juliusshufan edited a comment on issue #12591: USE_MKLDNN=1 is default in make build (mkldnn must be explicitly turned off) URL: https://github.com/apache/incubator-mxnet/pull/12591#issuecomment-423890706 Apart from ImageNet-1k traning test, training test also been executed on small dataset, that includes: | Training set | Validation set | Classes | Source -- | -- | -- | -- | -- CiFAR-10 | 50,000 | 10,000 | 10 | Released by MxNET official http://data.mxnet.io/data/cifar10/ CiFAR-100 | 50,000 | 10,000 | 100 | Released by MxNET official http://data.mxnet.io/data/cifar100.zip sampled ImageNet | 100,200 | 10,000 | 200 | Sampled from ImageNet-1k and converted following the structure and classes retrieved from tinyImageNet (https://www.kaggle.com/c/tiny-imagenet/) Due to the lackness of the SOTA accuracy data on these small dataset, the comparisons between MXNET-MKLDNN and MXNET-GPU on convergence trends and inference accuracy will be "indirectly" used as the correctness check of MXNET with MKLDNN backend. Below tables lists the validation accuracy on CIFAR10, CIFAR100 and the sampled-Imagenet and comparisons achieved on GPU, models including ResNet-50, VGG16 and Inception-v3. On Resnet-50: | HW Platform | Dataset | Validation Accuracy -- | -- | -- | -- CPU | SKX-8180 | sampled ImageNet | top1=0.629879 top5=0.842132 GPU | GTX-1080T | sampled ImageNet | top1=0.630609 top5=0.840345 CPU | SKX-8180 | CiFAR-10 | top1=0.917067 top5=0.997796 GPU | GTX-1080T | CiFAR-10 | top1=0.921474 top5=0.998397 CPU | SKX-8180 | CiFAR-100 | top1=0.734475 top5=0.915865 GPU | GTX-1080T | CiFAR-100 | top1= 0.723257 top5= 0.913161 On Inception-v3, (as inception-v3 only accepts input size 229, CIFAR is not applicable) | HW Platform | Dataset | Validation Accuracy -- | -- | -- | -- CPU | SKX-8180 | sampled ImageNet | top1= 0.684964 top5=0.866470 GPU | GTX-1080T | sampled ImageNet | top1= 0.684095 top5= 0.868890 On VGG-16: | HW Platform | Dataset | Validation Accuracy -- | -- | -- | -- CPU | SKX-8180 | sampled ImageNet | top1=0.528029 top5=0.759809 GPU | GTX-1080T | sampled ImageNet | top1=0.526834 top5= 0.761318 CPU | SKX-8180 | CiFAR-10 | top1=0.884615 top5=0.994391 GPU | GTX-1080T | CiFAR-10 | top1=0.888622 top5=0.995092 CPU | SKX-8180 | CiFAR-100 | top1=0.634415 top5=0.855569 GPU | GTX-1080T | CiFAR-100 | top1= 0.634916 top5= 0.855669 The below two figures are the top-5 validation accuracy trends collected on CPU and GPU respectively, On CPU:  On GPU: 
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