diff --git a/ScaFFold/utils/checkpointing.py b/ScaFFold/utils/checkpointing.py
index 0bab949..92c5203 100644
--- a/ScaFFold/utils/checkpointing.py
+++ b/ScaFFold/utils/checkpointing.py
@@ -257,14 +257,22 @@ def save_checkpoint(
     def _write_to_disk(state_dict, last_path, best_path, is_best):
         """Worker function to perform actual disk I/O."""
         # Save 'last'
-        torch.save(state_dict, last_path)
+        try:
+            torch.save(state_dict, last_path)
+        except Exception as e:
+            print("Saving checkpoint failed. Continuing...")
+            print(e)
         # Save 'best' (copy logic)
         if is_best:
             # Copy is often faster than re-serializing
             if last_path.exists():
                 shutil.copyfile(last_path, best_path)
             else:
-                torch.save(state_dict, best_path)
+                try:
+                    torch.save(state_dict, best_path)
+                except Exception as e:
+                    print("Saving checkpoint failed. Continuing...")
+                    print(e)
 
     def _transfer_dict_to_cpu(self, obj):
         """Recursively move tensors to CPU."""
diff --git a/ScaFFold/utils/trainer.py b/ScaFFold/utils/trainer.py
index c76646a..4196c3a 100644
--- a/ScaFFold/utils/trainer.py
+++ b/ScaFFold/utils/trainer.py
@@ -295,7 +295,11 @@ def train(self):
                         masks_pred_dc = self.model(images_dc)
 
                         # Convert predictions for loss
-                        if images.size(0) < ps.num_shards:
+                        if isinstance(ps.num_shards, tuple) and len(ps.num_shards) == 1:
+                            n_shards = ps.num_shards[0]
+                        else:
+                            n_shards = ps.num_shards
+                        if images.size(0) < n_shards:
                             # For small batches (e.g., N=1 with dc_num_shards=2), replicate outputs
                             masks_pred = masks_pred_dc.to_replicate()
                             labels_for_loss = true_masks
@@ -304,7 +308,9 @@ def train(self):
                             masks_pred = masks_pred_dc.to_ddp()
                             dt_labels = distribute_tensor(
                                 true_masks,
-                                device_mesh=ps.device_mesh["dc"],
+                                device_mesh=ps.device_mesh[
+                                    f"dc{self.config.shard_dim + 2}"
+                                ],
                                 placements=[Shard(0)],
                             )
                             labels_for_loss = dt_labels.to_local()
@@ -419,11 +425,15 @@ def train(self):
                             true_masks_ddp = (
                                 DTensor.from_local(
                                     true_masks_dp,
-                                    device_mesh=ps.device_mesh["dc"],
+                                    device_mesh=ps.device_mesh[
+                                        f"dc{self.config.shard_dim + 2}"
+                                    ],
                                     placements=[Replicate()],
                                 )
                                 .redistribute(
-                                    device_mesh=ps.device_mesh["dc"],
+                                    device_mesh=ps.device_mesh[
+                                        f"dc{self.config.shard_dim + 2}"
+                                    ],
                                     placements=[Shard(0)],
                                 )
                                 .to_local()
diff --git a/ScaFFold/worker.py b/ScaFFold/worker.py
index 33f8949..07bedf0 100644
--- a/ScaFFold/worker.py
+++ b/ScaFFold/worker.py
@@ -239,7 +239,8 @@ def main(kwargs_dict: dict = {}):
     outfile_path = trainer.outfile_path
     train_data = np.genfromtxt(outfile_path, dtype=float, delimiter=",", names=True)
     total_train_time = train_data["epoch_duration"].sum()
-    total_epochs = train_data["epoch"][-1]
+    epochs = np.atleast_1d(train_data["epoch"])
+    total_epochs = int(epochs[-1])
     log.info(
         f"Benchmark run at scale {config.problem_scale} complete. \n\
         Trained to >= 0.95 validation dice score in {total_train_time:.2f} seconds, {total_epochs} epochs."