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tensorflow/c/eager/parallel_device/BUILD
# tensorflow/python:_pywrap_parallel_device. filegroup( name = "lib_headers", srcs = ["parallel_device_lib.h"], ) filegroup( name = "lib_sources", srcs = ["parallel_device_lib.cc"], ) filegroup( name = "device_headers", srcs = ["parallel_device.h"], ) filegroup( name = "device_sources", srcs = ["parallel_device.cc"], ) filegroup( name = "headers",
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.github/ISSUE_TEMPLATE/tflite-other.md
- type: input id: OS attributes: label: OS Platform and Distribution description: placeholder: e.g., Linux Ubuntu 16.04 validations: required: false - type: input id: Mobile attributes: label: Mobile device description: placeholder: e.g., Linux Ubuntu 16.04 validations: required: false - type: input id: Python attributes: label: Python version description: placeholder: e.g., 3.9 validations: required: false
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.github/ISSUE_TEMPLATE/tflite-in-play-services.md
--- name: TensorFlow Lite in Play Services issue about: Use this template for issues with TensorFlow Lite in Google Play Services labels: 'comp:lite-in-play-services' --- **System information** - Android Device information (use `adb shell getprop ro.build.fingerprint` if possible): - TensorFlow Lite in Play Services SDK version (found in `build.gradle`): - Google Play Services version
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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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SECURITY.md
inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface) are skipped, and any checks implemented inside the shape inference code are not executed. The security impact of skipping those checks should be low, since the attack
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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"
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CITATION.cff
environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of...
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