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


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.json to avoid CLI payload/environment failures (Day 58).
  • Modularity and storage abstractions: introduced HFStorage and renamed/merged training scripts to reduce coupling; one import path mismatch remains (Day 59).
  • Single-script portability: consolidated tuning + training into new_tune_train.py for easier deployment; noted missing datasets dependency (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-jobs CLI, 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 to requirements.txt or 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):

  1. Fix import path mismatch in tune_and_train.py / jobs/hf_storage.py and run an end-to-end test (Tune → Train → Save → Upload).
  2. Add datasets to the environment requirements and rerun a short tuning job to validate portability.
  3. Implement per-trial metadata logging (job_id, shard_id, total_jobs) and a final shard summary to improve observability.
  4. 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.


Table of contents


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