Kvstore for distributed multi-gpu training


#1

I am running a distributed training job (resnet-34 / cifar10) on a 2-4 node cluster of p2.8xlarge instances (which have 8 gpus each). The advice in the multi-devices howto doc suggests dist_device_sync is a good choice for kvstore, but when I run the job this way, the model never learns – i.e. accuracy never gets better than random no matter how long I train.

When I use kvstore = dist_sync the training job works fine.

Am I doing something wrong, or is there a problem with dist_device_sync?

I am using mxnet-cu80 0.12 (the release version) on AWS p2.8xlarge instances.


#2

Hi,

Are you using module or gluon API for training? Do you mind pasting the training log and setup (batch_size, learning_rate) for dist_sync and dist_device_sync?


#4

Here’s the correct log.

kvstore was dist_device_sync
gpus = 16
nodes = 2
batch size was 128 * gpus
learning rate was 0.1 * gpus <-- maybe the problem?

Training log for node 1 (node 2 was similar):

[12:46:43] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/train.rec, use 31 threads for decoding..
 [12:47:27] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/test.rec, use 31 threads for decoding..
 [12:47:45] src/operator/././cudnn_algoreg-inl.h:106: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
 [12:48:00] src/kvstore/././comm.h:579: only 114 out of 240 GPU pairs are enabled direct access. It may affect the performance. You can set MXNET_ENABLE_GPU_P2P=0 to turn it off
 [12:48:00] src/kvstore/././comm.h:588: .vvvvvvvv.......
 [12:48:00] src/kvstore/././comm.h:588: v.vvvvvvv.......
 [12:48:00] src/kvstore/././comm.h:588: vv.vvvvvv.......
 [12:48:00] src/kvstore/././comm.h:588: vvv.vvvvv.......
 [12:48:00] src/kvstore/././comm.h:588: vvvv.vvvv.......
 [12:48:00] src/kvstore/././comm.h:588: vvvvv.vvv.......
 [12:48:00] src/kvstore/././comm.h:588: vvvvvv.vv.......
 [12:48:00] src/kvstore/././comm.h:588: vvvvvvv.v.......
 [12:48:00] src/kvstore/././comm.h:588: vvvvvvvv........
 [12:48:00] src/kvstore/././comm.h:588: ..........vvvvvv
 [12:48:00] src/kvstore/././comm.h:588: .........v.vvvvv
 [12:48:00] src/kvstore/././comm.h:588: .........vv.vvvv
 [12:48:00] src/kvstore/././comm.h:588: .........vvv.vvv
 [12:48:00] src/kvstore/././comm.h:588: .........vvvv.vv
 [12:48:00] src/kvstore/././comm.h:588: .........vvvvv.v
 [12:48:00] src/kvstore/././comm.h:588: .........vvvvvv.
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#5

What about dist_sync? Is it the same configuration for batch size and learning rate?


#6

Yes, we use exactly the same batch size and learning rate. The only thing that differs is the mode we use to create the kv store.

We’ve experienced this problem with both Gluon and Module.


#7

Here is training log from same job run on 2 x p2.16xl with dist_sync. The job seems to learn nothing for first 27 epochs, then starts learning for rest of job, but still ends up at much lower accuracy than we would expect.

batch_size and learning rate still (128, 0.1) * 16 gpus


 2017-11-03 22:25:25,661 INFO - mxnet_container.train - Starting distributed training task
 [22:25:25] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/train.rec, use 31 threads for decoding..
 017-11-03 22:25:28,741 INFO - mxnet_container.train - Starting distributed training task
 [22:25:28] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/train.rec, use 31 threads for decoding..
 [22:26:10] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/test.rec, use 31 threads for decoding..
 [22:26:13] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/test.rec, use 31 threads for decoding..
 [22:26:28] src/operator/././cudnn_algoreg-inl.h:106: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
 [22:26:31] src/operator/././cudnn_algoreg-inl.h:106: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
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 2017-11-03 22:33:11,998 INFO - root - [Epoch 27] training: accuracy=0.100728
 2017-11-03 22:33:11,999 INFO - root - [Epoch 27] time cost: 12.480929
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 2017-11-03 22:33:12,261 INFO - root - [Epoch 27] time cost: 12.609994
 2017-11-03 22:33:13,179 INFO - root - [Epoch 27] validation: accuracy=0.099316
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 2017-11-03 22:33:27,348 INFO - root - [Epoch 28] validation: accuracy=0.128809
 2017-11-03 22:33:27,301 INFO - root - [Epoch 28] validation: accuracy=0.129199
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...

