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Add OLive in perf tuning section (#1772)

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* Add OLive in perf tuning section

* Add OLive to perf tuning section

* Update README.md

* Update ONNX_Runtime_Perf_Tuning.md
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Emma Ning 2019-09-27 13:10:40 -07:00 committed by ybrnathan
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README.md
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@ -132,6 +132,8 @@ ONNX Runtime can be deployed to the cloud for model inferencing using [Azure Mac
## Performance Tuning
ONNX Runtime is open and extensible, supporting a broad set of configurations and execution providers for model acceleration. For performance tuning guidance, please see [this page](./docs/ONNX_Runtime_Perf_Tuning.md).
To tune performance for ONNX models, the [ONNX Go Live tool "OLive"](https://github.com/microsoft/OLive) provides an easy-to-use pipeline for converting models to ONNX and optimizing performance for inferencing with ONNX Runtime.
***
# Examples and Tutorials
## Python

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docs/ONNX_Runtime_Perf_Tuning.md
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@ -91,6 +91,8 @@ whether next task is ready or not. Use PASSIVE if your CPU usage already high, u
Yes, we have created a tool named onnxruntime_perf_test.exe, and you find it at the build drop.
You can use this tool to test all those knobs easily. Please find the usage of this tool by onnxruntime_perf_test.exe -h
The [ONNX Go Live "OLive" tool](https://github.com/microsoft/OLive) provides an easy-to-use pipeline for converting models to ONNX and optimizing performance with ONNX Runtime. The tool can help identify the optimal runtime configuration to get the best performance on the target hardware for the model.
## How to enable profiling and view the generated JSON file?
You can enable ONNX Runtime latency profiling in code: