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import torch | |
import logging | |
def select_device(min_memory = 2048): | |
logger = logging.getLogger(__name__) | |
if torch.cuda.is_available(): | |
available_gpus = [] | |
for i in range(torch.cuda.device_count()): | |
props = torch.cuda.get_device_properties(i) | |
free_memory = props.total_memory - torch.cuda.memory_reserved(i) | |
available_gpus.append((i, free_memory)) | |
selected_gpu, max_free_memory = max(available_gpus, key=lambda x: x[1]) | |
device = torch.device(f'cuda:{selected_gpu}') | |
free_memory_mb = max_free_memory / (1024 * 1024) | |
if free_memory_mb < min_memory: | |
logger.log(logging.WARNING, f'GPU {selected_gpu} has {round(free_memory_mb, 2)} MB memory left.') | |
device = torch.device('cpu') | |
else: | |
logger.log(logging.WARNING, f'No GPU found, use CPU instead') | |
device = torch.device('cpu') | |
return device | |