instadeepai/sebulba
📦 Open Source Projectinstadeepai
A high-performance JAX-based architecture designed to scale reinforcement learning workloads on Cloud TPUs.
Sebulba represents a specialized approach to high-performance reinforcement learning, focusing on the seamless integration of JAX with Google Cloud TPU infrastructure. The architecture is designed to overcome the bottlenecks typically associated with scaling RL, such as data throughput and synchronization latency. By leveraging JAX's XLA compilation and automatic differentiation, Sebulba allows for efficient parallelization of agent-environment interactions. Key technical features include optimized PPO implementations, support for multi-host TPU training, and a modular design that facilitates rapid experimentation with different RL environments. It is built to handle the demanding computational requirements of modern deep reinforcement learning, making it a powerful tool for those working at the intersection of HPC and AI.
💡Highlights
- ├─Native JAX-based TPU scaling
- ├─Optimized for PPO algorithms
- └─High-performance HPC integration
🎯For
- ├─Reinforcement Learning Researchers
- └─HPC Engineers