ONNX Runtime

ONNX Runtime is developed by Microsoft and partners as a open-source, cross-platform, high performance machine learning inferencing and training accelerator. This test profile runs the ONNX Runtime with various models available from the ONNX Zoo.

To run this test with the Phoronix Test Suite, the basic command is: phoronix-test-suite benchmark onnx.

Performance Metrics

ONNX Runtime 1.6

Model: fcn-resnet101-11 - Device: OpenMP CPU

OpenBenchmarking.org metrics for this test profile configuration based on 511 public results since 16 January 2021 with the latest data as of 15 April 2021.

Below is an overview of the generalized performance for components where there is sufficient statistically significant data based upon user-uploaded results. It is important to keep in mind particularly in the Linux/open-source space there can be vastly different OS configurations, with this overview intended to offer just general guidance as to the performance expectations.

Based on OpenBenchmarking.org data, the selected test / test configuration (ONNX Runtime 1.6 - Model: fcn-resnet101-11 - Device: OpenMP CPU) has an average run-time of 6 minutes. By default this test profile is set to run at least 3 times but may increase if the standard deviation exceeds pre-defined defaults or other calculations deem additional runs necessary for greater statistical accuracy of the result.

Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.3%.

Does It Scale Well With Increasing Cores?

Yes, based on the automated analysis of the collected public benchmark data, this test / test settings does generally scale well with increasing CPU core counts. Data based on publicly available results for this test / test settings, separated by vendor, result divided by the reference CPU clock speed, grouped by matching physical CPU core count, and normalized against the smallest core count tested from each vendor for each CPU having a sufficient number of test samples and statistically significant data.

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