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ci/official/utilities/code_check_changed_files.bats
if [[ "$(git rev-parse --abbrev-ref HEAD)" == "pull_branch" ]]; then # TF's CI runs 'git fetch origin "pull/PR#/merge:pull_branch"' # To get the as-merged branch during the CI tests git diff --diff-filter ACMRT --name-only pull_branch^ pull_branch > $BATS_FILE_TMPDIR/changed_files else # If the branch is not present, then diff against origin/master
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ci/official/envs/rbe
# This env is only valid when RBE is available on the selected platform, since # it enables a derived --config setting. If RBE is not available (i.e. there # is no --config setting), bazel would fail and quit. This script does a quick # check This script checks for such errors early if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then cat <<EOF ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable.
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 # Prevent double-compilation of some TF code, ref. b/183279666 (internal) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code # > on the host machine. If we don't have --distinct_host_configuration=false, # > the core TensorFlow code will be built once for the host and once for the # > target platform.
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ci/official/envs/disk_cache
# See the License for the specific language governing permissions and # 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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CONTRIBUTING.md
`comp:` etc. At this stage we check if the PR is valid and meets certain quality requirements. For example, we check if the CLA is signed, PR has sufficient description, if applicable unit tests are added, if it is a reasonable contribution (meaning it is not a single liner cosmetic PR). **2. Valid?** - If the PR passes all the quality checks then we go ahead and assign a reviewer.
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ci/official/README.md
# TFCI is a comma-separated list of filenames from the envs directory, which # are all settings for the scripts. TF's CI jobs are all made of a combination # of these env files. # # If you've clicked on a test result from our CI (via a dashboard or GitHub link), # click to "Invocation Details" and find BUILD_CONFIG, which will contain a TFCI # value in the "env_vars" list that you can choose to copy that environment.
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tensorflow/c/BUILD
load("@bazel_skylib//lib:selects.bzl", "selects") load("@local_config_tensorrt//:build_defs.bzl", "if_tensorrt") load("@local_tsl//tsl/platform:rules_cc.bzl", "cc_library") load( "//tensorflow:tensorflow.bzl", "check_deps", "if_google", "if_not_mobile", "tf_cc_test", "tf_copts", "tf_cuda_library", "tf_custom_op_library", "tf_kernel_library", )
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ci/official/requirements_updater/README.md
requirements_txt = "requirements_lock_3_11.txt", ) ``` 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
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SECURITY.md
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 reachable and exploitable through production-grade, benign models. ### Compilation
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tensorflow/BUILD
load("@bazel_skylib//rules:common_settings.bzl", "bool_flag", "bool_setting") load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda") load( "//tensorflow:tensorflow.bzl", "VERSION", "VERSION_MAJOR", "check_deps", "if_google", "if_oss", "if_xla_available", "tf_cc_shared_object", "tf_custom_op_library_additional_deps_impl", "tf_monitoring_python_deps",
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