# This file contains the changes to implement DDP training with the train.yaml config.

is_dist: '$dist.is_initialized()'
rank: '$dist.get_rank() if @is_dist else 0'
device: '$torch.device(f"cuda:{@rank}" if torch.cuda.is_available() else "cpu")'  # assumes GPU # matches rank #

# wrap the network in a DistributedDataParallel instance, moving it to the chosen device for this process
network:
  _target_: torch.nn.parallel.DistributedDataParallel
  module: $@network_def.to(@device)
  device_ids: ['@device']
  find_unused_parameters: true

train_sampler:
  _target_: DistributedSampler
  dataset: '@train_dataset'
  even_divisible: true
  shuffle: true

train_dataloader#sampler: '@train_sampler'
train_dataloader#shuffle: false

val_sampler:
  _target_: DistributedSampler
  dataset: '@val_dataset'
  even_divisible: false
  shuffle: false

val_dataloader#sampler: '@val_sampler'

run:
- $import torch.distributed as dist
- $dist.init_process_group(backend='nccl')
- $torch.cuda.set_device(@device)
- $monai.utils.set_determinism(seed=123)  # may want to choose a different seed or not do this here
- $@trainer.run()
- $dist.destroy_process_group()