41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
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# Resume all interrupted trainings in yolov5/ dir including DDP trainings
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# Usage: $ python utils/aws/resume.py
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import os
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import sys
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from pathlib import Path
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import torch
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import yaml
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[2] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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port = 0 # --master_port
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path = Path('').resolve()
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for last in path.rglob('*/**/last.pt'):
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ckpt = torch.load(last)
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if ckpt['optimizer'] is None:
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continue
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# Load opt.yaml
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with open(last.parent.parent / 'opt.yaml', errors='ignore') as f:
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opt = yaml.safe_load(f)
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# Get device count
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d = opt['device'].split(',') # devices
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nd = len(d) # number of devices
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ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel
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if ddp: # multi-GPU
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port += 1
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cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}'
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else: # single-GPU
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cmd = f'python train.py --resume {last}'
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cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread
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print(cmd)
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os.system(cmd)
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