基于Nvidia GPU和Docker容器的深度学习环境搭建

GPU云主机：

操作系统：Ubuntu 16.04 64位
GPU： 1 x Nvidia Tesla P40

1. 安装CUDA Driver

1.1 Pre-installation Actions

安装gcc、g++、make：

# sudo apt-get install gcc g++ make
# gcc --version
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

如果没有，需安装linux-headers：

# sudo apt-get install linux-headers-$(uname -r)

1.2 安装NVIDIA driver

CUDA安装有两种方式：
1.Package安装
2.Runfile安装

本文选择runfile安装方式。

首先禁用Nouveau：

# lsmod | grep nouveau
nouveau  1495040  0
mxm_wmi16384  1 nouveau
wmi20480  2 mxm_wmi,nouveau
video  40960  1 nouveau
i2c_algo_bit   16384  1 nouveau
ttm94208  1 nouveau
drm_kms_helper155648  1 nouveau
drm   364544  3 ttm,drm_kms_helper,nouveau
# vi /etc/modprobe.d/blacklist-nouveau.conf
blacklist nouveau
options nouveau modeset=0
# sudo update-initramfs -u
update-initramfs: Generating /boot/initrd.img-4.4.0-62-generic
W: mdadm: /etc/mdadm/mdadm.conf defines no arrays.

Reboot云主机：

# reboot

重启后check下Nouveau drivers没有被load：

# lsmod | grep nouveau
#

# wget https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_410.48_linux

开始安装CUDA Driver:

# chmod +x cuda_10.0.130_410.48_linux
# sudo sh ./cuda_10.0.130_410.48_linux 
Logging to /tmp/cuda_install_1699.log
Using more to view the EULA.
Do you accept the previously read EULA?
accept/decline/quit: accept

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
(y)es/(n)o/(q)uit: y

Do you want to install the OpenGL libraries?
(y)es/(n)o/(q)uit [ default is yes ]: y

Do you want to run nvidia-xconfig?
This will update the system X configuration file so that the NVIDIA X driver
is used. The pre-existing X configuration file will be backed up.
This option should not be used on systems that require a custom
X configuration, such as systems with multiple GPU vendors.
(y)es/(n)o/(q)uit [ default is no ]: 

Install the CUDA 10.0 Toolkit?
(y)es/(n)o/(q)uit: y

Enter Toolkit Location
 [ default is /usr/local/cuda-10.0 ]: 

Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y

Install the CUDA 10.0 Samples?
(y)es/(n)o/(q)uit: y

Enter CUDA Samples Location
 [ default is /root ]: 

Installing the NVIDIA display driver...
Installing the CUDA Toolkit in /usr/local/cuda-10.0 ...
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 /root ...
Copying samples to /root/NVIDIA_CUDA-10.0_Samples now...
Finished copying samples.

===========
= Summary =
===========

Driver:   Installed
Toolkit:  Installed in /usr/local/cuda-10.0
Samples:  Installed in /root, but missing recommended libraries

Please make sure that
 -   PATH includes /usr/local/cuda-10.0/bin
 -   LD_LIBRARY_PATH includes /usr/local/cuda-10.0/lib64, or, add /usr/local/cuda-10.0/lib64 to /etc/ld.so.conf and run ldconfig as root

To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-10.0/bin
To uninstall the NVIDIA Driver, run nvidia-uninstall

Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-10.0/doc/pdf for detailed information on setting up CUDA.

Logfile is /tmp/cuda_install_1699.log

安装成功！

Reboot云主机：

# reboot

设备验证：

# ls /dev/nvidia*
ls: cannot access '/dev/nvidia*': No such file or directory
# vi nvidia-probe.sh

#!/bin/bash
### BEGIN INIT INFO
# Provides:          jd.com
# Required-Start:    $local_fs $network
# Required-Stop:     $local_fs
# Default-Start:     2 3 4 5
# Default-Stop:      0 1 6
# Short-Description: nvidia service
# Description:       nvidia service daemon
### END INIT INFO

/sbin/modprobe nvidia

if [ "$?" -eq 0 ]; then
  # Count the number of NVIDIA controllers found.
  NVDEVS=`lspci | grep -i NVIDIA`
  N3D=`echo "$NVDEVS" | grep "3D controller" | wc -l`
  NVGA=`echo "$NVDEVS" | grep "VGA compatible controller" | wc -l`

  N=`expr $N3D + $NVGA - 1`
  for i in `seq 0 $N`; do
mknod -m 666 /dev/nvidia$i c 195 $i
  done

  mknod -m 666 /dev/nvidiactl c 195 255

else
  exit 1
fi

/sbin/modprobe nvidia-uvm

if [ "$?" -eq 0 ]; then
  # Find out the major device number used by the nvidia-uvm driver
  D=`grep nvidia-uvm /proc/devices | awk '{print $1}'`

  mknod -m 666 /dev/nvidia-uvm c $D 0
else
  exit 1
fi
    
# chmod +x nvidia-probe.sh 
# ./nvidia-probe.sh
# ls /dev/nvidia*
/dev/nvidia0  /dev/nvidiactl  /dev/nvidia-uvm

/dev下成功发现设备!