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 2017-11-03 22:38:15,275 INFO - root - Epoch [49] Batch [12]#011Speed: 3936.092128 samples/sec#011accuracy=0.674692
 2017-11-03 22:38:15,516 INFO - root - Epoch [49] Batch [12]#011Speed: 3845.120841 samples/sec#011accuracy=0.675669
 2017-11-03 22:38:16,032 INFO - root - Epoch [49] Batch [13]#011Speed: 3972.326956 samples/sec#011accuracy=0.677002
 2017-11-03 22:38:15,785 INFO - root - Epoch [49] Batch [13]#011Speed: 4015.262128 samples/sec#011accuracy=0.675956
 2017-11-03 22:38:16,317 INFO - root - Epoch [49] Batch [14]#011Speed: 3848.640462 samples/sec#011accuracy=0.675033
 2017-11-03 22:38:16,844 INFO - root - Epoch [49] Batch [15]#011Speed: 3889.283874 samples/sec#011accuracy=0.673462
 2017-11-03 22:38:17,368 INFO - root - Epoch [49] Batch [16]#011Speed: 3912.294076 samples/sec#011accuracy=0.673656
 2017-11-03 22:38:17,888 INFO - root - Epoch [49] Batch [17]#011Speed: 3939.270836 samples/sec#011accuracy=0.673367
 2017-11-03 22:38:18,394 INFO - root - Epoch [49] Batch [18]#011Speed: 4047.295096 samples/sec#011accuracy=0.674805
 2017-11-03 22:38:18,934 INFO - root - Epoch [49] Batch [19]#011Speed: 3791.329042 samples/sec#011accuracy=0.675122
 2017-11-03 22:38:16,522 INFO - root - Epoch [49] Batch [14]#011Speed: 4181.750581 samples/sec#011accuracy=0.675944
 2017-11-03 22:38:17,047 INFO - root - Epoch [49] Batch [15]#011Speed: 3902.878289 samples/sec#011accuracy=0.674377
 2017-11-03 22:38:17,559 INFO - root - Epoch [49] Batch [16]#011Speed: 4000.774355 samples/sec#011accuracy=0.674719
 2017-11-03 22:38:18,113 INFO - root - Epoch [49] Batch [17]#011Speed: 3701.922943 samples/sec#011accuracy=0.674561
 2017-11-03 22:38:18,699 INFO - root - Epoch [49] Batch [18]#011Speed: 3494.474129 samples/sec#011accuracy=0.675961
 2017-11-03 22:38:19,192 INFO - root - Epoch [49] Batch [19]#011Speed: 4151.650253 samples/sec#011accuracy=0.676367
 2017-11-03 22:38:19,732 INFO - root - Epoch [49] Batch [20]#011Speed: 3799.507693 samples/sec#011accuracy=0.677176
 2017-11-03 22:38:20,248 INFO - root - Epoch [49] Batch [21]#011Speed: 3965.188515 samples/sec#011accuracy=0.678267
 2017-11-03 22:38:19,483 INFO - root - Epoch [49] Batch [20]#011Speed: 3738.372265 samples/sec#011accuracy=0.676293
 2017-11-03 22:38:20,002 INFO - root - Epoch [49] Batch [21]#011Speed: 3949.026639 samples/sec#011accuracy=0.677246
 2017-11-03 22:38:20,761 INFO - root - Epoch [49] Batch [22]#011Speed: 3996.744230 samples/sec#011accuracy=0.678605
 2017-11-03 22:38:21,288 INFO - root - Epoch [49] Batch [23]#011Speed: 3885.838969 samples/sec#011accuracy=0.678955
 2017-11-03 22:38:21,288 INFO - root - [Epoch 49] training: accuracy=0.678955
 2017-11-03 22:38:21,289 INFO - root - [Epoch 49] time cost: 12.643478
 2017-11-03 22:38:20,508 INFO - root - Epoch [49] Batch [22]#011Speed: 4046.031182 samples/sec#011accuracy=0.677671
 2017-11-03 22:38:21,033 INFO - root - Epoch [49] Batch [23]#011Speed: 3900.418511 samples/sec#011accuracy=0.678162
 2017-11-03 22:38:21,034 INFO - root - [Epoch 49] training: accuracy=0.678162
 2017-11-03 22:38:21,034 INFO - root - [Epoch 49] time cost: 13.004197
 2017-11-03 22:38:22,160 INFO - root - [Epoch 49] validation: accuracy=0.563379

