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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 '
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 18:25:36 GMT 2024 - 53.8K bytes - Viewed (0) -
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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