| | |
|---|
| | | description = ( |
|---|
| | | f"rank: {self.local_rank}, " |
|---|
| | | f"epoch: {epoch}/{self.max_epoch}, " |
|---|
| | | f"step: {batch_idx}/{len(self.dataloader_train)}, total: {self.batch_total}, " |
|---|
| | | f"step: {batch_idx+1}/{len(self.dataloader_train)}, total: {self.batch_total}, " |
|---|
| | | f"(loss: {loss.detach().cpu().item():.3f}), " |
|---|
| | | f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, " |
|---|
| | | f"{speed_stats}, " |
|---|
| | |
|---|
| | | description = ( |
|---|
| | | f"rank: {self.local_rank}, " |
|---|
| | | f"validation epoch: {epoch}/{self.max_epoch}, " |
|---|
| | | f"step: {batch_idx}/{len(self.dataloader_val)}, " |
|---|
| | | f"step: {batch_idx+1}/{len(self.dataloader_val)}, " |
|---|
| | | f"(loss: {loss.detach().cpu().item():.3f}), " |
|---|
| | | f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, " |
|---|
| | | f"{speed_stats}, " |
|---|