#8

And here is another log. Using dist_sync again, exact same config as previous example. Accuracy rises through early epochs, but slowly and not very steadily, then collapses and bounces up and down for rest of job.


 2017-11-04 09:19:31,445 INFO - mxnet_container.train - Starting distributed training task
 [09:19:31] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/train.rec, use 31 threads for decoding..
 2017-11-04 09:19:31,850 INFO - mxnet_container.train - Starting distributed training task
 [09:19:31] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/train.rec, use 31 threads for decoding..
 [09:20:17] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/test.rec, use 31 threads for decoding..
 [09:20:16] src/io/iter_image_recordio_2.cc:169: ImageRecordIOParser2: /opt/ml/input/data/training/test.rec, use 31 threads for decoding..
 [09:20:35] src/operator/././cudnn_algoreg-inl.h:106: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
 [09:20:35] src/operator/././cudnn_algoreg-inl.h:106: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
 2017-11-04 09:21:01,013 INFO - root - [Epoch 0] training: accuracy=0.098574
 2017-11-04 09:21:01,013 INFO - root - [Epoch 0] time cost: 43.719973
 2017-11-04 09:21:01,296 INFO - root - [Epoch 0] training: accuracy=0.097988
 2017-11-04 09:21:01,296 INFO - root - [Epoch 0] time cost: 44.116472
 2017-11-04 09:21:02,137 INFO - root - [Epoch 0] validation: accuracy=0.091406
 2017-11-04 09:21:02,331 INFO - root - [Epoch 0] validation: accuracy=0.090332
 2017-11-04 09:21:14,716 INFO - root - [Epoch 1] training: accuracy=0.104736
 2017-11-04 09:21:14,717 INFO - root - [Epoch 1] time cost: 12.086653
 2017-11-04 09:21:14,975 INFO - root - [Epoch 1] training: accuracy=0.104533
 2017-11-04 09:21:14,975 INFO - root - [Epoch 1] time cost: 12.642990
 2017-11-04 09:21:15,856 INFO - root - [Epoch 1] validation: accuracy=0.105469
 2017-11-04 09:21:15,970 INFO - root - [Epoch 1] validation: accuracy=0.105566
 2017-11-04 09:21:28,916 INFO - root - [Epoch 2] training: accuracy=0.110625
 2017-11-04 09:21:28,916 INFO - root - [Epoch 2] time cost: 12.602215
 2017-11-04 09:21:29,183 INFO - root - [Epoch 2] training: accuracy=0.110273
 2017-11-04 09:21:29,183 INFO - root - [Epoch 2] time cost: 13.212795
 2017-11-04 09:21:30,050 INFO - root - [Epoch 2] validation: accuracy=0.105371
 2017-11-04 09:21:30,192 INFO - root - [Epoch 2] validation: accuracy=0.105176
 2017-11-04 09:21:42,421 INFO - root - [Epoch 3] training: accuracy=0.114237
 2017-11-04 09:21:42,421 INFO - root - [Epoch 3] time cost: 12.370330
 2017-11-04 09:21:42,681 INFO - root - [Epoch 3] training: accuracy=0.114156
 2017-11-04 09:21:42,681 INFO - root - [Epoch 3] time cost: 12.488236
 2017-11-04 09:21:43,546 INFO - root - [Epoch 3] validation: accuracy=0.116406
 2017-11-04 09:21:43,667 INFO - root - [Epoch 3] validation: accuracy=0.116406
