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Building the Embedded Learning Library (ELL) on macOS

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 macOS.

Cloning the ELL repository

The instructions below assume that ELL was obtained from github.com/Microsoft/ELL using git. One way to install a git client is to open a Terminal and type

brew install git

To clone the ELL repository, type

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

Prerequisites for building ELL

Homebrew is a package manager that makes it easy to install the prerequesits. Homebrew can be downloaded and installed by

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

If you already have Homebrew installed, update it to the latest version by typing

brew update

LLVM and Clang

ELL depends on the LLVM compiler framework, version 8.0. Clang is a C++ compiler built on top of LLVM, and it is the best choice for building ELL. To download and install LLVM and Clang, type

brew install llvm@8

Alternatively, if you have already have Xcode installed and prefer to use the version of Clang included with it, you must still install LLVM and make sure that the command-line version of the Xcode tools are available.

brew install llvm@8
xcode-select --install

If you already have LLVM installed, ensure that you have version 6.0. Note that CMake assumes that the LLVM binary files are located in the standard Homebrew location, which is /usr/local/opt.

CMake 3.8, SWIG 4.0.0, OpenBLAS, and Doxygen via Homebrew

ELL uses the CMake build system, version 3.8 or newer.

Optionally, ELL can take advantage of these additional tools:

  • SWIG version 4.0.0 - a tool that generates Python interfaces to C++ libraries. Required if you intend to use ELL from Python.
  • OpenBLAS - version 0.2.19.3 - fast linear algebra. OpenBLAS can make models execute up to 10 times faster.
  • Doxygen - version 1.8.13 - it is used to generate nice code documentation for the ELL API.

To install all of the above, type

brew install cmake
brew install swig
brew install openblas
brew install doxygen

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

Building ELL

We 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 ..

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

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

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

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

Troubleshooting

LLVM not found, please check that LLVM is installed.

Try telling CMake where to find LLVM as follows:

cmake -DLLVM_DIR=$(brew --prefix llvm) ..

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.