| | |
|---|
| | | logging.warning("No keep_nbest_models is given. Change to [1]") |
|---|
| | | trainer_options.keep_nbest_models = [1] |
|---|
| | | keep_nbest_models = trainer_options.keep_nbest_models |
|---|
| | | |
|---|
| | | #assert batch_interval is set and >0 |
|---|
| | | assert trainer_options.batch_interval > 0 |
|---|
| | | |
|---|
| | | output_dir = Path(trainer_options.output_dir) |
|---|
| | | reporter = Reporter() |
|---|
| | |
|---|
| | | if num_batch_updates % batch_interval == 0: |
|---|
| | | if options.use_pai and options.oss_bucket is not None: |
|---|
| | | buffer = BytesIO() |
|---|
| | | torch.save(model.state_dict(), buffer) |
|---|
| | | if hasattr(model, "module"): |
|---|
| | | torch.save(model.module.state_dict(), buffer) |
|---|
| | | else: |
|---|
| | | torch.save(model.state_dict(), buffer) |
|---|
| | | options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}step.pb"), buffer.getvalue()) |
|---|
| | | else: |
|---|
| | | torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb")) |
|---|
| | | if hasattr(model, "module"): |
|---|
| | | torch.save(model.module.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb")) |
|---|
| | | else: |
|---|
| | | torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb")) |
|---|
| | | |
|---|
| | | if distributed: |
|---|
| | | torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM) |
|---|
