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by Dean Wampler
What Is Ray?
1. Distributed Computing Is Hard but Necessary
Why Ray?
The Trends That Led to Ray
What’s Next?
2. The Ray API
Just Six API Methods
Installing Ray
Initializing Ray
From Functions to Ray Tasks
Task Dependencies
From Classes to Ray Actors
What’s Next?
3. Machine Learning Libraries That Use Ray
What Is Reinforcement Learning?
Reinforcement Learning with Ray RLlib
What Did We Learn?
Hyperparameter Tuning with Ray Tune
Distributed Training with Ray SGD
Model Serving with Ray Serve
What’s Next?
4. Ray for Applications
Why Microservices?
Production Services Built with Ray
Ray for Serverless
What’s Next?
5. Recap and Next Steps
Next Steps with Ray
For More Information
Final Thoughts
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