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ci/official/utilities/code_check_changed_files.bats
git diff --diff-filter ACMRT --name-only pull_branch^ pull_branch > $BATS_FILE_TMPDIR/changed_files else # If the branch is not present, then diff against origin/master git diff --diff-filter ACMRT --name-only origin/master > $BATS_FILE_TMPDIR/changed_files fi } # Note: this is excluded on the full code base, since any submitted code must # have passed Google's internal style guidelines.
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ci/official/envs/disk_cache
# limitations under the License. # ============================================================================== # Sourcing this enables local disk cache if [[ $(uname -s) == "Darwin" ]]; then echo "Please note that using disk cache on macOS is not recommended because the" echo "cache can end up being pretty big and make the build process inefficient." fi
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ci/official/envs/rbe
TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" # These flags share the user's gcloud credentials with the container, so that bazel # inside the container can authenticate. Note: TF's CI does not have any credential # stored here.
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ci/official/README.md
# generated artifacts like .whl files, and "script.log", from the script. # Note that files created under Docker will be owned by "root". ls build_output ``` ## Contribution & Maintenance The TensorFlow team does not yet have guidelines in place for contributing to this directory. We are working on it. Please join a TF SIG Build meeting (see: bit.ly/tf-sig-build-notes) if you'd like to discuss the future of contributions.
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ci/official/requirements_updater/requirements.in
astunparse == 1.6.3 dill == 0.3.7 astor == 0.7.1 typing_extensions == 4.8.0 gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 # Install tensorboard, and keras # Note that here we want the latest version that matches TF major.minor version # Note that we must use nightly here as these are used in nightly jobs # For release jobs, we will pin these on the release branch keras-nightly ~= 3.0.0.dev tb-nightly ~= 2.17.0.a
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ci/official/requirements_updater/README.md
# Hermetic Python Hermetic Python allows not to rely on system-installed Python, and system-installed Python packages. \ Instead, an independent Python toolchain is registered, ensuring the right dependencies are always used. \ See https://github.com/bazelbuild/rules_python/ for more details. ### Specifying the Python version Note: Only a number of minor Python versions are supported at any given time.
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CONTRIBUTING.md
bazel test ${flags} //tensorflow/python/saved_model:load_test --test_filter=*LoadTest.test_capture_variables* ``` **Note:** You can add `--test_sharding_strategy=disabled` to the `flags` to disable the sharding so that all the test outputs are in one file. However, it may slow down the tests for not running in parallel and may cause the test to timeout but it could be useful when you need to execute a single
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tensorflow/c/experimental/ops/README.md
***tensorflow/c/experimental/ops/\*_ops.{cc,h}***. Run this `update_cpp_ops.sh` script when Op definitions change in the registry. To generate additional operators, extend the lists in this script. Note that category names correspond to generated source file names, and should be consistent with the original source files registering each operator. For example
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ci/official/envs/public_cache_push
# limitations under the License. # ============================================================================== # Sourcing this enables Bazel remote cache (read and write) # Note that "_push" cache configs write to GCS buckets and require # authentication. If you are not a Googler, source "public_cache" to enable the # public read-only cache. # The cache configs are different for MacOS and Linux if [[ $(uname -s) == "Darwin" ]]; then
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ci/official/envs/linux_arm64
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_arm64_linux" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64 # Note: this is not set to "--cpu", because that changes the package name # to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow" # despite lacking Nvidia CUDA support.
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