Installation Environment:
Windows 64bit
Gpu:geforce GT 720
python:3.5.3
Cuda:8
First download the Anaconda3 version of Win10 64bit and install the Python3.5 release. Because currently TensorFlow only supports Python3.5 for Windows. You can download the Anaconda installation package directly, there is no problem. (Tsinghua Mirror https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/)
There are two versions of TensorFlow:CPU version and
CPU and GPU implementations JuliaThe main objective is to learn how to write Cuda programs by contrast. Julia's algorithm is still a certain difficulty, but not the focus. Since the GPU is also an image recognition program, the default is to combine with OpenCV. First, CPU implementation (JULIA_CPU.CPP)Julia_cpu using the CPU to implement the Julia transform#include"StdAfx.h"#include#include"OPENCV2/CORE/CO
9-7-6
Author: Xu yuanchun Liu Yong
Source: Wanfang data
Keywords: GPU virtual expression Shader Language This article proposes a GPU-based Virtual Character Expression rendering method, which uses GPU computing technology and uses the Shader Language to process interpolation data, this allows you to quickly draw emoticon animations of virtual characters. The exp
Graphics performance depends on the display core, so to distinguish the graphics performance, you must know some of the graphics card parameters!
To facilitate the viewing of parameters, a tool designed to view the parameters of the graphics card is gpu-z.
Through gpu-z, we can compare the graphics card parameters to identify the performance of the graphics card, or even distinguish between true and false
---Internet Explorer 9 browser under the WIN7 system, when you open "advanced Internet Options", you can't find the "accelerated graphics-using software rendering without GPU rendering" option?
(This process is only for WIN7 Internet Explorer 9 browsers, the display card-driven model is installed correctly)
Processing method:
Method One: Reload IE9
Reloading is a relatively complicated but thorough method. If you're having trouble, you can also mo
Bandwidth issues for NVIDIA GPU on HP Xeon 55xx servers
But through the SDK bandwidth test, the numerical value is rather strange.
Host-> device up to 3gb/s, device-> host highest only 3.2gb/s, which is the same as the PCI Express Gen 2.0 5-6gb/s data is relatively large.
Found that other people encountered similar problems on the same HP ProLiant DL370 G6 servers.
http://forums.nvidia.com/index.php?showtopic=104243
The problem with the motherboa
viewing memory and CPUcommand to view memory usage separately: free-mTo view memory and CPU usage commands: TopYou can also install the Htop tool, which is more intuitive,The installation commands are as follows: sudo apt-get install htopAfter installation, direct input command: htopYou can see the memory or CPU usage.
View GPU
Using the Nvidia-smi command
$ nvidia-smiBut this command can only be displayed once, if you want to display in real time,
driver API level. Above these are not many difficulties, in fact, the interpretation of the API, generally not many difficulties, the difficulty lies in how to flexible use of the API, it has to go through many exercises, more with API practice; In fact, sometimes can also find the lack of APIs, will produce a new API. There is no specific code here, the code can refer to the programming manual.
5. OK, here is the left OpenGL and Direct3D interface functions, he also has two levels of API, the
Due to a lot of reasons I cuda9.0+cudnn7.0.5+tensorflow-gpu1.6 the environment of the machine into: cuda8.0+cudnn6.0+tensorflow-gpu1.6After the introduction of:
Import TensorFlow
Throws an exception when you:
tensorflow:importerror:libcusolver.so.9.0
At first I was very puzzled, thought it was cuda did not uninstall clean, and re-uninstall + installation, but still this problem. Try the Tensorflow-gpu 1.5, 1.6, 1.7 No, the final installation of th
It might be a bit earlier. GPU computing developers will do a common GPU computing OpenGL, with the rise of GPU computing technology, more and more technologies, such as OpenCL, CUDA, OPENACC, etc., are specifically used to do parallel computing standard or interface.OpenGL is used to do general-purpose GPU computing,
As we all know, GPU acceleration technology has a great impact on image processing, in the previous blog in contrast to verify the GPU acceleration technology for image filtering efficiency. But GPU technology is not omnipotent, this paper compares the efficiency of GPU computing histogram is not the traditional method
Original title: The OpenCL language binding package that can be used in the go GPU operationFirst page Access https://github.com/pseudomind/go-opencl/Find out and then download it
C \Go\src\src>go get github.com/pseudomind/go-opencl/cl
Search your OpenCL.dll file again and copy it to the Lib directory of the GCC compilerLike I was searching for Opencl.dllin C, and I copied it into the C:\TDM-GCC-32\lib\ . Open wi
Ie9 will automatically detect the GPU on your machine. If the GPU exists,Ie9 automatically enables GPU hardware acceleration. Therefore, you do not need to make any settings.
How to determine whether ie9 has enabled GPU hardware acceleration:
Open"Internet Options", In the"Advanced"Tab, You can see"Accelerated gr
Install the SDK in the correct order and strictly install the specified version.
1. download and install the strict version of Cuda and cudnn. Other versions do not work. For example, if 9.0 is required, you cannot set 9.1. Https://www.tensorflow.org/install/install_windows
1.1. Delete c: \ Program Files \ NVIDIA Corporation \ installer2 before installing 9.0 pattern. Otherwise, the system will crash.
1.2. After cudnn is installed, check whether c: \ Program Files \ nvidia
Document directory
The GPU acceleration replacement routine provided by gpucv is compatible with opencv. Image processing application programmers do not need to care about the graphic context or hardware, and sample applications are provided by the program. Programmers can automatically manage colors, textures, and advanced OpenGL extensions. Its framework transparently manages hardware functions, data synchronization, low-level glsl and Cuda solut
1. Architecture
2. Development process
3. Mali GPU Linux kernel device driver
The Linux version of the Mali GPU DDK contains the following three components running in the kernel:
1) device driver:It is the most important component that provides low-level access to the Mali-200 or Mali-400 GPU. Its main functions are as follows:• Access to Mali
Catalogue
Graphics driver Installation
Cuda Installation
CUDNN Installation
TENSORFLOW-GPU Installation
this time using the host configuration:CPU:i7-8700k graphics :gtx-1080tiFirst, install the video driverOpen a Command Window (ctrl+alt+t)sudo apt-get purge nvidia*sudo add-apt-repository ppa:graphics-drivers/ppasudo apt-sudoinstall nvidia-384 nvidia-settingsif the error Add-apt-repository does not exist, run the following c
Windows Caffe in the GPU compilation processGeForce8800 gts512:cc=1.1CUDA6.5Question one:SRC/CAFFE/LAYERS/CONV_LAYER.CU: Error:too Few arguments in function callError in Conv_layer.cu:forward_gpu_gemm needs the argument Skip_im2col #1962Solve:https://github.com/BVLC/caffe/issues/1962As @liqing-ustc replied, just add "false" as the fourth argument.Question two:1>d:\dev\caffe-master-gpu\include\caffe/util/gpu
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:Anacon
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