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hf sync
Syed Ibrahim Omer edited this page Apr 12, 2026
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Deep dive into src/collection/hf_sync.py — the bidirectional synchronization utility between local data and the Hugging Face Hub.
All datasets and models are stored under the sdsc2005-migration Hugging Face organization, enabling:
- Full reproducibility: Any team member can bootstrap from scratch
- Version control: Datasets and models are versioned via HF Hub
- Collaboration: Centralized data sharing
Syncs 6 default datasets from HF Hub to local paths:
| HF Dataset | Local Path |
|---|---|
sdsc2005-migration/trends |
data/raw/trends/ |
sdsc2005-migration/news |
data/raw/news/ |
sdsc2005-migration/news_embeddings |
data/processed/news_embeddings/ |
sdsc2005-migration/news_embeddings_labeled |
data/processed/news_embeddings_labeled/ |
sdsc2005-migration/production_outputs |
data/processed/production_outputs/ |
sdsc2005-migration/cluster_labels |
data/processed/cluster_labels_test/ |
Syncs 3 TensorRT model artifacts:
| HF Model | Local Path |
|---|---|
| Flan-T5 engine | src/models/tensor-rt/flan-t5-large/ |
| Jina v5 engine | (configured externally) |
| LED engine | (configured externally) |
Uploads local processed data back to HF Hub for team sharing:
- Scans local directories for files not yet in the remote repo
- Supports dry-run mode for validation without actual upload
python -m src.main bootstrap --org sdsc2005-migrationThis triggers:
-
download_defaults()— all datasets -
download_models()— TensorRT engines - Validation — checks all expected files exist
All sync operations support --dry-run to preview what would be downloaded/uploaded without actually performing transfers. Essential for CI/CD validation.
- Main Entry Point — CLI that invokes sync
- Reproducibility — Full setup guide
- Data Collection — Where synced data is used
- Collection Module — Full package reference
- Project Overview — Goals, research questions, methodology, and team
- Glossary — Key terms used throughout this wiki
Raw inputs that feed the prediction system.
| Page | Description |
|---|---|
| Visa Data | US Department of State visa issuance statistics (108 monthly PDFs) |
| Encounter Data | CBP Southwest border encounter statistics (FY2019–2026) |
| Google News | 170K+ news articles across 15 countries × 8 topics |
| Google Trends | Monthly search-interest time series (15 countries × 8 keywords) |
| Exchange Rates | IMF Real Effective Exchange Rate for 6 countries |
The end-to-end flow from raw data to production forecasts.
| Page | Description |
|---|---|
| Data Collection | Ingestion layer: async scraping, bounded concurrency, retry logic |
| Data Processing | PDF parsing, JSON→Parquet, encounter merging |
| NLP Enrichment | Embedding → Clustering → Labeling → Sentiment |
| Panel Construction | Feature engineering: 18 lag features, 6 lead targets |
| Training Pipeline | Out-of-time train/test split, 4 architectures |
| Inference Pipeline | Horizon-aware ensemble, production prediction flow |
Machine learning architectures and their roles in the ensemble.
| Page | Description |
|---|---|
| Random Forest | cuML GPU Random Forest — best at short horizons (Lead 1–2) |
| LSTM | MigrationLSTM — country-aware with SurgeJointLoss |
| Transformer | MigrationTransformer — best at long horizons (Lead 5–6) |
| Horizon-Aware Ensemble | Dynamic weighting: RF→short, Transformer→long |
| SurgeJointLoss | Dual-objective loss: Huber + BCE for crisis detection |
| Jina v5 Embeddings | TensorRT INT8 news article embeddings (768-dim) |
| Flan-T5 Summarization | TensorRT INT8 cluster labeling engine |
Statistical techniques driving the lead-lag and surge analysis.
| Page | Description |
|---|---|
| Lead-Lag Analysis | Pearson correlation at 0–6 month offsets |
| Surge Detection | Quantile-based and σ-threshold spike identification |
| Sentiment Analysis | Rule-based lexicon scoring for migration-relevant news |
| Event Clustering | HDBSCAN GPU clustering + LED label generation |
| Cross-Correlation Analysis | CCF analysis, VAR benchmarking, ADF stationarity tests |
| Multiple Comparison Correction | Benjamini-Hochberg FDR for 58 significant signals |
What the system discovered about migration predictability.
| Page | Description |
|---|---|
| Event-Visa Findings | News events as leading indicators (r=0.617 at 3-month lag) |
| Exchange Rate Findings | Exchange rate signals (DR r=0.498 at 2-month lag) |
| Model Performance | Ensemble results: F1=0.96 at Lead 1, F1=0.86 at Lead 6 |
Reference documentation for every src/ subpackage and key files.
| Page | Description |
|---|---|
| Main Entry Point |
src/main.py CLI: bootstrap, collect-live, sync-data |
| Collection Module |
src/collection/* — visa, encounter, news, trends, HF sync |
| Processing Module |
src/processing/* — parse, merge, build_panel, summarize |
| Analysis Module |
src/analysis/* — events, exchange_rate, trends_analysis, plots |
| Models Module |
src/models/* — surge_model, train_and_evaluate, inference |
| News Scraper | Deep dive: batch decoding, checkpoint recovery, throttling |
| PDF Parser | Deep dive: PyMuPDF table extraction, VISA_MAP normalization |
| TensorRT Engines | Deep dive: Jina-v5, Flan-T5, LED TensorRT engines |
| Build Panel Detail | Deep dive: lag/lead construction, forward-fill strategies |
| HF Sync | Deep dive: bidirectional Hugging Face Hub sync |
Compute, reproducibility, and operational details.
| Page | Description |
|---|---|
| GPU Acceleration | TensorRT INT8, cuML, CUDA streams, NVML profiling |
| Reproducibility | HF bootstrap, run.sh pipeline, dependency checking |