Back to corpus
research noteexperiment writeup candidatescore 28

Phase 3.4: End-to-End Pipeline - Completion Report

Phase 3.4 implements the complete end-to-end training pipeline for DLM coordinates. This phase provides orchestration infrastructure that ties together data loading (Phase 3.1), IRCP integration (Phase 3.2), and evaluation metrics (Phase 3.3) into a production-ready training system.

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 **Integration Point:** Week 3, Phase 3.4 Phase 3.4 implements the complete end-to-end training pipeline for DLM coordinates. This phase provides orchestration infrastructure that ties together data loading (Phase 3.1), IRCP integration (Phase 3.2), and evaluation metrics (Phase 3.3) into a production-ready training system. #### 1. **Checkpoint Manager** ([packages/dlm/pipeline/checkpoint_manager.py](packages/dlm/pipeline/checkpoint_manager.py)) - 370+ lines **Features:** - ✅ Checkpoint metadata tracking - ✅ Training state persistence - ✅ Metrics history storage - ✅ Configuration snapshots - ✅ Artifact paths tracking **Features:** - ✅ Save/load checkpoints - ✅ Track best checkpoints by metric - ✅ Automatic cleanup (keep max_checkpoints) - ✅ Resume from checkpoint - ✅ PyTorch artifact storage - ✅ Metadata persistence

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.