Install ONNX Runtime for Radeon GPUs

Install ONNX Runtime for Radeon GPUs#

Refer to this section to install ONNX via the PIP installation method.

Overview#

Ensure that the following prerequisite installations are successful before proceeding to install ONNX Runtime for use with ROCm™ on Radeon™ GPUs.

Prerequisites#

Verify if MIGraphX is installed with the half library#

$ dpkg -l | grep migraphx
$ dpkg -l | grep half

Expected result:

root@aus-navi3x-02:/workspace/AMDMIGraphX# dpkg -l | grep migraphx
ii  migraphx                      2.9.0                             amd64        AMD's graph optimizer
ii  migraphx-dev                  2.9.0                             amd64        AMD's graph optimizer
ii  migraphx-tests                2.9.0                             amd64        AMD's graph opt


$ dpkg -l | grep half
ii  half                          1.12.0.60000-91~20.04             amd64        HALF-PRECISION FLOATING POINT LIBRARY

NOTE Versions may vary between ROCm builds and installed versions of MIGraphX, but the desired result is the same.

Enter the following install command if the half library is not installed with MIGraphX:

$ dpkg -l | grep migraphx
$ dpkg -l | grep half

NOTE The half library should come packaged with MIGraphX. If not, it can be installed with the following command:

$ sudo apt install half

Install ONNX Runtime#

Important!

  • Use the provided pre-built Python wheel files from the PIP installation method, unless adding custom features.

  • The wheel file contains the MIGraphX and ROCm Execution Providers (EP). Refer to Install MIGraphX for ONNX RT for more information.

  • Refer to ONNX Runtime Documentation for additional information on ONNX Runtime topics.

  • See ONNX Runtime Tutorials to try out real applications and tutorials on how to get started.

AMD recommends the PIP install method to create an ONNX Runtime environment when working with ROCm for machine learning development.

Note
The latest version of Python module numpy v2.0 is incompatible with the ONNX Runtime wheels for this version. Downgrade to an older version is required.
Example: pip3 install numpy==1.26.4

To install via PIP,

Enter this command to download and install the ONNX Runtime wheel.

pip3 uninstall onnxruntime-rocm numpy
pip3 install https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1.3/onnxruntime_rocm-1.17.0-cp310-cp310-linux_x86_64.whl numpy==1.26.4

Next, verify the ONNX Runtime installation.

Option B: Build from source for your environment, followed by local wheel file installation (Advanced)

Use this method for advanced customization usecases. This requires the user install the desired ROCm and MIGraphX versions, and creation of softlink prior to starting the build.

NOTE The build time typically takes ~45 minutes.

Prerequisites to build ONNX from source

To build from source,

  1. Clone the ONNX Runtime repository into the root directory.

    cd /
git clone https://github.com/microsoft/onnxruntime.git

  2. Git clone AMDMIGraphX into the home folder.

    cd ~
git clone https://github.com/ROCm/AMDMIGraphX.git

  3. Create a docker image for MIGraphX.

    Note

    • Use the groups command to ensure that the user is part of the video, render, and docker groups in Linux to run the docker container.

      groups
tthemist@aus-navi3x-02 ~/groups
tthemist sudo video render docker

    • Run the following for a simple MIGraphX apt install:

      cd AMDMIGraphX
docker build -t migraphx .
docker run --device='/dev/kfd' --device='/dev/dri' -v=`pwd`:/code/AMDMIGraphX -v /onnxruntime:/onnxruntime -w /code/AMDMIGraphX --group-add video -it migraphx
apt install migraphx migraphx-dev half

  4. Run rocm-smi to ensure that ROCm is installed and detects the supported GPU(s).

    $ rocm-smi

    Expected result:

    ======================================= ROCm System Management Interface =======================================
================================================= Concise Info =================================================
Device  [Model : Revision]    Temp    Power  Partitions      SCLK     MCLK   Fan     Perf  PwrCap  VRAM%  GPU% 
        Name (20 chars)       (Edge)  (Avg)  (Mem, Compute)                                                    
================================================================================================================
0       [0x0e0d : 0x00]       32.0°C  73.0W  N/A, N/A        1526Mhz  96Mhz  31.76%  auto  241.0W    0%   50%  
        0x7448                                                                                                 
================================================================================================================
============================================= End of ROCm SMI Log ==============================================

  5. Configure Git to treat all directories as safe to use and run the build script.

    cd AMDMIGraphX
git config --global --add safe.directory "*"
tools/build_and_test_onnxrt.sh

    This builds ONNX Runtime and adds ROCm and MIGraphX EP support to the ONNX Runtime interface and requires multiple external repo pieces be checked out automatically prior to the build.

  6. Install ONNX Runtime once MIGraphX is built.

    $ pip3 install /onnxruntime/build/Linux/Release/dist/*.whl

Next, verify your ONNX Runtime installation.

Verify ONNX Runtime installation#

Confirm if ONNX Runtime is correctly installed.

$ python3
$ import onnxruntime as ort
  ort.get_available_providers()

Expected result: The following EPs are displayed.

>>> ort.get_available_providers()
['MIGraphXExecutionProvider', 'ROCMExecutionProvider', 'CPUExecutionProvider']

Environment set-up is complete, and the system is ready for use with ONNX Runtime to work with machine learning models, and algorithms.

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