try

examples/1_load_lerobot_dataset.py

@@ -80,7 +80,7 @@ print(f"{dataset[0]['action'].shape=}\n")  # (64,c)
 # PyTorch datasets.
 dataloader = torch.utils.data.DataLoader(
     dataset,
-    num_workers=1,
+    num_workers=0,
     batch_size=32,
     shuffle=True,
 )

examples/3_train_policy.py

@@ -49,7 +49,7 @@ optimizer = torch.optim.Adam(policy.parameters(), lr=1e-4)
 # Create dataloader for offline training.
 dataloader = torch.utils.data.DataLoader(
     dataset,
-    num_workers=4,
+    num_workers=0,
     batch_size=64,
     shuffle=True,
     pin_memory=device != torch.device("cpu"),

tests/test_examples.py

@@ -37,7 +37,7 @@ def test_examples_3_and_2():
         file_contents,
         [
             ("training_steps = 5000", "training_steps = 1"),
-            ("num_workers=4", "num_workers=0"),
+            # ("num_workers=4", "num_workers=0"),
             ('device = torch.device("cuda")', 'device = torch.device("cpu")'),
             ("batch_size=64", "batch_size=1"),
         ],