setup torch.distributed for slurm platform and set num_aug_splits

train.py

@@ -319,21 +319,54 @@ def main():
              
     args.prefetcher = not args.no_prefetcher
     args.distributed = False
-    if 'WORLD_SIZE' in os.environ:
+    if 'SLURM_PROCID' in os.environ:
+        backend='nccl'
+        port = "13333"
+        proc_id = int(os.environ['SLURM_PROCID'])
+        ntasks = int(os.environ['SLURM_NTASKS'])
+        node_list = os.environ['SLURM_NODELIST']
+        if '[' in node_list:
+            beg = node_list.find('[')
+            pos1 = node_list.find('-', beg)
+            if pos1 < 0:
+                pos1 = 1000
+            pos2 = node_list.find(',', beg)
+            if pos2 < 0:
+                pos2 = 1000
+            node_list = node_list[:min(pos1, pos2)].replace('[', '')
+        addr = node_list[8:].replace('-', '.')
+        os.environ['MASTER_PORT'] = port
+        os.environ['MASTER_ADDR'] = addr
+        os.environ['WORLD_SIZE'] = str(ntasks)
+        os.environ['RANK'] = str(proc_id)
+        if backend == 'nccl':
+            torch.distributed.init_process_group(backend='nccl')
+        else:
+            torch.distributed.init_process_group(backend='gloo', rank=proc_id, world_size=ntasks)
+        rank = torch.distributed.get_rank()
+        device = rank % torch.cuda.device_count()
+        torch.cuda.set_device(device)
+
         args.distributed = int(os.environ['WORLD_SIZE']) > 1
-    args.device = 'cuda:0'
-    args.world_size = 1
-    args.rank = 0  # global rank
-    if args.distributed:
-        args.device = 'cuda:%d' % args.local_rank
-        torch.cuda.set_device(args.local_rank)
-        torch.distributed.init_process_group(backend='nccl', init_method='env://')
-        args.world_size = torch.distributed.get_world_size()
-        args.rank = torch.distributed.get_rank()
-        _logger.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'
-                     % (args.rank, args.world_size))
-    else:
-        _logger.info('Training with a single process on 1 GPUs.')
+        args.world_size = int(os.environ['WORLD_SIZE'])
+        args.rank = rank
+        args.local_rank = rank
+        args.device = device
+    elif 'WORLD_SIZE' in os.environ:
+        args.distributed = int(os.environ['WORLD_SIZE']) > 1
+        args.device = 'cuda:0'
+        args.world_size = 1
+        args.rank = 0  # global rank
+        if args.distributed:
+            args.device = 'cuda:%d' % args.local_rank
+            torch.cuda.set_device(args.local_rank)
+            torch.distributed.init_process_group(backend='nccl', init_method='env://')
+            args.world_size = torch.distributed.get_world_size()
+            args.rank = torch.distributed.get_rank()
+            _logger.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'
+                        % (args.rank, args.world_size))
+        else:
+            _logger.info('Training with a single process on 1 GPUs.')
     assert args.rank >= 0
 
     # resolve AMP arguments based on PyTorch / Apex availability
@@ -524,7 +557,6 @@ def main():
         vflip=args.vflip,
         color_jitter=args.color_jitter,
         auto_augment=args.aa,
-        num_aug_repeats=args.aug_repeats,
         num_aug_splits=num_aug_splits,
         interpolation=train_interpolation,
         mean=data_config['mean'],