THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
Inference Performance
This group of the performance test is gathered on AWS EC2 instances in 1 socket.
Performance boost with Intel MKL-DNN backend in release 1.3
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Performance gain from operator fusion by subgraph
- R1.3 w/ MKL-DNN, pip install mxnet-mkl==1.3.0
- master w/ subgraph, CI https://github.com/apache/incubator-mxnet/commit/213ab09e7a2924da436c0d0526d62fefeeea6aa7
build: make USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel/ -j
runtime env: export MXNET_SUBGRAPH_BACKEND=MKLDNN
Category | Model | Latency batchsize=1 (ms, small is better) | Throughput batchsize=128 (fps, big is better) | ||||
R1.3 w/ MKL-DNN | master w/ subgraph | speedup | R1.3 w/ MKL-DNN | master w/ subgraph | speedup | ||
CNN/classification | ResNet-50 v1 | ||||||
ResNet-50 v2 | |||||||
Inception v3 | |||||||
Inception v4 | |||||||
DenseNet | |||||||
MobileNet | |||||||
VGG16 | |||||||
AlexNet | |||||||
inception-resnet v2 | |||||||
CNN/object detection | Faster R-CNN | ||||||
SSD-VGG16 | |||||||
SSD-MobileNet | |||||||
RNN | GNMT | ||||||
GAN | DCGAN |
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