 2017-11-04 09:21:56,602 INFO - root - [Epoch 4] training: accuracy=0.124805
 2017-11-04 09:21:56,603 INFO - root - [Epoch 4] time cost: 12.611940
 2017-11-04 09:21:56,845 INFO - root - [Epoch 4] training: accuracy=0.124961
 2017-11-04 09:21:56,845 INFO - root - [Epoch 4] time cost: 13.177747
 2017-11-04 09:21:57,834 INFO - root - [Epoch 4] validation: accuracy=0.104785
 2017-11-04 09:21:57,731 INFO - root - [Epoch 4] validation: accuracy=0.105566
 2017-11-04 09:22:10,009 INFO - root - [Epoch 5] training: accuracy=0.130127
 2017-11-04 09:22:10,010 INFO - root - [Epoch 5] time cost: 12.278370
 2017-11-04 09:22:10,263 INFO - root - [Epoch 5] training: accuracy=0.130452
 2017-11-04 09:22:10,263 INFO - root - [Epoch 5] time cost: 12.428541
 2017-11-04 09:22:11,118 INFO - root - [Epoch 5] validation: accuracy=0.135742
 2017-11-04 09:22:11,242 INFO - root - [Epoch 5] validation: accuracy=0.135352
 2017-11-04 09:22:23,704 INFO - root - [Epoch 6] training: accuracy=0.142761
 2017-11-04 09:22:23,705 INFO - root - [Epoch 6] time cost: 12.126450
 2017-11-04 09:22:23,982 INFO - root - [Epoch 6] training: accuracy=0.142924
 2017-11-04 09:22:23,982 INFO - root - [Epoch 6] time cost: 12.739633
 2017-11-04 09:22:24,820 INFO - root - [Epoch 6] validation: accuracy=0.104980
 2017-11-04 09:22:24,955 INFO - root - [Epoch 6] validation: accuracy=0.104883
 2017-11-04 09:22:37,612 INFO - root - [Epoch 7] training: accuracy=0.142891
 2017-11-04 09:22:37,612 INFO - root - [Epoch 7] time cost: 12.791654
 2017-11-04 09:22:37,849 INFO - root - [Epoch 7] training: accuracy=0.143535
 2017-11-04 09:22:37,849 INFO - root - [Epoch 7] time cost: 12.893820
 2017-11-04 09:22:38,538 INFO - root - [Epoch 7] validation: accuracy=0.158569
 2017-11-04 09:22:38,642 INFO - root - [Epoch 7] validation: accuracy=0.159058
 2017-11-04 09:22:51,132 INFO - root - [Epoch 8] training: accuracy=0.159078
 2017-11-04 09:22:51,133 INFO - root - [Epoch 8] time cost: 12.111735
 2017-11-04 09:22:51,390 INFO - root - [Epoch 8] training: accuracy=0.159770
 2017-11-04 09:22:51,390 INFO - root - [Epoch 8] time cost: 12.747310
 2017-11-04 09:22:52,283 INFO - root - [Epoch 8] validation: accuracy=0.170313
 2017-11-04 09:22:52,382 INFO - root - [Epoch 8] validation: accuracy=0.170605
 2017-11-04 09:23:05,664 INFO - root - [Epoch 9] training: accuracy=0.170234
 2017-11-04 09:23:05,664 INFO - root - [Epoch 9] time cost: 13.281223
 2017-11-04 09:23:06,650 INFO - root - [Epoch 9] validation: accuracy=0.153320
 2017-11-04 09:23:05,403 INFO - root - [Epoch 9] training: accuracy=0.170059
 2017-11-04 09:23:05,403 INFO - root - [Epoch 9] time cost: 12.627474
 2017-11-04 09:23:06,528 INFO - root - [Epoch 9] validation: accuracy=0.153027
 2017-11-04 09:23:18,769 INFO - root - [Epoch 10] training: accuracy=0.179036
 2017-11-04 09:23:18,769 INFO - root - [Epoch 10] time cost: 12.240948
