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SECURITY.md
## TensorFlow models are programs TensorFlow [**models**](https://developers.google.com/machine-learning/glossary/#model) (to use a term commonly used by machine learning practitioners) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
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ci/official/README.md
# export TFCI=py311,linux_x86,no_docker # Advanced: Use Remote Build Execution (RBE) (internal developers only) # # RBE dramatically speeds up builds and testing. It also gives you a # public URL to share your build results with collaborators. However, # it is only available to a limited set of internal TensorFlow developers. # # RBE is incompatible with local caching, so you must remove
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README.md
[libraries](https://www.tensorflow.org/resources/libraries-extensions), and [community](https://www.tensorflow.org/community) resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. TensorFlow was originally developed by researchers and engineers working within the Machine Intelligence team at Google Brain to conduct research in machine
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CONTRIBUTING.md
1. Using tools and libraries installed directly on your system. Refer to the [CPU-only developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu.Dockerfile) and [GPU developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu.Dockerfile)
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tensorflow/c/c_op_requires.h
// C structs, including `TF_OpKernelContext`, `TF_Status`, etc. This is analogus // to the macros in tensorflow/core/framework/op_requires.h. This is provided // for plugin OpKernel developer's convenience. #define C_OPKERNELCONTEXT_REQUIRES_OK(CTX, C_STATUS, __VA_ARGS__) \ do { \ ::tensorflow::Status _s(__VA_ARGS__); \
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ci/official/envs/macos_x86_cross_compile
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RELEASE.md
`tf.distribute.experimental.MultiWorkerMirroredStrategy` * Update NVIDIA `NCCL` to `2.5.7-1` for better performance and performance tuning. Please see [nccl developer guide](https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/docs/env.html) for more information on this. * Support gradient `allreduce` in `float16`. See this
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.bazelrc
build:cuda_clang --config=cuda # Enable TensorRT optimizations https://developer.nvidia.com/tensorrt build:cuda_clang --config=tensorrt build:cuda_clang --action_env=TF_CUDA_CLANG="1" build:cuda_clang --@local_config_cuda//:cuda_compiler=clang # Select supported compute capabilities (supported graphics cards). # This is the same as the official TensorFlow builds. # See https://developer.nvidia.com/cuda-gpus#compute
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configure.py
ask_cuda_compute_capabilities = ( 'Please specify a list of comma-separated CUDA compute capabilities ' 'you want to build with.\nYou can find the compute capability of your ' 'device at: https://developer.nvidia.com/cuda-gpus. Each capability ' 'can be specified as "x.y" or "compute_xy" to include both virtual and' ' binary GPU code, or as "sm_xy" to only include the binary '
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