linux RTX2080显卡驱动

安装RTX2080显卡驱动

服务器配置#

服务器型号：DELL PowerEdge R730

CPU：2*Intel(R) Xeon(R) E5-2650 v4

内存：8*32G

磁盘：2*1.2T，raid 0

显卡：2*Nvidia RTX2080

系统：Ubuntu 18.04

使用标准Ubuntu 仓库进行自动化安装#

首先，检测显卡型号和推荐的驱动程序的模型。在命令行中输入如下命令：

Copy

root@rohn-PowerEdge-R730:/home/rohn# ubuntu-drivers devices == /sys/devices/pci0000:80/0000:80:02.0/0000:82:00.0 == modalias : pci:v000010DEd00001E82sv00001043sd00008674bc03sc00i00 vendor : NVIDIA Corporation driver : nvidia-driver-410 - third-party free driver : nvidia-driver-415 - third-party free driver : nvidia-driver-430 - third-party free recommended driver : nvidia-driver-418 - third-party free driver : xserver-xorg-video-nouveau - distro free builtin

从输出结果可以看到，目前系统已连接Nvidia RTX2080显卡，CUDA 10.0 需要 410.x 或更高版本。并且建议安装驱动程序是 nvidia-430版本的驱动。

安装驱动：

Copy

sudo ubuntu-drivers autoinstall

由于DELL对未认证的PCI设备的热量估算不准确造成的，默认会加大风扇风速。可以用ipmi有关命令关闭PCIE卡的响应。

Copy

sudo apt install ipmitool ipmitool raw 0x30 0xce 0x00 0x16 0x05 0x00 0x00 0x00 0x05 0x00 0x01 0x00 0x00

安装完成后重启系统：

Copy

reboot

查看：

Copy

root@rohn-PowerEdge-R730:~# nvidia-smi Mon Jun 3 09:56:45 2019 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 430.14 Driver Version: 430.14 CUDA Version: 10.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 GeForce RTX 2080 Off | 00000000:04:00.0 Off | N/A | | 22% 28C P8 17W / 215W | 0MiB / 7982MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 1 GeForce RTX 2080 Off | 00000000:82:00.0 Off | N/A | | 22% 29C P8 20W / 215W | 0MiB / 7982MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+

安装CUDA#

Copy

# Add NVIDIA package repositories wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu1804_10.0.130-1_amd64.deb sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub sudo apt-get update wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb sudo apt-get update # Install NVIDIA driver sudo apt-get install --no-install-recommends nvidia-driver-410 # Reboot. Check that GPUs are visible using the command: nvidia-smi # Install development and runtime libraries (~4GB) sudo apt-get install --no-install-recommends \ cuda-10-0 \ libcudnn7=7.4.1.5-1+cuda10.0 \ libcudnn7-dev=7.4.1.5-1+cuda10.0 # Install TensorRT. Requires that libcudnn7 is installed above. sudo apt-get update && \ sudo apt-get install nvinfer-runtime-trt-repo-ubuntu1804-5.0.2-ga-cuda10.0 \ && sudo apt-get update \ && sudo apt-get install -y --no-install-recommends libnvinfer-dev=5.0.2-1+cuda10.0

出处：https://www.cnblogs.com/Rohn/p/10971326.html

Linux

版权声明：本文内容由网络用户投稿，版权归原作者所有，本站不拥有其著作权，亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容，请联系我们jiasou666@gmail.com 处理，核实后本网站将在24小时内删除侵权内容。