dtourolleandClaude Opus 5
39a22875b1
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Measured on a Radeon RX 7900 XT against Arch's onnxruntime-rocm 1.29 (docs/inference.md §1.3): MIGraphX fp16 runs the detectors at 2.4–3.4 ms against 10–58 ms on the CPU provider, the inpainter at 8 ms against 514, with a 15–135 s compile per graph the first time and under a second from its cache after. A compiling rung on TensorRT's terms, wired the same way. The ROCm execution provider is gone (removed in ONNX Runtime 1.23), so the AMD ladder is MIGraphX then the CPU, with no non-compiling rung between. MIGraphX is registered through the runtime's generic key/value entry point rather than ort's builder: 1.29 reads the legacy options struct for its precision flags only, and the compiled-program cache directory (`migraphx_model_cache_dir`) only travels the generic way. The provider's cache key omits the precision, so f32 and fp16 programs get their own directories. The probe fingerprint now includes the provider libraries beside the runtime and the ROCm version, since a distribution's CPU and ROCm builds are the same file at the same path. `status().failed` reports only the rungs above the selection, so an AMD desktop's About line says why MIGraphX won rather than that the NVIDIA providers are not in the build. Two examples: `ep_probe` times each provider cold and from cache, and `ladder` drives `init` as the app does to watch the first-run sequence. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
38 lines
1.9 KiB
Bash
Executable File
38 lines
1.9 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Put a GPU-capable ONNX Runtime where the desktop app looks for one
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# (docs/inference.md §3): `runtime/` beside the models in the user data
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# directory, ahead of the system library.
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#
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# ./tools/fetch-desktop-runtime.sh [DEST]
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#
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# The source is the `onnxruntime-gpu` wheel: the one build that carries the
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# CUDA *and* TensorRT providers against the cuDNN and TensorRT majors current
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# on this machine. Distribution packages tend to have neither — Arch's
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# `onnxruntime-opt-cuda` is built without TensorRT and against cuDNN 8 — and
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# the probe rejects them correctly and leaves the app on the CPU provider,
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# which is what this script exists to fix. Nothing NVIDIA is bundled here:
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# the providers load CUDA, cuDNN and TensorRT from the system, and if those
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# are missing the probe says so and the app stays on the CPU.
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#
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# This is the NVIDIA script. On AMD there is nothing to fetch: the
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# distribution's ROCm build of ONNX Runtime (Arch's `onnxruntime-rocm`)
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# carries the MIGraphX provider, and the app finds it in the system library
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# directory (docs/inference.md §1.3).
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set -euo pipefail
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DEST="${1:-${XDG_DATA_HOME:-${HOME}/.local/share}/darkroom/runtime}"
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WORK="$(mktemp -d -p /var/tmp fetch-desktop-runtime.XXXXXX)"
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trap 'rm -rf "${WORK}"' EXIT
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echo "==> downloading the onnxruntime-gpu wheel"
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uv venv --python 3.12 "${WORK}/venv" >/dev/null
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VIRTUAL_ENV="${WORK}/venv" uv pip install --quiet onnxruntime-gpu
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CAPI="$(find "${WORK}/venv" -type d -path '*/onnxruntime/capi' | head -1)"
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[[ -n "${CAPI}" ]] || { echo "error: no capi directory in the wheel" >&2; exit 1; }
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mkdir -p "${DEST}"
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# The runtime and its provider libraries; not the Python binding.
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cp "${CAPI}"/libonnxruntime.so* "${CAPI}"/libonnxruntime_providers_*.so "${DEST}/"
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echo "==> runtime in ${DEST}:"
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ls -1 "${DEST}" | sed 's/^/ /'
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echo " (the app finds it on its next launch; Settings › About › Inference says what it chose)"
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