Update train_unconditional.py (#3899)

increase the time of timeout when using big dataset or high resolution

examples/unconditional_image_generation/train_unconditional.py

@@ -4,6 +4,7 @@ import logging
 import math
 import os
 import shutil
+from datetime import timedelta
 from pathlib import Path
 from typing import Optional
 
@@ -11,7 +12,7 @@ import accelerate
 import datasets
 import torch
 import torch.nn.functional as F
-from accelerate import Accelerator
+from accelerate import Accelerator, InitProcessGroupKwargs
 from accelerate.logging import get_logger
 from accelerate.utils import ProjectConfiguration
 from datasets import load_dataset
@@ -286,11 +287,13 @@ def main(args):
     logging_dir = os.path.join(args.output_dir, args.logging_dir)
     accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
 
+    kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=7200))#a big number for high resolution or big dataset
     accelerator = Accelerator(
         gradient_accumulation_steps=args.gradient_accumulation_steps,
         mixed_precision=args.mixed_precision,
         log_with=args.logger,
         project_config=accelerator_project_config,
+        kwargs_handlers=[kwargs],
     )
 
     if args.logger == "tensorboard":