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Win10 + python3.6 + VSCode + tensorflow-gpu + keras + cuda8 + cuDN6N environment configuration, win10cudn6n Preface: Before getting started, I knew almost nothing about python or tensorflow, so I took a lot of detours When configuring this environment, it took a whole week to complete the environment... However, the most annoying thing is that it is difficult to set up the environment. Because my laptop is low in configuration, the program provided by
1. Update NVIDIA Graphics drivers?? After installing the system, first update the graphics driver in the System Update Manager, as Click Apply Changes2. Installing Numpy,scipy,theanoPIP installation cansudo pip install 3. Installing Cuda7.5sudo apt-get install Nvidia-cuda-toolkit5. Configuration. Theanorc?? Generate Files sudo gedit ~/.theanorc (note Do not miss a point in front of Theano) and copy the following, and then save, where Cuda one of the content is the location of Cuda installed.??
Three-dimensional spatial analysis based on GPU accelerationTags: supermap geographic information System GisitArticle: SyedWith the rapid development and popularization of three-dimensional GIS, three-dimensional spatial analysis technology has become the hotspot of GIS technology in the application of its practicability. In the face of the increasingly large-scale data processing situation, in order to meet the practical needs of GIS industry for thr
Recently used Theano wrote the MLP and CNN program, because the training sample large, CPU speed so slow, and then found a computer with Naivid graphics card configuration using the GPU, encountered a lot of problems, recorded as follows:Platform Description:System: WindowsXPpython:2.7, it is recommended to use Python (x, y) directly, including the Theano required NumPy library, save your own configurationtheano:0.6cuda:3.01 DownloadsDownload Install
Preface How to optimize existing programs in parallel is the most important practical issue in GPU parallel programming technology. This article provides several optimization ideas to point out the path for parallel program optimization. Preparation before optimization First, we need to clarify the goal of Optimization-is it necessary to speed up the program twice? Or 10 times? 100 times? Maybe you will not think about it. Of course, the higher the im
1.Glossary GPU: Graphic Processing Unit (graphics processor) OpenGL: Open Graphic Library defines the specification of a cross-programming language and cross-platform programming interface. Different vendors have different implementation methods. It is mainly used for 3D image (two-dimensional) painting. Surfaceflinger:Dynamic library in Android that is responsible for surface overlay and hybrid operations Skia:2d graphics library in Android Libagl:A
The following is a chrome user's usage tips, hoping to help readers. Here we will introduce the methods for enabling hardware acceleration and pre-rendering: Go to about: flags in the chrome address bar and pull down the page to find GPU accelerated compositing and GPU accelerated canvas 2D. enable these two items. Chrome 11 does not have the GPU accelerated c
Beware of GPU memory bandwidth For personal use only, do not reprint, do not use for any commercial purposes. Some time ago, I wrote a series of post-process effect, including the motion blur, refraction, and scattering of screen spance. Most shader is very simple. It is nothing more than rendering a full screen quad to the screen. Generally, there are no more than 10 lines of PS Code, without any branch or loop commands. It can be run only after sm1.
Entertainment, mobile phone-hosted graphics operations are growing. Especially for the glory of the mobile phone brand for young people, users of large online games, AR/VR and other functions of the smoothness, clarity requirements are rising, but also hope that mobile phone prices as close as possible to the people. The scary technique is to honor the secret law of balance between the two needs.It's a scary technology. The "scientific name", called the GPU
Sometimes it is necessary to do coding work through Remote Desktop Connection, such as the general web, such as the need for the GPU and other support coding work directly with Windows Remote Desktop Connection coding and then debug, and some need to rely on graphics support work such as rendering, When GPU operations such as CUDA, Remote Desktop Connection debug will fail. Because when using Remote Desktop
implementation of 2-D FFT algorithm--base 2 fast two-dimensional Fourier transform based on GPU The first one-dimensional FFT of the GPU implementation (FFT algorithm implementation-based on the GPU base 2 fast Fourier transform), and then I need to do a second-dimensional FFT, probably the following ideas. The first thing to look at is definitely the formula:
Abstract: Can the Ewa Rendering Method of dot rendering have the graphic effects produced by real-time GPU oversampling of our workers? Certainly not.Abstract: Is the Ewa splatting will be better than my GPU multipass supersampling method? Of course not!Zusammemfasloud: ist die Ewa splatting so besser als meine GPU multipass supersampling methode? Naturlich nicht
Getting started with http://www.cnblogs.com/Fancyboy2004/archive/2009/04/28/1445637.html cuda-GPU hardware architecture Here we will briefly introduce that NVIDIA currently supports Cuda GPU, Which is executing CudaProgram(Basically, its shader unit) architecture. The data here is a combination of the information posted by nvidia and the data provided by NVIDIA in various seminars and school courses. There
Reprinted from: http://www.cnbeta.com/articles/145526.htm This is an interesting little tool that allows you to use GPU to brute force password cracking, from the description in the news, radeon5770 operations per second for HD 3.3 billionRadeon HD 5770 can crack a five-digit password "fjr8n" in one second "...... If you have four HD 5970 images, the cracking speed will reach 33.1 billion times per second, and the CPU we generally use is only about 9
With the enhancement of GPU's programmable performance and the continuous development of gpgpu technology, it is hoped that the stream processor model-based GPU can be like a CPU, while supporting the process branch, it also allows flexible read/write operations on the memory. Ian Buck [1] has pointed out that the lack of flexible memory operations is the key to restricting the GPU to complete complex compu
Today, the GPU is used to speed up computing, that feeling is soaring, close to graduation season, we are doing experiments, the server is already overwhelmed, our house server A pile of people to use, card to the explosion, training a model of a rough calculation of the iteration 100 times will take 3, 4 days of time, not worth the candle, Just next door there is an idle GPU depth learning server, decided
After the Cuda is installed, you can use Devicequery to look at the related properties of the GPU, so that you have a certain understanding of the GPU, which will help cuda programming in the future. #include "cuda_runtime.h" #include "device_launch_parameters.h" #include The number of Nvidia GPU in the system is first obtained by Cudagetdevicecount , and th
http://blog.itpub.net/23057064/viewspace-629236/ Nvidia graphics cards on the market are based on the Tesla architecture, divided into G80, G92, GT200 three series. The Tesla architecture is a processor array with the number of extendable places. Each GT200 GPU consists of 240 stream processors (streaming processor,sp), and each of the 8 stream processors is comprised of one stream multiprocessor (streaming multiprocessor,sm), thus a total of 30 strea
Comprehensive Guide: Install the Caffe2 translator with GPU support from source on Ubuntu 16.04:Originally from: https://tech.amikelive.com/node-706/ Comprehensive-guide-installing-caffe2-with-gpu-support-by-building-from-source-on-ubuntu-16-04/?tdsourcetag=s_ Pctim_aiomsg, have to say that the author's knowledge is rich, the research is more thorough, the environment configuration explained more detailed.
When using TensorFlow to run deep learning, there is often a lack of memory, so we want to be able to view the GPU usage at any time. If you are the NVIDIA GPU, you can do this at the command line with just one line of command.1. Show current GPU usageNvidia comes with a NVIDIA-SMI command-line tool that displays video memory usage:Nvidia-smiOutput:2. Periodic ou
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