Fix errors when no CUDA devices are available#1334
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soumith merged 1 commit intopytorch:masterfrom Apr 23, 2017
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Fixes pytorch#1267 This fixes a number of issues when PyTorch was compiled with CUDA support but run on a machine without any GPUs. Now, we treat all errors from cudaGetDeviceCount() as if the machine has no devices.
soumith
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Apr 23, 2017
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This PR relaxes the constraint so that arbitrary padding sizes can be used as long as output domains don't get larger than input domains.
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* update ngc link and dockerhub container tag * update * update * update * Update README.md Co-authored-by: Masaki Kozuki <mkozuki@nvidia.com>
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Fixes #1267
This fixes a number of issues when PyTorch was compiled with CUDA
support but run on a machine without any GPUs. Now, we treat all errors
from cudaGetDeviceCount() as if the machine has no devices.
Now all tests pass with:
CUDA_VISIBLE_DEVICES= ./run_test.shI also changed _cuda_init and _cuda_sparse_init to return None on success and raise an exception on failure. Previously, they set the exception but returned a Python bool False which isn't allowed.
We should probably add a smoke test for the PyTorch compiled with CUDA but run without any GPUs (or with no visible device).