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feat(ml): rocm (#16613)

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* feat(ml): introduce support of onnxruntime-rocm for AMD GPU

* try mutex for algo cache

use OrtMutex

* bump versions, run on mich

use 3.12

use 1.19.2

* acquire lock before any changes can be made

guard algo benchmark results

mark mutex as mutable

re-add /bin/sh (?)

use 3.10

use 6.1.2

* use composite cache key

1.19.2

fix variable name

fix variable reference

aaaaaaaaaaaaaaaaaaaa

* bump deps

* disable algo caching

* fix gha

* try ubuntu runner

* actually fix the gha

* update patch

* skip mimalloc preload for rocm

* increase build threads

* increase timeout for rocm

* Revert "increase timeout for rocm"

This reverts commit 2c4452f5d132198ed381a7b262b4a5cab5114b5f.

* attempt migraphx

* set migraphx_home

* Revert "set migraphx_home"

This reverts commit c121d3e48754b3bce100636f8d666deec58a44b7.

* Revert "attempt migraphx"

This reverts commit 521f9fb72dbe506dc6cb8faeb6494817d87265c6.

* migraphx, take two

* bump rocm

* allow cpu

* try only targeting migraphx

* skip tests

* migraph ❌

* known issues

* target gfx900 and gfx1102

* mention `HSA_USE_SVM`

* update lock

* set device id for rocm

---------

Co-authored-by: Mehdi GHESH <mehdi.ghesh@hotmail.fr>
This commit is contained in:
Mert
2025-03-17 17:08:19 -04:00
committed by GitHub
parent 6a40aa83b7
commit 2b37caba03
17 changed files with 340 additions and 50 deletions
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machine-learning/README.md
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@@ -7,7 +7,7 @@
This project uses [uv](https://docs.astral.sh/uv/getting-started/installation/), so be sure to install it first.
Running `uv sync --extra cpu` will install everything you need in an isolated virtual environment.
CUDA and OpenVINO are supported as acceleration APIs. To use them, you can replace `--group cpu` with either of `--group cuda` or `--group openvino`. In the case of CUDA, a [compute capability](https://developer.nvidia.com/cuda-gpus) of 5.2 or higher is required.
CUDA, ROCM and OpenVINO are supported as acceleration APIs. To use them, you can replace `--extra cpu` with either of `--extra cuda`, `--extra rocm` or `--extra openvino`. In the case of CUDA, a [compute capability](https://developer.nvidia.com/cuda-gpus) of 5.2 or higher is required.
To add or remove dependencies, you can use the commands `uv add $PACKAGE_NAME` and `uv remove $PACKAGE_NAME`, respectively.
Be sure to commit the `uv.lock` and `pyproject.toml` files with `uv lock` to reflect any changes in dependencies.