Authors: Maksim Eremeev (eremeev@nyu.edu), Anya Trivedi (anya.trivedi@nyu.edu), Anastasia Shaura (as15846@nyu.edu)
Project Proposal: https://drive.google.com/file/d/1Cj-Gn0p4NIREuzCJ2YG5nmodHVr2dDmC/view?usp=sharing
Project Report: https://drive.google.com/file/d/1EoG3EQLmKB9aXoaBZ_t9TUVfMWsr8vVE/view?usp=sharing
Presentation recording: https://drive.google.com/file/d/1yluJDCI8L45zqTKUxVTLz1vbdOGvCFxo/view?usp=sharing
Presentation slides: https://drive.google.com/file/d/1XA4Gi4-4plwS-6PKdVRsxaDNMZSPucz3/view?usp=sharing
Trained model: https://drive.google.com/file/d/1FP9qzlZBJDmXEhgZX1Pps3Hu3JiTP-2u/view?usp=sharing
Dataset: https://www.kaggle.com/andrewmvd/okcupid-profiles
This is a scalable deployable system that generates a dating app bio from user features. We used a micro-services architecture to ensure the flexibility and scalability of the system. RabbitMQ is used as a mediator along with the Node.JS-powered backend, allowing us to stick to the Publish-Fetch-Listen communication interface. Training was performed on T4 and RTX8000 GPUs, using the transformers framework. We use distillied GPT-2 model to speed up CPU-only inference in production. We utilize ancestral sampling instead of beam search for diversity. Finally, we test the model offline and online, as well as conducting bandwidth tests of the proposed architecture. All separate components are wrapped into Docker containers, and therefore the system deployment becomes straighforward.
Make sure ports 22111,5672, and 80 are not busy.
git clone https://github.com/maks5507/idls.git
sudo snap install docker
cd idls/
cd services/
docker pull rabbitmq:3.7.14-management
docker-compose up -d
cd ../backend/
docker-compose build
docker-compose up -d
cd ../frontend
docker-compose build
docker-compose up -d
cd ../
docker-compose build
docker-compose up -dThe frontend is then available at http://localhost:80
/frontend features the jQuery ajax-powered web interface for the product
/backend features the Node.JS async-based backend that efficiently packs the requests from frontend and passes them to the message queue. Backend also takes care of CORS blocking the frontend requests
/services docker-compose for launching the message queue
/launcher is a framework for efficient scale of the workers in the system
/modeling comprises of the scripts for training the model
/core contains the worker class and configuration
Make sure to check out the READMEs of each component carefully before modifying the system
Offline evaluation: ROUGE-L-F1 score on test set: 0.37
Online evaluation: Average user rating of generated sequences: 3.3 out of 5
Bandwidth tests:
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