diff --git a/only_for_me/narval/train.sh b/only_for_me/narval/train.sh
index ceff355e..be3032cd 100644
--- a/only_for_me/narval/train.sh
+++ b/only_for_me/narval/train.sh
@@ -13,7 +13,7 @@ PYTHON=/home/walml/envs/zoobot39_dev/bin/python
 mkdir $SLURM_TMPDIR/cache
 # mkdir /tmp/cache
 
-# export NCCL_BLOCKING_WAIT=1  #Set this environment variable if you wish to use the NCCL backend for inter-GPU communication.
+export NCCL_BLOCKING_WAIT=1  #Set this environment variable if you wish to use the NCCL backend for inter-GPU communication.
 # export MASTER_ADDR=$(hostname) #Store the master node’s IP address in the MASTER_ADDR environment variable.
 # echo "r$SLURM_NODEID master: $MASTER_ADDR"
 # echo "r$SLURM_NODEID Launching python script"
diff --git a/zoobot/pytorch/training/webdatamodule.py b/zoobot/pytorch/training/webdatamodule.py
index f1a94680..a965a777 100644
--- a/zoobot/pytorch/training/webdatamodule.py
+++ b/zoobot/pytorch/training/webdatamodule.py
@@ -76,7 +76,8 @@ def make_loader(self, urls, mode="train"):
         dataset = (
             # https://webdataset.github.io/webdataset/multinode/ 
             # WDS 'knows' which worker it is running on and selects a subset of urls accordingly
-            wds.WebDataset(urls, cache_dir=self.cache_dir, shardshuffle=shuffle>0, nodesplitter=nodesplitter_func)
+            wds.WebDataset(urls, cache_dir=self.cache_dir, shardshuffle=shuffle>0)
+                        #    , nodesplitter=nodesplitter_func)
             .shuffle(shuffle)
             .decode("rgb")
             .to_tuple('image.jpg', 'labels.json')
@@ -137,14 +138,14 @@ def val_dataloader(self):
     #     parser.add_argument("--valshards", default="imagenet-val-{000000..000006}.tar")
     #     return parser
 
-def nodesplitter_func(urls): # SimpleShardList
-    # print(urls)
-    try:
-        node_id, node_count = torch.distributed.get_rank(), torch.distributed.get_world_size()
-        return list(urls)[node_id::node_count]
-    except RuntimeError:
-        print('Distributed not initialised. Hopefully single node.')
-        return urls
+# def nodesplitter_func(urls): # SimpleShardList
+#     # print(urls)
+#     try:
+#         node_id, node_count = torch.distributed.get_rank(), torch.distributed.get_world_size()
+#         return list(urls)[node_id::node_count]
+#     except RuntimeError:
+#         print('Distributed not initialised. Hopefully single node.')
+#         return urls
 
 def identity(x):
     return x
\ No newline at end of file