diff --git a/test/test_prototype_builtin_datasets.py b/test/test_prototype_builtin_datasets.py
index f8dc3a0542b..06f86c0a016 100644
--- a/test/test_prototype_builtin_datasets.py
+++ b/test/test_prototype_builtin_datasets.py
@@ -1,5 +1,6 @@
 import functools
 import io
+import os
 import pickle
 from pathlib import Path
 
@@ -7,6 +8,7 @@
 import torch
 from builtin_dataset_mocks import parametrize_dataset_mocks, DATASET_MOCKS
 from torch.testing._comparison import assert_equal, TensorLikePair, ObjectPair
+from torch.utils.data import DataLoader
 from torch.utils.data.graph import traverse
 from torch.utils.data.graph_settings import get_all_graph_pipes
 from torchdata.datapipes.iter import Shuffler, ShardingFilter
@@ -30,6 +32,23 @@ def test_home(mocker, tmp_path):
     yield tmp_path
 
 
+@pytest.fixture
+def ddp_fixture():
+    # Note: we only test DDP with world_size=1, but it should be enough for our purpose.
+    # If we ever need to go full DDP, we'll need to implement a much more complex logic, similar to
+    # MultiProcessTestCase from torch core.
+
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29501"
+    torch.distributed.init_process_group(backend="gloo", world_size=1, rank=0)
+    torch.distributed.barrier()
+
+    yield
+
+    torch.distributed.barrier()
+    torch.distributed.destroy_process_group()
+
+
 def test_coverage():
     untested_datasets = set(datasets.list_datasets()) - DATASET_MOCKS.keys()
     if untested_datasets:
@@ -109,7 +128,7 @@ def test_transformable(self, test_home, dataset_mock, config):
 
         next(iter(dataset.map(transforms.Identity())))
 
-    @pytest.mark.xfail(reason="See https://github.com/pytorch/data/issues/237")
+    # @pytest.mark.xfail(reason="See https://github.com/pytorch/data/issues/237")
     @parametrize_dataset_mocks(DATASET_MOCKS)
     def test_serializable(self, test_home, dataset_mock, config):
         dataset_mock.prepare(test_home, config)
@@ -118,10 +137,20 @@ def test_serializable(self, test_home, dataset_mock, config):
 
         pickle.dumps(dataset)
 
+    @parametrize_dataset_mocks(DATASET_MOCKS)
+    def test_ddp(self, test_home, dataset_mock, config, ddp_fixture):
+        dataset_mock.prepare(test_home, config)
+
+        dataset = datasets.load(dataset_mock.name, **config)
+
+        dl = DataLoader(dataset, collate_fn=lambda batch: batch)
+
+        next(iter(dl))
+
     # TODO: we need to enforce not only that both a Shuffler and a ShardingFilter are part of the datapipe, but also
     #  that the Shuffler comes before the ShardingFilter. Early commits in https://github.com/pytorch/vision/pull/5680
     #  contain a custom test for that, but we opted to wait for a potential solution / test from torchdata for now.
-    @pytest.mark.xfail(reason="See https://github.com/pytorch/data/issues/237")
+    # @pytest.mark.xfail(reason="See https://github.com/pytorch/data/issues/237")
     @parametrize_dataset_mocks(DATASET_MOCKS)
     @pytest.mark.parametrize("annotation_dp_type", (Shuffler, ShardingFilter))
     def test_has_annotations(self, test_home, dataset_mock, config, annotation_dp_type):