Week 31 - Daily Log
Continuing the detailed daily logging format. Each day’s work and learning outcomes are tracked in separate files for clarity and granularity.
Table of Contents
- Day 1 - April 13, 2026
- Day 2 - April 14, 2026
- Day 3 - April 15, 2026
- Day 4 - April 16, 2026
- Day 5 - April 17, 2026
- Day 6 - April 18, 2026
Overview
This week focuses on the AnanaCare Relabel project, with emphasis on automating the setup process and planning the Undo/Redo functionality.
Weekly Summary
Key themes for Week 31 (Days 58–63):
- Production hardening of the training/control plane: stabilized HF job submission via a bootstrap flow and
job_config.jsonto avoid CLI payload/environment failures (Day 58). - Modularity and storage abstractions: introduced
HFStorageand renamed/merged training scripts to reduce coupling; one import path mismatch remains (Day 59). - Single-script portability: consolidated tuning + training into
new_tune_train.pyfor easier deployment; noted missingdatasetsdependency (Day 60). - Observability & stability: reduced log noise, added epoch/device-level logging, fixed model-dimension and checkpoint-tracking bugs to improve reliability (Day 61).
- Distributed tuning & sharding: implemented deterministic hash-based sharding, shard-first filtering,
--total-jobsCLI, and per-shard aggregation for scalable hyperparameter search (Days 62–63).
Blockers & risks observed:
- Runtime import path mismatch for the HF storage wrapper (fix needed before full end-to-end runs).
- Missing runtime dependency (
datasets) in some environments blocks execution until added torequirements.txtor installed at runtime. - Secrets handling (
HF_TOKEN) and debug logs need secure/clean handling before production use. - Shard imbalance and missing per-trial metadata can complicate debugging and traceability for distributed runs.
Recommended next steps (priority):
- Fix import path mismatch in
tune_and_train.py/jobs/hf_storage.pyand run an end-to-end test (Tune → Train → Save → Upload). - Add
datasetsto the environment requirements and rerun a short tuning job to validate portability. - Implement per-trial metadata logging (
job_id,shard_id,total_jobs) and a final shard summary to improve observability. - Move secrets to secure storage and remove temporary debug logs.
Daily Work Logs
See the sidebar or the links above for each day’s detailed log.