Use Core ML 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 train or 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.

Converting from TensorFlow

Starting with Core ML Tools 4.0, you can convert neural network models from TensorFlow 1 and TensorFlow 2 to Core ML using the Unified Converter API.


Minimum Deployment Target

The Unified Converter API produces Core ML models for iOS 13, macOS 10.15, watchOS 6, tvOS 13 or newer deployment targets.

If your primary deployment target is iOS 12 or earlier, you can find limited conversion support for TensorFlow 1 models in the tfcoreml package.

To convert from TensorFlow 1.x to Core ML, use one of the following formats for the source model:

  • Frozen tf.Graph
  • Frozen graph (.pb) file path

For instructions, see the following:

Updated about a month ago

Converting from TensorFlow

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