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ci/official/utilities/code_check_changed_files.bats
} @test "API compatibility test passes, ensuring no unexpected changes to the TF API" { bazel test $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/api/tests:api_compatibility_test echo "You have to re-generate the TF API goldens and have the API changes reviewed." echo "Look at the instructions for ':api_compatibility_test -- --update_goldens=True'" } teardown_file() { bazel shutdown
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ci/official/envs/rbe
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ci/official/wheel_test/README.md
## Wheel Test This directory is dedicated to tests that require a built TensorFlow wheel file for testing, such as: * Ensuring the entire API is importable * Testing downstream projects against the wheel Ensure you have Bazel installed and accessible from your command line. These tests use hermetic Python. They also require a built TensorFlow wheel file and a requirements_lock file. The requirements_lock file is generated by the
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# 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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tensorflow/c/eager/dlpack_test.cc
TestHandleFromDLPack(status, ctx, {4}, {1}); TestHandleFromDLPack(status, ctx, {4, 3, 2}, {}); TestHandleFromDLPack(status, ctx, {4, 3, 2}, {6, 2, 1}); // Test that dims with size=1 can have any stride. TestHandleFromDLPack(status, ctx, {1}, {1}); TestHandleFromDLPack(status, ctx, {1}, {0}); TestHandleFromDLPack(status, ctx, {4, 1, 2}, {2, 1, 1}); TestHandleFromDLPack(status, ctx, {4, 1, 2}, {2, 0, 1});
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ci/official/envs/linux_arm64
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. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1 TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python
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tensorflow/api_template.__init__.py
# Don't fail if these modules are not available. # For e.g. this file will be originally placed under tensorflow/_api/v1 which # does not have "python", "core" directories. Then, it will be copied # to tensorflow/ which does have these two directories. try: del python except NameError: pass try: del core except NameError: pass try: del compiler except NameError:
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ci/official/containers/linux_arm64/Dockerfile
COPY cuda.packages.txt /cuda.packages.txt RUN /setup.sources.sh && /setup.packages.sh /devel.packages.txt # Install various tools. # - bats: bash unit testing framework # NOTE: v1.6.0 seems to have a bug that made "git" in setup_file break # - bazelisk: always use the correct bazel version # - buildifier: clean bazel build deps # - buildozer: clean bazel build deps
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tensorflow/c/eager/c_api_unified_experimental.cc
} // end namespace tensorflow // ============================================================================= // Public C API entry points // // These are only the generic entry points for the C API. This file does not // have any visibility into the graph/eager implementation and is only providing // C bindings to the abstract classes defined in the // c_api_unified_experimental_internal.h header. //
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build --linkopt="-Wl,--undefined-version" # 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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