How do I build TensorFlow Lite for Deep Learning C++ code generation and deployment?

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Bill Chou - 2023-04-01T11:33:57+00:00
Question: How do I build TensorFlow Lite for Deep Learning C++ code generation and deployment?

I see a few deep learning networks supported for code generation using MATLAB Coder: Deep Learning Networks and Layers Supported for C++ Code Generation I'm looking to generate code from my deep learning network (like ResNet, GoogLeNet, SqueezeNet, VGG-16/19, etc) to run on boards supported by TensorFlow Lite using MATLAB Coder. What are the steps to do this?

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    • Expert Answer

    Profile picture of Prashant Kumar Prashant Kumar answered . 2025-11-20

    Use on Windows targets (For Release 2022b and newer)
     
    To generate and run C++ code that performs inference with TensorFlow Lite models on Windows targets, you must have the?TensorFlow Lite library on the Windows hardware. To build the TensorFlow Lite library version 2.4.1 for Windows targets on your host Windows platform, execute the following steps.
     
    Requirements:
    • To build the TensorFlow Lite dynamic library, you must install bazel (version 3.1.0 to 3.99.0) on the host Windows computer. See this link for more information: Installing Bazel on Windows
    • You may need to add PYTHON_BIN_PATH to the bazel command if Bazel is not able to find the python paths
    • Start the process of bazel-build from the Developer Command Prompt terminals provided with Visual Studio installations
    Build Instructions:
    1. Open Command Prompt
    2. Execute the following commands:
    % clone the git repo
git clone https://github.com/tensorflow/tensorflow.git  

% git clone will automatically download the code into a folder named `tensorflow`
cd tensorflow

% checkout version 2.4.1
git checkout v2.4.1

% install dependencies
pip --no-cache-dir install numpy future

% Download flatbuffers and other dependent files by running the following command from within the command prompt in Windows:
./tensorflow/lite/tools/make/download_dependencies.sh

% configure tensorflowlite installation
python ./configure.py

% build the dynamic library
bazel build -c opt //tensorflow/lite:tensorflowlite.dll

% it might be necessary to provide python path if it's not picked up automatically
bazel build -c opt //tensorflow/lite:tensorflowlite.dll --action_env PYTHON_BIN_PATH=

% Create a directory named lib and copy the contents of the bazel-bin directory into it.
cd bazel-bin
cp -r ../lib/

% To configure the MATLAB environment for TensorFlow Lite code generation, set the environment variables TFLITE_PATH
%  is the full-path to `tensorflow` folder where you have downloaded the source files
setenv('TFLITE_PATH’, ‘’);

     


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