Migration Workflow

If you used coremltools 3 for neural network model conversion from TensorFlow or ONNX/PyTorch to Core ML, update your workflow as follows when you upgrade to coremltools 4 or 5:

Conversion from	coremltools 3	coremltools 4 and newer
TensorFlow	Install coremltools 3.4 and tfcoreml 1.1 and use the tfcoreml.convert API.	Use the new coremltools.convert API. See Unified Conversion API.
PyTorch	First export the PyTorch model to the ONNX format and then install coremltools 3.4 and onnx-coreml 1.3 and use the onnx_coreml.convert API.	Use the new coremltools.convert API. See Unified Conversion API.

Convert from TensorFlow

With coremltools 4 and newer versions, you do not need to install the tfcoreml package to convert TensorFlow models. The TensorFlow converter is fully integrated in coremltools and available in the Unified Conversion API.

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For older deployment targets

To deploy the Core ML model to a target that is iOS12, macOS 10.13, watchOS 5, tvOS 12, or an older version, use coremltools 3 and tfcoreml 1.

Convert from PyTorch

You can directly convert from PyTorch using the newest version of coremltools, which includes a PyTorch converter available through the Unified Conversion API. You no longer need to use the two-step process for converting PyTorch models using the ONNX format.

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For older deployment targets

To deploy the Core ML model to a target that is iOS12, macOS 10.13, watchOS 5, tvOS 12, or an older version, use coremltools 3 and onnx-coreml 1

Deprecated Methods and Support

In coremltools 4 and newer, the the following class and methods available in previous versions are deprecated:

• convert_neural_network_weights_to_fp16(), convert_neural_network_spec_weights_to_fp16(), and quantize_spec_weights(). Use the quantize_weights() method instead. For instructions, see Quantization.
• The NeuralNetworkShaper class.
• get_allowed_shape_ranges().
• can_allow_multiple_input_shapes().
• visualize_spec() method of the MLModel class. You can use the netron open source viewer to visualize CoreML models.
• get_custom_layer_names(), replace_custom_layer_name(), and has_custom_layer(): These were moved to internal methods.
• Caffe converter
• Keras.io and ONNX converters will be deprecated in coremltools 6. Users are recommended to transition to the TensorFlow/PyTorch conversion via the unified converter API.

Support for Python 2 has been deprecated since coremltools 4.1. The current version of coremltools includes wheels for Python 3.5, 3.6, 3.7, and 3.8.