安装RTX2080显卡驱动

近日新购了一台DELL服务器，用于TensorFlow，由于显卡是另加的，需要安装显卡驱动。

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：

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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：

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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。

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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：

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reboot

查看：

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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

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# 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