In this chapter, we look at how Keras can be installed on Ubuntu and CentOS. We will use Ubuntu 16.04, 64-bit (Canonical, Ubuntu, 16.04 LTS, and amd64 xenial image build on 2017-10-26) for the installation.

Before installing Keras, we have to install the Theano and TensorFlow packages and their dependencies. Since it is a fresh OS, make sure Python is installed. Let's look at the following section for Python installation.

First you need to make sure you have a blank Ubuntu 16.04 OS locally or remotely available in the cloud and with root access. 

In the following sections, we take a at the installation of each component that needs to be done before we can go ahead with the installation of Keras.

Before we proceed further, let's install miniconda to install the rest of the packages. Miniconda is a smaller version of the conda package manager. Python is bundled along with miniconda.

1. To install miniconda, let's first download the sh installer from the continuum repository:

wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh
chmod 755 Miniconda2-latest-Linux-x86_64.sh 
./Miniconda2-latest-Linux-x86_64.sh

2. Once conda has been installed, we can use it to install the dependencies of Theano, TensorFlow, and Keras.

The numpy and scipy packages are prerequisites for Theano installation. The following versions are recommended:

• NumPy >= 1.9.1 <= 1.12
• SciPy >= 0.14 < 0.17.1: Highly recommended for sparse matrix and support for special functions in Theano, SciPy >=0.8 would do the work
• BLAS installation (with Level 3 functionality) the recommended: MKL, this is free through conda with the mkl-service package

1. Execute the following command to install numpy and scipy. (Make sure conda is in your PATH):

conda install numpy
conda install scipy

The output of the scipy installation is shown as follows. Notice that it installs libgfortran as part of the scipy installation:

Fetching package metadata ...........
Solving package specifications: .
Package plan for installation in environment /home/ubuntu/miniconda2:

2. The following new packages will also be installed:

libgfortran-ng: 7.2.0-h9f7466a_2 
scipy: 1.0.0-py27hf5f0f52_0
Proceed ([y]/n)?
libgfortran-ng 100% |#############################################################| Time: 0:00:00 36.60 MB/s
scipy-1.0.0-py 100% |#############################################################| Time: 0:00:00 66.62 MB/s

1. mkl is a math library for Intel and compatible processors. It is a part of numpy, but we want to make sure it is installed before we install Theano and TensorFlow:

conda install mkl

The output of the installation is given as follows. In our case, miniconda2 has already installed the latest version of mkl:

Fetching package metadata ...........
Solving package specifications: .
# All requested packages already installed.
# packages in environment at /home/ubuntu/miniconda2:
#
mkl 2018.0.1 h19d6760_4

2. Once all the prerequisites are installed, let's install TensorFlow.

1. Execute the following command to install tensorflow using conda:

conda install -c conda-forge tensorflow 

The output of this command will fetch metadata and install a list of packages, as follows:

Fetching package metadata .............
Solving package specifications: .
Package plan for installation in environment /home/ubuntu/miniconda2:

2. The following new packages will also be installed:

bleach: 1.5.0-py27_0 conda-forge
funcsigs: 1.0.2-py_2 conda-forge
futures: 3.2.0-py27_0 conda-forge
html5lib: 0.9999999-py27_0 conda-forge
markdown: 2.6.9-py27_0 conda-forge
mock: 2.0.0-py27_0 conda-forge
pbr: 3.1.1-py27_0 conda-forge
protobuf: 3.5.0-py27_0 conda-forge
tensorboard: 0.4.0rc3-py27_0 conda-forge
tensorflow: 1.4.0-py27_0 conda-forge
webencodings: 0.5-py27_0 conda-forge
werkzeug: 0.12.2-py_1 conda-forge

3. A higher-priority channel will supersede the following packages, as follows:

conda: 4.3.30-py27h6ae6dc7_0 --> 4.3.29-py27_0 conda-forge
conda-env: 2.6.0-h36134e3_1 --> 2.6.0-0 conda-forge
Proceed ([y]/n)? y
conda-env-2.6. 100% |#############################################################| Time: 0:00:00 1.67 MB/s
...
mock-2.0.0-py2 100% |#############################################################| Time: 0:00:00 26.00 MB/s
conda-4.3.29-p 100% |#############################################################| Time: 0:00:00 27.46 MB/s

4. Once TensorFlow has been installed, let's test it with a simple program. Create a new file called hello_tf.py with the following command:

vi hello_tf.py

5. Add the following code to this file and save the file:

import tensorflow as tf
hello = tf.constant('Greetings, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

6. Execute the file created from the command line:

python hello_tf.py

The output will make sure the library has been successfully installed:

Greetings, TensorFlow!

