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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/containers/linux_arm64/Dockerfile
RUN wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 --tries=5 https://github.com/bazelbuild/buildtools/releases/download/4.2.5/buildozer-linux-arm64 -O /usr/local/bin/buildozer && chmod +x /usr/local/bin/buildozer RUN groupadd -g 1001 buildslave && useradd -m -u 1001 -g buildslave buildslave RUN mkdir -p /tf/venv RUN chown -R buildslave:buildslave /tf RUN dpkg -i /patchelf/patchelf_0.14.3-1_arm64.deb
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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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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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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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.bazelrc
# Linux x86 so that we can use RBE. Since tests still need to run on the single # host Arm64 machine, the build becomes too slow (~30 min) to be a presubmit. # For testing purposes, we want to see the runtime performance of an # experimental job that is build-only, i.e, we only build the test targets and # do not run them. By prefixing the configs with "build", we can run both
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ci/official/containers/linux_arm64/devel.usertools/wheel_verification.bats
test "$WHEEL_MEGABYTES" -le "$LARGEST_OK_SIZE" } # Note: this runs before the tests further down the file, so TF is installed in # the venv and the venv is active when those tests run. The venv gets cleaned # up in teardown_file() above. @test "Wheel is installable" { python3 -m venv /tf/venv source /tf/venv/bin/activate python3 -m pip install --upgrade setuptools wheel
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tensorflow/BUILD
":fuchsia", ":emscripten", ":ios", ], visibility = ["//visibility:public"], ) # This flag disables all google production dependencies, intended for # applications run with non-prod environment. # TODO(timshen): Currently this option only disables some dependencies. # See b/122528503. # copybara:uncomment_begin(google-only) # bool_flag( # name = "tf_no_prod_deps",
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