 2017-11-04 09:23:19,041 INFO - root - [Epoch 10] training: accuracy=0.179016
 2017-11-04 09:23:19,041 INFO - root - [Epoch 10] time cost: 12.390525
 2017-11-04 09:23:19,899 INFO - root - [Epoch 10] validation: accuracy=0.186914
 2017-11-04 09:23:20,031 INFO - root - [Epoch 10] validation: accuracy=0.186914
 2017-11-04 09:23:32,326 INFO - root - [Epoch 11] training: accuracy=0.197062
 2017-11-04 09:23:32,326 INFO - root - [Epoch 11] time cost: 11.943790
 2017-11-04 09:23:32,572 INFO - root - [Epoch 11] training: accuracy=0.196391
 2017-11-04 09:23:32,572 INFO - root - [Epoch 11] time cost: 12.540984
 2017-11-04 09:23:33,437 INFO - root - [Epoch 11] validation: accuracy=0.177832
 2017-11-04 09:23:33,566 INFO - root - [Epoch 11] validation: accuracy=0.177539
 2017-11-04 09:23:46,132 INFO - root - [Epoch 12] training: accuracy=0.192344
 2017-11-04 09:23:46,132 INFO - root - [Epoch 12] time cost: 12.695587
 2017-11-04 09:23:46,411 INFO - root - [Epoch 12] training: accuracy=0.192246
 2017-11-04 09:23:46,411 INFO - root - [Epoch 12] time cost: 12.845486
 2017-11-04 09:23:47,312 INFO - root - [Epoch 12] validation: accuracy=0.184180
 2017-11-04 09:23:47,412 INFO - root - [Epoch 12] validation: accuracy=0.184473
 2017-11-04 09:23:59,553 INFO - root - [Epoch 13] training: accuracy=0.194967
 2017-11-04 09:23:59,553 INFO - root - [Epoch 13] time cost: 12.240608
 2017-11-04 09:23:59,786 INFO - root - [Epoch 13] training: accuracy=0.195089
 2017-11-04 09:23:59,787 INFO - root - [Epoch 13] time cost: 12.374072
 2017-11-04 09:24:00,665 INFO - root - [Epoch 13] validation: accuracy=0.211719
 2017-11-04 09:24:00,797 INFO - root - [Epoch 13] validation: accuracy=0.211523
 2017-11-04 09:24:13,738 INFO - root - [Epoch 14] training: accuracy=0.200664
 2017-11-04 09:24:13,738 INFO - root - [Epoch 14] time cost: 12.615486
 2017-11-04 09:24:14,023 INFO - root - [Epoch 14] training: accuracy=0.200723
 2017-11-04 09:24:14,023 INFO - root - [Epoch 14] time cost: 13.225932
 2017-11-04 09:24:14,912 INFO - root - [Epoch 14] validation: accuracy=0.211426
 2017-11-04 09:24:15,019 INFO - root - [Epoch 14] validation: accuracy=0.211523
 2017-11-04 09:24:27,153 INFO - root - [Epoch 15] training: accuracy=0.213908
 2017-11-04 09:24:27,153 INFO - root - [Epoch 15] time cost: 12.240951
 2017-11-04 09:24:27,349 INFO - root - [Epoch 15] training: accuracy=0.214274
 2017-11-04 09:24:27,349 INFO - root - [Epoch 15] time cost: 12.329125
 2017-11-04 09:24:28,227 INFO - root - [Epoch 15] validation: accuracy=0.196191
 2017-11-04 09:24:28,336 INFO - root - [Epoch 15] validation: accuracy=0.196289
 2017-11-04 09:24:40,983 INFO - root - [Epoch 16] training: accuracy=0.219297
 2017-11-04 09:24:40,983 INFO - root - [Epoch 16] time cost: 12.755961
 2017-11-04 09:24:41,253 INFO - root - [Epoch 16] training: accuracy=0.220059
