| | |
|---|
| | | prepare_data(args, distributed_option) |
|---|
| | | |
|---|
| | | model = build_model(args) |
|---|
| | | optimizer = build_optimizer(args, model=model) |
|---|
| | | scheduler = build_scheduler(args, optimizer) |
|---|
| | | optimizers = build_optimizer(args, model=model) |
|---|
| | | schedulers = build_scheduler(args, optimizers) |
|---|
| | | |
|---|
| | | logging.info("world size: {}, rank: {}, local_rank: {}".format(distributed_option.dist_world_size, |
|---|
| | | distributed_option.dist_rank, |
|---|
| | | distributed_option.local_rank)) |
|---|
| | | logging.info(pytorch_cudnn_version()) |
|---|
| | | logging.info(model_summary(model)) |
|---|
| | | logging.info("Optimizer: {}".format(optimizer)) |
|---|
| | | logging.info("Scheduler: {}".format(scheduler)) |
|---|
| | | logging.info("Optimizer: {}".format(optimizers)) |
|---|
| | | logging.info("Scheduler: {}".format(schedulers)) |
|---|
| | | |
|---|
| | | # dump args to config.yaml |
|---|
| | | if not distributed_option.distributed or distributed_option.dist_rank == 0: |
|---|
