| commit | 819b81b47622696cb3c70445653a0c2e8dde6fe0 | [log] [tgz] |
|---|---|---|
| author | Ritul Jasuja <ritul.jasuja@intel.com> | Fri Sep 13 07:14:04 2024 |
| committer | Chromeos LUCI <chromeos-scoped@luci-project-accounts.iam.gserviceaccount.com> | Wed Jan 15 10:36:09 2025 |
| tree | c1995f744d71aad080dc88dc53a1cac242174772 | |
| parent | a7fd77940fed192b76328783a36f7a64f55a875d [diff] |
tflite: npu: Async Kernel API implementation BUG=b:353367133 TEST=async_delegate_test --stable_delegate_settings_file=/etc/tflite/settings.json Change-Id: I7ee15eceb3d5f98ff16271340944bd13176be0ab Signed-off-by: Ritul Jasuja <ritul.jasuja@intel.com> Reviewed-on: https://chromium-review.googlesource.com/c/chromiumos/platform/tflite/+/5872382 Reviewed-by: 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.