Before using a GPU-accelerated ECS, make sure that the desired Tesla driver and CUDA toolkit have been installed on the ECS for computing acceleration.
This section describes how to install a Tesla driver and CUDA toolkit on a GPU-accelerated ECS.
The following uses Ubuntu 16.04 64bit as an example to describe how to install the Tesla driver matching CUDA 10.1 on a GPU-accelerated ECS.
The Linux kernel version is compatible with the driver version. If installing the driver failed, check the driver installation log, which is generally stored in /var/log/nvidia-installer.log. If the log shows that the failure was caused by a driver compilation error, for example, the get_user_pages parameter setting is incorrect, the kernel version is incompatible with the driver version. In such a case, select the desired kernel version and driver version and reinstall them. It is recommended that the release time of the kernel version and driver version be the same.
Update the software installation source: apt-get -y update
Install necessary programs: apt-get install gcc g++ make
Update the software installation source: yum -y update --exclude=kernel* --exclude=centos-release* --exclude=initscripts*
Install the desired program: yum install -y kernel-devel-`uname -r` gcc gcc-c++
Select a driver version at NVIDIA Driver Downloads based on the ECS type. Click SEARCH.
wget Copied link
For example, wget http://us.download.nvidia.com/tesla/418.67/NVIDIA-Linux-x86_64-418.67.run
If the /etc/modprobe.d/blacklist.conf file is unavailable, create it.
vi /etc/modprobe.d/blacklist.conf
Add the following statement to the end of the file:
blacklist nouveau options nouveau modeset=0
The following uses Windows Server 2016 Standard 64bit as an example to describe how to install a Tesla driver on a GPU-accelerated ECS.
Select a driver version at NVIDIA Driver Downloads based on the ECS type.
cd C:\Program Files\NVIDIA Corporation\NVSMI
nvidia-smi
If the command output contains the installed driver version, the driver has been installed.
The following uses Ubuntu 16.04 64bit as an example to describe how to install the CUDA 10.1 toolkit on a GPU-accelerated ECS.
Update the software installation source: apt-get -y update
Install necessary programs: apt-get install gcc g++ make
Update the software installation source: yum -y update --exclude=kernel* --exclude=centos-release* --exclude=initscripts*
Install the desired program: yum install -y kernel-devel-`uname -r` gcc gcc-c++
wget Copied link
For example, wget https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.39_linux.run
cd /usr/local/cuda-10.1/samples/1_Utilities/deviceQuery
If the command output contains the CUDA version, CUDA has been installed.
Enabling the persistent mode optimizes the GPU performance on Linux ECSs.
The following uses Windows Server 2016 Standard 64bit as an example to describe how to install the CUDA 10.1 toolkit on a GPU-accelerated ECS.