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### Description Staged: https://faxu.github.io/onnxruntime/docs/performance/ Main changes: - Restructure performance section to break into sub-categories - Move CUDA specific perf tuning tips to [CUDA EP page](https://faxu.github.io/onnxruntime/docs/execution-providers/CUDA-ExecutionProvider.html#performance-tuning) - Update [Transformer optimizer page](https://faxu.github.io/onnxruntime/docs/performance/transformers-optimization.html) to remove version-specific content... will be supported along with https://github.com/microsoft/onnxruntime/pull/14964 - Fix links to point to new pages
88 lines
2.5 KiB
Markdown
88 lines
2.5 KiB
Markdown
---
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title: AMD - MIGraphX
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description: Instructions to execute ONNX Runtime with the AMD MIGraphX execution provider
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parent: Execution Providers
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nav_order: 10
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redirect_from: /docs/reference/execution-providers/MIGraphX-ExecutionProvider
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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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## Install
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**NOTE** Please make sure to install the proper version of Pytorch specified here [PyTorch Version](../install/#training-install-table-for-all-languages).
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For Nightly PyTorch builds please see [Pytorch home](https://pytorch.org/) and select ROCm as the Compute Platform.
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Pre-built binaries of ONNX Runtime with MIGraphX EP are published for most language bindings. Please reference [Install ORT](../install).
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## Requirements
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|ONNX Runtime|MIGraphX|
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|---|---|
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|main|5.4|
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|1.14|5.4|
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|1.13|5.4|
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|1.13|5.3.2|
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|1.12|5.2.3|
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|1.12|5.2|
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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 so;
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int device_id = 0;
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Ort::ThrowOnError(OrtSessionOptionsAppendExecutionProvider_MIGraphX(so, device_id));
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```
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The C API details are [here](../get-started/with-c.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.
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Python APIs details are [here](https://onnxruntime.ai/docs/api/python/api_summary.html).
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*Note that the next release (ORT 1.10) will require explicitly setting the providers parameter if you want to use execution provider other than the default CPU provider when instantiating InferenceSession.*
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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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## Samples
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### Python
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```python
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import onnxruntime as ort
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model_path = '<path to model>'
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providers = [
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'MIGraphXExecutionProvider',
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'CPUExecutionProvider',
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]
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session = ort.InferenceSession(model_path, providers=providers)
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```
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