cudnn

Pytorch 训练时，使用Resnet18遇到错误： RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED 通过设置以下语句，解决： torch.backends.cudnn.enabled = False 关于torch.backends.cudnn.enabled 的使用： 设置这个 flag 可以让内置的 cuDNN 的 auto-tuner 自动寻找最适合当前配置的高效算法，来达到优化运行效率的问题。 应该遵循以下准则： 如果网络的输入数据维度或类型上变化不大，设置 torch.backends.cudnn.benchmark = true 可以增加运行效率； 如果网络的输入数据在每次 iteration 都变化的话，会导致 cnDNN 每次都会去寻找一遍最优配置，这样 反而会降低 运行效率。 具体导致出现 CUDNN_STATUS_EXECUTION_FAILED的原因，暂时不清楚！ 来源： CSDN 作者： 挡不住三千问的BlueCat 链接： https://blog.csdn.net/qq_23944915/article/details/99740830

参考文档 https://blog.csdn.net/nwpushuai/article/details/79935740 https://blog.csdn.net/qq_43030766/article/details/91513501 https://blog.csdn.net/zhqh100/article/details/77646497 https://www.cnblogs.com/zixuan-L/p/11023051.html https://blog.csdn.net/huangfei711/article/details/79230446 https://www.cnblogs.com/yjlch1016/p/8641910.html 硬件环境 CPU I7-7700,8M,3.6GHZ,4核 内存 DDR4 16G 硬盘 SSD 500G 系统 Ubuntu 16.04 Desktop版（需要用到图像界面） 显卡 NVDIA GeForce GTX1050Ti 4G 系统环境 1.双网卡绑定 root@mec03:~# cat /etc/modules # /etc/modules: kernel modules to load at boot time. # # This file contains the names of kernel modules

Ubuntu16.04下安装多版本cuda和cudnn 原文 https://blog.csdn.net/tunhuzhuang1836/article/details/79545625 前言 因为之前针对Pytorch，caffe，torch等，装了cuda8.0和对应cudnn5.1，但是最近在装MxNet的时候，发现官网上能下载到的MxNet版本仅支持cuda9.0和对应cudnn7.0.5，所以无奈不想卸载cuda8.0只能在电脑上安装多个版本的cuda和对应cudnn。 安装cuda 好了进入正题，首先安装cuda 需要去nvidia官网注册一个账号登录下载。这里给一下历史版本的cuda链接（现在官网好像进去默认下载最新版本）。这里我们举例下载cuda9.0：（当你电脑上只有一个cuda时候下载哪种类型无所谓，如果已存在一个或以上时候需要选择runfile安装） 安装按照官方指引就好 # if deb sudo dpkg -i cuda-repo-ubuntu1604-9-0-local-rc_9.0.103-1_amd64.deb sudo apt-get update sudo apt-get install cuda # if runfile sudo chmod +x cuda_9.0.176_384.81_linux.run ./cuda_9.0.176_384

I've successfully installed tensorflow (GPU) on Linux Ubuntu 16.04 and made some small changes in order to make it work with the new Ubuntu LTS release. However, I thought (who knows why) that my GPU met the minimum requirement of a compute capability greater than 3.5. That was not the case since my GeForce 820M has just 2.1. Is there a way of making tensorflow GPU version working with my GPU? I am asking this question since apparently there was no way of making tensorflow GPU version working on Ubuntu 16.04 but by searching the internet I found out that was not the case and indeed I made it

Tensorflow2.0+Anaconda + Windows10+cuda10.0+python 发布时间： 2019-04-19 20:10:27 Tensorflow2.0用起来比tensorflow1.x要好用很多。 安装过程与1.x基本差不多。 1、下载安装Anaconda https://www.anaconda.com/distribution/#download-section 确认Anaconda已经安装 2、安装cuda10.0 本机显卡是GTX 1080ti cuda下载地址： https://developer.nvidia.com/cuda-10.0-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal cuda安装过程勾选要仔细点，不要粗错 上图红框部分不相等没有关系，但是必须保证Current Version <= New Version 然后确认cuda安装： CUPTI确认： 3、下载安装cudnn 下载这个需注册账号先（烦） https://developer.nvidia.com/rdp/cudnn-download 解压，将文件夹名改为cudnn，复制到下图位置 cudnn安装确认： 更改系统环境变量：

I have installed Theano framework and enabled CUDA on my machine, however when I "import theano" in my python console, I got the following message: >>> import theano Using gpu device 0: GeForce GTX 950 (CNMeM is disabled, CuDNN not available) Now that "CuDNN not available", I downloaded cuDnn from Nvidia website. I also updated 'path' in environment, and added 'optimizer_including=cudnn' in '.theanorc.txt' config file. Then, I tried again, but failed, with: >>> import theano Using gpu device 0: GeForce GTX 950 (CNMeM is disabled, CuDNN not available) Traceback (most recent call last): File "

一、Ubuntu16.04 GTX 1080Ti+CUDA9.0+GPU driver 390.77+cuDNN7 https://www.jianshu.com/p/a9dcf59bd896 1.1 CUDA与GPU Driver，cuDNN三者版本要匹配 https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html 安装CUDA 9.2 cuda-drivers 390.77，显示CUDA driver version is insufficient for CUDA runtime version 重新安装beta版 cuda-drivers 396.18 PASS，但是TensorFlow1.9不支持 1.2 重新安装CUDA9.0和GPU Driver390.77 $ sudo ./cuda_8.0.61_375.26_linux.run --no-opengl-libs在这里插入图片描述 找了很久的原因，为什么Driver Not Selected。其实已经安装成功，可以直接下一步。 sudo nvidia - smi 二、安装 TensorFlow 1.9 https://www.tensorflow.org/install/install_linux 2.1 确定安装哪种 TensorFlow

I have noticed that some newer TensorFlow versions are incompatible with older CUDA and cuDNN versions. Does an overview of the compatible versions or even a list of officially tested combinations exist? I can't find it in the TensorFlow documentation. Generally: Check the CUDA version: cat /usr/local/cuda/version.txt and cuDNN version: grep CUDNN_MAJOR -A 2 /usr/local/cuda/include/cudnn.h and install a combination as given below in the images or here . The following images and the link provide an overview of the officially supported/tested combinations of CUDA and TensorFlow on Linux, macOS