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Subject [GitHub] [singa] chrishkchris edited a comment on issue #577: SINGA-502 Avoid moving data between host and gpu in SoftmaxCrossEntropy
Date Tue, 21 Jan 2020 08:46:48 GMT
chrishkchris edited a comment on issue #577: SINGA-502 Avoid moving data between host and gpu
in SoftmaxCrossEntropy
URL: https://github.com/apache/singa/pull/577#issuecomment-576577786
 
 
   Some examples running using a T4 GPU:
   ```
   ubuntu@ip-172-31-17-75:~/singa/examples/autograd$ python3 mlp.py
   train_data_shape: (400, 2)
   train_label_shape: (400, 2)
   training loss =  0.6908968
   training loss =  0.59333104
   training loss =  0.5687339
   training loss =  0.5405281
   training loss =  0.46624574
   training loss =  0.35966498
   training loss =  0.28517348
   training loss =  0.23358232
   training loss =  0.19751398
   training loss =  0.17123318
   training loss =  0.15162155
   ubuntu@ip-172-31-17-75:~/singa/examples/autograd$ python3 mnist_cnn.py
   Starting Epoch 0:
   Training loss = 585.281616, training accuracy = 0.791572
   Evaluation accuracy = 0.940204, Elapsed Time = 2.901998s
   Starting Epoch 1:
   Training loss = 234.667984, training accuracy = 0.920758
   Evaluation accuracy = 0.961839, Elapsed Time = 2.898471s
   Starting Epoch 2:
   Training loss = 168.530197, training accuracy = 0.943420
   Evaluation accuracy = 0.970753, Elapsed Time = 2.903471s
   Starting Epoch 3:
   Training loss = 137.636353, training accuracy = 0.953875
   Evaluation accuracy = 0.974860, Elapsed Time = 2.908554s
   Starting Epoch 4:
   Training loss = 118.752136, training accuracy = 0.959779
   Evaluation accuracy = 0.971955, Elapsed Time = 2.916549s
   Starting Epoch 5:
   Training loss = 105.220406, training accuracy = 0.964131
   Evaluation accuracy = 0.974359, Elapsed Time = 2.926336s
   Starting Epoch 6:
   Training loss = 95.145279, training accuracy = 0.968350
   Evaluation accuracy = 0.980569, Elapsed Time = 2.918108s
   Starting Epoch 7:
   Training loss = 86.757538, training accuracy = 0.971251
   Evaluation accuracy = 0.982572, Elapsed Time = 2.920413s
   Starting Epoch 8:
   Training loss = 81.706383, training accuracy = 0.972252
   Evaluation accuracy = 0.984075, Elapsed Time = 2.924388s
   Starting Epoch 9:
   Training loss = 77.409966, training accuracy = 0.973769
   Evaluation accuracy = 0.981270, Elapsed Time = 2.929271s
   ubuntu@ip-172-31-17-75:~/singa/examples/autograd$ python3 resnet.py
   Start intialization............
   100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████|
100/100 [00:29<00:00,  3.45it/s]
   Throughput = 110.30029570709767 per second
   Total=0.2901170825958252, forward=0.09263657093048096, softmax=0.0016593122482299804, backward=0.19582119941711426,
sgd=0.009393131732940674
   ```
   

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