tflite: Add `per_os_targets = True` to delegates This ensures delegate libs to get the right SONAME. BUG=b:452498510 TEST=build and readelf to check SONAME is correct Change-Id: If2c1d744971ea4588fabff5a1a4cb9978f891748 Reviewed-on: https://chromium-review.googlesource.com/c/chromiumos/platform/tflite/+/7047860 Commit-Queue: Ching-Kang Yen <chingkang@chromium.org> Tested-by: Tommy Chiang <ototot@google.com> Reviewed-by: Shik Chen <shik@chromium.org> Auto-Submit: Tommy Chiang <ototot@google.com> Reviewed-by: Ching-Kang Yen <chingkang@chromium.org>
This repository hosts the core ChromeOS TFLite components, enabling on-device machine learning (ODML) workloads accelerated by NPU.
The corresponding ebuild can be found at: tensorflow-9999.ebuild
Patches are stored in the patch/ directory and explicitly listed in WORKSPACE.bazel. A helper script, ./script/patcher.py, is included to facilitate patch management within a TFLite workspace.
The typical workflow:
Eject (Download) TensorFlow Source Code
Download the TensorFlow source code into a local git repository with patches applied as individual commits:
./script/patcher.py eject
This creates a new local git repository at tensorflow/.
Modify the TensorFlow Repository
Make changes to the tensorflow/ repository as needed, following standard git workflows. Optionally, include a PATCH_NAME= tag in commit messages to specify the filename of the corresponding patch.
Seal the Repository
Regenerate the patch files and update the WORKSPACE.bazel file:
./script/patcher.py seal
This updates the patches in the patch/ directory and reflects the changes in WORKSPACE.bazel.
It's preferred to submit changes to upstream TensorFlow first and cherry-pick them as patches. This helps minimize divergence and makes TensorFlow updates easier.