In order to avoid the reader to step on the pit, this article successfully tested the Ubuntu18.04 environment configuration deep learning environment:

(GPU:NVIDIA TITAN Xp), including:

Installation and testing of CUDA+CUDNN+TensorFlow1.9+Pytorch1.1.

I. Hardware configuration \

Ultra – micro tower server

Graphics card NVIDIA TITAN Xp *4 Memory 128G CPU 2620V4* 2 Power supply 1600W *2 Hard disk 256G*2+2T*2

1. Install Ubuntu. Use a USB flash drive to install the Ubuntu OPERATING system.

Jingyan.baidu.com/article/a37… At the beginning of the installation, select “Install Ubuntu” and press enter. After a while, if the screen displays “Input not supported”, which is related to Ubuntu’s support for graphics cards, select Nomodeset on the F6 screen of the main installation screen and proceed to the next installation. The installation process is omitted. Download the installation image from ubuntu.com/download/des: Ubuntu-18.04.2-desktop-amd64.iso

Note: Enter SSH in the command window on the server desktop. After this step, you can use SSH to remotely connect to the server. XShell is used in this document.

sudo apt-get install openssh-server
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3. Install NVIDIA TITAN Xp graphics drivers The default graphics drivers installed are not NVIDIA drivers, so remove the old drivers first.

sudo apt-get purge nvidia*
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Add Graphic Drivers PPA

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
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View the appropriate driver version:

ubuntu-drivers devices
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Figure: List of available Nvidia drivers

It can be seen that the recommended driver is the latest 430 version, install the driver: \

sudo apt-get install nvidia-driver- 430.
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Restart the machine after installation:

sudo reboot
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Restart complete run

nvidia-smi
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Take a look at the graphics card driver in effect:

Figure: Graphics card driver in effect

4. Install the dependency library \

sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-devlibgl1-mesa-glx libglu1-mesa libglu1-mesa-dev
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GCC Lowered version CUDA9.0 requires the GCC version to be 5.x or 6.x. You need to configure the GCC version by yourself. \

  • Version installed
Sudo apt-get install GCC- 5
sudo apt-get install g++- 5
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  • Replace the previous version with a command
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc- 5 50
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++- 5 50
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6. Install Anaconda and TensorFlow, Keras and PyTorch

** Key: Let Conda install CUDA and CUDNN automatically!! ** Since Anaconda provides a complete scientific library, you can use Anaconda directly for installation. 1) installation Anaconda download address: www.anaconda.com/download/ here we download Python 3.7 64 – bit Linux version. Installation:

bash Anaconda32019.03-Linux-x86_64.sh
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2) Change PIP and conda to domestic source. Since it is slow to access PIP and conda in China, it is suggested to change to domestic source: a. Change the source of PIP to Aliyun:

mkdir ~/.pip
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cat > ~/.pip/pip.conf << EOF
[global]
trusted-host=mirrors.aliyun.com
index-url=https://mirrors.aliyun.com/pypi/simple/
EOF
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3) Install Python3.7 in Anaconda to create a Python virtual environment

conda create --name tf python=3.7Create a TF environmentCopy the code

Virtual environment commands:

Conda remove --name tf --all # delete the tf environmentCopy the code

4) Install TensorFlow GPU 1.9 in Anaconda

conda install tensorflow-gpu==1.9
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Cuda, CUDNN and other related components will be installed automatically.

source activate tf
python
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Enter the following code from the Python command:

import tensorflowas tf
hello= tf.constant('Hello, TensorFlow! ')
sess= tf.Session()
print(sess.run(hello))
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No error is configured. 6) Install Keras directly in this virtual environment:

pip install keras
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Install Pytorch directly in this virtual environment:

conda install pytorch torchvision -c pytorch
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Cuda and CUDNN will be automatically installed to test Pytorch.

source activate tf
python
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Enter the following code from the python command:

import torch
print(torch.cuda.is_available())
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If True is returned, the installation succeeded.

In order to avoid the reader to step on the pit, this article successfully tested the installation and test of CUDA+CUDNN+TensorFlow1.9+Pytorch1.1 in Ubuntu18.04 environment. Reference blog.csdn.net/weixin_4186…

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