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CONTRIBUTING.md
``` This will catch most license, Python coding style and BUILD file issues that may exist in your changes. #### Running unit tests There are two ways to run TensorFlow unit tests. 1. Using tools and libraries installed directly on your system. Refer to the
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ci/official/utilities/rename_and_verify_wheels.sh
if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)' "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)' fi # VERY basic check to ensure the [and-cuda] package variant is installable. # Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
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RELEASE.md
`CUDA_ILLEGAL_ADDRESS` failures. Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9 and CUDA 9.1 sometimes does not properly compute the carry bit when decomposing 64-bit address calculations with large offsets (e.g. `load [x + large_constant]`) into 32-bit arithmetic in SASS. As a result, these versions of `ptxas` miscompile most XLA programs which
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.bazelrc
# This is the same as the official TensorFlow builds. # See https://developer.nvidia.com/cuda-gpus#compute # `compute_XY` enables PTX embedding in addition to SASS. PTX # is forward compatible beyond the current compute capability major # release while SASS is only forward compatible inside the current # major release. Example: sm_80 kernels can run on sm_89 GPUs but
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