Lecture Notes on Tiny Grad and Recent Developments

Introduction

• Speaker: George Hots (likely)
• Location: Shared office
• Discussion on the return to streaming after a hiatus.
• Mentioned progress in Tiny Grad, a machine learning framework.

Current State of Tiny Grad

• Significant updates and commits have been made to Tiny Grad.
• Improvements in accuracy due to better kernel initialization methods.
• Features of Tiny Grad include:
  • Documentation available on GitHub.
  • Tutorials: Quick Start and AMNESS Tutorial.

Personal Updates

• Speaker traveled to Poland and Italy.
• Tiny Grad operates as a remote company.

Competition and Market

• Tiny Grad aims to compete with established ML libraries like PyTorch and JAX.
• Speaker expresses disdain for previous competitors in self-driving car space.
• Acknowledges respect for current competition in ML frameworks.

Technical Details of Tiny Grad Improvements

• Discussion on MCTS (Monte Carlo Tree Search) and its implementation in Tiny Grad:
  • MCTS allows for better kernel searches and optimizations.
  • Speaker intends to improve search speeds, targeting 500 nodes per second.
• Challenges in compiling and rendering times mentioned.
• Talk about recent implementations to enhance performance, particularly on different hardware.

Driver Quality and Hardware Support

• Speaker discusses the development of AMD and NVIDIA drivers.
• AMD's driver quality compared to NVIDIA's is criticized.
• Tiny Grad's relationship with hardware:
  • The need for optimized drivers for different chips.
  • Discussion on how to implement and optimize kernel executions.

Future Goals

• Plans to rewrite Open Pilot using Tiny Grad, focusing on efficiency and competitive performance.
• Discussion on using search methods to enhance performance.
• Aspiration to develop their own chips eventually, starting with inference chips for their products.

Community Engagement and Documentation

• Speaker encourages community engagement through GitHub and Discord.
• Updated documentation aims to make Tiny Grad more user-friendly.

Conclusion

• Positive outlook on progress made with Tiny Grad.
• Ongoing efforts to improve performance and community involvement.
• Encouragement for audience to participate and follow developments.