tflite: Add sync driver Although we have the async driver helper, not every delegate are assumed to be supporting async API. Hence, this CL adds a similar helper but only uses the traditional sync API, so we can easily control delegate behaviors without requiring the delegate to support async API. BUG=b:374245241 TEST=`bazelisk run --config=host_clang '//common:sync_driver_test'` with sample delegate Change-Id: I25b8f4b27ff7fed09a7d336b1b4eca36a8cbc6f0 Reviewed-on: https://chromium-review.googlesource.com/c/chromiumos/platform/tflite/+/6176630 Auto-Submit: Tommy Chiang <ototot@google.com> Tested-by: Tommy Chiang <ototot@google.com> Reviewed-by: Shik Chen <shik@chromium.org> Commit-Queue: Tommy Chiang <ototot@google.com>
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.