Ray
Distributed execution framework for scaling Python workloads.
Resumen
Ray turns ordinary Python functions and classes into distributed tasks and actors with a scheduler that handles placement, failure and state. It solves the problem that scaling a training or inference job across machines otherwise means rewriting it around a cluster API. It exists as the substrate several training and serving systems are built on, which is why it appears beneath them rather than beside them.
Grafo de tecnologías
Cómo se conecta Ray con el resto del stack.
Aplicado a
- Inference concept Infrastructure Ray used for Inference High ✓ source
- Pretraining technique Techniques Ray used for Pretraining High ✓ source
Herramientas
Software que implementa o se integra con Ray.