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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. 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.

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

cmake .

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; 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:

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:

About

Converter tools for Core ML.

Resources

Readme
License
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
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.
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.
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
cmake .
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; 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:
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:
About
Converter tools for Core ML.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
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.
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.
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
cmake .
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; 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:
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:
About
Converter tools for Core ML.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
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.
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.
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
cmake .
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; 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:
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:
About
Converter tools for Core ML.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
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.
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.
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
cmake .
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; 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:
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:
About
Converter tools for Core ML.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
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.
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.
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
cmake .
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; 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:
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:
About
Converter tools for Core ML.
Resources
Readme
License
Activity
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Watchers
1 watching
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, 'i');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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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.
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.
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
cmake .
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; 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:
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:
About
Converter tools for Core ML.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
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html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
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::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
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.
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.
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
cmake .
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; 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:
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:
About
Converter tools for Core ML.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages