onnxruntime/docs/performance/tune-performance/profiling-tools.md

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Profiling tools	Performance	Tune performance	1

Profiling Tools

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ONNX Go Live Tool (OLive)

The ONNX Go Live "OLive" tool is a Python package that automates the process of accelerating models with ONNX Runtime. It contains two parts: (1) model conversion to ONNX with correctness validation (2) auto performance tuning with ORT. Users can run these two together through a single pipeline or run them independently as needed.

As a quickstart, please see the notebook tutorials and command line examples

In-code performance profiling

The onnxruntime_perf_test.exe tool (available from the build drop) can be used to test various knobs. Please find the usage instructions using onnxruntime_perf_test.exe -h. The perf_view tool can also be used to render the statistics as a summarized view in the browser.

You can enable ONNX Runtime latency profiling in code:

import onnxruntime as rt

sess_options = rt.SessionOptions()
sess_options.enable_profiling = True

If you are using the onnxruntime_perf_test.exe tool, you can add -p [profile_file] to enable performance profiling.

In both cases, you will get a JSON file which contains the detailed performance data (threading, latency of each operator, etc). This file is a standard performance tracing file, and to view it in a user-friendly way, you can open it by using chrome://tracing:

• Open Chrome browser
• Type chrome://tracing in the address bar
• Load the generated JSON file

To profile CUDA kernels, please add the cupti library to your PATH and use the onnxruntime binary built from source with --enable_cuda_profiling. To profile ROCm kernels, please add the roctracer library to your PATH and use the onnxruntime binary built from source with --enable_rocm_profiling.

Performance numbers from the device will then be attached to those from the host. For example:

{"cat":"Node", "name":"Add_1234", "dur":17, ...}
{"cat":"Kernel", "name":"ort_add_cuda_kernel", dur:33, ...}

Here, the "Add" operator from the host initiated a CUDA kernel on device named "ort_add_cuda_kernel" which lasted for 33 microseconds. If an operator called multiple kernels during execution, the performance numbers of those kernels will all be listed following the call sequence:

{"cat":"Node", "name":<name of the node>, ...}
{"cat":"Kernel", "name":<name of the kernel called first>, ...}
{"cat":"Kernel", "name":<name of the kernel called next>, ...}