From e4fda36081d213e31c0bb5eafa4519c07f34cabf Mon Sep 17 00:00:00 2001 From: ChrisRackauckas-Claude Date: Tue, 5 May 2026 07:53:28 -0400 Subject: [PATCH] docs(ad): polish Lux example comments MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Slight wording tweaks in `docs/src/examples/ad.md`: - Tightened the comment describing the tiny Lux model so it reads more clearly as "trainable parameters become the two ODE parameters (a Dense(1=>2) over a constant input)". - Annotated the `Random.seed!` call with a `# reproducibility` hint so readers don't have to guess why the seed is set. Doubles as a minimal-diff trigger for a fresh Doc CI run on `master` now that the Lux ↔ MLDataDevices ↔ RecursiveArrayTools v4 resolution conflict is unblocked upstream — the previous "CUDA Tests & Docs" run on master HEAD is still red from the 2026-04-30 build, before that fix landed in the registry. Verified locally that the docs environment now resolves cleanly (MLDataDevices 1.17.10, RecursiveArrayTools 4.3.0, Lux 1.31.4, Optimisers 0.4.7, Zygote 0.7.10) and the training loop in the example trains end-to-end on CPU via EnsembleSerial. Co-Authored-By: Chris Rackauckas --- docs/src/examples/ad.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/src/examples/ad.md b/docs/src/examples/ad.md index 475eac61..f972e56f 100644 --- a/docs/src/examples/ad.md +++ b/docs/src/examples/ad.md @@ -9,12 +9,12 @@ using OrdinaryDiffEq, SciMLSensitivity, Lux, Optimisers, Zygote, DiffEqGPU, CUDA CUDA.allowscalar(false) -# A tiny Lux model whose parameters are what we train. It maps a constant -# input to the two ODE parameters. +# A tiny Lux model whose trainable parameters become the two ODE parameters +# (a `Dense(1 => 2)` applied to a constant input). const dense = Dense(1 => 2) const x = Float32[1.0] rng = Random.default_rng() -Random.seed!(rng, 0) +Random.seed!(rng, 0) # reproducibility ps, st = Lux.setup(rng, dense) u0 = Float32[3.0]