{
    "device": "$torch.device(f'cuda:{dist.get_rank()}')",
    "network": {
        "_target_": "torch.nn.parallel.DistributedDataParallel",
        "module": "$@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,
    "train#trainer#train_handlers": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
    "validate#sampler": {
        "_target_": "DistributedSampler",
        "dataset": "@validate#dataset",
        "even_divisible": false,
        "shuffle": false
    },
    "validate#dataloader#sampler": "@validate#sampler",
    "validate#evaluator#val_handlers": "$None if dist.get_rank() > 0 else @validate#handlers",
    "training": [
        "$import torch.distributed as dist",
        "$dist.init_process_group(backend='nccl')",
        "$torch.cuda.set_device(@device)",
        "$monai.utils.set_determinism(seed=123)",
        "$setattr(torch.backends.cudnn, 'benchmark', True)",
        "$import logging",
        "$@train#trainer.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
        "$@validate#evaluator.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
        "$@train#trainer.run()",
        "$dist.destroy_process_group()"
    ]
}