Update tutorials to use TensorPipeRpcBackendOptions. (#1164)

* Update tutorials to use TensorPipeRpcBackendOptions.

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Co-authored-by: pritam <pritam.damania@fb.com>

Author: pritamdamania87

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.

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(