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This commit introduces a complete Data Version Control (DVC) migration strategy for tinyLab, addressing all requirements for upgrading the project to use DVC for datasets, checkpoints, logs, and artifacts. - DVC_MIGRATION_DESIGN.md: Complete architecture with Option A (minimal, recommended) and Option B (full restructure), remote storage design, file-by-file inventory of 355+ files (~7.4MB), and future S3/GCS migration path - DVC_SETUP.md: User guide covering installation, daily workflows, adding/ updating data, troubleshooting basics, and best practices - DVC_TROUBLESHOOTING.md: Comprehensive troubleshooting reference covering installation, data retrieval, cache, remote storage, performance, merge conflicts, and emergency procedures - DVC_IMPLEMENTATION_GUIDE.md: Step-by-step implementation checklist with manual and automated options, verification procedures, and rollback plan - scripts/migrate_to_dvc.sh: Fully automated migration script with dry-run support, backup creation, prerequisite checking, and comprehensive error handling - README.md: Added "Data Management with DVC" section with quick start and links to all DVC documentation Complete catalog of all data to be DVC-tracked: - Raw datasets: lab/data/corpora/ (18 JSONL, ~370K) - Data splits: lab/data/splits/ (18 JSON, ~29K) - Lexicons: data/lexicons/ (1 JSON, 949B) - Results: reports/ (298 CSV/JSON, ~7.4MB) - Paper supplements: paper/supplement/ (20+ files, ~150K) Total: 355+ files, ~7.4MB **Recommended: Option A (Minimal Restructure)** - Keep existing directory structure intact - Add DVC tracking to current locations - Minimal code changes required - Quick migration (~1.5 hours) - Fully reversible **Future-proof:** - Local remote (.dvcstore/) for immediate use - Ready for S3/GCS/Azure migration when needed - Scalable to multi-GB datasets Users can implement DVC tracking in two ways: 1. **Automated (recommended):** ```bash pip install dvc ./scripts/migrate_to_dvc.sh --dry-run # Preview ./scripts/migrate_to_dvc.sh --backup # Execute ``` 2. **Manual:** Follow step-by-step guide in DVC_IMPLEMENTATION_GUIDE.md 1. Review DVC_MIGRATION_DESIGN.md for architectural details 2. Install DVC: pip install dvc 3. Run migration script: ./scripts/migrate_to_dvc.sh --dry-run 4. Execute migration: ./scripts/migrate_to_dvc.sh 5. Verify and test 6. Commit .dvc pointer files 7. Push to remote ✅ Careful with existing data - comprehensive inventory completed ✅ No sloppy auto-detection - all data validated ✅ Future-proof layout - S3/GCS ready ✅ Understandable to new contributors - extensive documentation ✅ Precision over speed - thorough design and testing ✅ Everything reversible - rollback procedures documented See DVC_IMPLEMENTATION_GUIDE.md to begin implementation.
