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tensorflow/BUILD
) # This flag enables experimental TPU support bool_flag( name = "enable_tpu_support", build_setting_default = False, ) config_setting( name = "with_tpu_support_define", define_values = {"with_tpu_support": "true"}, visibility = ["//visibility:public"], ) config_setting( name = "with_tpu_support_flag", flag_values = {":enable_tpu_support": "True"},
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# odd to attempt to validate the quality of the pip package. The wheel is # installed into a virtual environment, and then that venv is used to run all # bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which # drops all the bazel dependencies for each py_test; this makes all the tests # use the wheel's TensorFlow installation instead of the one made available
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CONTRIBUTING.md
bazel by doing as follows: ```bash export flags="--config=opt -k" ``` If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and add the `cuda` option flag ```bash export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH" export flags="--config=opt --config=cuda -k" ```
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# odd to attempt to validate the quality of the pip package. The wheel is # installed into a virtual environment, and then that venv is used to run all # bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which # drops all the bazel dependencies for each py_test; this makes all the tests # use the wheel's TensorFlow installation instead of the one made available
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.bazelrc
build:release_arm64_linux --config=mkl_aarch64_threadpool build:release_arm64_linux --copt=-flax-vector-conversions test:release_arm64_linux --flaky_test_attempts=3 # The old gcc linux build options are preserved in the unsupported_*_linux # configs. If your project fails to build with Clang, you can use these # unsupported flags to replace the release flags in your build command.
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tensorflow/c/BUILD
"//tensorflow/core:lib_internal", "//tensorflow/core:op_gen_lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core/config:flag_defs", "//tensorflow/core/config:flags", "//tensorflow/core/distributed_runtime:server_lib", "//tensorflow/core/kernels:logging_ops", "@com_google_absl//absl/strings", ], }),
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ci/official/envs/rbe
That setting must exist for this script to function correctly. 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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tensorflow/c/eager/BUILD
hdrs = ["tape.h"], visibility = ["//tensorflow:internal"], deps = [ "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core/config:flag_defs", "//tensorflow/core/config:flags", ], ) filegroup( name = "headers", srcs = [ "c_api.h", "c_api_experimental.h", "dlpack.h", ],
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RELEASE.md
* CPU * Fix an issue w/ dynamic weights and `Conv2D` on x86. * Add a runtime Android flag for enabling `XNNPACK` for optimized CPU performance. * Add a runtime iOS flag for enabling `XNNPACK` for optimized CPU performance. * Add a compiler flag to enable building a TFLite library that applies `XNNPACK` delegate automatically when the model has a `fp32` operation.
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README.md
A smaller CPU-only package is also available: ``` $ pip install tensorflow-cpu ``` To update TensorFlow to the latest version, add `--upgrade` flag to the above commands. *Nightly binaries are available for testing using the [tf-nightly](https://pypi.python.org/pypi/tf-nightly) and [tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPi.*
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