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Compiled TensorFlow lite runtime. This interpreter-only package is a fraction the size of the full TensorFlow package and includes the bare minimum code required to run inferences with TensorFlow Lite—it includes only the tf.lite.Interpreter Python class. This small package is ideal when all you want to do is execute .tflite models and avoid was…

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iCorv/tflite-runtime

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tflite-runtime

Compiled TensorFlow lite runtime. This interpreter-only package is a fraction the size of the full TensorFlow package and includes the bare minimum code required to run inferences with TensorFlow Lite—it includes only the tf.lite.Interpreter Python class. This small package is ideal when all you want to do is execute .tflite models and avoid wasting disk space with the large TensorFlow library.

The original build instructions can be found here: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/pip_package

Installation

To install, run pip3 install and pass it the appropriate Python wheel URL from the following table.

For example, if you have Raspberry Pi that's running Raspbian Buster (which has Python 3.7), install the Python wheel as follows:

pip3 install https://github.com/iCorv/tflite-runtime/raw/master/tflite_runtime-2.4.0-cp37-cp37m-linux_armv7l.whl

Platform Python URL
Linux (x86-64) 3.6 https://github.com/iCorv/tflite-runtime/raw/master/tflite_runtime-2.4.0-cp36-cp36m-linux_x86_64.whl
Linux (x86-64) 3.7 https://github.com/iCorv/tflite-runtime/raw/master/tflite_runtime-2.4.0-cp37-cp37m-linux_x86_64.whl
Linux (ARM 32) 3.7 https://github.com/iCorv/tflite-runtime/raw/master/tflite_runtime-2.4.0-cp37-cp37m-linux_armv7l.whl
any? 3.x https://github.com/iCorv/tflite-runtime/raw/master/tflite_runtime-2.4.0-py3-none-any.whl

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Compiled TensorFlow lite runtime. This interpreter-only package is a fraction the size of the full TensorFlow package and includes the bare minimum code required to run inferences with TensorFlow Lite—it includes only the tf.lite.Interpreter Python class. This small package is ideal when all you want to do is execute .tflite models and avoid was…

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