worlds best gpu

The source code is running, the experimental process is recorded as follows, for beginners to get started.Today and elder sister to run through, to share the next experience. (Pre-Training network: ImageNet, Training set: PASCAL VOC2007, GPU)First, the entire train and test process is not unique, and the deeper you understand it, the more skilled you are.Come down and get to the point:1.git Clone source code. Be sure to choose recursive mode. (No Caff

1, install Cuda Toolkit and CUDNN (Baidu Cloud can download, version needs corresponding)2. Configure Environment variables:3, install CUDNN (need to copy some DLLs and Lib to configure)4, go to cmd, find the Anaconda3 pip path, with the following command to execute, you can uninstall the CPU version of TensorFlow, install the GPU version of the TensorFlowpip uninstall tensorflowpip install TensorFlow-GPUComplete, TensorFlow automatically calls the

Install Theano Anaconda installation Theano available Conda Direct installationConda Install Theano Configuration. Theanorc Generate file sudo gedit ~/.theanorc (note that you do not omit a point in front of Theano) and copy the following, and then save, where cuda the contents of the item is the location installed by Cuda.[Global]Floatx=float32Device=gpu[Cuda]Root=/usr/lib/nvidia-cuda-toolkit[NVCC]Flags=-d_force_inlines Now that the installation

their own can be. But Caffe's compilation blogger was wrong.In general, we use the source file installation method is the use of the following stepsmkdir buildcd buildcmake ..makeHowever, bloggers are ready to use some of the file settings make all -j8 . I didn't think much of it at the time, just follow the order. However, no matter how you modify nvcc fatal: Unsupported gpu architecture ‘compute_20‘ the error prompts that appear. Changed 3 times, j

GPU's parallel computing capability is higher than the CPU, so recently there are also a lot of projects using GPU appear in our field of view, on InfoQ saw this article about Accelerator-V2, it is a research project of Microsoft Research Institute. It needs to be registered before it can be downloaded. I feel that it is a good first step in accessing general GPU computing, So I downloaded it back. In the

bc00 [size = 128][Virtual] Expansion ROM at f8f80000 [disabled] [size = 512 K]Capabilities: [60] Power Management version 3Capabilities: [68] MSI: Enable-Count = 1/1 Maskable-64bit +Capabilities: [78] Express Endpoint, MSI 00Capabilities: [b4] Vendor Specific Information: Len = 14 Capabilities: [100] Virtual ChannelCapabilities: [128] Power Budgeting Capabilities: [600] Vendor Specific Information: ID = 0001 Rev = 1 Len = 024 Kernel driver in use: nvidiaKernel modules: nvidiafb, nvidia We ca

"Python 3.6 + tensorflow GPU 1.4.0 + CUDA 8.0 + CuDNN 6.0"There is no pycharm to install the Pycharm first.1, python:https://www.python.org/downloads/release/python-364/Pull to the bottom and select Windows x86-64 executable installer download.Note the Add Python 3.6 to path check box, and then select Install Now.2, TensorFlow GPU 1.4.0 in Pycharm settings--project interpreter to add the corresponding versi

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