cuda in python

Deep learning is an important tool for the study of computer vision, especially in the field of image classification and recognition, which has epoch-making significance. Now there are many deep learning frameworks, and Caffe is one of the more common ones. This article describes the basic steps for configuring Caffe in the Ubuntu 14.04 (64-bit) system, referring to the official website of Caffe http://caffe.berkeleyvision.org/.First, the system environment configuration1.1 First install some de

Based on years of Cuda development experience, we will briefly introduce the general development steps of the Cuda program, and follow the principle of first modifying the CPU serial program and then porting it to the GPU platform, modify the work that needs to be done on the GPU as much as possible on the CPU platform, reducing the difficulty of Program Development and debugging with bugs. By implementing

Install some dependencies first$sudo apt-get Install Freeglut3-dev build-essential libx11-dev libxmu-dev libgl1-mesa-dev Libglu1-mesa Libglu1-mesa-dev Libxi-devThere are two options for offline Installation: Debian installation package; Run Installation methodDeb Installation MethodThis is the case with Cuda 7.5 for 14.04, preferably MD5 validation of cuda installation files prior to this$ md5sum

For the first time, I officially wrote a technical blog. First, I exercised my ability to write summaries. Second, I felt ashamed to have read many technical posts but never contributed myself. 1. preparation 1. first, the runtime environment is ubuntu14.04. Therefore, this document assumes that ubuntu14.04 has been installed. In addition, nvidia designed its gpu programming architecture for cuda. For the first time, I officially wrote a technical blo

First verify that you have an NVIDIA graphics card (Http://developer.nvidia.com/cuda-gpus this site to see if you have a graphics card that supports GPU): $ LSPCI | Grep-i nvidia See your Linux distributions (mostly 64-bit or 32-bit): $ uname-m cat/etc/*release Look at the version of GCC: $ gcc--versionFirst download the NVIDIA Cuda Warehouse installation package (my Ubuntu 14.04 64 bit, so the down

http://blog.csdn.net/augusdi/article/details/12833235 Cuda from entry to Mastery (0): written in front At the request of the boss, this Bo master from 2012 on the High Performance Computing course began to contact Cuda programming, and then the technology applied to the actual project, so that the processing program accelerated more than 1K, visible based on the parallel computing graphics display for t

Since this book contains a lot of content, a lot of content is repeated with other books that explain cuda, so I only translate some key points. Time is money. Let's learn Cuda together. If any errors occur, please correct them. Since Chapter 1 and Chapter 2 do not have time to take a closer look, we will start from Chapter 3. I don't like being subject to people, so I don't need its header file. I will re

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

Part of the content is transferred fromHttps://chenrudan.github.io/blog/2015/07/22/cudastream.htmlHttp://stackoverflow.com/questions/10415204/how-to-create-a-cuda-contextEarly on, it was discovered that the first function that was run on Cuda would take a long time to explain because Cuda initialization.So what's the main problem with

Summary of accelerated installation of Amber11 + AmberTools1.5 + CUDA The following installation method is based on some of the previous posts on the Forum simulated by the numerator. The installation and testing can be successful as long as the operation is correct. Considering that Amber11 is generally installed on clusters, the intel compiler and Openmpi parallel tool are used for installation. You need to purchase the Amber11 software to obtain th

Compiling the environmentVMWare Workstation PlayerUbuntu-15.10-desktop-amd64CPU 4700MQ, allocating 6 cores +4GB memory +80GB HDD to VMCompile stepThe main reference is Caffe official websiteHttp://caffe.berkeleyvision.org/install_apt.html1. Install the Basic Package sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev Protobuf-compilersudo apt-get install--no-install-recommends Libboost-all-dev Cuda