| from pathlib import Path | |
| import sys,os,numpy as np,torch;import torch.distributed as dist | |
| from torch.nn.parallel import DistributedDataParallel as DDP | |
| R=Path(__file__).resolve().parents[1];sys.path.insert(0,str(R));from model.weatherbench2 import * | |
| c=cfg(R);rank=int(os.getenv('RANK',0));world=int(os.getenv('WORLD_SIZE',1));ddp=world>1 | |
| if ddp:dist.init_process_group('gloo') | |
| d=np.load(R/c['data']['path']);base=WB2Baseline(**c['model']);m=DDP(base) if ddp else base;opt=torch.optim.Adam(m.parameters(),lr=c['train']['lr']);ids=np.arange(rank,len(d['input']),world);loss=((m(torch.tensor(d['input'][ids]))-torch.tensor(d['target'][ids]))**2).mean();opt.zero_grad();loss.backward();opt.step();v=loss.detach().double() | |
| if ddp:dist.all_reduce(v);v/=world | |
| p=R/c['paths']['checkpoint'] | |
| if rank==0:p.parent.mkdir(parents=True,exist_ok=True);torch.save({'model':base.state_dict(),'model_config':c['model']},p);write(R/c['paths']['training_metrics'],{'mse':float(v),'world_size':world});print(p) | |
| if ddp:dist.destroy_process_group() | |