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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# 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 # 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
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tensorflow/api_template.__init__.py
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import. _os.environ["TF2_BEHAVIOR"] = "1" from tensorflow.python import tf2 as _tf2 _tf2.enable() # API IMPORTS PLACEHOLDER # WRAPPER_PLACEHOLDER # Make sure directory containing top level submodules is in
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# 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 # Use lld as the linker build --linkopt="-fuse-ld=lld" build --linkopt="-lm" build --linkopt="-Wl,--undefined-version" # Prevent double-compilation of some TF code, ref. b/183279666 (internal)
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.bazelrc
test:win_clang_xla --host_linkopt=/FORCE:MULTIPLE # Options to build TensorFlow 1.x or 2.x. # TODO(kanglan): Change v2's define to default behavior build:v2 --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 # Enable all targets in XLA build:cpu_cross --define=with_cross_compiler_support=true # Disable XLA on mobile. build:xla --define=with_xla_support=true # TODO: remove, it's on by default.
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RELEASE.md
* `tf.lite` * Keras-based LSTM models must be converted with an explicit batch size in the input layer. ## Bug Fixes and Other Changes ### TF Core: * Set `tf2_behavior` to 1 to enable V2 for early loading cases. * Add `execute_fn_for_device function` to dynamically choose the implementation based on underlying device placement. * Eager:
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