从0开始Ubuntu上搭深度学习框架

原文地址：https://github.com/saiprashanths/dl-setup

A detailed guide to setting up your machine for deep learning research. Includes instructions to install drivers, tools and various deep learning frameworks. This was tested on a 64 bit machine with Nvidia Titan X, running Ubuntu 14.04

There are several great guides with a similar goal. Some are limited in scope, while others are not up to date. This guide is based on (with some portions copied verbatim from):

Caffe Installation for Ubuntu
Running a Deep Learning Dream Machine

Table of Contents

Basics
Nvidia Drivers
CUDA
cuDNN
Python Packages
Tensorflow
OpenBLAS
Common Tools
Caffe
Theano
Keras
Torch
X2Go

Basics

First, open a terminal and run the following commands to make sure your OS is up-to-date

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install build-essential cmake g++ gfortran git pkg-config python-dev software-properties-common wget
sudo apt-get autoremove
sudo rm -rf /var/lib/apt/lists/*

Nvidia Drivers

Find your graphics card model

lspci | grep -i nvidia

Go to the Nvidia website and find the latest drivers for your graphics card and system setup. You can download the driver from the website and install it, but doing so makes updating to newer drivers
and uninstalling it a little messy. Also, doing this will require you having to quit your X server session and install from a Terminal session, which is a hassle.

We will install the drivers using apt-get. Check if your latest driver exists in the

"Proprietary GPU Drivers" PPA. Note that the latest drivers are necessarily the most stable. It is advisable to install the driver version recommended on that page. Add the "Proprietary GPU Drivers" PPA repository. At the time of this writing, the latest
version is 361.42, however, the recommended version is 352:

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-352

Restart your system

sudo shutdown -r now

Check to ensure that the correct version of NVIDIA drivers are installed

cat /proc/driver/nvidia/version

CUDA

Download CUDA 7.5 from
Nvidia. Go to the Downloads directory and install CUDA

sudo dpkg -i cuda-repo-ubuntu1404*amd64.deb
sudo apt-get update
sudo apt-get install cuda

Add CUDA to the environment variables

echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc

Check to ensure the correct version of CUDA is installed

nvcc -V

Restart your computer

sudo shutdown -r now

Checking your CUDA Installation (Optional)

Install the samples in the CUDA directory. Compile them (takes a few minutes):

/usr/local/cuda/bin/cuda-install-samples-7.5.sh ~/cuda-samples
cd ~/cuda-samples/NVIDIA*Samples
make -j $(($(nproc) + 1))

Note: (

-j $(($(nproc) + 1))

) executes the make command in parallel using the number of cores in your machine, so the compilation is faster

Run deviceQuery and ensure that it detects your graphics card and the tests pass

bin/x86_64/linux/release/deviceQuery

cuDNN

cuDNN is a GPU accelerated library for DNNs. It can help speed up execution in many cases. To be able to download the cuDNN library, you need to register in the Nvidia website at
https://developer.nvidia.com/cudnn. This can take anywhere between a few hours to a couple of working days to get approved. Once your registration is approved, download
cuDNN v4 for Linux. The latest version is cuDNN v5, however, not all toolkits support it yet.

Extract and copy the files

cd ~/Downloads/
tar xvf cudnn*.tgz
cd cuda
sudo cp */*.h /usr/local/cuda/include/
sudo cp */libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

Check

You can do a check to ensure everything is good so far using the

nvidia-smi

command. This should output some stats about your GPU

Python Packages

Install some useful Python packages using apt-get. There are some version incompatibilities with using pip install and TensorFlow ( see
https://github.com/tensorflow/tensorflow/issues/2034)

sudo apt-get update && apt-get install -y python-numpy python-scipy python-nose \
python-h5py python-skimage python-matplotlib \
python-pandas python-sklearn python-sympy
sudo apt-get clean && sudo apt-get autoremove
rm -rf /var/lib/apt/lists/*

Tensorflow

This installs v0.8 with GPU support. Instructions below are from
here

sudo apt-get install python-pip python-dev
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

Run a test to ensure your Tensorflow installation is successful. When you execute the

import

command, there should be no warning/error.

python
>>> import tensorflow as tf
>>> exit()

OpenBLAS

OpenBLAS is a linear algebra library and is faster than Atlas. This step is optional, but note that some of the following steps assume that OpenBLAS is installed. You'll need to install gfortran to compile it.

mkdir ~/git
cd ~/git
git clone https://github.com/xianyi/OpenBLAS.git cd OpenBLAS
make FC=gfortran -j $(($(nproc) + 1))
sudo make PREFIX=/usr/local install

Add the path to your LD_LIBRARY_PATH variable

echo 'export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH' >> ~/.bashrc

Common Tools

Install some common tools from the Scipy stack

sudo apt-get install -y libfreetype6-dev libpng12-dev
pip install -U matplotlib ipython[all] jupyter pandas scikit-image

Caffe

The following instructions are from
here. The first step is to install the pre-requisites

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

Clone the Caffe repo

cd ~/git
git clone https://github.com/BVLC/caffe.git cd caffe
cp Makefile.config.example Makefile.config

If you installed cuDNN, uncomment the

USE_CUDNN := 1

line in the Makefile

sed -i 's/# USE_CUDNN := 1/USE_CUDNN := 1/' Makefile.config

If you installed OpenBLAS, modify the

BLAS

parameter value to

open

sed -i 's/BLAS := atlas/BLAS := open/' Makefile.config

Install the requirements, build Caffe, build the tests, run the tests and ensure that all tests pass. Note that all this takes a while

sudo pip install -r python/requirements.txt
make all -j $(($(nproc) + 1))
make test -j $(($(nproc) + 1))
make runtest -j $(($(nproc) + 1))

Build PyCaffe, the Python interface to Caffe

make pycaffe -j $(($(nproc) + 1))

Add Caffe to your environment variable

echo 'export CAFFE_ROOT=$(pwd)' >> ~/.bashrc
echo 'export PYTHONPATH=$CAFFE_ROOT/python:$PYTHONPATH' >> ~/.bashrc
source ~/.bashrc

Test to ensure that your Caffe installation is successful. There should be no warnings/errors when the import command is executed.

ipython
>>> import caffe
>>> exit()

Theano

Install the pre-requisites and install Theano. These instructions are sourced from

here

sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ python-pygments python-sphinx python-nose
sudo pip install Theano

Test your Theano installation. There should be no warnings/errors when the import command is executed.

python
>>> import theano
>>> exit()

Keras

Keras is a useful wrapper around Theano and Tensorflow. By default, it uses Theano as the backend. See
here for instructions on how to change this to Tensorflow.

sudo pip install keras

Torch

Instructions to install Torch below are sourced from
here. The installation takes a little while

git clone https://github.com/torch/distro.git ~/git/torch --recursive
cd torch; bash install-deps;
./install.sh

X2Go

If your deep learning machine is not your primary work desktop, it helps to be able to access it remotely.
X2Go is a fantastic remote access solution. You can install the X2Go server on your Ubuntu machine using the instructions below.

sudo apt-get install software-properties-common
sudo add-apt-repository ppa:x2go/stable
sudo apt-get update
sudo apt-get install x2goserver x2goserver-xsession

X2Go does not support the Unity desktop environment (the default in Ubuntu). I have found XFCE to work pretty well. More details on the supported environmens
here

sudo apt-get update
sudo apt-get install -y xfce4 xfce4-goodies xubuntu-desktop

Find the IP of your machine using

hostname -I

You can install a client on your main machine to connect to your deep learning server using the above IP. More instructions
here depending on your Client OS