Use 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 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.

The coremltools 5 package offers several performance improvements over previous versions, including the following new features:

• Core ML model package: A new model container format that separates the model into components and offers more flexible metadata editing and better source control.
• ML program: A new model type that represents computation as programmatic instructions, offers more control over the precision of its intermediate tensors and better performance.

To install coremltools, use the following command in your terminal:

pip install coremltools

To install the first beta of coremltools 6.0 use the following command in your terminal:

pip install coremltools==6.0b1

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 the “Installation“ page. For more information, see the following:

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