diff --git a/advanced_source/rpc_ddp_tutorial/main.py b/advanced_source/rpc_ddp_tutorial/main.py
index f83384d0a8d..3d0d6ba2219 100644
--- a/advanced_source/rpc_ddp_tutorial/main.py
+++ b/advanced_source/rpc_ddp_tutorial/main.py
@@ -6,7 +6,7 @@
 import torch.distributed as dist
 import torch.distributed.autograd as dist_autograd
 import torch.distributed.rpc as rpc
-from torch.distributed.rpc import ProcessGroupRpcBackendOptions
+from torch.distributed.rpc import TensorPipeRpcBackendOptions
 import torch.multiprocessing as mp
 import torch.optim as optim
 from torch.distributed.optim import DistributedOptimizer
@@ -128,7 +128,7 @@ def run_worker(rank, world_size):
     os.environ['MASTER_PORT'] = '29500'
 
 
-    rpc_backend_options = ProcessGroupRpcBackendOptions()
+    rpc_backend_options = TensorPipeRpcBackendOptions()
     rpc_backend_options.init_method='tcp://localhost:29501'
 
     # Rank 2 is master, 3 is ps and 0 and 1 are trainers.
diff --git a/intermediate_source/dist_pipeline_parallel_tutorial.rst b/intermediate_source/dist_pipeline_parallel_tutorial.rst
index 693043478fb..2c8cc730258 100644
--- a/intermediate_source/dist_pipeline_parallel_tutorial.rst
+++ b/intermediate_source/dist_pipeline_parallel_tutorial.rst
@@ -316,7 +316,7 @@ where the ``shutdown`` by default will block until all RPC participants finish.
     def run_worker(rank, world_size, num_split):
         os.environ['MASTER_ADDR'] = 'localhost'
         os.environ['MASTER_PORT'] = '29500'
-        options = rpc.ProcessGroupRpcBackendOptions(num_send_recv_threads=128)
+        options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=128)
 
         if rank == 0:
             rpc.init_rpc(