Tutorial on virtual envs configuration

DeepLearning framework Tensorflow/Pytorch installation tutorial for newbees...

Prerequisites
DL-Framework configuration
Server Port Table
Others

Prerequisites

Build your own docker instance

Go to the GPU-management platform http://station.csgrandeur.com/gpu/faqs
Register an account using the inviting-code provided by @LiuNing
Select an available server and build a new docker instance.

DL-Framework configuration

Step0. Choose compatible CUDA cudnn version.

CUDA:

The NVIDIA® CUDA® Toolkit provides a development environment for creating high performance GPU-accelerated applications. With the CUDA Toolkit, you can develop, optimize and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler and a runtime library to deploy your application.

cuDNN:

The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.

CUDA & cuDNN compatible table:
Refer to [https://docs.nvidia.com/deeplearning/sdk/cudnn-support-matrix/index.html]

Step1. CUDA Installation

install CUDA Toolkit[https://developer.nvidia.com/cuda-toolkit] v8.0,or any other version you need(It has already been install on our Servers,you can skip this step.)

instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))

Fetch the .deb cuda package.

$CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"       
$wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}

Install cuda

$dpkg -i ${CUDA_REPO_PKG}            
$apt-get update           
$apt-get -y install cuda          
$vim ~/.bashrc
Append the following two env paths:

$export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
$export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
[Explanation]: ${PATH:+:${PATH}} means that if PATH exists and PATH is not null then append the directory ${PATH} to PATH.

General info
Technologies
Setup

Step2. install cuDNN v6.0

Install the compatible(with CUDA) cuDNN version:

$CUDNN_TAR_FILE="cudnn-8.0-linux-x64-v6.0.tgz"   
$wget http://developer.download.nvidia.com/compute/redist/cudnn/v6.0/${CUDNN_TAR_FILE}   
$tar -xzvf ${CUDNN_TAR_FILE}   
$cp -P cuda/include/cudnn.h /usr/local/cuda-8.0/include    
$cp -P cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64/   
$chmod a+r /usr/local/cuda-8.0/lib64/libcudnn*

Step3. Set environment variables

Step4. Download and install anaconda

Download anaconda3:

$wget https://repo.continuum.io/archive/Anaconda3-2.4.0-Linux-x86_64.sh

[Recommendation]:You can update conda to the lastest version using command as follow:

conda update conda

Step5. Install anaconda

$bash Anaconda3-2.4.0-Linux-x86_64.sh

Notice:
Approve the licence at last and follow the installation navigation:
Do you approve the license terms? [yes|no]
[no] >>> yes

Step6. Create virtual envs

Add tsinghua conda source to accelerate the download speed.

$conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/    
$conda config --set show_channel_urls yes    

$conda create -n YOUR_ENV_NAME python=3.6   
$source activate YOUR_ENV_NAME

Step7. Update the pip source to accelerate the download speed

$source activate YOUR_ENV_NAME
$mkdir .pip    
$vim .pip/pip.conf

copy the following source to .pip/pip.conf

(Notice: Enter the insert mode(press key "i") before you copy the following source to ./pip/pip.conf):
"
[global]
index-url = http://mirrors.aliyun.com/pypi/simple/
[install]
trusted-host = mirrors.aliyun.com
"
After you copy this source to the pip.conf,press "esc" to escape from the insert mode(and enter into normal mode), and then type ":wq" to save ./pip/pip.conf and exit

then you can install the libs you need using pip install xxx(e.g. pip install opencv-python)

Step8. Install Pytorch

$pip install torch==1.2.0 torchvision==0.4.0 -f https://download.pytorch.org/whl/torch_stable.html

Server Port Table

Server IP	Port	Owner	Public
192.168.50.113	33010	None	Yes
192.168.50.113	33020	Wenxiaobin	No
192.168.50.113	33030	Zouzhongquan	No
192.168.50.113	33040	RenHui	Yes
192.168.50.113	33050	Not Allocated	No
192.168.50.113	33060	TainLi	No
192.168.50.113	33070	None	Yes
192.168.50.113	33080	HeJiaLi	Yes
192.168.50.113	33090	TangPing	Yes
192.168.50.50	30320	RenHui	Yes
192.168.50.50	31010	None	Yes
192.168.50.50	31020	Wenxiaobin(Scorbin)	Yes
192.168.50.50	31030	Yangxiaodi	No
192.168.50.50	31040	Zouzhongquan	No
192.168.50.50	31050	Hongxuesong	No
192.168.50.50	31060	HeJiaLi	Yes

Show tensorboard on your local machine

reference link[https://blog.csdn.net/bryant_meng/article/details/79153531]

Stuck at apt-get update

Sometimes you may encounter : 0%[working...] when you update the source using command

$ apt-get update

This problem probably stems from the directory: /etc/apt where apt-get update works

$ls /etc/apt/
apt.conf.d  auth.conf.d  preferences.d  sources.list source.list.d  trusted.gpg  trusted.gpg~

SOLUTION:

Find out the source you stuck when update ,for our cases,it's source.list.d,this directory stores additional source for some package.

$ rm -r /etc/apt/source.list.d
$ apt-get update

scorbinwen / tensorflow-installation Goto Github PK

tensorflow-installation's Introduction