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1.7 KiB
Markdown
50 lines
No EOL
1.7 KiB
Markdown
---
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title: AMD MI GraphX
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parent: Execution Providers
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nav_order: 6
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---
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# MIGraphX Execution Provider
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{: .no_toc }
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The [MIGraphX](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX/) execution provider uses AMD's Deep Learning graph optimization engine to accelerate ONNX model on AMD GPUs.
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## Contents
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{: .no_toc }
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* TOC placeholder
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{:toc}
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## Build
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For build instructions, please see the [BUILD page](../build/eps.md#amd-migraphx).
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## Usage
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### C/C++
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```c++
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Ort::Env env = Ort::Env{ORT_LOGGING_LEVEL_ERROR, "Default"};
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Ort::SessionOptions sf;
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int device_id = 0;
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Ort::ThrowOnError(OrtSessionOptionsAppendExecutionProvider_MiGraphX(sf, device_id));
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```
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You can check [here](https://github.com/scxiao/ort_test/tree/master/char_rnn) for a specific c/c++ program.
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The C API details are [here](../get-started/with-c.html.md).
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### Python
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When using the Python wheel from the ONNX Runtime build with MIGraphX execution provider, it will be automatically
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prioritized over the default GPU or CPU execution providers. There is no need to separately register the execution
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provider. Python APIs details are [here](/python/api_summary).
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You can check [here](https://github.com/scxiao/ort_test/tree/master/python/run_onnx) for a python script to run an
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model on either the CPU or MIGraphX Execution Provider.
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## Configuration Options
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MIGraphX providers an environment variable ORT_MIGRAPHX_FP16_ENABLE to enable the FP16 mode.
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## Performance Tuning
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For performance tuning, please see guidance on this page: [ONNX Runtime Perf Tuning](../performance/tune-performance.md)
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When/if using [onnxruntime_perf_test](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/test/perftest#onnxruntime-performance-test), use the flag `-e migraphx` |