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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_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then cat <<EOF
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false # Change the value of CACHEBUSTER when upgrading the toolchain, or when testing # different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325
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ci/official/utilities/code_check_changed_files.bats
if [[ "$(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/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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tensorflow/c/eager/parallel_device/BUILD
"//tensorflow/core:test_main", ], ) tf_cc_test( name = "parallel_device_remote_test", srcs = ["parallel_device_remote_test.cc"], # TODO(b/136478427): Enable global heap checking when servers shut down # cleanly. args = ["--heap_check="], deps = [ ":parallel_device", ":parallel_device_testlib", "//tensorflow/c:c_api",
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ci/official/README.md
# # Bazel will cache your builds in tensorflow/build_output/cache, # and will also try using public build cache results to speed up # your builds. This usually saves a lot of time, especially when # re-running tests. However, note that: # # - New environments like new CUDA versions, changes to manylinux, # compilers, etc. can cause undefined behavior such as build failures # or tests passing incorrectly.
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WORKSPACE
# # The cascade of load() statements and tf_workspace?() calls works around the # restriction that load() statements need to be at the top of .bzl files. # E.g. we can not retrieve a new repository with http_archive and then load() # a macro from that repository in the same file. load("@//tensorflow:workspace3.bzl", "tf_workspace3") tf_workspace3() load("@//tensorflow:workspace2.bzl", "tf_workspace2") tf_workspace2()
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SECURITY.md
some form of isolation when dealing with untrusted data. As a result, this document also outlines what issues we consider as TensorFlow security vulnerabilities. We recognize issues as vulnerabilities only when they occur in scenarios that we outline as safe; issues that have a security impact only when TensorFlow is used
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false # Change the value of CACHEBUSTER when upgrading the toolchain, or when testing # different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325
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ci/official/requirements_updater/requirements.in
# TODO(b/304751256): Adjust the numpy pin to a single version, when ready numpy ~= 1.23.5 ; python_version <= "3.11" numpy ~= 1.26.0 ; python_version >= "3.12" wheel ~= 0.41.2 h5py >= 3.10.0 lit ~= 17.0.2 opt_einsum == 3.3.0 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
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