Per-component convergence diagnostics: write_nd trajectory and run log
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Active
- Tech stack
- numpy, python, pytorch
- Domain
- machine-learning, observability
Research direction
Start by reviewing .context/feature_parity.md and the referenced amica15.f90 sections for the expected gradient trajectory and run-log fields. Then inspect the existing NumPy tracking and the torch/MLX reporting paths; done requires an agreed scope and consistent diagnostics or an explicit record that the feature remains deliberate parity debt.
Written by the indexing model from the issue text.
Description
From the epic #278 feature-parity audit (Fortran-coverage pass). Two related reporting gaps vs amica15.f90, unrecorded:
write_nd(amica15.f90:658, 1760-1761, output at 2398-2429): the per-component, per-iteration squared-gradient trajectory. pamica exposes only the scalar RMS aggregate (ndtmpsumfeeding themin_ndstop); the legacy NumPy backend keeps a public per-iterationndlist that torch/MLX lack. Diagnosing WHICH sources converge slowly is not possible on the primary backends.- Per-iteration run log (Fortran's
outstep-gated console + unit-20 log line: iter/lrate/LL/nd/Dsum/timing-ETA, amica15.f90:1005-1012): pamica'sverbosetqdm shows LL/lrate only, and no on-disk log exists on any array backend.
Also from the audit, for the record when this is picked up: .context/feature_parity.md's "History tracking ... grad norms" row implies a tracked gradient-norm trajectory that does not publicly exist on torch/MLX. Scope decision (port, partial, or record-as-deliberate) left open.
- Dominant language
- Python
- Stars
- 8
- Forks
- 3
- Avg merge
- 5h 37m
- Merged PRs (30d)
- 36
Contributor guide
First steps
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- Comment on the issue to say you are picking it up — it saves two people doing the same work.
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- Open a pull request that references the issue number.
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