The macOS ladder was the CPU provider alone, with CoreML listed as a gap.
It is now CoreML, then the CPU, then tract — unmeasured, since nobody here
has a Mac, and safe to ship unmeasured because the probe's clock rejects a
CoreML slower than the CPU and `attempt` refuses one that crashes.
- `Rung::CoreMl`, a compiling rung like TensorRT: an ML Program with every
compute unit allowed, falling back to the CPU until each model's program
is built. The embedder stays on the CPU, as on the Hexagon (§7).
- The cache is one directory per model and runtime version. CoreML keys a
model committed from memory on its input and node names, not its
weights (ONNX Runtime 1.29, coreml_execution_provider.cc), so two
exports of one architecture would otherwise share a program.
- The fingerprint on macOS is the chip and the OS release, which ships
CoreML.
- The desktop looks for the runtime in the bundle's Contents/Frameworks
and Homebrew's prefixes; fetch-desktop-runtime.sh on a Mac downloads
ONNX Runtime 1.29.0 for Apple silicon, which carries CoreML.
docs/dev/macos.md says what exists, how to build it, and which log lines
to ask a Mac user for.
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.
dtourolle
