Back to corpus
research noteexperiment writeup candidatescore 20

Phase 3.4: End-to-End Pipeline - Executive Summary

**Status:** ✅ COMPLETE **Date:** 2025-12-08 **Duration:** ~3-4 hours **Lines of Code:** 1,882+ lines (core + tests + examples)

Open full HTMLDownload HTMLCorpus JSONPaper schema

Full HTML reader

Read the full artifact

Open in new tab

Extracted abstract or opening context

**Status:** ✅ COMPLETE **Date:** 2025-12-08 **Duration:** ~3-4 hours **Lines of Code:** 1,882+ lines (core + tests + examples) A complete, production-ready training pipeline orchestration system for DLM coordinates, consisting of three main components: ### 1. Checkpoint Manager **File:** [packages/dlm/pipeline/checkpoint_manager.py](packages/dlm/pipeline/checkpoint_manager.py) (370+ lines) - Save/load training state with full metadata - Track best checkpoints by configurable metrics - Automatic cleanup (max_checkpoints limit) - Resume training from any checkpoint - PyTorch artifact persistence ### 2. Data Pipeline **File:** [packages/dlm/pipeline/data_pipeline.py](packages/dlm/pipeline/data_pipeline.py) (330+ lines)

Promotion decision

What has to happen next

Attach run IDs, datasets, metrics, and reproduction commands.

Why this is not always a full paper yet

Corpus pages are public-safe readers for discovered workspace artifacts. They are not automatically final papers. A corpus item becomes a polished paper only after the editable source, evidence checkpoints, references, figures, render path, and release status are attached through the paper schema.