Setting up a deep learning machine from Scratch (software)

Setting up a deep learning machine from Scratch (software)

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

There is several great guides with a similar goal. Some is limited in scope, while others is not up to date. This are 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 are 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 wgetsudo 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. 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 would require you have to quit your X server session and install from a Terminal session, which are A has Sle.

• We'll install the drivers using Apt-get. Check If your latest driver exists in the "proprietary GPU Drivers" PPA. Note that the latest drivers is necessarily the most stable. It's advisable to install the driver version recommended on the page. ADD the "proprietary GPU Drivers" PPA repository. At the time of this writing, the latest version was 361.42, however, the recommended version is 352:

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

• Restart your system

  sudo shutdown -r now

• Check to ensure the correct version of NVIDIA drivers is 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.debsudo apt-get updatesudo apt-get install cuda

• ADD CUDA to the environment variables

  echo ‘export PATH=/usr/local/cuda/bin:$PATH‘ >> ~/.bashrcecho ‘export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH‘ >> ~/.bashrcsource ~/.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-samplescd ~/cuda-samples/NVIDIA*Samplesmake -j $(($(nproc) + 1))

Note: ( -j $(($(nproc) + 1)) ) executes the make command is 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 GPUs accelerated library for Dnns. It can help speed up execution in many cases. To being 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 from 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*.tgzcd cudasudo 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 are 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 is some version incompatibilities with using PIP install and TensorFlow (see Https://github.com/tensorflow/tensorf low/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-sympysudo apt-get clean && sudo apt-get autoremoverm -rf /var/lib/apt/lists/*

TensorFlow

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

  sudo apt-get install python-pip python-devsudo 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 import the command, there should is no warning/error.

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

Openblas

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

  mkdir ~/gitcd ~/gitgit clone https://github.com/xianyi/OpenBLAS.gitcd OpenBLASmake 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-devpip install -U matplotlib ipython[all] jupyter pandas scikit-image

Caffe

• The following instructions is 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-compilersudo apt-get install --no-install-recommends libboost-all-devsudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

• Clone the Caffe Repo

  cd ~/gitgit clone https://github.com/BVLC/caffe.gitcd caffecp Makefile.config.example Makefile.config

• If you installed CuDNN, uncomment the "line" in the USE_CUDNN := 1 Makefile

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

• If you installed Openblas, modify the BLAS parameter value toopen

  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.txtmake 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=$(pwd)‘ >> ~/.bashrcecho ‘export PYTHONPATH=$CAFFE_ROOT/python:$PYTHONPATH‘ >> ~/.bashrcsource ~/.bashrc

• Test to ensure this 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 is sourced from here

  sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ python-pygments python-sphinx python-nosesudo 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 what to TensorFlow.

  sudo pip install keras

Torch

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

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

X2go

• If Your deep learning machine was not your primary work desktop, it helps to was 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-commonsudo add-apt-repository ppa:x2go/stablesudo apt-get Updatesudo apt-get Install x2goserver x2goserver-xsession  

• X2go does not support the Unity Deskto P Environment (the default in Ubuntu). I have the found XFCE to work pretty well. More details on the supported environmens here

    sudo apt-get updatesudo apt-get install-y xfce4 Xfce4-goodie s 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)