Ray - A System for High-performance, Distributed Python Applications

סקירה כללית

Explore Ray, an open-source framework for scaling Python applications from laptops to clusters, focusing on ML/AI performance challenges and its key features for distributed computing.

סילבוס

• Introduction to Ray
Overview of Ray
Key benefits of using Ray for distributed computing
Comparison with other distributed computing frameworks
• Setting Up Ray
Installation and setup
Running Ray locally on a laptop
Configuring Ray for clusters
• Core Concepts of Ray
Ray’s architecture
Tasks and Actors in Ray
Managing Ray objects and object stores
• Distributed Computing with Ray
Scheduling and execution of tasks
Combining tasks and actors
Handling resource constraints and dependencies
• Scaling Machine Learning Applications
Using Ray with popular ML frameworks (TensorFlow, PyTorch)
Hyperparameter tuning with Ray Tune
Distributed data processing with Ray Datasets
• Advanced Features of Ray
Ray's fault-tolerance and failure recovery
Monitoring and debugging Ray applications
Ray Serve for scalable model serving
• Real-world Applications and Use Cases
Case studies of Ray in industry
Best practices for deploying Ray in production
• Ray Ecosystem and Tools
Overview of Ray libraries (RLLib, Ray Tune, Ray Serve, etc.)
Choosing the right Ray tool for your application
• Hands-on Projects
Building a distributed application with Ray
Scaling a machine learning model using Ray
Performance optimization and tuning with Ray
• Conclusion and Future of Ray
Emerging trends in distributed computing
Future developments in Ray
Resources for further learning
• Final Q&A and Wrap-up

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נושאים

Conference Talks