenient. One thing that needs to be explained in advance here is that in the official installation documentation, the following options are available for installation OpenCV >= 2.4 including 3.0 IO Libraries:lmdb, LEVELDB (note:leveldb requires snappy)

CUDNN the latest version of the download address MKDIR/HOME/HOK/SOFTWARE/CUDA+CUDNN/CUDNN TAR-XZVF cudnn-5.1-linux-r1.tgz-c/home/hok/software/cuda+cudnn/ CUDNN cd Cudnn/cuda sudo cp li

Reference website:Http://blog.sina.com.cn/s/blog_a5fdbf010102w7f6.htmlHttp://www.linuxidc.com/Linux/2015-04/116445.htmUbuntu Configuration CUDNN Download Https://developer.nvidia.com/rdp/cudnn-downloadRegister, download, select the appropriate version.tried it the same way. Cudnn-v3 No, cudnn-v4 it worked

Installation Order: OpenCV 3.1.0 Cuda 8.0 CUDNN 5.1.5 Caffe 1. Installation of OpenCV 3.1.0 1.1 installation relies on necessary installation sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev Libswscale-dev Optional Installation sudo apt-get install checkinstall yasm libtiff5-dev libjpeg-dev libjasper-dev libdc1394-22-dev Libxine2-dev Libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev Libv4l-d

Here take cudnn-7.0-linux-x64-v4.0-rc.tgz as an example (pre-download good, need to register)CD to CUDNN storage directory, unzip$ cd Storage Cudnn Directory$ sudo tar xvf cudnn-7.0-linux-x64-v4.0-rc.tgzCopy the appropriate files to the Cuda directory$ CD Cuda/include$ sudo cp *.h/usr/local/cuda/include/$ CD. /lib64$ s

Before and after installing the CUDNN, the GPU ran an algorithm that was 139ms and 26ms in speed, respectively!1. Select CuDNN v5.1 Library for Linux download at the following URLHttps://developer.nvidia.com/rdp/cudnn-download2. Download to get a cudnn-8.0-linux-x64-v5.1.tgz package, unzip theGet Cuda folder with two s

CUDNN Installation Note:CUDNN installation is actually very simple, the key point is to be sure to install cuda corresponding to the CUDNN package, this machine installed cuda7.5 so the corresponding CUDNN for v5.1 This is very important, I am installing the wrong version, resulting in the compilation of Caffe is always wrong.CUDNN Installation steps:1. Download

Win10 with CMake 3.5.2 and vs update1 compiling GPU version (Cuda 8.0, CUDNN v5 for Cuda 8.0) Open compile release and debug version with VS 2015 See the example on the net there are three inside the project Folders include (Include directories containing Mxnet,dmlc,mshadow)Lib (contains Libmxnet.dll, libmxnet.lib, put it in vs. compiled)Python (contains a mxnet,setup.py, and build, but the build contains the lib/mxnet, which is the same as the Python

1. go to the official download of the latest version of nVidia driver, the latest version is Nvidia-Linux-x86-270.41.06.run2. delete the previously installed nVidia Driver (skip this step without security) sudoapt-get -- purgeremovenvidia-* 3. this is found in the Nvidia official instructions, establish and modify the

It's been a long time today. Intel integrated graphics display. Finally it was all done, and here's a record.1. The first thing in the BIOS is to open Intel HD graphics. I set it up as the main video card, and the monitor is also connected to the port of the core graphics card. After restarting, I card warning low resolution, into the desktop 2. The command to switch the n/i card is prime-select (the installation package is Nvidia-prime, does not need

In Win7, how does one delete the Nvidia icon? When using Win7, some users find that there is a green graphics card icon (Nvidia) in the lower right corner of the desktop, which is actually a quick way to open Nvidia graphics card management software, if you do not need this shortcut, you can delete the Nvidia icon as f

1. Configure the Environment 1. This article compiles in the windows7+vs2013 environment, CUDA version 8.0,CUDNN version 5.1 2. Cuda Download Address: https://developer.nvidia.com/cuda-toolkit,cudnn:cudnn-8.0-windows7-x64-v5.1 Download Address: https:// Developer.nvidia.com/cudnn 3. Install CUDA8.0 (required after vs2013 installation) 4. Unzip the downloaded CUDNN