This repository demonstrates an implementation of a recommender system pipeline using PyTorch. The system covers the full pipeline from offline data processing and model training to online serving and prediction.

The pipeline was made as part of my freelance project for demonstation purposes.

Dependencies & tools

• Conda for offline data processing and model training with pandas and PyTorch
• Docker to run Redis, Elasticsearch, Feast, and Triton without affecting the local environment
• Flask simulates the backend, acting as a RESTful web server
• Redis stores user terms and vectors for recall
• Elasticsearch manages the term and vector indices for item recall
• Feast handles feature storage for online ranking
• Triton serves real-time model predictions

Architecture

This RS follows a three-phase development process:

1. Offline: Preprocessing, model training, and feature engineering
2. Offline-to-Online: Transition from offline data to online services using Docker containers
3. Online: Real-time serving and recommendation through a Flask web service

Offline part

Conda environment setup

Create a Conda environment for offline processing and model training:

conda create -n rsppl python=3.8
conda activate rsppl
conda install --file requirements.txt --channel anaconda --channel conda-forge

Preprocessing

Split the dataset, process labels, and transform features:

cd offline/preprocess/
python s1_data_split.py
python s2_term_trans.py

• Labels: Convert the MovieLens dataset to implicit feedback (positive if rating > 3)
• Samples: Reserve the last 10 rated items per user for online evaluation
• Features: Encode sparse features as integer sequences

Term and vector recall

Generate term and vector recalls based on user interactions and factorization machines:

cd offline/recall/
python s1_term_recall.py
python s2_vector_recall.py

• Term Recall: Matches user preferences based on item attributes (e.g., genres)
• Vector Recall: Trains a Factorization Machine model for user-item interactions

Feature engineering and ranking with DeepFM

Train the ranking model with a mix of sparse and dense features:

cd offline/rank/
python s1_feature_engi.py
python s2_model_train.py

• DeepFM Model: Trains using one-hot, multi-hot, and dense features. Modified for more flexible embedding dimensions and feature handling

Offline-to-online part

Docker setup

Ensure Docker is installed and pull necessary images:

docker pull redis:6.0.0
docker pull elasticsearch:8.8.0
docker pull feastdev/feature-server:0.31.0
docker pull nvcr.io/nvidia/tritonserver:20.12-py3

Load user data into Redis

Load user terms and vectors into Redis for fast retrieval:

docker run --name redis -p 6379:6379 -d redis:6.0.0
cd offline_to_online/recall/
python s1_user_to_redis.py

Load item data into Elasticsearch

Set up Elasticsearch to handle both term and vector-based recall:

docker run --name es8 -p 9200:9200 -it elasticsearch:8.8.0
docker start es8
cd offline_to_online/recall/
python s2_item_to_es.py

Load features into Feast

Feast acts as the feature store for the ranking phase:

cd offline_to_online/rank/
python s1_feature_to_feast.py
docker run --rm --name feast-server -v $(pwd):/home/hs -p 6566:6566 -it feastdev/feature-server:0.31.0

Initialize Feast and load features:

feast apply
feast materialize-incremental "$(date +'%Y-%m-%d %H:%M:%S')"
feast serve -h 0.0.0.0 -p 6566

Convert PyTorch model to ONNX and deploy to Triton

Convert the PyTorch ranking model to ONNX format for serving with Triton:

cd offline_to_online/rank/
python s2_model_to_triton.py
docker run --rm -p8000:8000 -p8001:8001 -v $(pwd)/:/models/ nvcr.io/nvidia/tritonserver:20.12-py3 tritonserver --model-repository=/models/

Validate consistency between offline and online scores:

python s3_check_offline_and_online.py

Online part

Flask web server

Start the Flask web server for handling recommendation requests:

conda activate rsppl
cd online/main
flask --app s1_server.py run --host=0.0.0.0

Test the recommender system

Simulate a client request to the Flask server, receiving the top 50 recommended items:

cd online/main
python s2_client.py