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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. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1
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tensorflow/c/eager/BUILD
"//tensorflow/core/distributed_runtime/eager:eager_client", "//tensorflow/core/distributed_runtime/eager:remote_mgr", "//tensorflow/core/distributed_runtime/rpc:grpc_channel", "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service",
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ci/official/containers/linux_arm64/Dockerfile
RUN dpkg -i /patchelf/patchelf_0.14.3-1_arm64.deb # All lines past this point are reset when $CACHEBUSTER is set. We need this # for Python specifically because we install some nightly packages which are # likely to change daily. ARG CACHEBUSTER=0 RUN echo $CACHEBUSTER # Setup build and environment COPY devel.usertools /usertools COPY devel.bashrc /root/.bashrc COPY ld.so.conf /dt10/etc/ # Setup JAX Python environment.
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# Use the public-access TF DevInfra cache (read only) 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/containers/linux_arm64/devel.usertools/code_check_full.bats
One or more test dependencies are not in the pip package. If these test dependencies need to be in the TensorFlow pip package, please add them to //tensorflow/tools/pip_package/BUILD. Otherwise, add the no_pip tag to the test, or change code_check_full.bats in the SIG Build repository. That's https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/code_check_full.bats Here are the affected tests: EOF
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ci/official/utilities/code_check_full.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'" }
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.bazelrc
build --experimental_cc_shared_library # cc_shared_library ensures no library is linked statically more than once. build --experimental_link_static_libraries_once=false # Prevent regressions on those two incompatible changes # TODO: remove those flags when they are flipped in the default Bazel version TF uses. build --incompatible_enforce_config_setting_visibility # TODO: also enable this flag after fixing the visibility violations
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ci/devinfra/docker_windows/Dockerfile
wheel win-unicode-console; # Hardcoding Android license since I did not find any solution on accepting it # through the docker build command. If the licensing agreement changes, this # will need to be updated as well. RUN New-Item -ItemType Directory -Path C:\Android\android-sdk\licenses; \
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README.md
* Clone the TensorFlow repo and switch to the corresponding branch for your desired TensorFlow version, for example, branch `r2.8` for version 2.8. * Apply (that is, cherry-pick) the desired changes and resolve any code conflicts. * Run TensorFlow tests and ensure they pass. * [Build](https://www.tensorflow.org/install/source) the TensorFlow pip package from source. ## Continuous build status
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ci/README.md
The CI folder contains the configuration files and scripts used to build, test, and deploy TensorFlow. This folder is typically used by continuous integration (CI) tools to build and test TensorFlow whenever there is a change to the code. This folder is broken into subfolders that represent the level of support
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