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From GitBox <...@apache.org>
Subject [GitHub] [singa] chrishkchris opened a new pull request #560: SINGA-487 Accumulate gradients to reduce network latency
Date Tue, 12 Nov 2019 08:37:09 GMT
chrishkchris opened a new pull request #560: SINGA-487 Accumulate gradients to reduce network
latency
URL: https://github.com/apache/singa/pull/560
 
 
   This PR reduces the network latency by accumulate gradients in a memory buffer before sending
out with NCCL.
   This can reduce much of the TCP/IP latency by reducing the number of NCCL API call.
   
   Together with the result of PR #555, here is a simple test to make sure the training is
correct:
   ```
   ubuntu@ip-172-31-26-214:~/singa/examples/autograd$ python3 mnist_multiprocess.py
   Starting Epoch 0:
   Training loss = 831.072205, training accuracy = 0.700454
   Evaluation accuracy = 0.927015, Elapsed Time = 0.676089s
   Starting Epoch 1:
   Training loss = 248.684601, training accuracy = 0.916183
   Evaluation accuracy = 0.958265, Elapsed Time = 0.545179s
   Starting Epoch 2:
   Training loss = 172.330597, training accuracy = 0.943042
   Evaluation accuracy = 0.967928, Elapsed Time = 0.543617s
   Starting Epoch 3:
   Training loss = 139.254807, training accuracy = 0.953425
   Evaluation accuracy = 0.973067, Elapsed Time = 0.530805s
   Starting Epoch 4:
   Training loss = 115.329491, training accuracy = 0.960737
   Evaluation accuracy = 0.976049, Elapsed Time = 0.530590s
   Starting Epoch 5:
   Training loss = 101.911728, training accuracy = 0.966179
   Evaluation accuracy = 0.974095, Elapsed Time = 0.529574s
   Starting Epoch 6:
   Training loss = 90.820244, training accuracy = 0.969969
   Evaluation accuracy = 0.980983, Elapsed Time = 0.530502s
   Starting Epoch 7:
   Training loss = 86.718071, training accuracy = 0.971037
   Evaluation accuracy = 0.977590, Elapsed Time = 0.531085s
   Starting Epoch 8:
   Training loss = 79.507553, training accuracy = 0.973675
   Evaluation accuracy = 0.976562, Elapsed Time = 0.529935s
   Starting Epoch 9:
   Training loss = 78.784409, training accuracy = 0.974025
   Evaluation accuracy = 0.980469, Elapsed Time = 0.530919s
   ```

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