配置开机自启动：

# cp nvidia-probe.sh /etc/init.d/
# sudo update-rc.d nvidia-probe.sh defaults 95

1.3 Post-installation Actions

配置环境变量：


# vi /etc/profile
......
export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

开机启动Persistence Daemon：

# vi /etc/rc.local
......
/usr/bin/nvidia-persistenced --verbose

exit 0

1.4 CUDA driver验证

查看Driver Version：


# cat /proc/driver/nvidia/version
NVRM version: NVIDIA UNIX x86_64 Kernel Module  410.48  Thu Sep  6 06:36:33 CDT 2018
GCC version:  gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.10)

使用deviceQuery示例验证：


# cd ~/NVIDIA_CUDA-10.0_Samples/1_Utilities/deviceQuery/
# make
"/usr/local/cuda-10.0"/bin/nvcc -ccbin g++ -I../../common/inc  -m64-gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o deviceQuery.o -c deviceQuery.cpp
"/usr/local/cuda-10.0"/bin/nvcc -ccbin g++   -m64  -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o deviceQuery deviceQuery.o 
mkdir -p ../../bin/x86_64/linux/release
cp deviceQuery ../../bin/x86_64/linux/release
# cd ../../bin/x86_64/linux/release/
# ls
deviceQuery
# ./deviceQuery 
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "Tesla P40"
  CUDA Driver Version / Runtime Version  10.0 / 10.0
  CUDA Capability Major/Minor version number:6.1
  Total amount of global memory: 22919 MBytes (24032378880 bytes)
  (30) Multiprocessors, (128) CUDA Cores/MP: 3840 CUDA Cores
  GPU Max Clock rate:1531 MHz (1.53 GHz)
  Memory Clock rate: 3615 Mhz
  Memory Bus Width:  384-bit
  L2 Cache Size: 3145728 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:Enabled
  Device supports Unified Addressing (UVA):  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 / 0 / 7
  Compute Mode:
 < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.0, CUDA Runtime Version = 10.0, NumDevs = 1
Result = PASS

参考：

https://github.com/NVIDIA/nvi...
https://docs.nvidia.com/cuda/...

2. 安装Nvidia-docker

2.1 安装Docker

安装docker-ce：


#sudo apt-get remove docker docker-engine docker.io

# sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
software-properties-common
# curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
# sudo add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable"
# sudo apt-get update
# sudo apt-get install docker-ce
# docker version
Client:
 Version:   18.06.1-ce
 API version:   1.38
 Go version:go1.10.3
 Git commit:e68fc7a
 Built: Tue Aug 21 17:24:56 2018
 OS/Arch:   linux/amd64
 Experimental:  false

Server:
 Engine:
  Version:  18.06.1-ce
  API version:  1.38 (minimum version 1.12)
  Go version:   go1.10.3
  Git commit:   e68fc7a
  Built:Tue Aug 21 17:23:21 2018
  OS/Arch:  linux/amd64
  Experimental: false

2.2 安装nvidia-docker

安装nvidia-docker：


# Add the package repositories
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update

# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

验证nvidia-docker：


# docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi
Thu Oct 25 09:03:27 2018   
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.48 Driver Version: 410.48|
|-------------------------------+----------------------+----------------------+
| GPU  NamePersistence-M| Bus-IdDisp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap| Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla P40   On   | 00000000:00:07.0 Off |0 |
| N/A   20CP8 9W / 250W |  0MiB / 22919MiB |  1%  Default |
+-------------------------------+----------------------+----------------------+
   
+-----------------------------------------------------------------------------+
| Processes:   GPU Memory |
|  GPU   PID   Type   Process name Usage  |
|=============================================================================|
|  No running processes found |
+-----------------------------------------------------------------------------+

2.3 配置Docker默认runtime

cat /etc/docker/daemon.json

{
    "default-runtime": "nvidia",
    "runtimes": {
        "nvidia": {
            "path": "nvidia-container-runtime",
            "runtimeArgs": []
        }
    }
}

重启服务：

# systemctl restart docker
# systemctl status docker

2.4 运行TensorFlow卷积神经Model

Docker运行：

# docker run --rm --name tensorflow -ti tensorflow/tensorflow:r0.9-devel-gpu
root@bd0fb3758da2:~# python --version
Python 2.7.6
root@bd0fb3758da2:~# python -m tensorflow.models.image.mnist.convolutional

参考：

https://docs.docker.com/insta...
https://github.com/NVIDIA/nvi...

基于Nvidia GPU和Docker容器的深度学习环境搭建

基于Nvidia GPU和Docker容器的深度学习环境搭建

1. 安装CUDA Driver

1.1 Pre-installation Actions

1.2 安装NVIDIA driver

1.3 Post-installation Actions

1.4 CUDA driver验证

2. 安装Nvidia-docker

2.1 安装Docker

2.2 安装nvidia-docker

2.3 配置Docker默认runtime

2.4 运行TensorFlow卷积神经Model

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