commit | d78d3c831e5a95cbcb9f6df444a018ef22b678fd | [log] [tgz] |
---|---|---|
author | Tommy Chiang <ototot@google.com> | Mon May 12 08:31:22 2025 |
committer | Chromeos LUCI <chromeos-scoped@luci-project-accounts.iam.gserviceaccount.com> | Mon May 12 12:46:20 2025 |
tree | c79e0b3728726fd21134bf5f2e4512dd647bc640 | |
parent | f6564426ce316734a733c337bfca6e56ec2396f6 [diff] |
tflite: Correct the name of SyncDelegateTest The test name in sync_driver_test should be SyncDelegateTest instead of AsyncDelegateTest BUG=b:374245241 TEST=CQ Change-Id: I5533a58c8231fb10c140953d9f85f2f27e687754 Reviewed-on: https://chromium-review.googlesource.com/c/chromiumos/platform/tflite/+/6535900 Commit-Queue: Shik Chen <shik@chromium.org> Auto-Submit: Tommy Chiang <ototot@google.com> Reviewed-by: Shik Chen <shik@chromium.org> Tested-by: Tommy Chiang <ototot@google.com> 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.