# 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)