Use Core ML Tools (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
• TensorFlow 2.x
• PyTorch
• Non-neural network frameworks:
  • scikit-learn
  • XGBoost
  • 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.

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

pip install -U coremltools

Core ML 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.

To install coremltools, see Installing Core ML Tools. For more information, see the following:

• Release Notes
• Guide and examples
• API Reference
• Core ML Specification
• Building from Source
• Contribution Guidelines