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Performance Results

Below are compared results captured from gRPC client run against a Docker container using TensorFlow Serving* image from dockerhub tensorflow/serving:1.10.1 and the docker image built according to the recipe from: https://github.com/NervanaSystems/OpenVINO-model-server with OpenVINO toolkit version 2018.3.

In the experiments there were applied standard models from: TensorFlow-Slim image classification models library, specifically resnet_v1_50, resnet_v2_50, resnet_v1_152 and resnet_v2_152.

Figure 1: Performance Results with Batch Size 1 (higher value is the better) performance chart

Table 1. OpenVINO Model Server gain over TensorFlow Serving with batch size 1

Model Performance gain
Resnet v1 50 598%
Resnet v2 50 456%
Resnet v1 152 585%
Resnet v2 152 511%

Figure 2: Performance Results with Batch Size 16 (higher value is the better) performance chart

Table 2. OpenVINO Model Server gain over TensorFlow Serving with batch size 16

Model Performance gain
Resnet v1 50 267%
Resnet v2 50 254%
Resnet v1 152 325%
Resnet v2 152 316%

Figure 3: Performance Results for model Resnet v1 50 depending on batch size (higher value is the better) performance chart

Table 3. OpenVINO Model Server and TensorFlow Serving relative to the TensorFlow Serving performance with batch size 1

TensorFlow Serving OpenVINO Model Server
batch size 1 100% 595%
batch size 8 304% 1006%
batch size 16 413% 1103%
batch size 32 451% 1238%

During the tests a physical server was used hosting the Docker containers without the capacity limits with two CPUs Intel(R) Xeon(R) Platinum 8180 on board.

Note: The complete hardware specification is described in a hw_spec.pdf file.

The results above, show that OpenVINO Model Server can provide even five times the performance gain over the default tensorflow/serving:1.10.1 docker image using the same CPU and identical client code.