Build WasmEdge With WasmEdge-TensorflowLite Plug-in
The WasmEdge-TensorflowLite plug-in is a software component that extends the functionality of the WasmEdge runtime to perform TensorFlow-Lite model inference. It allows WebAssembly applications to access TensorFlow-Lite functionality when executed on the WasmEdge runtime. The plugin provides a bridge between the WasmEdge runtime and the TensorFlow-Lite backend, allowing developers to execute machine learning models within WebAssembly applications.
Prerequisites
The prerequisites of the WasmEdge-TensorflowLite plug-in is the same as the WasmEdge building environment on the Linux platforms or MacOS platforms.
Build WasmEdge with WasmEdge-TensorflowLite Plug-in
To enable the WasmEdge WasmEdge-TensorflowLite, developers need to building the WasmEdge from source with the cmake option -DWASMEDGE_PLUGIN_TENSORFLOWLITE=On.
cd <path/to/your/wasmedge/source/folder>
cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_PLUGIN_TENSORFLOWLITE=On
cmake --build build
# For the WasmEdge-TensorflowLite plug-in, you should install this project.
cmake --install build
If the built wasmedge CLI tool cannot find the WasmEdge-TensorflowLite plug-in, you can set the WASMEDGE_PLUGIN_PATH environment variable to the plug-in installation path (such as /usr/local/lib/wasmedge/, or the built plug-in path build/plugins/wasmedge_tensorflowlite/) to try to fix this issue.
Then you will have an executable wasmedge runtime under /usr/local/bin and the WasmEdge-TensorflowLite plug-in under /usr/local/lib/wasmedge/libwasmedgePluginWasmEdgeTensorflowLite.so after installation.
Install the TensorFlowLite Dependency
Installing the necessary libtensorflowlite_c.so and libtensorflowlite_flex.so on both Linux and MacOS platforms, we recommend the following commands:
curl -s -L -O --remote-name-all https://github.com/second-state/WasmEdge-tensorflow-deps/releases/download/TF-2.12.0-CC/WasmEdge-tensorflow-deps-TFLite-TF-2.12.0-CC-manylinux2014_x86_64.tar.gz
# For the Linux aarch64 platforms, please use the `WasmEdge-tensorflow-deps-TFLite-TF-2.12.0-CC-manylinux2014_aarch64.tar.gz`.
# For the MacOS x86_64 platforms, please use the `WasmEdge-tensorflow-deps-TFLite-TF-2.12.0-CC-darwin_x86_64.tar.gz`.
# For the MacOS arm64 platforms, please use the `WasmEdge-tensorflow-deps-TFLite-TF-2.12.0-CC-darwin_arm64.tar.gz`.
tar -zxf WasmEdge-tensorflow-deps-TFLite-TF-2.12.0-CC-manylinux2014_x86_64.tar.gz
rm -f WasmEdge-tensorflow-deps-TFLite-TF-2.12.0-CC-manylinux2014_x86_64.tar.gz
The shared library will be extracted in the current directory ./libtensorflowlite_c.so and ./libtensorflowlite_flex.so on Linux platforms, or ./libtensorflowlite_c.dylib and ./libtensorflowlite_flex.dylib on MacOS platforms.
After building the plug-in, you can also find these shared libraries under the build/_deps/wasmedge_tensorflow_lib_tflite-src/ directory.
Then you can move the library to the installation path:
mv libtensorflowlite_c.so /usr/local/lib
mv libtensorflowlite_flex.so /usr/local/lib
If on MacOS platforms:
mv libtensorflowlite_c.dylib /usr/local/lib
mv libtensorflowlite_flex.dylib /usr/local/lib
Or set the environment variable export LD_LIBRARY_PATH=$(pwd):${LD_LIBRARY_PATH}.
For more information, you can refer to the GitHub repository.