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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# Give only the list of failed tests at the end of the log test --test_summary=short # "nonpip" tests are regular py_test tests. # Pass --config=nonpip to run the same suite of tests. If you want to run just # one test for investigation, you don't need --config=nonpip; just run the # bazel test invocation as normal.
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ci/official/wheel_test/README.md
[requirements updater readme](https://github.com/tensorflow/tensorflow/blob/master/ci/official/requirements_updater/README.md) ### Prerequisites for Local Testing To run tests locally, follow these steps: 1. Navigate to the relevant directory: ``` cd ci/official/wheel_test ``` 2. Run a script for creating requirements file: ``` bash update_requirements.sh <path_to_tensorflow_wheel> <python_version> e.g.:
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ci/official/README.md
TensorFlow's official CI jobs run the scripts in this folder. Our internal CI system, Kokoro, schedules our CI jobs by combining a build script with a file from the `envs` directory that is filled with configuration options: - Nightly jobs (Run nightly on the `nightly` branch) - Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh` - Continuous jobs (Run on every GitHub commit) - Uses `pycpp.sh`
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CONTRIBUTING.md
```bash docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10 ``` Once you have the packages installed, you can run a specific unit test in bazel by doing as follows: ```bash export flags="--config=opt -k" ``` If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and add the `cuda` option flag
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ci/official/requirements_updater/README.md
4) Add the version to `SUPPORTED_VERSIONS` in `updater.sh`, and `release_updater.sh` 5) Run the `updater.sh` shell script. \ If the base requirements file hasn't yet been updated to account for the new Python version, which will require different versions for at least some dependencies, it will need to be updated now, for the script to run successfully.
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
``` (You can paste links or attach files by dragging & dropping them below) - Provide links to your updated versions of the above two colab notebooks.
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RELEASE.md
* TensorBoard fixes: graphs with only one data point, Nan values, reload button and auto-reload, tooltips in scalar charts, run filtering, stable colors * Tensorboard graph visualizer now supports run metadata. Clicking on nodes while viewing a stats for a particular run will show runtime statistics, such as memory or compute usage. Unused nodes will be faded out. ## Thanks to our Contributors
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tensorflow/.clang-format
# Run manually to reformat a file: # clang-format -i --style=file <file> BasedOnStyle: Google
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SECURITY.md
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted models or graphs is equivalent to running untrusted code. If you need to run untrusted models, execute them inside a [**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions in TensorFlow ops can be recognized as security issues only if they are
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ci/official/envs/enable_pycpp_build
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # # Changes the behavior in pycpp.sh from "run all tests" to "verify that all # tests can compile." Used in some CI jobs (macOS and Linux Arm64) where test # execution is too expensive. TFCI_PYCPP_SWAP_TO_BUILD_ENABLE=1
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