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
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 CoreML 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.
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+)
Building from source
To build the project, you need CMake to configure the project
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=$(which python) -DPYTHON_CONFIG=$(which python-config)
after which you can use make to build the project
Building Installable Wheel
To make a wheel/egg that you can distribute, you can do the following
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
To install xgboost
git clone --recursive https://github.com/dmlc/xgboost cd xgboost; cp make/minimum.mk ./config.mk; make 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:
some tests are marked as slow because they test a lot of combinations. If you want to run, all tests, you can use:
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
In addition to the conversion tools in this package, TensorFlow and MXNet have their own conversion tools: