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Computation using data flow graphs for scalable machine learning

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TensorFlow Study Guide

CUDA

/third_party/gpus/cuda_configure.bzl does all the heavy lifting. The repository_rule depends on nvcc to obtain compiler and architecture information.

/usr/local/cuda/bin/nvcc -v /dev/null -o /dev/null
#$ _SPACE_=
#$ _CUDART_=cudart
#$ _HERE_=/usr/local/cuda/bin
#$ _THERE_=/usr/local/cuda/bin
#$ _TARGET_SIZE_=
#$ _TARGET_DIR_=
#$ _TARGET_DIR_=targets/x86_64-linux
#$ TOP=/usr/local/cuda/bin/..
#$ NVVMIR_LIBRARY_DIR=/usr/local/cuda/bin/../nvvm/libdevice
#$ LD_LIBRARY_PATH=/usr/local/cuda/bin/../lib:
#$ PATH=/usr/local/cuda/bin/../nvvm/bin:/usr/local/cuda/bin
#$ INCLUDES="-I/usr/local/cuda/bin/../targets/x86_64-linux/include"
#$ LIBRARIES=  "-L/usr/local/cuda/bin/../targets/x86_64-linux/lib/stubs" "-L/usr/local/cuda/bin/../targets/x86_64-linux/lib"
#$ CUDAFE_FLAGS=
#$ PTXAS_FLAGS=

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