diff --git a/train.py b/train.py
index ba69de43d255..18c8b531e6f0 100644
--- a/train.py
+++ b/train.py
@@ -294,7 +294,7 @@ def train(hyp, tb_writer, opt, device):
             loss, loss_items = compute_loss(pred, targets.to(device), model)
             # loss is scaled with batch size in func compute_loss. But in DDP mode, gradient is averaged between devices.
             if local_rank != -1:
-                loss *= dist.get_world_size()
+                loss *= opt.world_size
             if not torch.isfinite(loss):
                 print('WARNING: non-finite loss, ending training ', loss_items)
                 return results
@@ -449,6 +449,7 @@ def train(hyp, tb_writer, opt, device):
     opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
     device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)
     opt.total_batch_size = opt.batch_size
+    opt.world_size = 1
     if device.type == 'cpu':
         mixed_precision = False
     elif opt.local_rank != -1:
@@ -457,9 +458,10 @@ def train(hyp, tb_writer, opt, device):
         torch.cuda.set_device(opt.local_rank)
         device = torch.device("cuda", opt.local_rank)
         dist.init_process_group(backend='nccl', init_method='env://')  # distributed backend
-
-        assert opt.batch_size % dist.get_world_size() == 0
-        opt.batch_size = opt.total_batch_size // dist.get_world_size()
+        
+        opt.world_size = dist.get_world_size()
+        assert opt.batch_size % opt.world_size == 0, "Batch size is not a multiple of the number of devices given!"
+        opt.batch_size = opt.total_batch_size // opt.world_size
     print(opt)
 
     # Train
diff --git a/utils/datasets.py b/utils/datasets.py
index a3a5531f8f54..3d724e6fae62 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -59,7 +59,7 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
                                     pad=pad)
 
     batch_size = min(batch_size, len(dataset))
-    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
+    nw = min([os.cpu_count()//opt.world_size, batch_size if batch_size > 1 else 0, 8])  # number of workers
     train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) if local_rank != -1 else None
     dataloader = torch.utils.data.DataLoader(dataset,
                                              batch_size=batch_size,