Add DCP async checkpointing info to docs

docs/source/deep_dives/checkpointer.rst

@@ -266,6 +266,61 @@ for testing or for loading quantized models for generation.
 
 |
 
+:class:`DistributedCheckpointer <torchtune.training.DistributedCheckpointer>`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This checkpointer reads and writes checkpoints in a distributed format using Pytorch Distributed Checkpointing (DCP).
+The output format is DCP's default format, which saves the state dict across all ranks, as seperate files for each rank. This differs
+from the other checkpointer implementations, which consolidate to rank-0 and then save the state dict as full tensors.
+The distributed checkpointer is enabled when enabling asynchronous checkpointing during training and uses DCP's async_save API.
+Async distributed checkpointing is only used for intermediate checkpoints.
+
+When asynchronous checkpointing is enabled, intermediate checkpoints are saved using the DistributedCheckpointer
+without blocking the training process. This is particularly useful for large models where saving checkpoints
+can take significant time.
+
+**Key Features:**
+
+- **Asynchronous Saving**: Allows training to continue while checkpoints are being saved in the background
+- **Distributed-Aware**: Designed to work seamlessly in multi-GPU and multi-node training setups
+
+**Configuration Example:**
+
+To enable asynchronous checkpointing in your training config, you need to set the ``enable_async_checkpointing``
+flag to ``True``. The DistributedCheckpointer will be automatically used when this flag is enabled.
+
+.. code-block:: yaml
+
+    checkpointer:
+        # checkpointer to use for final checkpoints
+        _component_: torchtune.training.FullModelHFCheckpointer
+
+    # Set to True to enable asynchronous distributed checkpointing for intermediate checkpoints
+    enable_async_checkpointing: True
+
+**Resuming Training with DistributedCheckpointer:**
+
+If your training was interrupted and you had async checkpointing enabled, you can resume from the latest
+distributed checkpoint by setting both ``resume_from_checkpoint`` and ``enable_async_checkpointing`` to ``True``:
+
+.. code-block:: yaml
+
+    # Set to True to resume from checkpoint
+    resume_from_checkpoint: True
+
+    # Set to True to enable asynchronous checkpointing
+    enable_async_checkpointing: True
+
+The DistributedCheckpointer will automatically locate and load the latest intermediate checkpoint from the
+output directory.
+
+.. note::
+
+    The final checkpoint at the end of training is always saved synchronously to ensure all
+    data is properly persisted in safetensors or torch.save format before the training job completes.
+
+|
+
 Checkpoint Output
 ---------------------------------