 2017-11-04 09:24:41,253 INFO - root - [Epoch 16] time cost: 12.916951
 2017-11-04 09:24:41,952 INFO - root - [Epoch 16] validation: accuracy=0.223022
 2017-11-04 09:24:42,085 INFO - root - [Epoch 16] validation: accuracy=0.223511
 2017-11-04 09:24:54,514 INFO - root - [Epoch 17] training: accuracy=0.207581
 2017-11-04 09:24:54,514 INFO - root - [Epoch 17] time cost: 12.063521
 2017-11-04 09:24:54,789 INFO - root - [Epoch 17] training: accuracy=0.207723
 2017-11-04 09:24:54,789 INFO - root - [Epoch 17] time cost: 12.703974
 2017-11-04 09:24:55,671 INFO - root - [Epoch 17] validation: accuracy=0.099805
 2017-11-04 09:24:55,790 INFO - root - [Epoch 17] validation: accuracy=0.099805
 2017-11-04 09:25:08,027 INFO - root - [Epoch 18] training: accuracy=0.101542
 2017-11-04 09:25:08,027 INFO - root - [Epoch 18] time cost: 12.355660
 2017-11-04 09:25:08,254 INFO - root - [Epoch 18] training: accuracy=0.101420
 2017-11-04 09:25:08,254 INFO - root - [Epoch 18] time cost: 12.463921
 2017-11-04 09:25:09,120 INFO - root - [Epoch 18] validation: accuracy=0.132812
 2017-11-04 09:25:09,244 INFO - root - [Epoch 18] validation: accuracy=0.132422
 2017-11-04 09:25:21,767 INFO - root - [Epoch 19] training: accuracy=0.165957
 2017-11-04 09:25:21,767 INFO - root - [Epoch 19] time cost: 12.647036
 2017-11-04 09:25:22,021 INFO - root - [Epoch 19] training: accuracy=0.166895
 2017-11-04 09:25:22,021 INFO - root - [Epoch 19] time cost: 12.776301
 2017-11-04 09:25:22,887 INFO - root - [Epoch 19] validation: accuracy=0.178711
 2017-11-04 09:25:23,014 INFO - root - [Epoch 19] validation: accuracy=0.178711
 2017-11-04 09:25:35,266 INFO - root - [Epoch 20] training: accuracy=0.174316
 2017-11-04 09:25:35,266 INFO - root - [Epoch 20] time cost: 12.379125
 2017-11-04 09:25:36,353 INFO - root - [Epoch 20] validation: accuracy=0.099609
 2017-11-04 09:25:35,476 INFO - root - [Epoch 20] training: accuracy=0.174377
 2017-11-04 09:25:35,476 INFO - root - [Epoch 20] time cost: 12.461397
 2017-11-04 09:25:36,447 INFO - root - [Epoch 20] validation: accuracy=0.099609
 2017-11-04 09:25:49,051 INFO - root - [Epoch 21] training: accuracy=0.125703
 2017-11-04 09:25:49,051 INFO - root - [Epoch 21] time cost: 12.697860
 2017-11-04 09:25:49,324 INFO - root - [Epoch 21] training: accuracy=0.125938
 2017-11-04 09:25:49,324 INFO - root - [Epoch 21] time cost: 12.877100
 2017-11-04 09:25:50,191 INFO - root - [Epoch 21] validation: accuracy=0.103027
 2017-11-04 09:25:50,326 INFO - root - [Epoch 21] validation: accuracy=0.103027
 2017-11-04 09:26:02,858 INFO - root - [Epoch 22] training: accuracy=0.141866
 2017-11-04 09:26:02,858 INFO - root - [Epoch 22] time cost: 12.531573
 2017-11-04 09:26:03,849 INFO - root - [Epoch 22] validation: accuracy=0.164355
 2017-11-04 09:26:02,566 INFO - root - [Epoch 22] training: accuracy=0.141703
 2017-11-04 09:26:02,566 INFO - root - [Epoch 22] time cost: 12.374612
