# coremltools

**Repository Path**: deeplearningrepos/coremltools

## Basic Information

- **Project Name**: coremltools
- **Description**: Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-30
- **Last Updated**: 2024-04-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![Build Status](https://img.shields.io/gitlab/pipeline/zach_nation/coremltools/master)](https://gitlab.com/zach_nation/coremltools/-/pipelines?page=1&scope=branches&ref=master)
[![PyPI Release](https://img.shields.io/pypi/v/coremltools.svg)](#)
[![Python Versions](https://img.shields.io/pypi/pyversions/coremltools.svg)](#)

[Core ML Tools](https://coremltools.readme.io/docs)
=======================

Use *coremltools* to convert machine learning models from third-party libraries to the Core ML format. The 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/)
* [TensorFlow's Keras APIs](https://keras.io/)
* 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 do the following:

* 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.

## 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 the [“Installation“ page](https://coremltools.readme.io/docs/installation). For more information, see the following:

* [Release Notes](https://github.com/apple/coremltools/releases/) 
* [Guides and examples](https://coremltools.readme.io/) 
* [API Reference](https://coremltools.readme.io/reference/convertersconvert)
* [Core ML Specification](https://mlmodel.readme.io/)
* [Building from Source](BUILDING.md)
* [Contribution Guidelines](CONTRIBUTING.md)