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Subject [GitHub] [singa] chrishkchris commented on issue #651: Add new example APIs
Date Mon, 06 Apr 2020 12:16:56 GMT
chrishkchris commented on issue #651: Add new example APIs
URL: https://github.com/apache/singa/pull/651#issuecomment-609758264
 
 
   I added two args in train_mpi,py and train_multiprocess.py to support all the distributed
training options, but train.py don't need the argument because it doesn't have DistOpt
   
   Below are two examples for the use of arguments:
   ```
   root@71ac539cda77:~/dcsysh/singa/examples/autograd# mpiexec -np 2 python3 train_mpi.py
cnn mnist --op fp16 --lr 0.01
   Starting Epoch 0:
   Training loss = 625.136963, training accuracy = 0.776893
   Evaluation accuracy = 0.939704, Elapsed Time = 1.619492s
   Starting Epoch 1:
   Training loss = 235.280151, training accuracy = 0.920339
   Evaluation accuracy = 0.947115, Elapsed Time = 1.517764s
   Starting Epoch 2:
   Training loss = 171.311310, training accuracy = 0.942508
   Evaluation accuracy = 0.970252, Elapsed Time = 1.557606s
   Starting Epoch 3:
   Training loss = 139.594086, training accuracy = 0.953342
   Evaluation accuracy = 0.972356, Elapsed Time = 1.541372s
   Starting Epoch 4:
   Training loss = 120.380058, training accuracy = 0.959852
   Evaluation accuracy = 0.971655, Elapsed Time = 1.560016s
   Starting Epoch 5:
   Training loss = 104.767105, training accuracy = 0.965345
   Evaluation accuracy = 0.979467, Elapsed Time = 1.525147s
   Starting Epoch 6:
   Training loss = 98.995010, training accuracy = 0.966730
   Evaluation accuracy = 0.976963, Elapsed Time = 1.528173s
   Starting Epoch 7:
   Training loss = 88.012024, training accuracy = 0.970202
   Evaluation accuracy = 0.977865, Elapsed Time = 1.515204s
   Starting Epoch 8:
   Training loss = 86.540741, training accuracy = 0.970519
   Evaluation accuracy = 0.975361, Elapsed Time = 1.604774s
   Starting Epoch 9:
   Training loss = 80.653885, training accuracy = 0.973024
   Evaluation accuracy = 0.982071, Elapsed Time = 1.633480s
   root@71ac539cda77:~/dcsysh/singa/examples/autograd# mpiexec -np 2 python3 train_mpi.py
cnn mnist --op partialUpdate --lr 0.01
   Starting Epoch 0:
   Training loss = 623.692139, training accuracy = 0.777694
   Evaluation accuracy = 0.939804, Elapsed Time = 1.678683s
   Starting Epoch 1:
   Training loss = 235.369232, training accuracy = 0.920690
   Evaluation accuracy = 0.948017, Elapsed Time = 1.567066s
   Starting Epoch 2:
   Training loss = 171.335693, training accuracy = 0.942157
   Evaluation accuracy = 0.971855, Elapsed Time = 1.595140s
   Starting Epoch 3:
   Training loss = 139.396088, training accuracy = 0.953592
   Evaluation accuracy = 0.971755, Elapsed Time = 1.661490s
   Starting Epoch 4:
   Training loss = 120.173752, training accuracy = 0.960019
   Evaluation accuracy = 0.970753, Elapsed Time = 1.571710s
   Starting Epoch 5:
   Training loss = 105.472672, training accuracy = 0.965061
   Evaluation accuracy = 0.978065, Elapsed Time = 1.570710s
   Starting Epoch 6:
   Training loss = 99.055389, training accuracy = 0.966930
   Evaluation accuracy = 0.977163, Elapsed Time = 1.561047s
   Starting Epoch 7:
   Training loss = 88.166275, training accuracy = 0.970002
   Evaluation accuracy = 0.976663, Elapsed Time = 1.665272s
   Starting Epoch 8:
   Training loss = 85.920563, training accuracy = 0.970469
   Evaluation accuracy = 0.976162, Elapsed Time = 1.579652s
   Starting Epoch 9:
   Training loss = 79.568375, training accuracy = 0.973040
   Evaluation accuracy = 0.982472, Elapsed Time = 1.605414s
   ```

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