NeuralBlitz Ray
Ray is a unified framework for scaling AI and Python applications. It provides distributed computing primitives for ML workloads, enabling seamless scaling from single machines to clusters.
Key Concepts
- Ray Tasks and Actors
- Ray Serve for deployment
- Ray Datasets
- Ray Tune for hyperparameter search
- Ray RLlib for reinforcement learning
Common Use Cases
- Distributed training
- Hyperparameter tuning
- Batch inference
- Reinforcement learning
- Scalable Python applications
Best Practices
- Design for actor isolation
- Use object stores efficiently
- Configure proper resource allocation
- Monitor Ray dashboard
- Use Ray Serve for production
Resources
- Docs: docs.ray.io
- Related Skills: distributed-systems, pytorch, tensorflow