diff --git a/megatron/core/dist_checkpointing/strategies/two_stage.py b/megatron/core/dist_checkpointing/strategies/two_stage.py
index b76bf6a103..50b31e2497 100644
--- a/megatron/core/dist_checkpointing/strategies/two_stage.py
+++ b/megatron/core/dist_checkpointing/strategies/two_stage.py
@@ -25,9 +25,16 @@
 timers = defaultdict(list)
 
 logger = getLogger(__name__)
+logger.warning(
+    'megatron.core.dist_checkpointing.two_stage module is deprecated'
+    ' and will be removed in Megatron-Core v0.12. Please use'
+    ' FullyParallelLoadStrategyWrapper to accomplish a parallelized checkpoint load.'
+)
 
 
 def timed(verbose=True):
+    """Timing decorator."""
+
     def timed_dec(fn):
         name = fn.__name__
 
@@ -58,6 +65,7 @@ class _ShardedTensorMetadata:
 
 
 def sharded_tensor_chunk_id(sharded_tensor: ShardedTensor):
+    """Id of a sharded tensor."""
     return (sharded_tensor.key, sharded_tensor.global_offset)
 
 
@@ -100,6 +108,7 @@ def __init__(self, data_parallel_group, cpu_transfer=True):
         self.global_rank = torch.distributed.get_rank()
 
     def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
+        """Main load method."""
         self.maybe_init_gloo_group()
         all_tensors_sorted = self._build_load_plan(sharded_state_dict)
         self._exchange_loaded_tensors(all_tensors_sorted, sharded_state_dict, checkpoint_dir)
@@ -108,6 +117,7 @@ def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
         return sharded_state_dict
 
     def summarize_load_times(self):
+        """Summarize load times."""
         torch.distributed.barrier()
         logger.info('Checkpoint loading finished. Summary:')
         # TODO: `timers` keys are not guaranteed to be the same across ranks which causes hangs
@@ -123,6 +133,7 @@ def summarize_load_times(self):
 
     @timed(verbose=False)
     def load_tensor_from_storage(self, checkpoint_dir, ten_meta: _ShardedTensorMetadata):
+        """Load tensor from storage."""
         logger.debug(f'_load_from_array({ten_meta.sharded_tensor_no_data.key}) init')
         ret = _load_from_array(
             ten_meta.sharded_tensor_no_data,
@@ -135,6 +146,7 @@ def load_tensor_from_storage(self, checkpoint_dir, ten_meta: _ShardedTensorMetad
 
     @timed()
     def maybe_init_gloo_group(self):
+        """Create Gloo groups."""
         if not self.cpu_transfer:
             return
         all_groups = [None] * torch.distributed.get_world_size()