scripts/train.py

from pathlib import Path
import sys,os,numpy as np,torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
ROOT=Path(__file__).resolve().parents[1];sys.path.insert(0,str(ROOT))
from model.samudrace import *
c=load_config(ROOT);rank=int(os.getenv("RANK",0));world=int(os.getenv("WORLD_SIZE",1));distributed=world>1
if distributed:dist.init_process_group("gloo")
torch.manual_seed(c["seed"]);torch.set_num_threads(2);d=np.load(ROOT/c["data"]["path"]);base=SamudrACE(**c["model"]);m=DDP(base) if distributed else base;opt=torch.optim.AdamW(m.parameters(),lr=c["train"]["learning_rate"]);losses=[]
for i in range(rank,len(d["atmosphere"]),world):a=torch.tensor(d["atmosphere"][i:i+1]);o=torch.tensor(d["ocean"][i:i+1]);pa,po=m(a,o,20);loss=((pa-torch.tensor(d["atmosphere_target"][i:i+1]))**2).mean()+((po-torch.tensor(d["ocean_target"][i:i+1]))**2).mean();opt.zero_grad();loss.backward();opt.step();losses.append(float(loss))
v=torch.tensor([sum(losses),len(losses)],dtype=torch.float64)
if distributed:dist.all_reduce(v)
path=ROOT/c["paths"]["checkpoint"]
if rank==0:path.parent.mkdir(parents=True,exist_ok=True);torch.save({"model":base.state_dict(),"model_config":c["model"]},path);write_json(ROOT/c["paths"]["training_metrics"],{"coupled_mse":float(v[0]/v[1]),"world_size":world});print(path)
if distributed:dist.destroy_process_group()