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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):
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/*
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
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
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 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*
nvidia-smi command. This should output some stats about your GPUInstall 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/*
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 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
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
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()
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 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
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
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
Setting up a Deep Learning Machine from Scratch (Software)
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原文地址：http://www.cnblogs.com/shishupeng/p/5727000.html
