Skip to content
Permalink
Branch: master
Find file Copy path
Find file Copy path
12 contributors

Users who have contributed to this file

@aseemw @TobyRoseman @rxwei @znation @1duo @agnosticdev @kant @tkremenek @gustavla @Necross @bryant1410 @srikris
207 lines (155 sloc) 4.82 KB

Core ML Community Tools

Core ML community tools contains all supporting tools for Core ML model conversion and validation. This includes scikit-learn, LIBSVM, Caffe, Keras and XGBoost.

coremltools 3.0 beta

Release notes

# Install using pip
pip install coremltools==3.0b6

API

Installation

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

pip install virtualenv

The method for installing coremltools follows the standard python package installation steps. To create a Python virtual environment called pythonenv follow these steps:

# Create a folder for your virtualenv
mkdir mlvirtualenv
cd mlvirtualenv

# Create a Python virtual environment for your Core ML project
virtualenv pythonenv

To activate your new virtual environment and install coremltools in this environment, follow these steps:

# Active your virtual environment
source pythonenv/bin/activate


# Install coremltools in the new virtual environment, pythonenv
(pythonenv) pip install -U coremltools

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

Dependencies

coremltools has the following dependencies:

  • numpy (1.10.0+)
  • 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 corresponding TensorFlow version
  • XGBoost (0.7+)
  • scikit-learn (0.17+)
  • libSVM

Building from source

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

mkdir build
cd build
cmake ../

When several python virtual environments are installed, it may be useful to use the following command instead, to point to the correct intended version of python:

cmake \
  -DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.7/bin/python \
  -DPYTHON_INCLUDE_DIR=/Library/Frameworks/Python.framework/Versions/3.7/include/python3.7m/ \
  -DPYTHON_LIBRARY=/Library/Frameworks/Python.framework/Versions/3.7/lib/ \
  ../

after which you can use make to build the project

make

Building Installable Wheel

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

make dist

Running Unit Tests

In order to run unit tests, you need pytest, pandas, and h5py.

pip install pytest pandas h5py

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 -r test_requirements.pip

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
git checkout v0.90
git submodule update
make config=make/config.mk -j8
cd python-package; python setup.py develop

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

Finally, to run the most important unit tests, you can use:

pytest -rs

some tests are marked as slow because they test a lot of combinations. If you want to run, all tests, you can use:

pytest

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

External Tools

In addition to the conversion tools in this package, TensorFlow and MXNet have their own conversion tools:

You can’t perform that action at this time.