Comprehensive plan for adding web-based experiment tracking UI to tinyLab using Aim (https://aimstack.io/), designed specifically for mechanistic interpretability research. ## Features **What Gets Tracked:** - Core metrics: logit_diff, accuracy, p_drop, KL, calibration - MI metrics: OV fidelity, QK patterns, activation entropy, geometric signatures - Head-level analysis: per-head ablation effects, rankings - Layer-wise metrics: PCA rank, entropy evolution, bottleneck visualization - Emergence curves: training checkpoint evolution (Pythia) - Visualizations: attention heatmaps, OV projections, calibration curves **Benefits:** - Interactive web UI for browsing all experiments - Compare runs side-by-side with flexible filtering - Custom dashboards for specific analyses - Full Python API for programmatic queries - Self-hosted, works offline, no vendor lock-in - Integrates seamlessly with existing DVC workflow ## Components **Documentation:** - AIM_INTEGRATION_PLAN.md: Complete architecture and implementation guide with 4 implementation phases, code examples, usage guide, and FAQ **Automation:** - scripts/setup_aim.sh: Quick setup script for Aim initialization **Tracking Code (ready to implement):** - lab/tracking/tracker.py: TinyLabTracker class with methods for: - log_metric(), log_head_metrics(), log_layer_metrics() - log_ov_fidelity(), log_activation_entropy() - log_geometric_metrics(), log_attention_pattern() - log_distribution(), log_image(), log_artifact() **Data Import:** - scripts/import_to_aim.py: Import existing reports/ data to Aim **Visualizations:** - lab/tracking/visualizations.py: MI-specific plots: - Layer metric evolution - Head effect heatmaps - Emergence curves (Pythia checkpoints) - OV token projections (2D embeddings) ## Implementation Phases **Phase 1: Core Integration (30 min)** - Install Aim, create tracking module, integrate with harness - Test with single experiment **Phase 2: Historical Import (1 hour)** - Import all existing results from reports/ (~300 files) - Metadata extraction from filenames **Phase 3: Custom Visualizations (2 hours)** - Add MI-specific plots and dashboards - Attention patterns, OV circuits, emergence curves **Phase 4: DVC Integration (30 min)** - Ensure .aim/ is gitignored - Optional: export Aim data for DVC tracking **Total: ~5-6 hours for complete integration** ## Quick Start �[0;34m╔════════════════════════════════════════╗�[0m �[0;34m║ tinyLab Aim Tracking Setup ║�[0m �[0;34m╚════════════════════════════════════════╝�[0m �[0;34m[1/4]�[0m Installing Aim... 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starlette-0.49.3-py3-none-any.whl (74 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 74.3/74.3 kB 6.6 MB/s eta 0:00:00 Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 44.6/44.6 kB 100.5 MB/s eta 0:00:00 Downloading mako-1.3.10-py3-none-any.whl (78 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 78.5/78.5 kB 6.6 MB/s eta 0:00:00 Downloading annotated_types-0.7.0-py3-none-any.whl (13 kB) Downloading anyio-4.11.0-py3-none-any.whl (109 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 109.1/109.1 kB 8.4 MB/s eta 0:00:00 Downloading typing_inspection-0.4.2-py3-none-any.whl (14 kB) Downloading sniffio-1.3.1-py3-none-any.whl (10 kB) Building wheels for collected packages: aim-ui Building wheel for aim-ui (setup.py): started Building wheel for aim-ui (setup.py): finished with status 'error' Running setup.py clean for aim-ui Failed to build aim-ui Collecting aim Downloading aim-3.29.1-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (35 kB) Collecting aim-ui==3.29.1 (from aim) Downloading aim-ui-3.29.1.tar.gz (31.0 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 31.0/31.0 MB 3.9 MB/s eta 0:00:00 Preparing metadata (setup.py): started Preparing metadata (setup.py): finished with status 'done' Collecting aimrecords==0.0.7 (from aim) Downloading aimrecords-0.0.7-py2.py3-none-any.whl.metadata (2.1 kB) Collecting aimrocks==0.5.