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Building the Embedded Learning Library (ELL) on Ubuntu Linux

ELL enables you to design and deploy intelligent machine-learned models onto single-board computers, like Raspberry Pi and Arduino. Most of your interaction with ELL occurs on a laptop or desktop computer, rather than the single-board machine itself. The steps below describe how to build ELL on a laptop or desktop running Ubuntu Linux. Note, following instructions are only for Bionic Beaver (18.04). You can also use scripts/SETUP-Ubuntu.sh to setup your computer.

Cloning the ELL repository

The instructions below assume that ELL was obtained from https://github.com/Microsoft/ELL using git. The git scm client is typically installed by default on Ubuntu systems, but if it isn't, open a terminal and type

sudo apt-get install -y git

To clone the ELL repository, type

git clone https://github.com/Microsoft/ELL.git

Prerequisites for building ELL

There is a docker image already setup with all the pre-requisites for building and running ELL. This image was built using this Dockerfile.

Ubuntu provides the Advanced Packaging Tool apt for downloading and installing prerequisites. First, make sure that apt is up to date by running:

sudo apt-get -y update

GCC 8, CMake 3.8, libedit, zlib, OpenBLAS, and Doxygen via apt-get

ELL requires the following tools and libraries, some of which are installed by default on Ubuntu systems:

  • GCC 8 or newer - C++17 compiler
  • CMake version 3.8 or newer - build system
  • libedit and zlib libraries

Note that GCC 8 may not be available by default. It can be found in ubuntu-toolchain-r/test PPA.

To add ubuntu-toolchain-r/test PPA and update sources, type

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get -y update

Optionally, ELL can take advantage of these additional tools:

  • OpenBLAS - version 0.2.19.3 - fast linear algebra. This is optional but can make models execute up to 10 times faster.
  • Doxygen - version 1.8.13 - this is optional, it is used to generate nice code documentation for the ELL API.

To install all of the above, type

sudo apt-get install -y gcc-8 g++-8 cmake libedit-dev zlibc zlib1g zlib1g-dev make
sudo apt-get install -y libopenblas-dev doxygen

LLVM 8.0

ELL depends on the LLVM compiler framework, version 8.0. To install it use this command

sudo sh -c 'echo deb http://apt.llvm.org/bionic/ llvm-toolchain-bionic-8 main >> /etc/apt/sources.list'
sudo sh -c 'echo deb-src http://apt.llvm.org/bionic/ llvm-toolchain-bionic-8 main >> /etc/apt/sources.list'
wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
sudo apt-get update
sudo apt-get install llvm-8 -y

This works on Ubuntu 18.04 Bionic Beaver. If you have some other Linux version then LLVM has more instructions on how to install it manually.

curl

curl is a command line tool used to transfer data via URL. When files are required to be downloaded from a URL, the instructions assume you have curl available to perform the download. To install curl, type the following:

sudo apt-get install -y curl

SWIG 4.0.0

SWIG is a tool that generates Python interfaces to C++ libraries. If you intend to use ELL from Python, you must install SWIG version 4.0.0. At the time of writing this document, apt-get doesn't yet have the latest version of SWIG, so it must be installed manually

curl -O --location http://prdownloads.sourceforge.net/swig/swig-4.0.0.tar.gz
tar zxvf swig-4.0.0.tar.gz && cd swig-4.0.0
./configure --without-pcre && make && sudo make install

Using ELL in Python

ELL can optionally be used from Python 3.6. An easy way to install Python and all the required modules is with Miniconda. Download and install Miniconda from here https://conda.io/miniconda.html.

After installing Miniconda, create a Python 3.6 environment and include the numpy module by typing

conda create -n py36 numpy python=3.6

Next, activate the environment you just created by typing

source activate py36

You need to repeat this activation command each time you open a new terminal and intend to use ELL from Python. Also, make sure to activate the py36 environment before building ELL, to ensure that Python interfaces are created.

OpenCV is a library that helps with capturing and preprocessing images. To install OpenCV in the current Python environment, type

conda install -c conda-forge opencv -y

Building ELL

You can build ELL by using CMake to create a makefile, invoking that makefile, and optionally building Python interfaces. If you intend to build Python interfaces, make sure to activate the py36 miniconda environment as described above.

In the repository root directory, create a build subdirectory and change to that directory.

mkdir build
cd build

Invoke CMake by typing

cmake -DCMAKE_CXX_COMPILER=/usr/bin/g++-8 -DCMAKE_C_COMPILER=/usr/bin/gcc-8 -DONNX=ON ..

Don't forget the two dots (..) at the end of the command! This creates a makefile for the project. Next, invoke the makefile by typing

make

Optionally, build Python interfaces by typing

make _ELL_python

The generated executables will appear in ELL/build/bin.

You can test that the python interface is working by running the following test:

ctest . --build-config release -R ell-python-interface-test

Advanced Installation

The instructions above are enough to start using ELL. For more advanced topics, like testing and generating documentation, please see our advanced installation instructions.