diff --git a/docs/spmd.md b/docs/spmd.md
index c139c613d8d..5d6e554092d 100644
--- a/docs/spmd.md
+++ b/docs/spmd.md
@@ -235,7 +235,7 @@ train_loader = pl.MpDeviceLoader(
 
 PyTorch/XLA SPMD is compatible with the [torch.distributed.checkpoint](https://pytorch.org/docs/stable/distributed.checkpoint.html) library through a dedicated `Planner` instance. Users are able to synchronously save and load checkpoints through this common interface.
 
-The SPMDSavePlanner and SPMDLoadPlanner ([src](https://github.com/pytorch/xla/blob/master/torch_xla/experimental/distributed_checkpoint.py)) classes enable the `save_state_dict` and `load_state_dict` functions to operate directly on the shards of an `XLAShardedTensor`, enabling all of the benefits of distributed checkpointing in SPMD training.
+The SPMDSavePlanner and SPMDLoadPlanner ([src](https://github.com/pytorch/xla/blob/master/torch_xla/experimental/distributed_checkpoint.py)) classes enable the `save` and `load` functions to operate directly on the shards of an `XLAShardedTensor`, enabling all of the benefits of distributed checkpointing in SPMD training.
 
 Here is a demonstration of the synchronous distributed checkpointing API:
 
@@ -249,7 +249,7 @@ state_dict = {
     "optim": optim.state_dict(),
 }
 
-dist_cp.save_state_dict(
+dist_cp.save(
     state_dict=state_dict,
     storage_writer=dist_cp.FileSystemWriter(CHECKPOINT_DIR),
     planner=xc.SPMDSavePlanner(),
@@ -262,7 +262,7 @@ state_dict = {
     "model": model.state_dict(),
 }
 
-dist_cp.load_state_dict(
+dist_cp.load(
     state_dict=state_dict,
     storage_reader=dist_cp.FileSystemReader(CHECKPOINT_DIR),
     planner=xc.SPMDLoadPlanner(),
diff --git a/test/spmd/test_xla_distributed_checkpoint.py b/test/spmd/test_xla_distributed_checkpoint.py
index 98c465e0718..f4467e6a85f 100644
--- a/test/spmd/test_xla_distributed_checkpoint.py
+++ b/test/spmd/test_xla_distributed_checkpoint.py
@@ -76,7 +76,6 @@ def _save_and_restore(self,
                         save_planner=None,
                         load_planner=None,
                         is_sharded_cpu_state_dict=False,
-                        no_dist=True,
                         chkpt_path=None):
     """
     Checkpoint model_in using the provided save_planner and load into model_out
@@ -90,24 +89,22 @@ def _save_and_restore(self,
     if is_sharded_cpu_state_dict:
       model_in_state_dict = _sharded_cpu_state_dict(model_in_state_dict)
     model_out_state_dict = model_out.state_dict()
-    dist_cp.save_state_dict(
+    dist_cp.save(
         state_dict=model_in_state_dict,
         storage_writer=dist_cp.FileSystemWriter(
             chkpt_path,
             per_thread_copy_ahead=0,
         ),
         planner=save_planner,
-        no_dist=no_dist,
     )
     # Load the checkpoint using the provided load planner
     for p1, p2 in zip(model_in.parameters(), model_out.parameters()):
       self.assertFalse(torch.allclose(p1.cpu(), p2.cpu()))
 
-    dist_cp.load_state_dict(
+    dist_cp.load(
         state_dict=model_out_state_dict,
         storage_reader=dist_cp.FileSystemReader(chkpt_path),
         planner=load_planner,
-        no_dist=no_dist,
     )
     for p1, p2 in zip(model_in.parameters(), model_out.parameters()):
       self.assertTrue(torch.allclose(p1.cpu(), p2.cpu()))
@@ -142,15 +139,13 @@ def test_resharding_different_device_mesh(self):
         save_planner=SPMDSavePlanner(),
         load_planner=SPMDLoadPlanner())
 
-  @unittest.skipUnless(
-      {'CHKPT_PATH', 'MASTER_ADDR', 'MASTER_PORT', 'RANK', 'WORLD_SIZE'
-      } <= os.environ.keys(),
-      'CHKPT_PATH and distributed config must be set for multihost checkpoint')
+  @unittest.skipUnless('CHKPT_PATH' in os.environ,
+                       'CHKPT_PATH must be set for multihost checkpoint')
   def test_multihost_checkpoint(self):
     torch.manual_seed(42)
 
     # Initialize the default CPU process group from the environment.
-    dist.init_process_group()
+    dist.init_process_group(backend='gloo', init_method='xla://')
 
     model1 = self._get_sharded_model(mesh_shape=(1, self.n_devices))
     model2 = self._get_sharded_model(mesh_shape=(self.n_devices, 1))
@@ -160,7 +155,6 @@ def test_multihost_checkpoint(self):
         model2,
         save_planner=SPMDSavePlanner(),
         load_planner=SPMDLoadPlanner(),
-        no_dist=False,
         chkpt_path=os.environ['CHKPT_PATH'])
 
     # Destroy the CPU process group after the test
diff --git a/torch_xla/experimental/distributed_checkpoint/manager.py b/torch_xla/experimental/distributed_checkpoint/manager.py
index 13b6abfacfc..4ce57b5fb38 100644
--- a/torch_xla/experimental/distributed_checkpoint/manager.py
+++ b/torch_xla/experimental/distributed_checkpoint/manager.py
@@ -218,7 +218,7 @@ def _save(self, step, state_dict):
       path = self._get_path(step)
       # Delete any existing checkpoint at the current step.
       self._delete_chkpt_at_step(step)
-      dist_cp.save_state_dict(
+      dist_cp.save(
           state_dict=state_dict,
           storage_writer=FsspecWriter(
               path,
@@ -244,7 +244,7 @@ def should_save(self, step: int) -> bool:
     """
     preemption_detected = False
     if self.chkpt_on_preemption and self.reached_preemption(step):
-      logging.warn(
+      logging.warning(
           f"Preemption sync point reached at step {step}. Triggering a checkpoint."
       )
       preemption_detected = True
@@ -319,7 +319,7 @@ def restore(self, step: int, state_dict: STATE_DICT_TYPE) -> None:
     tracked_steps = set(x.step for x in self._tracked_chkpts)
     assert step in tracked_steps, f'Cannot restore from untracked step {step}. Valid steps are: {tracked_steps}'
     path = self._get_path(step)
-    dist_cp.load_state_dict(
+    dist_cp.load(
         state_dict=state_dict,
         storage_reader=FsspecReader(path),
         planner=xc.SPMDLoadPlanner(),