Argo

Argo, the ship that carried Jason and the Argonauts on their quest for the Golden Fleece

This is a playground to re-implement model architectures from industry/academic papers in Pytorch. The primary goal is educational and the target audience is for those who would like to start journey in machine learning & machine learning infra. The code implementation is optimized for readability and expandability, while not for the best of performance.

Repo structure

• data: functions for dataset management, such as downloading public dataset, cache management, etc
• feature: functions for featuer engeering, right now primarily read data from benchmark and use Pandas to do certain feature engineer
• model: model code implementation
• trainer: simple wrapper around train/val/eval loop
• server: simple inference stack for recommendation system, including retrieval engine, feature server, model manager and inference engine
• scripts: some scripts used for setup the system, such as DB ingestion
• get-started: some userful notebooks to help you get faimilar with common techniques & concept in machine learning and recommendation system
• embedding: scripts used for generating embedding

Prepare Step

Embedding Based Retrieval Setup

1. run python movie_len_embedding.py to generate the embeddings (only support the collabrative embedding)
2. run python movie_len_index.py to generate the FAISS index
3. run python scripts/vector_db.py to ingest embedding into DuckDB

How to run locally

1. install the dependency pip install -r requirements.txt, pip install -e .
2. run python main.py to train the model with current env config.
3. run python -m server/ebr_server.py to start the grpc server for embedding based retrieval, it would listen on port 50051 by default; if you use DuckDB then this step could be skipped
4. run python server/inference_engine.py to start the inference server, it would listen on 8000 port
5. run bash scripts/server_request.sh to send a dummy request (there is one for DIN and one for TransAct as of now, will parameterized the request in the future)

Papers

• ✅ Deep Interest Network for Click-Through Rate Prediction
• ✅ TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
• Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
• Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations
• ✅ DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
• LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders
• MTGR: Industrial-Scale Generative Recommendation Framework in Meituan
• TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation

Road Map

Modeling

• ✅ Deep Interest Network E2E training & inference example, MovieLen Small
• ✅ TransAct training & inference example, MovieLen Large
• ✅ MovieLen item embedding generation, collaborative filtering, two-towers, LLM (QWen3-embedding is out)
• HSTU training & inference example, MoiveLen Small
• RQ-VAE

Data & Feature Engineering

• Kuaishou Dataset: https://kuairand.com/
• Ray integration (DPP reader + trainer arch)
• Daft, Polars exploartion

Infra

• ✅ Embedding Based Retrieval (EBR): DuckDB, FAISS
• Nearline item embedding update
• Feature store integration: FEAST
• Feature logging & training data generation pipeline

GPU

• GPU training & inference enablement
• Integrate profiling, benchmarking, tuning, and monitoring for accelerator optimization
• Optimize representative models with auto-tuning, kernel fusion, quantization, dynamic batching, etc

Reference

• DuckDB
• QWen3