 2017-11-04 09:26:03,704 INFO - root - [Epoch 22] validation: accuracy=0.164551
 2017-11-04 09:26:15,989 INFO - root - [Epoch 23] training: accuracy=0.161458
 2017-11-04 09:26:15,989 INFO - root - [Epoch 23] time cost: 12.284893
 2017-11-04 09:26:16,260 INFO - root - [Epoch 23] training: accuracy=0.161580
 2017-11-04 09:26:16,260 INFO - root - [Epoch 23] time cost: 12.410921
 2017-11-04 09:26:17,149 INFO - root - [Epoch 23] validation: accuracy=0.127930
 2017-11-04 09:26:17,250 INFO - root - [Epoch 23] validation: accuracy=0.127930
 2017-11-04 09:26:30,197 INFO - root - [Epoch 24] training: accuracy=0.150918
 2017-11-04 09:26:30,197 INFO - root - [Epoch 24] time cost: 12.947518
 2017-11-04 09:26:30,986 INFO - root - [Epoch 24] validation: accuracy=0.186768
 2017-11-04 09:26:29,924 INFO - root - [Epoch 24] training: accuracy=0.150742
 2017-11-04 09:26:29,924 INFO - root - [Epoch 24] time cost: 12.774948
 2017-11-04 09:26:30,893 INFO - root - [Epoch 24] validation: accuracy=0.186768
 2017-11-04 09:26:43,268 INFO - root - [Epoch 25] training: accuracy=0.173706
 2017-11-04 09:26:43,268 INFO - root - [Epoch 25] time cost: 12.374998
 2017-11-04 09:26:43,386 INFO - root - [Epoch 25] training: accuracy=0.173889
 2017-11-04 09:26:43,386 INFO - root - [Epoch 25] time cost: 12.399672
 2017-11-04 09:26:44,430 INFO - root - [Epoch 25] validation: accuracy=0.177734
 2017-11-04 09:26:44,411 INFO - root - [Epoch 25] validation: accuracy=0.178125
 2017-11-04 09:26:57,201 INFO - root - [Epoch 26] training: accuracy=0.214102
 2017-11-04 09:26:57,201 INFO - root - [Epoch 26] time cost: 12.770151
 2017-11-04 09:26:57,416 INFO - root - [Epoch 26] training: accuracy=0.214609
 2017-11-04 09:26:57,416 INFO - root - [Epoch 26] time cost: 13.004954
 2017-11-04 09:26:58,310 INFO - root - [Epoch 26] validation: accuracy=0.189648
 2017-11-04 09:26:58,407 INFO - root - [Epoch 26] validation: accuracy=0.188477
 2017-11-04 09:27:10,548 INFO - root - [Epoch 27] training: accuracy=0.207926
 2017-11-04 09:27:10,548 INFO - root - [Epoch 27] time cost: 12.237620
 2017-11-04 09:27:10,724 INFO - root - [Epoch 27] training: accuracy=0.207642
 2017-11-04 09:27:10,724 INFO - root - [Epoch 27] time cost: 12.317256
 2017-11-04 09:27:11,691 INFO - root - [Epoch 27] validation: accuracy=0.176953
 2017-11-04 09:27:11,794 INFO - root - [Epoch 27] validation: accuracy=0.176660
 2017-11-04 09:27:24,447 INFO - root - [Epoch 28] training: accuracy=0.187891
 2017-11-04 09:27:24,447 INFO - root - [Epoch 28] time cost: 12.756263
 2017-11-04 09:27:24,702 INFO - root - [Epoch 28] training: accuracy=0.187422
 2017-11-04 09:27:24,702 INFO - root - [Epoch 28] time cost: 12.907530
 2017-11-04 09:27:25,578 INFO - root - [Epoch 28] validation: accuracy=0.173535
 2017-11-04 09:27:25,700 INFO - root - [Epoch 28] validation: accuracy=0.173730
