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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/01/14 19:31:57 UTC
[GitHub] ashokei commented on issue #12502: [NGRAPH] MXNet - nGraph initial
integration
ashokei commented on issue #12502: [NGRAPH] MXNet - nGraph initial integration
URL: https://github.com/apache/incubator-mxnet/pull/12502#issuecomment-454131909
@zheng-da MKLDNN issue has been resolved, this PR uses the default MXNet MKLDNN. So, when users build with `make USE_NGRAPH=1` , all the following options work
```
MXNET_SUBGRAPH_BACKEND="" python example/image-classification/benchmark_score.py --network resnet-50
MXNET_SUBGRAPH_BACKEND="MKLDNN" python example/image-classification/benchmark_score.py --network resnet-50
MXNET_SUBGRAPH_BACKEND="ngraph" python example/image-classification/benchmark_score.py --network resnet-50
```
With this PR (built with `make USE_NGRAPH=1`) , We ran resnet-50 on AWS c5.18xlarge instance with benchmark_score.py, below are the results (`MXNET_SUBGRAPH_BACKEND=""` uses default MXNet MKLDNN ops)
```
ubuntu@aws-c5-18xlarge:~/mxnet-upstream$ MXNET_SUBGRAPH_BACKEND="" OMP_NUM_THREADS=18 env KMP_BLOCKTIME=1 env KMP_AFFINITY=granularity=fine,compact,1,0 python example/image-classification/benchmark_score.py --network resnet-50
INFO:root:run batchsize [1, 32, 64, 128, 256] by default, set --batch-size to run a specific one
INFO:root:network: resnet-50
INFO:root:device: cpu(0)
/home/ubuntu/mxnet-upstream/python/mxnet/module/base_module.py:67: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])
warnings.warn(msg)
INFO:root:batch size 1, dtype float32, images/sec: 90.258317
INFO:root:batch size 32, dtype float32, images/sec: 152.033810
INFO:root:batch size 64, dtype float32, images/sec: 151.357087
INFO:root:batch size 128, dtype float32, images/sec: 150.144761
INFO:root:batch size 256, dtype float32, images/sec: 148.233113
ubuntu@aws-c5-18xlarge:~/mxnet-upstream$ MXNET_SUBGRAPH_BACKEND="MKLDNN" OMP_NUM_THREADS=18 env KMP_BLOCKTIME=1 env KMP_AFFINITY=granularity=fine,compact,1,0 python example/image-classification/benchmark_score.py --network resnet-50
INFO:root:run batchsize [1, 32, 64, 128, 256] by default, set --batch-size to run a specific one
INFO:root:network: resnet-50
INFO:root:device: cpu(0)
/home/ubuntu/mxnet-upstream/python/mxnet/module/base_module.py:67: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])
warnings.warn(msg)
[19:27:50] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
INFO:root:batch size 1, dtype float32, images/sec: 110.765558
[19:27:50] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
INFO:root:batch size 32, dtype float32, images/sec: 191.871125
[19:27:53] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
INFO:root:batch size 64, dtype float32, images/sec: 191.062513
[19:27:58] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
INFO:root:batch size 128, dtype float32, images/sec: 193.064107
[19:28:09] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
INFO:root:batch size 256, dtype float32, images/sec: 191.515350
ubuntu@aws-c5-18xlarge:~/mxnet-upstream$ MXNET_SUBGRAPH_BACKEND="ngraph" OMP_NUM_THREADS=18 env KMP_BLOCKTIME=1 env KMP_AFFINITY=granularity=fine,compact,1,0 python example/image-classification/benchmark_score.py --network resnet-50
INFO:root:run batchsize [1, 32, 64, 128, 256] by default, set --batch-size to run a specific one
INFO:root:network: resnet-50
INFO:root:device: cpu(0)
/home/ubuntu/mxnet-upstream/python/mxnet/module/base_module.py:67: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])
warnings.warn(msg)
INFO:root:batch size 1, dtype float32, images/sec: 125.942216
INFO:root:batch size 32, dtype float32, images/sec: 224.910995
INFO:root:batch size 64, dtype float32, images/sec: 227.315916
INFO:root:batch size 128, dtype float32, images/sec: 225.936182
INFO:root:batch size 256, dtype float32, images/sec: 226.969117
```
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