How to Install LightGBM Model on Raspberry Pi

The seamless transition of machine learning models across different operating systems can be a challenge, and this is particularly true when dealing with libraries compiled for specific architectures. This article will dissect a common issue encountered when attempting to load a LightGBM model, previously trained on a Windows machine, onto a Raspberry Pi 4 running Ubuntu 20.04. We'll delve into the root cause of the error and provide a practical solution for successfully deploying your model on this popular embedded platform.

The Problem: A Mismatched Library

The error message "ERROR: could not load library "/home/pi/.julia/packages/LightGBM/My6MK/src/lib_lightgbm.so" /home/pi/.julia/packages/LightGBM/My6MK/src/lib_lightgbm.so: cannot open shared object file: No such file or directory" indicates that the lib_lightgbm.so file, essential for interacting with the LightGBM library, is either missing or incompatible with your Raspberry Pi's architecture. The library included with the LightGBM package for Windows is typically optimized for a different system, and thus, cannot be directly utilized on an ARM-based device like the Raspberry Pi.

The Solution: Compiling LightGBM for Raspberry Pi

The solution involves building a custom lib_lightgbm.so file specifically tailored for the Raspberry Pi's ARM architecture. This ensures compatibility with your target environment and eliminates the library mismatch. Follow these steps to compile LightGBM on your Raspberry Pi:

1. Clone the LightGBM Repository: Start by obtaining the source code from the official LightGBM GitHub repository:

      git clone --recursive https://github.com/microsoft/LightGBM
    

The --recursive flag ensures that all necessary submodules are also cloned, which is crucial for successful compilation.

1. Navigate to the Build Directory: Move into the build directory within the cloned repository:

      cd LightGBM
mkdir build 
cd build
    

1. Configure with CMake: CMake is used to generate build files specific to your system's architecture. Execute the following command:

      cmake ..
    

This will configure the build process, detecting the correct compiler and libraries for your Raspberry Pi.

1. Build the Library: After the configuration step, compile the LightGBM library using make:

      make -j4
    

The -j4 flag indicates to use four parallel processes for compilation, potentially speeding up the process.

1. Locate the Compiled Library: Upon successful compilation, the lib_lightgbm.so file will be located in the build directory:

      ./build/lib_lightgbm.so
    

1. Replace the Existing Library: Now, replace the existing lib_lightgbm.so file in your LightGBM package directory on your Raspberry Pi with the newly compiled one. You might need to navigate to the package location based on your Julia installation.

Post-Compilation Verification

After replacing the lib_lightgbm.so file, try reloading your LightGBM model using the loadmodel function:

      loadmodel(estimator, "/home/pi/softwares/julia/lightgbmModel.jld2")
    

If the model loads successfully, it means you have successfully resolved the incompatibility issue and your LightGBM model is now functional on your Raspberry Pi.

Additional Considerations:

• System Dependencies: Ensure that you have the necessary system dependencies installed on your Raspberry Pi before attempting the compilation process. This may include packages like gcc, g++, and other development tools.

• Memory Limitations: If you face issues with memory during compilation, consider using a smaller -j flag to reduce the number of parallel processes.

• Library Version Compatibility: It's important to use a version of LightGBM that is compatible with the version of Julia you're using. Check for compatibility notes and updates for your specific versions.

Conclusion:

This guide provides a comprehensive solution for resolving the incompatibility issue when loading LightGBM models on Raspberry Pi 4. By compiling the lib_lightgbm.so library tailored to the ARM architecture, you ensure compatibility and enable your trained model to run smoothly on your embedded platform. This process allows you to leverage the power of LightGBM for machine learning tasks on resource-constrained devices, opening up new possibilities for development and deployment.