Deep Learning Setup – Tensorflow GPU 1.4 on Ubuntu 16.04


The application of deep learning models to real world problems has been growing exponentially over the past several years. The widespread availability of NVIDIA GPUs, packages such as Tensorflow and Keras, and large online data sets has democratized this technology and ignited interest among developers, analysts, data scientists, and others looking to leverage the power that deep learning offers.

However, setting up a functional and effective deep learning environment can be challenging for a number of reasons. First, there are several moving parts and versions of the various software components must be compatible with one another. As new editions are released, configuration instructions that worked several months ago may no longer be viable. Also, searching the Web for help will return instructions that take differing (and sometimes incorrect) approaches – adding further confusion.

The objective of this post is to provide a rock-solid Ubuntu configuration that is anchored to the most current GPU-based version of Tensorflow – to get your deep learning environment up and running quickly and painlessly.

Note: This installation has been tested on both an AWS p2.xlarge  instance as well as one of my personal Intel-based PCs.


  1. Ubuntu 16.04 x86_64
  2. NVIDIA GPU with a Compute Capability of 3.0 or higher (see for a list of supported GPUs)
  3. An Internet connection


We’re going to build out an Anaconda environment that leverages the Keras library on top of the latest version of Tensorflow (1.4 for Python 3.6 at the time of this writing) with full GPU support to maximize performance.

Tensorflow 1.4 GPU, in turn, requires :

  1. NVIDIA CUDA® Toolkit 8.0
  2. NVIDIA drivers associated with CUDA Toolkit 8.0
  3. NVIDIA CUDA® Deep Neural Network library (cudNN) v6.0
  4. GPU with CUDA Compute Capability 3.0 or higher (as stated above)


Open a terminal or SSH into your running Ubuntu instance.

Remove any existing NVIDIA components

sudo apt-get purge 'nvidia-*'

Update and install needed packages

sudo apt-get update
sudo apt-get install gcc make g++ build-essential
sudo apt-get upgrade

Download Anaconda and NVIDIA software

mkdir Downloads
cd Downloads
wget "" -O ""
chmod +x
wget "" -O "cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb"
wget "" -O "cuda-repo-ubuntu1604-8-0-local-cublas-performance-update_8.0.61-1_amd64-deb"
# Also be sure to download cuDNN 6.0 for CUDA 8.0 from here:
# The file should be named cudnn-8.0-linux-x64-v6.0.tgz

Here are the software versions used at the time of this writing:

  1. Anaconda 5.0.1, Python 3.6.  (Check for updates here:
  2. CUDA Toolkit 8.0 GA2 (Feb 2017) and cuBLAS Patch Update to CUDA 8 (Released Jun 26, 2017) – local Debian packages.  (Check for updates here:
  3. cuDNN 6.0 for CUDA 8.0 – local Debian package.  Please note that you must join the NVIDIA developer community (free) and download this file from  The filename is cudnn-8.0-linux-x64-v6.0.tgz

Install Anaconda and NVIDIA software

sh "" -b
sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb
sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-cublas-performance-update_8.0.61-1_amd64-deb
sudo apt-get update
sudo apt-get install cuda
tar -zxf cudnn-8.0-linux-x64-v6.0.tgz
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

Note that we are installing Anaconda for the current user, and the CUDA Toolkit 8.0  using the default path.

Add environmental variables and reboot

echo "export CUDA_HOME=\"/usr/local/cuda\"" >> ~/.bashrc
echo "export LD_LIBRARY_PATH=\"/usr/local/cuda-8.0\":\$LD_LIBRARY_PATH" >> ~/.bashrc
echo "export LD_LIBRARY_PATH=\"/usr/local/cuda-8.0/lib64\":\$LD_LIBRARY_PATH" >> ~/.bashrc
echo "export PATH=\"/usr/local/cuda-8.0/bin:\$PATH\"" >> ~/.bashrc
echo "export PATH=\"$HOME/anaconda3/bin:\$PATH\"" >> ~/.bashrc
sudo reboot

Check if NVIDIA drivers installed correctly

# Enable Nvidia drivers 
sudo modprobe nvidia 
# check if drivers were installed

You should receive output similar to the following:

nvidiasmiresultsTest building and running CUDA sample code

# Compile and run the deviceQuery sample from the CUDA distribution
cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery
sudo make

You should receive output similar to the following:


Create and activate Anaconda “tensorflow” source

cd $HOME
conda create -n tensorflow python=3.6
source activate tensorflow

Install Tensorflow with GPU support

pip install --ignore-installed --upgrade

Test Tensorflow installation

# Enter the following program inside the Python interactive shell
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(
# You should see b'Hello Tensorflow!' printed to the terminal
# Exit the shell

Install Keras, ipkernel (for Jupyter Notebook), and any other python libraries you would like

pip install keras
conda install ipykernel
python -m ipykernel install --user --name tensorflow --display-name "Python (Tensorflow)"
source deactivate

Optional: Set up Jupyter Notebook

# configure jupyter and prompt for password
jupyter notebook --generate-config
jupass=`python -c "from notebook.auth import passwd; print(passwd())"`
echo "c.NotebookApp.password = u'"$jupass"'" >> $HOME/.jupyter/
echo "c.NotebookApp.ip = '*'" >> $HOME/.jupyter/
echo "c.NotebookApp.open_browser = False" >> $HOME/.jupyter/
# prompt to start notebook
cd $HOME
# Start jupyter notebook on port 8888 (edit $HOME/.jupyter/ if different port is desired)
# If you get an error instead, try restarting your session so your $PATH is updated
jupyter notebook

If you are installing on an instance to which you only have SSH/command-line access (and even if you are not),  Jupyter Notebook offers a feature-rich, browser-based interface.  (See  Just be sure that your firewall is open to port 8888 (or whichever port you configure it to run on).



Additionally, if you have local or remote access to the GUI then you could also install an IDE such as Spyder – either alone or (my preference) in conjunction with Anaconda Navigator (, an integrated desktop graphical user interface included with Anaconda – which you already installed  above.   To run Anaconda Navigator, simply open a terminal window and type:



From this screen, select your “tensorflow” environment in the “Applications on” drop-down.  Then click the Install button for Spyder.


Verifying GPU utilization

To ensure that the GPU is being leveraged, while running your deep learning model, enter:


in the terminal to check the GPU utilization.  You should see something like this:


Note that, in this example, about 10GB (10964 MB) of GPU memory and 16% of compute is being used by the Tensorflow Anaconda python process.

I hope you have found this setup guide helpful. Did you encounter any issues or have suggestions for improvement?
Let me know in the comments.

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