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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.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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ci/official/containers/linux_arm64/devel.usertools/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/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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CONTRIBUTING.md
development to avoid installing the packages directly on your system (in which case remember to change the directory from `/root` to `/tensorflow` once you get into the running container so `bazel` can find the `tensorflow` workspace). you can do this by using the following command. As an example- ```bash docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10
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tensorflow/c/BUILD
"//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", "//tensorflow/core/kernels:ops_testutil", "@com_google_absl//absl/container:inlined_vector", "@com_google_absl//absl/strings:str_format", "@eigen_archive//:eigen3", ], ) tf_cc_test( name = "ops_test", size = "medium",
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ci/official/envs/rbe
EOF exit 1 fi TFCI_BAZEL_COMMON_ARGS="$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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ci/official/wheel_test/README.md
"tf.foo" being imported first. The `_api/v2/api_packages.txt` file is generated during the TensorFlow API v2 init files creation process and is subsequently stored in the wheel file after the build. It also contains a few paths that cannot be directly imported. These paths point to attributes or sub-modules within a module's namespace, but they don't correspond to an actual file or directory on the filesystem. The list of
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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/BUILD
"//tensorflow/core/common_runtime/eager:tensor_handle", "//tensorflow/core/profiler/lib:traceme", "@com_google_absl//absl/algorithm:container", "@com_google_absl//absl/types:span", "@com_google_absl//absl/types:variant", ], }) + [ ":abstract_tensor_handle", "//tensorflow/c:c_api_macros_hdrs",
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RELEASE.md
* <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER> ### Bug Fixes and Other Changes * <SIMILAR TO ABOVE SECTION, BUT FOR OTHER IMPORTANT CHANGES / BUG FIXES> * <IF A CHANGE CLOSES A GITHUB ISSUE, IT SHOULD BE DOCUMENTED HERE> * <NOTES SHOULD BE GROUPED PER AREA> ## Thanks to our Contributors This release contains contributions from many people at Google, as well as:
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