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:
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:
pip install coremltoolsCore 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: