Daily Report - March 11, 2026

  • Summary: Performed a SvelteKit frontend audit and substantial backend orchestration work to enable parallel hyperparameter tuning. README and docs were updated; remote push verification remains pending.

Key accomplishments

  • Reviewed the SvelteKit frontend (SvelteKit 2 / Svelte 5) and key files: package.json, vite.config.ts, svelte.config.js, src/lib/components/ScatterPlot.svelte.
  • Updated the project README to reflect the SvelteKit migration (replaced outdated React/Konva references, set frontend folder to frontend-sveltekit, updated dev port to 3001 and commands).
  • Implemented distributed tuning support: deterministic job slicing (--job-id, --trials-per-job), seeded sampling (seed=42), and graceful out-of-bounds handling.
  • Reduced noisy HF Hub sync messages by batching commits only when files changed.

Files changed / touched

  • train.py - parallel slicing, trial aggregation, HF sync improvements
  • PS1_USAGE.md - updated usage notes for PowerShell wrapper
  • README.md - front-end migration notes and updated dev commands

Tests & verification

  • Smoke tests: ran Job 0 and Job 1 locally; each processed separate trial slices and produced distinct best-validation results.
  • End-to-end train --predict completed in local runs.

Technical highlights

  1. Distributed orchestration (parallel jobs)
  • Job slicing: added --job-id and --trials-per-job to compute non-overlapping trial ranges per worker.
  • Reproducible sampling: fixed-seed shuffle ensures consistent trial ordering across workers.
  • Safety: jobs with an index beyond available trials now exit with a clear message rather than crashing.
  1. SvelteKit audit & README updates
  • Confirmed SvelteKit version compatibility and adjusted README commands and dev port to 3001.
  • Replaced outdated references to React/Konva; pointed maintainers to frontend-sveltekit folder.
  1. Sync & logging improvements
  • HF Hub sync now batches commits only when files changed, eliminating repeated “No files have been modified” warnings while preserving full debug logs in app.debug.log.

Current project status

  • Training Engine: ✅ Distributed (parallel workers supported via job-id)
  • Platform UI: ✅ Stable (Legend Gallery: R_1–R_11)
  • Data Integrity: ✅ Verified (LFS image pointers fixed)
  • Orchestration: ✅ Complete (run.ps1 updated for cluster-style launches)

Notes: I preserved the detailed technical notes from the day’s work but removed duplicated lines and consolidated repetitive TODO messages into a clear, scannable report.


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