Yuangang Li

Mater Student in Computer Science at the University of Southern California. & Interests include Cloud Computing, Machine Learning, and Distributed Systems.

ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation Experiments

Python, MLOps, Docker, Github Action, Pytorch, Pytest

Github / Paper

  • A distributed experiment execution framework for ablation studies. ABLATOR provides a wrapper for your model and a Trainer class for you to prototype on your method and scale to thousands of experimental trials with 1 code change.
  • Automates analysis artifacts, cutting down manual review processes.
  • Integrates comprehensive functionalities (HPO, training, tuning, analysis) outdoing counterparts like Ray, Optuna, etc.
  • Integration with established libraries (e.g., PyTorch Lighting), ensuring versatility without complexity.
  • Reduces dev-ops burdens, redirecting focus to innovative aspects of ML research.

  • RocketMqOperator: Auto Mangane RocketMq Cluster Instance on an Cloud Platform

    Kubernetes, Docker, Go, CI/CD, CRD, Operator-SDK, Helm3, Prometheus, Grafana

    SenseTime (The first listed AI company in China), 2023
    Video Introduction

    The RocketMQ Operator automatically deploys and manages RocketMQ clusters on the Kubernetes-based cloud environment. Apache RocketMQ is a popular distributed messaging and streaming platform.
    Support Features:

  • Horizontal Scaling - Safely and seamlessly scale up each component of RocketMQ.
  • Rolling Update - Gracefully perform rolling updates in order with no downtime.
  • Multi-cluster Support - Users can deploy and manage multiple RocketMQ name server clusters and broker clusters on a single Kubernetes cluster using RocketMQ Operator.

  • WebRR: Automatic testing tool based on record/replay for web application cross-browser compatibility

    Docker, Node.js, JSON, Vue.js, RobotFramework

    Chinese Academy of Sciences (Top Research Institute), 2021-2022

  • The tool features a low-code approach, allowing users without extensive programming skills to easily create and deploy complex automation tests.
  • Utilized a low-code model, simplifying test creation for users with limited programming background.
  • Ensured cross-platform testing, enhancing application consistency across diverse environments.
  • Automated script generation from user interactions to JSON, then to Robot Framework formats.
  • Deployed in key enterprises like Southern Power Grid, streamlining their testing processes and reducing errors.
  • Broadened testing participation due to the tool's ease of use, boosting software quality and user satisfaction.

  • College professional development analysis platform

    Full Stack Website, Web crawler, Python, MongoDB, MySQL, Java, Spring, Spring MVC, MyBatis, React, Docker

    National-level Innovation Training Program, 2019

  • Led a team to collect and analyze job recruitment data using Web crawler and Python libraries, aiding university major and curriculum planning.
  • Stored data using MongoDB and MySQL databases for subsequent data processing and analysis.
  • Built data visualization interfaces using Spring and React technologies, improving data readability.
  • Used Docker for containerization and CI/CD technologies for automated operations, enhancing the efficiency of project deployment and operation, and ensuring project stability and reliability.
  • Achieved recognition as a "National Level Innovative Excellence Project for College Students" and adopted by school for professional development data support.

  • EventMaster

    Full Stack Website, Node.js, AngularJS, GCP, AJAX

    USC CSCI571, 2023

  • Developed a full-stack event search platform using JavaScript/Node.js, Express, AngularJS, TypeScript, and Bootstrap, aggregating event information and enhancing user interaction.
  • Integrated APIs such as Ticketmaster, Spotify, Google Maps, and social media to enrich data display on the frontend.
  • Deployed the project on the Google Cloud Platform(App Engine) with performance optimization.