soltrinox / coremltools

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

Core ML Tools

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
• TensorFlow 2.x
• PyTorch
• TensorFlow's Keras APIs
• 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.

Version 5

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:

pip install coremltools

Core ML

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.

Resources

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