ubuntu18.04上安装cuda
下⾯是从NVIDIA官⽹搬运过来的。
Some actions must be taken before the CUDA Toolkit and Driver can be installed on Linux:
Verify the system has a CUDA-capable GPU.
Verify the system is running a supported version of Linux.
Verify the system has gcc installed.
Verify the system has the correct kernel headers and development packages installed.
Download the NVIDIA CUDA Toolkit.
Handle conflicting installation methods.
c语言怎么写漂亮的界面Note: You can override the install-time prerequisite checks by running the installer with the -override flag. Remember that the prerequisites will still be required to use the NVIDIA CUDA Toolkit.
To verify that your GPU is CUDA-capable, go to your distribution's equivalent of System Properties, or, from the command line, enter:
$ lspci | grep -i nvidia
If you do not see any settings, update the PCI hardware database that Linux maintains by entering update-pciids(generally found in/sbin) at the command line and rerun the previous lspci command.
The Release Notes for the CUDA Toolkit also contain a list of supported products.tcp udp区别优缺点
The CUDA Development Tools are only supported on some specific distributions of Linux. These are listed in the CUDA Toolkit release notes.
To determine which distribution and release number you're running, type the following at the command line:
$ uname -m && cat /etc/*release
You should see output similar to the following, modified for your particular system:
x86_64
Red Hat Enterprise Linux Workstation release 6.0 (Santiago)
The x86_64line indicates you are running on a 64-bit system. The remainder gives information about your distribution.
The gcc compiler is required for development using the CUDA Toolkit. It is not required for running CUDA applications. It is generally installed as part of the Linux installation, and in most cases the version of gcc installed with a supported version of Linux will work correctly. To verify the version of gcc installed on your system, type the following on the command line:
$ gcc --version
If an error message displays, you need to install the development tools from your Linux distribution or obtain a version of gcc and its accompanying toolchain from the Web.
The CUDA Driver requires that the kernel headers and development packages for the running version of the kernel be installed at the time of the driver installation, as well whenever the driver is rebuilt. For example, if your system is running kernel version 3.17.4-301, the 3.17.4-301 kernel headers and development packages must also be installed.unsigned char数组初始化
While the Runfile installation performs no package validation, the RPM and Deb installations of the driver will make an attempt to install the kernel header and development packages if no version of these packages is currently installed. However, it will install the latest version of these packages, which may or may not match the version of the kernel your system is using. Therefore, it is best to manually ensure the correct version of the kernel headers and development packages are installed prior to installing the CUDA Drivers, as well as whenever you change the kernel version.
The version of the kernel your system is running can be found by running the following command:
$ uname -r
This is the version of the kernel headers and development packages that must be installed prior to installing the CUDA Drivers. This command will be used multiple times below to specify the version of the packages to install. Note that below are the common-case scenarios for kernel usage. More advanced cases, such as custom kernel branches, should ensure that their kernel headers and sources match the kernel build they are running.
Note: If you perform a system update which changes the version of the linux kernel being used, make sure to rerun the commands below to ensure you have the correct kernel headers and kernel d
长沙新闻网站模板怎么设计evelopment packages installed. Otherwise, the CUDA Driver will fail to work with the new kernel.
Note: If you perform a system update which changes the version of the linux kernel being used, make sure to rerun the commands below to ensure you have the correct kernel headers and kernel development packages installed. Otherwise, the CUDA Driver will fail to work with the new kernel.
RHEL/CentOS
The kernel headers and development packages for the currently running kernel can be installed with:
$ sudo yum install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
Fedora
The kernel headers and development packages for the currently running kernel can be installed with:
$ sudo dnf install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
OpenSUSE/SLES
Use the output of the uname command to determine the running kernel's version and variant:
$ uname -r
3.16.6-2-default
In this example, the version is3.16.6-2and the variant is default. The kernel headers and development packages can then be installed with the following command, replacing<variant>and<version>with the variant and version discovered from the previous uname command:
$ sudo zypper install kernel-<variant>-devel=<version>
centos和ubuntu
Ubuntu
The kernel headers and development packages for the currently running kernel can be installed with:
$ sudo apt-get install linux-headers-$(uname -r)
The CUDA Toolkit can be installed using either of two different installation mechanisms: distribution-specific packages (RPM and Deb packages), or a distribution-independent package (runfile packages). The distribution-independent package has the advantage of working across a wider set o
f Linux distributions, but does not update the distribution's native package management system. The distribution-specific packages interface with the distribution's native package management system. It is recommended to use the distribution-specific packages, where possible.
Note: Standalone installers are not provided for architectures other than the x86_64 release. For both native as well as cross development, the toolkit must be installed using the distribution-specific installer. See the section for more details.
cuda历史版本下载：developer.nvidia/cuda-toolkit-archive
最终使⽤的是 .run⽂件安装的，没有降级gcc，安装的cuda10.2，因为之前安装的nvidia驱动版本够⽤ 440.95.01。
执⾏ sudo sh *.run后会输出声明信息，扫⼀遍，后⾯提⽰下输⼊：accept
接着就进⼊安装界⾯:
上图右边[ ]中的x是将光标移动到那⼀⾏后按Enter键实现的。
然后就可以执⾏安装了。
安装完成，⽐期待的时间快多了。plsql试用期结束了怎么办
安装完后，在.bashrc⽂件末尾添加环境变量
sudo vim ~/.bashrc （没有安装vim 可以使⽤gedit：sudo gedit ~/.bashrc）
export CUDA_HOME=/usr/local/cuda
export PATH=$PATH:$CUDA_HOME/bin
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
保存退出后，输⼊以下命名
source ~/.bashrc
测试cuda:
cd /usr/local/cuda/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery
能正常运⾏的话显⽰的是你的显卡信息，说明CUDA安装成功
安装CUDNN：
下载的是cuDNN Library for Linux
进⼊解压后的cudnn⽬录应该能看到cuda⽂件夹
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
查看cudnn版本
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
输出信息如下：
$ cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2#define CUDNN_MAJOR 7
#define CUDNN_MINOR 6
#define CUDNN_PATCHLEVEL 5
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL) #include "driver_types.h"
从上到下输出7 6 5 即表⽰cudnn7.6.5

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