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.
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>
The reference desktop's only system ONNX Runtime is Arch's
onnxruntime-opt-cuda: 1.29, built without TensorRT and against cuDNN 8
on a cuDNN 9 machine. The probe rejects both providers correctly and
the app runs on the CPU provider, which is right and not what anyone
wants. runtime/ beside the models is now searched ahead of /usr/lib,
tools/fetch-desktop-runtime.sh fills it with the four libraries from
the current onnxruntime-gpu wheel (cuDNN 9, TensorRT 10), and the
About caption lists every rung that lost and why, not only the first.
Verified: the app selects TensorRT from that directory with no
environment variable set.
A border fill acquires the filler once a tile, and each acquire hashed
the 28 MB model twice — 60 ms a tile, a third of the tile's run on a
throttled TensorRT. The Model keeps its hash from open.
MI-GAN is plain convolutions, so every rung serves it and none needs a
special form; the role exists so resolve_model and the probe's fingerprint
know the model, and so the merge job can open it through the engine rather
than tract, which takes 7.4 s a tile for it.
On the tablet the engine compiled arcface for the NPU: the routing
compared the form a rung wants with the form on offer, and for the
embedder both are f32, so nothing said no. A rung now says which roles
it serves at all, and the Hexagon does not serve the embedder (§7 —
its vectors must compare across devices). Tested at the routing seam.
dr-segment's onnx_probe example still named ort-tract, which is what
stopped the workspace test build.
The strict flag refused the Hexagon over the ten quantise/dequantise
nodes at the graph's edges that QNN declines by policy, which cost
microseconds. A provider that hands real work to the CPU is slower than
the CPU floor and the timing already rejects it; the tablet measured
2.3 ms on the NPU against a 29.7 ms floor.
XFeat's two exports are a Keypoints role now; the crate no longer names
tract, and the app compiles TensorRT engines for both ahead of the
first merge. The probe picks the smallest *detector* rather than the
smallest file: the tablet's first run chose the 112 KB eye classifier,
which has no int8 form, and reported the Hexagon as failed for want of
one.
The desktop names where a package may have put libonnxruntime — an
override variable, beside the executable, the package's own library
directory, the Flatpak prefix, the system library directory — and
Android points at the APK's native library directory, which is also
what Qualcomm's DSP loader must be told for the Hexagon skel. Android
starts the engine at the end of the model unpack rather than at launch,
because the probe fingerprints the model files and a first launch has
none until then.
The About panel gains an Inference row beside Graphics, re-read every
two seconds while the probe runs and engines land, and faces.model_id
carries the detector's form: an int8 detector finds a different set of
faces and is a different population (docs/inference.md §7). A
low-memory signal drops every idle session with the GPU caches.
The APK assembly bundles ONNX Runtime and the Qualcomm HTP libraries
from Maven, fetched by tools/fetch-android-runtime.sh with their
published checksums; RUNTIME_DIR=none builds the tract-only APK, which
is a slower app and not a broken one. The desktop packages carry no
runtime yet.
Two probe fixes from the first desktop run: the floor must not be
built with CPU fallback disabled, and a versioned libonnxruntime.so is
a runtime too. On the reference desktop the probe now loads ONNX
Runtime 1.30, measures 30 ms on the CPU provider, and selects TensorRT
at 1.5 ms.
One crate names the runtime, the providers and the devices; dr-face and
dr-segment ask it for a session by role. It hands ort an API table once
per process — from a libonnxruntime it dlopens when the app names a
directory holding one, otherwise from tract — so the Rust build stays
free of C on every target and a package can install the runtime as a
file (docs/inference.md §3).
Sessions live in a registry behind a Model handle that holds the bytes,
not the session: every use refreshes a timestamp and a reaper unloads
whatever sat idle past the decay. A scan that runs the detector on each
image never lets it go idle; a click in the develop view lets the
segmenter go after thirty seconds; a handle used after that reloads,
and reloads on a higher rung if a compiled engine has landed meanwhile.
The probe walks the platform's ladder by building strict sessions and
timing them against the CPU provider, caches the choice against a
fingerprint of the runtime, driver, hardware and models, and compiles
engines for the selected rung in the background, smallest model first.
Nothing in this commit turns the native path on: the apps still run on
tract until they call init with a runtime directory.
dtourolle
