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https://github.com/apple/coremltools

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
https://github.com/apple/coremltools

coreml coremltools machine-learning model-conversion model-converter pytorch tensorflow

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Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

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README 

        
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[Core ML Tools](https://apple.github.io/coremltools/docs-guides/source/overview-coremltools.html)

=======================



![Core ML Tools logo](docs/logo.png)



Use [Core ML Tools](https://apple.github.io/coremltools/docs-guides/source/overview-coremltools.html) (*coremltools*) to convert machine learning models from third-party libraries to the Core ML format. This Python package contains the supporting tools for converting models from training libraries such as the following:



* [TensorFlow 1.x](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf)

* [TensorFlow 2.x](https://www.tensorflow.org/api_docs)

* [PyTorch](https://pytorch.org/)

* Non-neural network frameworks:

	* [scikit-learn](https://scikit-learn.org/stable/)

	* [XGBoost](https://xgboost.readthedocs.io/en/latest/)

	* [LibSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)



With coremltools, you can:



* Convert trained models to the Core ML format.

* Read, write, and optimize Core ML models.

* Verify conversion/creation (on macOS) by making predictions using Core ML.



After conversion, you can integrate the Core ML models with your app using Xcode.



## Install 8.0 Beta

The [coremltools version 8 beta 2](https://github.com/apple/coremltools/releases/tag/8.0b2) is now out. To install, run the following command in your terminal:

```shell

pip install coremltools==8.0b2

```



## Install Version 7.2

To install the latest non-beta version, run the following command in your terminal:

```shell

pip install -U coremltools

```



## Core ML



[Core ML](https://developer.apple.com/documentation/coreml) is an Apple framework to integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to fine-tune models, all on the user’s device. Core ML optimizes on-device performance by leveraging the CPU, GPU, and Neural Engine while minimizing its memory footprint and power consumption. Running a model strictly on the user’s device removes any need for a network connection, which helps keep the user’s data private and your app responsive.



## Resources



To install coremltools, see [Installing Core ML Tools](https://apple.github.io/coremltools/docs-guides/source/installing-coremltools.html). For more information, see the following:



* [Release Notes](https://github.com/apple/coremltools/releases/)

* [Guide and examples](https://apple.github.io/coremltools/docs-guides/index.html)

* [API Reference](https://apple.github.io/coremltools/index.html)

* [Core ML Specification](https://apple.github.io/coremltools/mlmodel/index.html)

* [Building from Source](BUILDING.md)

* [Contribution Guidelines](CONTRIBUTING.md)