dtourolle
84fade99ec
docs/ had 26 developer documents flat beside the manual, and the two audiences are very differently sized: most readers want the manual and the gesture reference, a few want the register, the designs and the measurements. The manual and gestures.md stay at the top; everything for someone changing the code moves to docs/dev/, and the two documents that name their own successors — the v0.1 milestone and the UI-refinement plan — go to docs/dev/archive/ rather than being deleted, since both are still cited. docs/README.md is the index, users first. Every reference follows: code comments, Cargo manifests, the workflows, the pre-commit hook, the bench and traceability tools (which locate the repo root by docs/dev/requirements.md now), packaging, the Docker READMEs, CLAUDE.md, CONTRIBUTING.md and the README. The matrix links one level deeper and is regenerated. Links out of the moved documents into the tree gain a level; a link checker over every Markdown file finds none broken.
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/dev/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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