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### Description Update support Matrix for MIGraphX EP and add related info about prebuilt wheels and python support. ### Motivation and Context Updating the latest info and pointing uses to the wheel we support with each corresponding ROCm release. --------- Co-authored-by: Ted Themistokleous <tedthemistokleous@amd.com>
105 lines
4.4 KiB
Markdown
105 lines
4.4 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: 11
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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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Since ROCm 6.0.2, AMD supplies pre-built python wheels hosted on (https://repo.radeon.com/rocm/manylinux)
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## Requirements
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Below is the matrix of supported ROCm versions corresponding to Ubuntu builds.
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Links for prebuild Python Wheels (.whl) are linked below corresponding to python versions for the host OS based on Ubuntu support.
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|ONNX Runtime Version|MIGraphX ROCm Release| Repo.Radeon link|
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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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|1.13|5.3||
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|1.14|5.4||
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|1.15|5.4.x||
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|1.16|6.0.0||
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|1.16|6.0.2| [Python 3.10](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.0.2/onnxruntime_rocm-inference-1.17.0-cp310-cp310-linux_x86_64.whl)|
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|1.17|6.1| [Python 3.8](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1/onnxruntime_rocm-inference-1.17.0-cp38-cp38-linux_x86_64.whl) [Python 3.10](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1/onnxruntime_rocm-inference-1.17.0-cp310-cp310-linux_x86_64.whl)|
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|1.17|6.1.3| [Python 3.10](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1.3/onnxruntime_rocm-inference-1.17.0-cp310-cp310-linux_x86_64.whl)|
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|1.18|6.2| [Python 3.8](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.2/onnxruntime_rocm-1.18.0-cp38-cp38-linux_x86_64.whl) [Python 3.10](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.2/onnxruntime_rocm-1.18.0-cp310-cp310-linux_x86_64.whl)|
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|1.18|6.2.3| [Python 3.10](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.2.3/onnxruntime_rocm-1.18.0-cp310-cp310-linux_x86_64.whl)|
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|1.18|6.2.4| [Python 3.10](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.2.4/onnxruntime_rocm-1.18.0-cp310-cp310-linux_x86_64.whl)|
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|1.19|6.3| [Python 3.10](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/onnxruntime_rocm-1.19.0-cp310-cp310-linux_x86_64.whl) [Python 3.12](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/onnxruntime_rocm-1.19.0-cp312-cp312-linux_x86_64.whl)|
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|1.19|6.3.1| [Python 3.10](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3.1/onnxruntime_rocm-1.19.0-cp310-cp310-linux_x86_64.whl) [Python 3.12](https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3.1/onnxruntime_rocm-1.19.0-cp312-cp312-linux_x86_64.whl)|
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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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