無論采用哪一種方式，首先都需要更新 Ubuntu 軟件源和升級到最新版本的軟件包。由於國內從 Ubuntu 官方軟件源下載速度比較慢，所以，建議采用國內 Ubuntu 鏡像源，比如阿里 Ubuntu 軟件源或清華大學 Ubuntu 軟件源。具體的配置方式是修改配置文件 /etc/apt/sources.list，將其中的 archive.ubuntu.com 替換為 mirrors.alibaba.com 或 mirrors.tuna.tsinghua.edu.cn 。也可以在圖形界面應用 "Software & Update" 中，修改 Ubuntu Software 標簽頁中的 Download from 后的軟件源地址。
配置軟件源后，采用如下命令進行軟件源的更新和軟件包的升級。
sudo apt update sudo apt upgrade
下面介紹在 Ubuntu 20.04 長期支持版本中，安裝 CUDA Tools 的三種方式：
方式一：采用 Ubuntu 軟件源中的 CUDA Tools 軟件包
這種方式安裝簡單，但安裝的 CUDA Toolkit 版本往往不是最新版本。查詢目前可安裝的 CUDA Toolkit 版本的命令，如下所示
apt search nvidia-cuda-toolkit
具體安裝命令如下：
sudo apt install nvidia-cuda-toolkit
方式二：先采用圖形界面安裝 CUDA 驅動，再安裝從 NVIDIA 官網下載的 CUDA Toolkit 安裝包
1）圖形界面安裝 CUDA 驅動
在所有應用中，選擇 “Software & Update” 應用，在標簽頁 "Additional Drivers" 中選擇 “nvidia-driver-450-server”，如下圖所示：
選擇后，單擊 “Apply Changes” 按鈕，這樣就更新並切換到所選驅動。
快捷鍵 Ctrl + Alt + T 打開 Terminal ，運行 nvidia-smi 命令以驗證切換到 CUDA 驅動是否成功。我嘗試過 nvidia-driver-460 這個版本，但沒有成功，因此使用稍低的版本 nvidia-driver-450-server 。
2）下載並安裝 CUDA Toolkit
本機安裝的 CUDA Toolkit 版本為 11.0.3，與上一步安裝 CUDA 驅動 450 兼容（可以參考下載文件名的尾綴）， 具體下載命令，如下
wget https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda_11.0.3_450.51.06_linux.run
安裝命令，如下
sudo sh cuda_11.0.3_450.51.06_linux.run
需要注意，安裝時，選擇不安裝 CUDA 驅動，安裝記錄如下：
===========
= Summary =
===========
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-11.0/
Samples: Installed in /home/klchang/, but missing recommended libraries
Please make sure that
- PATH includes /usr/local/cuda-11.0/bin
- LD_LIBRARY_PATH includes /usr/local/cuda-11.0/lib64, or, add /usr/local/cuda-11.0/lib64 to /etc/ld.so.conf and run ldconfig as root
To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-11.0/bin
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least .00 is required for CUDA 11.0 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
sudo <CudaInstaller>.run --silent --driver
Logfile is /var/log/cuda-installer.log
安裝結束后，添加環境變量到 ~/.bashrc 文件的末尾，具體添加內容如下：
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64 export PATH=$PATH:/usr/local/cuda/bin export CUDA_HOME=$CUDA_HOME:/usr/local/cuda
保存后退出。
在 Terminal 中，激活環境變量命令為 source ~/.bashrc 。
測試 CUDA Toolkit 。 通過編譯自帶 Samples並執行， 以驗證是否安裝成功。具體命令如下所示：
cd /usr/local/cuda/samples/1_Utilities/deviceQuery sudo make ./deviceQuery
如果安裝成功，則輸出類似於如下信息：
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce RTX 2070 with Max-Q Design"
CUDA Driver Version / Runtime Version 11.0 / 11.0
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 7982 MBytes (8370061312 bytes)
(36) Multiprocessors, ( 64) CUDA Cores/MP: 2304 CUDA Cores
GPU Max Clock rate: 1125 MHz (1.12 GHz)
Memory Clock rate: 5501 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 4194304 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1024
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 3 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Managed Memory: Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.0, CUDA Runtime Version = 11.0, NumDevs = 1
Result = PASS
3）下載並安裝 cuDNN
從 NVIDIA 官方網址 https://developer.nvidia.com/rdp/cudnn-download 下載 cudnn-11.0-linux-x64-v8.0.5.39.tgz 。
解壓壓縮包，並把相應的文件，復制到指定目錄即可。如下所示：
tar zxvf cudnn-11.0-linux-x64-v8.0.5.39.tgz sudo cp cuda/include/cudnn* /usr/local/cuda/include sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn* /usr/local/cuda/lib64/libcudnn*
方式三：CUDA 驅動和 CUDA Toolkit 都采用命令行方式安裝
首先，需要卸載原有的 NVIDIA 驅動並禁用自帶的驅動 nouveau；然后，重啟電腦，使用 lsmod | grep nouveau 命令檢查禁用自帶驅動是否成功；如果禁用成功，則安裝從 NVIDIA 官方地址下載的 CUDA Toolkit。其步驟則與方式二相同，差別在於這次需要安裝 CUDA 驅動 。更多內容，參見 How to Install CUDA ToolKit 11.0, and Nvidia Display Driver on Ubuntu 20.04。
問題與解答
問題 1，sudo apt update 時，出現有鎖無法更新的情況
$ sudo apt update Reading package lists... Done E: Could not get lock /var/lib/apt/lists/lock. It is held by process 1379 (packagekitd) N: Be aware that removing the lock file is not a solution and may break your system. E: Unable to lock directory /var/lib/apt/lists/
解決方法：
停用 packagekitd，並禁止開機啟動，具體命令如下：
systemctl stop packagekitd
systemcrl disable packagekit.service
參考資料
[1] How to Install cuda on Ubuntu 20.04. https://linuxconfig.org/how-to-install-cuda-on-ubuntu-20-04-focal-fossa-linux
[2] Ubuntu16.04安裝NVIDIA驅動、實現GPU加速. https://blog.csdn.net/zhang970187013/article/details/81012845