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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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.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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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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