Core ML Community Tools

Core ML community tools contains all supporting tools for CoreML model conversion and validation. This includes Scikit Learn, LIBSVM, Caffe, Keras and XGBoost.

We recommend using virtualenv to use, install, or build coremltools. Be sure to install virtualenv using your system pip.

pip install virtualenv

Installation

The method for installing coremltools follows the standard python package installation steps. Once you have set up a python environment, run::

pip install -U coremltools

The package documentation contains more details on how to use coremltools.

Dependencies

coremltools has the following dependencies:

numpy (1.12.1+)
protobuf (3.1.0+)

In addition, it has the following soft dependencies that are only needed when you are converting models of these formats:

Keras (1.2.2, 2.0.4+) with Tensorflow (1.0.x, 1.1.x)
Xgboost (0.6+)
scikit-learn (0.15+)
libSVM

Building from source

To build the project, you need CMake to configure the project

cmake .

after which you can use make to build the project

make -j4

Building Installable Wheel

To make a wheel/egg that you can distribute, you can do the following

make dist

Running Unit Tests

To run the unit tests, from the repo root, run the following command:

make test

To add a new unit test, add it to the coremltools/test folder. Make sure you name the file with a 'test' as the prefix.

Additionally, running unit-tests would require more packages (like libsvm)

pip install numpy scikit-learn

To install libsvm

git clone https://github.com/cjlin1/libsvm.git
cd libsvm/
make
cd python/
make

To make sure you can run libsvm python bindings everywhere, you need the following command, replacing <LIBSVM_PATH> with the path to the root of your repository.

export PYTHONPATH=${PYTHONPATH}:<LIBSVM_PATH>/python

To install xgboost

git clone --recursive https://github.com/dmlc/xgboost
cd xgboost; cp make/minimum.mk ./config.mk; make -j4
cd python-package; python setup.py develop --user

To install keras (Version >= 2.0)

pip install keras tensorflow

If you'd like to use the old keras version, you can:

pip install keras==1.2.2 tensorflow

Building Documentation

First install all external dependencies.

pip install Sphinx==1.5.3 sphinx-rtd-theme==0.2.4 numpydoc
pip install -e git+git://github.com/michaeljones/sphinx-to-github.git#egg=sphinx-to-github

You also must have the coremltools package install, see the Building section.

Then from the root of the repository:

cd docs
make html
open _build/html/index.html