nvidia cuda工具包更新9.0版本記錄
阿新 • • 發佈：2019-01-09
因工作需要，對公司2臺伺服器升級cuda到9.0版本。本文記錄其過程。
介紹
cuda在nvidia官方網站有下載，最新版本為9.2，但公司採用的tensorflow只使用9.0，為了版本的相容，決定還是選用9.0版本。官網預設使用最新版本，舊版本位於：https://developer.nvidia.com/cuda-toolkit-archive。9.0版本地址：https://developer.nvidia.com/cuda-90-download-archive。提供Windows、Linux和MacOS三大平臺安裝包。而Linux平臺支援CentOS、Ubuntu、Fedora、RHEL等等主流發行版本。Ubuntu只支援16.04和17.04版本。本文采用16.04版本。一共需要下載5個檔案，一個安裝包cuda_9.0.176_384.81_linux.run，四個補丁包，名稱為cuda_9.0.176.X_linux.run，其中X為1~4。
下載
cuda包下載比較麻煩，去年曾經安裝過，在md5問題上卡了很久，各種下載工具都嘗試過，但md5都不正確，所以安裝失敗。還是託在深圳工作的前同事幫忙下載，再傳回來的。現在認識到docker，就使用docker來做中轉。
下載docker：
$ docker pull latelee/cuda:tar-9.09.0安裝包位於容器根目錄下。
$ docker run -it -v /home:/home --rm latelee/cuda:tar-9.0 bash
[email protected]98dfc8d4f016:/# ls
bin cuda_9.0 然後將.run檔案拷貝到宿主機目錄中。
安裝
在安裝前，先檢視核心：
uname -a
Linux localhost 4.13.0-38-generic #43~16.04.1-Ubuntu SMP Wed Mar 14 17:48:43 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux再檢視nvidia顯示卡驅動版本：
$ nvidia-smi
Thu Sep 13 09:56:26 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.130 Driver Version: 384.130 |
|-------------------------------+----------------------+----------------------+
| 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 GTX 1070 Off | 00000000:01:00.0 Off | N/A |
| 79% 82C P2 131W / 200W | 2865MiB / 8112MiB | 100% Default |
+-------------------------------+----------------------+----------------------+上面表明驅動是正常的，由於已經安裝了驅動，在安裝cuda時就不需要再安裝了。
先解除安裝舊版本：
···
cd /usr/local/cuda-8.0/bin
sudo ./uninstall_cuda_8.0.pl
···
再進行安裝：
sudo ./cuda_9.0.176_384.81_linux.run首先顯示協議，按q退出。下面根據步驟來安裝，如下所示(各步驟給出說明)：
Do you accept the previously read EULA?
accept/decline/quit: accept # 輸入accept表示同意協議
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81?
(y)es/(n)o/(q)uit: n # 輸入n不安裝nvidia驅動，因為已經安裝過了。
Install the CUDA 9.0 Toolkit?
(y)es/(n)o/(q)uit: y # 輸入y安裝cuda工具包
Enter Toolkit Location
[ default is /usr/local/cuda-9.0 ]: # 直接回車即可，使用預設目錄
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y # 輸入y，安裝連結目錄
Install the CUDA 9.0 Samples?
(y)es/(n)o/(q)uit: y # 輸入y安裝示例
Enter CUDA Samples Location
[ default is /home/latelee ]: # 使用預設目錄，直接回車
Installing the CUDA Toolkit in /usr/local/cuda-9.0 ...
Missing recommended library: libGLU.so
Missing recommended library: libX11.so
Missing recommended library: libXi.so
Missing recommended library: libXmu.so
Installing the CUDA Samples in /home/latelee ...
Copying samples to /home/latelee/NVIDIA_CUDA-9.0_Samples now...
Finished copying samples.
===========
= Summary =
===========
Driver: Not Selected # 由於沒選擇驅動，會這樣列印資訊出來
Toolkit: Installed in /usr/local/cuda-9.0
Samples: Installed in /home/latelee, but missing recommended libraries
# 設定PATH和庫目錄
Please make sure that
- PATH includes /usr/local/cuda-9.0/bin
- LD_LIBRARY_PATH includes /usr/local/cuda-9.0/lib64, or, add /usr/local/cuda-9.0/lib64 to /etc/ld.so.conf and run ldconfig as root
To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-9.0/bin
Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-9.0/doc/pdf for detailed information on setting up CUDA.
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 384.00 is required for CUDA 9.0 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 /tmp/cuda_install_5468.log補丁
下面安裝補丁檔案：
sudo ./cuda_9.0.176.1_linux.run
sudo ./cuda_9.0.176.2_linux.run
sudo ./cuda_9.0.176.3_linux.run
sudo ./cuda_9.0.176.4_linux.run按q退出協議，輸入accept，再直接回車即可，每個一補丁都如此。
環境變數
由於本文是升級，而且，安裝目錄做了連結，所以無須任何修改即可。如果是首次安裝，則要設定環境變數，——正如上面提示資訊所述那樣。設定環境變數有很多種方式，這裡只提一種，由於是伺服器，所以最好是全域性設定，所有開發人員都能使用。開啟/etc/profile檔案，在最後新增：
# cuda-9.0
export PATH=$PATH:/usr/local/cuda/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64重新登陸，可執行source /etc/profile，均可生效。
驗證
檢視nvcc編譯器版本以確認cuda版本：
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176最後一行表示cuda已經是9.0了。接著編譯並執行示例程式碼，進入相應示例程式碼目錄：
$ cd /home/latelee/NVIDIA_CUDA-9.0_Samples/1_Utilities/deviceQuery編譯並執行：
$ make -j
$ ./deviceQuery輸出資訊如下：
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 1070"
CUDA Driver Version / Runtime Version 9.0 / 9.0
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 8113 MBytes (8506769408 bytes)
(15) Multiprocessors, (128) CUDA Cores/MP: 1920 CUDA Cores
GPU Max Clock rate: 1709 MHz (1.71 GHz)
Memory Clock rate: 4004 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 2097152 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: 2048
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 2 copy engine(s)
Run time limit on kernels: No
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
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 = 9.0, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS最後的PASS表示程式測試通過。
小結
如果已經安裝過nvidia顯示卡驅動，在安裝cuda時就不能選擇安裝驅動，否則，因為驅動已經被載入，無法安裝成功。需要先解除安裝，但制裁重新安裝比較麻煩，考慮到年代較近（去年年底才安裝），因為就不升級驅動了。
李遲 2018.9.14 週五 夜