LiteRT

GitHub repository for Google's open-source high-performance runtime for
on-device AI which has been renamed from TensorFlow Lite to LiteRT.

More details of the LiteRT announcement are in this blog
post.

The official documentation can be found at https://ai.google.dev/edge/litert

PyPi Installation Requirements

• Python versions: 3.9, 3.10, 3.11, 3.12
• Operating system: Linux, MacOS

FAQs

1. How do I contribute code?

   For now, please contribute code to the
   existing TensorFlow Lite repository.

2. What is happening to the .tflite file extension and file format?

   No changes are being made to the .tflite file extension or format.
   Conversion tools will continue to output .tflite flatbuffer files, and
   .tflite files will be readable by LiteRT.

3. How do I convert models to .tflite format?

   For Tensorflow,
   Keras and
   Jax you can
   continue to use the same flows. For PyTorch support check out
   ai-edge-torch.

4. Will there be any changes to classes and methods?

   No. Aside from package names, you won't have to change any code you've
   written for now.

5. Is TensorFlow Lite still being actively developed?

   Yes, but under the name LiteRT. Active development will continue on the
   runtime (now called LiteRT), as well as the conversion and optimization
   tools. To ensure you're using the most up-to-date version of the runtime,
   please use LiteRT.

Build From Source with Bazel

1. From the LiteRT root folder (where this README file is), run

   git submodule init && git submodule update --remote
   

   to make sure you have the latest version of the tensorflow submodule.

2. You will need docker, but nothing else. Create the image with

   docker build . -t tflite-builder -f ci/tflite-py3.Dockerfile
   
   # Confirm the container was created with
   docker image ls
   

3. Run bash inside the container

   docker run -it -w /host_dir -v $PWD:/host_dir -v $HOME/.cache/bazel:/root/.cache/bazel tflite-builder bash
   

   where -v $HOME/.cache/bazel:/root/.cache/bazel is optional, but useful to
   map your Bazel cache into the container.

4. Run configure script, use default settings for this example.

   ./configure
   

5. Build and run e.g. //tflite:interpreter_test

   bazel test //tflite:interpreter_test