Languages
Features
distributedparallelscalingclusterhigh-performance
Pros and Cons
Ventajas
- + Easy scaling of existing Python code
- + Complete ecosystem (Tune, Serve, Train, Data)
- + High performance for distributed ML
- + Simple APIs with decorators
- + Supports multiple ML frameworks
- + Active community and Anyscale backing
Desventajas
- - Learning curve for distributed patterns
- - Overhead for small tasks
- - Complex distributed debugging
- - Cluster configuration can be difficult
- - Extensive but fragmented documentation
Casos de Uso
- Distributed model training
- Hyperparameter tuning at scale
- Model serving in production
- Parallel data processing
- Distributed reinforcement learning
- Large-scale batch inference