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Update tutorials to use TensorPipeRpcBackendOptions. #1164

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Sep 25, 2020
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4 changes: 2 additions & 2 deletions advanced_source/rpc_ddp_tutorial/main.py
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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.
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2 changes: 1 addition & 1 deletion intermediate_source/dist_pipeline_parallel_tutorial.rst
Original file line number Diff line number Diff line change
Expand Up @@ -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(
Expand Down