 2017-11-04 09:27:38,035 INFO - root - [Epoch 29] training: accuracy=0.160156
 2017-11-04 09:27:38,035 INFO - root - [Epoch 29] time cost: 12.457565
 2017-11-04 09:27:38,300 INFO - root - [Epoch 29] training: accuracy=0.159688
 2017-11-04 09:27:38,300 INFO - root - [Epoch 29] time cost: 12.599412
 2017-11-04 09:27:39,178 INFO - root - [Epoch 29] validation: accuracy=0.105859
 2017-11-04 09:27:39,281 INFO - root - [Epoch 29] validation: accuracy=0.105762
 2017-11-04 09:27:51,418 INFO - root - [Epoch 30] training: accuracy=0.163391
 2017-11-04 09:27:51,419 INFO - root - [Epoch 30] time cost: 12.240153
 2017-11-04 09:27:51,648 INFO - root - [Epoch 30] training: accuracy=0.163554
 2017-11-04 09:27:51,649 INFO - root - [Epoch 30] time cost: 12.367119
 2017-11-04 09:27:52,531 INFO - root - [Epoch 30] validation: accuracy=0.181250
 2017-11-04 09:27:52,646 INFO - root - [Epoch 30] validation: accuracy=0.181348
 2017-11-04 09:28:05,408 INFO - root - [Epoch 31] training: accuracy=0.189805
 2017-11-04 09:28:05,408 INFO - root - [Epoch 31] time cost: 12.876806
 2017-11-04 09:28:05,695 INFO - root - [Epoch 31] training: accuracy=0.189043
 2017-11-04 09:28:05,695 INFO - root - [Epoch 31] time cost: 13.048735
 2017-11-04 09:28:06,599 INFO - root - [Epoch 31] validation: accuracy=0.101270
 2017-11-04 09:28:06,692 INFO - root - [Epoch 31] validation: accuracy=0.101465
 2017-11-04 09:28:18,842 INFO - root - [Epoch 32] training: accuracy=0.118795
 2017-11-04 09:28:18,842 INFO - root - [Epoch 32] time cost: 12.242412
 2017-11-04 09:28:19,118 INFO - root - [Epoch 32] training: accuracy=0.118835
 2017-11-04 09:28:19,118 INFO - root - [Epoch 32] time cost: 12.425529
 2017-11-04 09:28:19,966 INFO - root - [Epoch 32] validation: accuracy=0.124316
 2017-11-04 09:28:20,107 INFO - root - [Epoch 32] validation: accuracy=0.124316
 2017-11-04 09:28:32,794 INFO - root - [Epoch 33] training: accuracy=0.151797
 2017-11-04 09:28:32,794 INFO - root - [Epoch 33] time cost: 12.827599
 2017-11-04 09:28:33,057 INFO - root - [Epoch 33] training: accuracy=0.152500
 2017-11-04 09:28:33,057 INFO - root - [Epoch 33] time cost: 12.949730
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#9

The training log for dist_sync is quite bouncy. Did you try with smaller learning rate and check if it’s more stable?

I didn’t find a unit test with dist_device_sync mode in MXNet. In theory it should give the same result as dist_sync. Let me verify if dist_device_sync passes a simple test. I’ll let you know how it goes.


#10

I verified at my end with this unit test on dist_device_sync kvstore, and the basic functionality seems to be working:

Does reducing learning rate help though?


#11

BTW - what model are you training?


#12

we (i’m working w/ @owenataws) were training resnet_v2_34 using cifar10 data. our learning rate was too high. we will have new test results soon and give update then.