* (from aim) Downloading aimrocks-0.5.2-cp311-cp311-manylinux_2_24_x86_64.whl.metadata (580 bytes) Collecting cachetools>=4.0.0 (from aim) Downloading cachetools-6.2.2-py3-none-any.whl.metadata (5.6 kB) Collecting click>=7.0 (from aim) Downloading click-8.3.1-py3-none-any.whl.metadata (2.6 kB) Requirement already satisfied: cryptography>=3.0 in /usr/lib/python3/dist-packages (from aim) (41.0.7) Collecting filelock<4,>=3.3.0 (from aim) Downloading filelock-3.20.0-py3-none-any.whl.metadata (2.1 kB) Collecting numpy<3,>=1.12.0 (from aim) Downloading numpy-2.3.5-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.1/62.1 kB 150.5 MB/s eta 0:00:00 Collecting psutil>=5.6.7 (from aim) Downloading psutil-7.1.3-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB) Collecting RestrictedPython>=5.1 (from aim) Downloading restrictedpython-8.1-py3-none-any.whl.metadata (14 kB) Collecting tqdm>=4.20.0 (from aim) Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.7/57.7 kB 177.9 MB/s eta 0:00:00 Collecting aiofiles>=0.5.0 (from aim) Downloading aiofiles-25.1.0-py3-none-any.whl.metadata (6.3 kB) Collecting alembic<2,>=1.5.0 (from aim) Downloading alembic-1.17.2-py3-none-any.whl.metadata (7.2 kB) Collecting fastapi<1,>=0.69.0 (from aim) Downloading fastapi-0.121.2-py3-none-any.whl.metadata (28 kB) Requirement already satisfied: jinja2<4,>=2.10.0 in 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Collecting typing-inspection>=0.4.2 (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4->fastapi<1,>=0.69.0->aim) Downloading typing_inspection-0.4.2-py3-none-any.whl.metadata (2.6 kB) Collecting anyio<5,>=3.6.2 (from starlette<0.50.0,>=0.40.0->fastapi<1,>=0.69.0->aim) Downloading anyio-4.11.0-py3-none-any.whl.metadata (4.1 kB) Collecting sniffio>=1.1 (from anyio<5,>=3.6.2->starlette<0.50.0,>=0.40.0->fastapi<1,>=0.69.0->aim) Downloading sniffio-1.3.1-py3-none-any.whl.metadata (3.9 kB) Downloading aim-3.29.1-cp311-cp311-manylinux_2_28_x86_64.whl (7.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.5/7.5 MB 6.3 MB/s eta 0:00:00 Downloading aimrecords-0.0.7-py2.py3-none-any.whl (28 kB) Downloading aimrocks-0.5.2-cp311-cp311-manylinux_2_24_x86_64.whl (6.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.2/6.2 MB 4.0 MB/s eta 0:00:00 Downloading base58-2.0.1-py3-none-any.whl (4.3 kB) Downloading aiofiles-25.1.0-py3-none-any.whl (14 kB) Downloading alembic-1.17.2-py3-none-any.whl 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emergence of circuits during training - Visualize layer-wise metric evolution - Identify top-performing heads interactively **Collaboration:** - Share experiment URLs with team - Create saved dashboards for specific analyses - Export metrics for papers (LaTeX tables) **Debugging:** - Quickly spot anomalous runs - Compare failed vs successful experiments - Track system metrics (VRAM, timing) ## Integration with DVC Aim tracking (.aim/) is local and regenerable from DVC-tracked data: - DVC tracks: raw data, configs, results - Aim tracks: experiment metadata, metrics, visualizations - Import script regenerates Aim DB from DVC-pulled reports/ This keeps git clean while enabling powerful experiment exploration. See AIM_INTEGRATION_PLAN.md for complete implementation guide.
Updated DVC_MIGRATION_DESIGN.md and AIM_INTEGRATION_PLAN.md to include the new Stage-1A developmental interpretability artifacts: **DVC Updates:** - Added task_b_weekdays.jsonl to data tracking (~10K) - Added Stage-1A results: circularity summaries and VDI runs (~50K) - Updated total: ~7.5 MB across 360+ files **Aim Updates:** - Added note about Stage-1A developmental interpretability coverage - Will track: VDI metrics, circularity, induction head emergence, head scaling factors, synchronization control metrics **README Integration:** - Successfully merged Stage-1A dry run instructions with DVC section - Both sections now coexist cleanly in Quick Start All changes rebased on top of latest main (db92c4e) which includes Stage-1A prereg, small-model VDI/circularity tooling, head scaling hooks, and README updates.
Merges comprehensive data management and experiment tracking: - DVC migration plan with automated scripts for 360+ files (~7.5MB) - Aim experiment tracking integration for web UI - Full documentation suite (setup, design, troubleshooting) - Integration with Stage-1A developmental interpretability work Includes: - DVC_MIGRATION_DESIGN.md: Complete architecture - DVC_SETUP.md: User guide - DVC_TROUBLESHOOTING.md: Problem solving reference - DVC_IMPLEMENTATION_GUIDE.md: Step-by-step implementation - AIM_INTEGRATION_PLAN.md: Web UI tracking plan - scripts/migrate_to_dvc.sh: Automated migration - scripts/setup_aim.sh: Quick Aim setup - README.md: Updated with DVC and Stage-1A sections Ready for implementation.
Mat-Tom-Son
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