"Failed to load _MLModelProxy: No module named 'coremltools.libcoremlpython'"when converting an ONNX model to CoreML with onnx-coreml lib. How to fix?

GIVANDRA

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01-11-2024

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When working with machine learning models, you may encounter the error message:

Failed to load _MLModelProxy: No module named 'coremltools.libcoremlpython'

This typically arises during the conversion of ONNX (Open Neural Network Exchange) models to CoreML using the onnx-coreml library. Understanding the cause of this error and learning how to resolve it can enhance your productivity and streamline your workflow when working with machine learning frameworks.

The Original Code That Triggers the Error

Here's an example of code that might trigger the above error while attempting to convert an ONNX model to CoreML:

import onnx
from onnx_coreml import convert

# Load your ONNX model
onnx_model = onnx.load("your_model.onnx")

# Convert to CoreML
coreml_model = convert(onnx_model)

When executing the conversion, you might see the error message indicating that the libcoremlpython module is missing, which can be frustrating.

Understanding the Problem

The error you're encountering can occur due to a few reasons:

1. Missing Dependency: The coremltools library may not be properly installed, or the required version of coremltools might not be compatible with your Python environment.
2. Library Conflict: Conflicts between different versions of libraries can also lead to this issue.
3. Incomplete Installation: Sometimes, libraries may not install completely due to network issues or other interruptions.

How to Fix the Issue

Here are some effective strategies to resolve the issue:

1. Install or Upgrade coremltools: Ensure you have the coremltools library installed. You can do this by running:

   pip install --upgrade coremltools
   

   This command will install the latest version of the library, which should include the necessary components.

2. Check Compatibility: The onnx-coreml library and coremltools should be compatible with each other. You can check the documentation for the correct versions. For example, if you are using onnx-coreml version 1.0.1, ensure your coremltools is up-to-date.

3. Verify Installation: After installation, verify if coremltools is correctly installed and accessible. You can check the installation by running:

   import coremltools
   print(coremltools.__version__)
   

4. Create a New Virtual Environment: If issues persist, consider creating a fresh virtual environment. This ensures that your project dependencies are isolated:

   python -m venv myenv
   source myenv/bin/activate  # On Windows use: myenv\Scripts\activate
   pip install onnx-coreml coremltools
   

5. Check Python Version: Compatibility can also depend on your Python version. Ensure you are using Python 3.6 or higher, as earlier versions may not support the required libraries effectively.

Practical Example

Here's how you might implement the above fixes in a practical scenario:

1. Creating a Virtual Environment:

   python -m venv my_venv
   source my_venv/bin/activate  # or my_venv\Scripts\activate on Windows
   

2. Installing Required Libraries:

   pip install onnx onnx-coreml coremltools
   

3. Testing the Conversion:

   After ensuring your environment is correctly set up, you can try the conversion again:

   import onnx
   from onnx_coreml import convert
   
   onnx_model = onnx.load("your_model.onnx")
   coreml_model = convert(onnx_model)
   

If all goes well, you should no longer see the error, and your model should convert seamlessly.

Conclusion

The "Failed to load _MLModelProxy: No module named 'coremltools.libcoremlpython'" error can be an obstacle in your machine learning projects, but with the right steps, you can resolve it efficiently. Always ensure that your libraries are up-to-date and compatible, and don't hesitate to create isolated environments for your projects.

Useful Resources

• ONNX CoreML Documentation
• coremltools Documentation
• Creating a Virtual Environment

By following these guidelines, you can effectively troubleshoot and overcome conversion issues when working with ONNX models and CoreML. Happy coding!