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ci/official/README.md
# and scripts on Linux create a persistent container called "tf" # which mounts your TensorFlow directory into the container. # # Important: because the container is persistent, you cannot change TFCI # variables in between script executions. To forcibly remove the # container and start fresh, run "docker rm -f tf". Removing the container # destroys some temporary bazel data and causes longer builds. #
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tensorflow/c/experimental/next_pluggable_device/c_api.h
TF_CAPI_EXPORT extern PJRT_Client* TF_GetPjRtCClient(const char* device_type, TF_Status* status); // Gets the `PJRT_Buffer*` stored in the tensor. The status will contain error // if the tensor does not have a `PjRtCApiBuffer`. TF_CAPI_EXPORT extern PJRT_Buffer* TF_GetPjRtCBuffer(TF_Tensor* c_tensor, TF_Status* status);
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tensorflow/c/c_api_test.cc
Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst); namespace { static void ExpectHasSubstr(StringPiece s, StringPiece expected) { EXPECT_TRUE(absl::StrContains(s, expected)) << "'" << s << "' does not contain '" << expected << "'"; } // Returns the GPU device name if there is one (with arbitrary tie breaking if // there are more than one), or "" otherwise. string GPUDeviceName(TF_Session* session) {
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ci/official/envs/rbe
EOF exit 1 fi 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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.github/workflows/arm-ci.yml
if [[ $running_containers == "" ]]; then echo "No running containers"; else echo "Running container(s) found" && \ docker stop $running_containers; fi docker container prune -f - name: Clean repository shell: bash
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.gitignore
/.tf_configure.bazelrc /xla_configure.bazelrc /bazel-* /bazel_pip /tools/python_bin_path.sh /tensorflow/tools/git/gen /pip_test /_python_build *.pyc __pycache__ *.swp .vscode/ cmake_build/ tensorflow/contrib/cmake/_build/ .idea/** /build/ [Bb]uild/ /build_output/ /tensorflow/core/util/version_info.cc /tensorflow/python/framework/fast_tensor_util.cpp /tensorflow/lite/gen/** /tensorflow/lite/tools/make/downloads/**
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# 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 # Use Python 3.X as installed in container image build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3" build --python_path="/usr/local/bin/python3" # Build TensorFlow v2 build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
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tensorflow/c/eager/c_api_experimental.h
// which is passed to the functions referenced in the TFE_CustomDevice struct // `device` (execute, delete_device, etc.). It can for example contain the // names of wrapped devices. // // There are currently no graph semantics implemented for registered custom // devices, so executing tf.functions which contain operations placed on the // custom devices will fail. // // `device_name` must not name an existing physical or custom device. It must
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ci/official/utilities/code_check_full.bats
# targets matching the provided query, which start with // or @ but not # //tensorflow (so it looks for //third_party, //external, etc.), and then # gathers the list of all packages (i.e. directories) which contain those # targets. license_query() { bazel cquery --experimental_cc_shared_library "$1" --keep_going \ | grep -e "^//" -e "^@" \ | grep -E -v "^//tensorflow" \ | sed -e 's|:.*||' \ | sort -u }
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ci/official/utilities/setup.sh
# limitations under the License. # ============================================================================== # # Common setup for all TF scripts. # # Make as FEW changes to this file as possible. It should not contain utility # functions (except for tfrun); use dedicated scripts instead and reference them # specifically. Use your best judgment to keep the scripts in this directory
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