fixed a NVIDIA-SMI problem submitted by 찬우 전

This commit is contained in:
k4yt3x 2019-03-13 11:59:42 -04:00 committed by K4YT3X
parent 7f3a377ea8
commit a963e407e0

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@ -108,9 +108,12 @@ def check_memory():
Avalon.warning('Nvidia-smi not available, skipping available memory check') Avalon.warning('Nvidia-smi not available, skipping available memory check')
Avalon.warning('If you experience error \"cudaSuccess out of memory\", try reducing number of threads you\'re using') Avalon.warning('If you experience error \"cudaSuccess out of memory\", try reducing number of threads you\'re using')
else: else:
# "0" is GPU ID. Both waifu2x drivers use the first GPU available, therefore only 0 makes sense try:
gpu_memory_available = (GPUtil.getGPUs()[0].memoryTotal - GPUtil.getGPUs()[0].memoryUsed) / 1024 # "0" is GPU ID. Both waifu2x drivers use the first GPU available, therefore only 0 makes sense
memory_status.append(('GPU', gpu_memory_available)) gpu_memory_available = (GPUtil.getGPUs()[0].memoryTotal - GPUtil.getGPUs()[0].memoryUsed) / 1024
memory_status.append(('GPU', gpu_memory_available))
except ValueError:
pass
# Go though each checkable memory type and check availability # Go though each checkable memory type and check availability
for memory_type, memory_available in memory_status: for memory_type, memory_available in memory_status: