What Is Ray?

Chapter 5. Recap and Next Steps

We have learned that Ray is a system for building distributed applications, born out of pragmatic challenges of modern, computationally intensive ML/AI systems yet broadly applicable to applications that need to scale to a cluster for performance and resilience.

If your team is building ML libraries or general-purpose applications, Ray provides high performance and an intuitive, concise core API.

If your team is building ML-based applications, Ray provides libraries for reinforcement learning (RLlib), hyperparameter tuning (Tune), distributed training (SGD), and model serving (Serve). The list of third-party libraries and systems based on Ray is also growing. Two examples of NLP (natural language processing) libraries built on Ray are spaCy and Hugging Face.

The Ray and Anyscale websites and blogs have stories from Ray users in different industries and communities.

Next Steps with Ray

To take the next steps with Ray, check out the following resources:

• To learn more about the Ray API, go through my Ray class on the O’Reilly Learning Platform. With the material organized into notebooks, you can work with live Ray code, try the Ray replacements for several popular multiprocessing libraries, and try your hand at reinforcement learning with RLlib.

• The Ray documentation provides a lot of details on the more advanced aspects of the Ray API and on all the included libraries, like RLlib, Tune, SGD, and Serve.

• If you have Python code that uses Python’s ...