In this tutorial I will be going through the process of building the latest TensorFlow from sources for Ubuntu 16.04. TensorFlow now supports using Cuda 8.0 & CuDNN 5.1 so you can use the pip’s from their website for a much easier install. If you would like to install into a Anaconda environment the easiest method is to ‘conda install pip’ and just use the pip packages. If you prefer to build from sources using Ubuntu 14.04 please see my other tutorial.
In order to use TensorFlow with GPU support you must have a Nvidia graphic card with a minimum compute capability of 3.0.
Getting started I am going to assume you know some of the basics of using a terminal in Linux.
Install Required Packages
Open a terminal by pressing Ctrl + Alt + T
Paste each line one at a time (without the $) using Shift + Ctrl + V
$ sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev
Update & Install Nvidia Drivers
You must also have the 367 (or later) NVidia drivers installed, this can easily be done from Ubuntu’s built in additional drivers after you update your driver packages.
$ sudo add-apt-repository ppa:graphics-drivers/ppa $ sudo apt update
Once installed using additional drivers restart your computer. If you experience any troubles booting linux or logging in: try disabling fast & safe boot in your bios and modifying your grub boot options to enable nomodeset.
Install Nvidia Toolkit 8.0 & CudNN
Skip if not installing with GPU support
To install the Nvidia Toolkit download base installation .run file from Nvidiawebsite. MAKE SURE YOU SAY NO TO INSTALLING NVIDIA DRIVERS! Also make sure you select yes to creating a symbolic link to your cuda directory.
$ cd ~/Downloads # or directory to where you downloaded file $ sudo sh cuda_8.0.44_linux.run --override # hold s to skip
This will install cuda into: /usr/local/cuda
To install CudNN download cudNN v5.1 for Cuda 8.0 from Nvidia website and extract into /usr/local/cuda via:
$ sudo tar -xzvf cudnn-8.0-linux-x64-v5.1.tgz $ sudo cp cuda/include/cudnn.h /usr/local/cuda/include $ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 $ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
Then update your bash file:
$ gedit ~/.bashrc
This will open your bash file in a text editor which you will scroll to the bottom and add these lines:
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64" export CUDA_HOME=/usr/local/cuda
Once you save and close the text file you can return to your original terminal and type this command to reload your .bashrc file:
$ source ~/.bashrc
Install Bazel
Instructions also on Bazel website
$ echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list$ curl https://storage.googleapis.com/bazel-apt/doc/apt-key.pub.gpg | sudo apt-key add - $ sudo apt-get update $ sudo apt-get install bazel $ sudo apt-get upgrade bazel
Clone TensorFlow
$ cd ~ $ git clone https://github.com/tensorflow/tensorflow
Configure TensorFlow Installation
$ cd ~/tensorflow $ ./configure
Use defaults by pressing enter for all except:
Please specify the location of python. [Default is /usr/bin/python]:
For Python 2 use default or If you wish to build for Python 3 enter:
$ /usr/bin/python3.5
Please input the desired Python library path to use. Default is [/usr/local/lib/python2.7/dist-packages]:
For Python 2 use default or If you wish to build for Python 3 enter:
$ /usr/local/lib/python3.5/dist-packages
Unless you have a Radeon graphic card you can say no to OpenCL support. (has anyone tested this? ping me if so!)
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]:
$ 8.0
Please specify the Cudnn version you want to use. [Leave empty to use system default]:
$ 5
You can find the compute capability of your device at:https://developer.nvidia.com/cuda-gpus
If all was done correctly you should see:
INFO: All external dependencies fetched successfully.
Configuration finished
Build TensorFlow
Warning Resource Intensive I recommend having at least 8GB of computer memory.
If you want to build TensorFlow with GPU support enter:
$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
For CPU only enter:
$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
Build & Install Pip Package
This will build the pip package required for installing TensorFlow in your /tmp/ folder
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
To Install Using Python 3 (remove sudo if using a virtualenv)
$ sudo pip3 install /tmp/tensorflow_pkg/tensorflow # with no spaces after tensorflow hit tab before hitting enter to fill in blanks
For Python 2 (remove sudo if using a virtualenv)
$ sudo pip install /tmp/tensorflow_pkg/tensorflow # with no spaces after tensorflow hit tab before hitting enter to fill in blanks
Test Your Installation
Close all your terminals and open a new terminal to test.
$ python # or python3 $ import tensorflow as tf $ sess = tf.InteractiveSession() $ sess.close()
TensorFlow also has instructions on how to do a basic test and a list of common installation problems.
There you have it, you should now have TensorFlow installed on your computer. This tutorial was tested on a fresh install of Ubuntu 16.04 with a GeForce GTX 780 and a GTX 970m.
If you want to give your GPU a workout maybe try building a massive image classifier following this tutorial.