1. Next, we will install Keras using conda from conda-forge
2. Execute the following command on the Terminal:

conda install -c conda-forge keras

The following listed output will confirm that Keras is installed:

Fetching package metadata .............
Solving package specifications: .
Package plan for installation in environment /home/ubuntu/miniconda2:

The following new packages will also be installed:

h5py: 2.7.1-py27_2 conda-forge
hdf5: 1.10.1-1 conda-forge
keras: 2.0.9-py27_0 conda-forge
libgfortran: 3.0.0-1 
pyyaml: 3.12-py27_1 conda-forge
Proceed ([y]/n)? y
libgfortran-3. 100% |#############################################################| Time: 0:00:00 35.16 MB/s
hdf5-1.10.1-1. 100% |#############################################################| Time: 0:00:00 34.26 MB/s
pyyaml-3.12-py 100% |#############################################################| Time: 0:00:00 60.08 MB/s
h5py-2.7.1-py2 100% |#############################################################| Time: 0:00:00 58.54 MB/s
keras-2.0.9-py 100% |#############################################################| Time: 0:00:00 45.92 MB/s

3. Let's verify the Keras installation with the following code:

$ python
Python 2.7.14 |Anaconda, Inc.| (default, Oct 16 2017, 17:29:19)

4. Execute the following command to verify that Keras has been installed:

> from keras.models import Sequential
Using TensorFlow backend.
>>>

Notice that Keras is using the TensorFlow backend.

1. Let's modify the default configuration and change TensorFlow to Theano as the backend of Keras. Modify the keras.json file:

vi .keras/keras.json

The default file has the following content:

{ "image_data_format": "channels_last", 
  "epsilon": 1e-07, 
  "floatx": "float32", 
  "backend": "tensorflow" 
}

2. The modified file will look like the following file. The "backend" value has been changed to "theano":

{ "image_data_format": "channels_last", 
  "epsilon": 1e-07, 
  "floatx": "float32", 
  "backend": "theano" 
}

3. Run the Python console and import Sequential from keras.model using the Theano backend:

$ python
Python 2.7.14 |Anaconda, Inc.| (default, Oct 16 2017, 17:29:19) 
[GCC 7.2.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from keras.models import Sequential

Notice how the backend has changed to Theano.

We have installed miniconda, all the dependencies of TensorFlow, and Theano. This was followed by installing TensorFlow and Theano itself. Finally, we installed Keras. We also learned how to change the backend of Keras from TensorFlow to Theano.

In this recipe, we learn how to install and use a Docker container running Keras inside a container and access it using Jupyter.

Install the latest version of the Docker CLI from https://docs.docker.com/engine/installation/.

In the following section, we will be learning how to install the Docker container.

1. Execute the following command on the Terminal to run the container. The container image is available with the tag rajdeepd/jupyter-keras:

docker run -d -p 8888:8888 rajdeepd/jupyter-keras start-notebook.sh --NotebookApp.token=''

2. This will install the Notebook locally and start it as well. You can execute the docker ps -a command and see the output in the Terminal, as follows:

CONTAINER ID        IMAGE                         COMMAND                  CREATED             STATUS                         PORTS                     NAMES
45998a5eea89        rajdeepd/jupyter-keras        "tini -- start-not..."             About an hour ago Up About an hour               0.0.0.0:8888->8888/tcp    admiring_wing

Please note that the host port of 8888 is mapped to the container port of 8888.

3. Open the browser at the following URL http://localhost:8888:

You will notice that Jupyter is running. You can create a new Notebook and run Keras-specific code.

In this section, we look at how to map the local volume $(pwd)/keras-samples to the work directory in the container.

1. Execute the note -v flag command, which does the volume mapping:

docker run -d -v /$(pwd)/keras-samples:/home/jovyan/work \
 -p 8888:8888 rajdeepd/jupyter-keras start-notebook.sh --NotebookApp.token=''

If you go to the URL, you will notice the sample page being displayed.

2. If you got /$(pwd)/keras-samples, you will notice that the Notebooks are available in the host directory, and they also can be seen being loaded by Jupyter:

rdua1-ltm:keras-samples rdua$ pwd
 /Users/rdua/personal/keras-samples
 rdua1-ltm:keras-samples rdua$ ls
 MNIST CNN.ipynb sample_one.ipynb

If you open MNIST CNN.ipynb, it is a Keras CNN sample, which we will learn more about in the subsequent chapters.

In this recipe, we used the Docker image rajdeepd/jupyter-keras to create a Keras environment and access it from Jupyter running in the host environment.

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

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 ubuntu@34.201.110.131

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

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.

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:

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*

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

Execute the following command to install the TensorFlow GPU version:

sudo pip install tensorflow-gpu

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.