I've spent a fair amount of time running ML experiments on remote GPU clusters, and I kept running into the same frustration: the code that actually matters — the training logic — drowns in infrastructure noise.
A typical training script looks something like this:
parser = argparse.ArgumentParser()
parser.add_argument("--lr", type=float, default=1e-3)
args = parser.parse_args()
if args.resume:
ckpt = torch.load(args.resume)
model.load_state_dict(ckpt["model"])
optimizer.load_state_dict(ckpt["optimizer"])
start_step = ckpt["step"]
else:
start_step = 0
for step in range(start_step, total_steps):
loss = train_step(batch)
wandb.log({"loss": loss})
if step % 1000 == 0:
torch.save({...}, f"ckpt_{step}.pt")About 6 min