Installing Keras on Ubuntu 16.04 with GPU enabled

In this recipe, we will install Keras on Ubuntu 16.04 with NVIDIA GPU enabled.

Getting ready

We are going to launch a GPU-enabled AWS EC2 instance and prepare it for the installed TensorFlow with the GPU and Keras. Launch the following AMI: Ubuntu Server 16.04 LTS (HVM), SSD Volume Type - ami-aa2ea6d0:

This is an AMI with Ubuntu 16.04 64 bit pre-installed, and it has the SSD volume type.

Choose the appropriate instance type: g3.4xlarge:

Once the VM is launched, assign the appropriate key that you will use to SSH into it. In our case, we used a pre-existing key:

SSH into the instance:

ssh -i aws/rd_app.pem [email protected]

How to do it...

1. Run the following commands to update and upgrade the OS:

sudo apt-get update
sudo apt-get upgrade

2. Install the gcc compiler and make the tool:

sudo apt install gcc
sudo apt install make

Installing cuda

1. Execute the following command to execute cuda:

sudo apt-get install -y cuda

2. Check that cuda is installed and run a basic program:

ls /usr/local/cuda-8.0
bin extras lib64 libnvvp nvml README share targets version.txt
doc include libnsight LICENSE nvvm samples src tools

3. Let's run one of the cuda samples after compiling it locally:

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64\${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
cd /usr/local/cuda-8.0/samples/5_Simulations/nbody

4. Compile the sample and run it as follows:

sudo make

./nbody

You will see output similar to the following listing:

Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
 -fullscreen (run n-body simulation in fullscreen mode)
 -fp64 (use double precision floating point values for simulation)
 -hostmem (stores simulation data in host memory)
 -benchmark (run benchmark to measure performance)
 -numbodies=<N> (number of bodies (>= 1) to run in simulation)
 -device=<d> (where d=0,1,2.... for the CUDA device to use)
 -numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation)
 -compare (compares simulation results running once on the default GPU and once on the CPU)
 -cpu (run n-body simulation on the CPU)
 -tipsy=<file.bin> (load a tipsy model file for simulation)

5. Next we install cudnn, which is a deep learning library from NVIDIA. You can find more information at https://developer.nvidia.com/cudnn.

Installing cudnn

1. Download cudnn from the NVIDIA site (https://developer.nvidia.com/rdp/assets/cudnn-8.0-linux-x64-v5.0-ga-tgz) and decompress the binary:

Please note, you will need an NVIDIA developer account.

tar xvf cudnn-8.0-linux-x64-v5.1.tgz

We obtain the following output after decompressing the .tgz file:

cuda/include/cudnn.h
cuda/lib64/libcudnn.so
cuda/lib64/libcudnn.so.5
cuda/lib64/libcudnn.so.5.1.10
cuda/lib64/libcudnn_static.a

2. Copy these files to the /usr/local folder, as follows:

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*

Installing NVIDIA CUDA profiler tools interface development files

Install the NVIDIA CUDA profiler tools interface development files that are needed for TensorFlow GPU installation with the following code:

sudo apt-get install libcupti-dev

Installing the TensorFlow GPU version

Execute the following command to install the TensorFlow GPU version:

sudo pip install tensorflow-gpu

Installing Keras

For Keras, use the sample command, as used for the installation with GPUs:

sudo pip install keras

In this recipe, we learned how to install Keras on top of the TensorFlow GPU hooked to cuDNN and CUDA.