This document discusses Ray, a distributed Python framework designed to handle complex computing requirements driven by machine learning and AI workloads. It covers Ray's advantages over Spark in handling non-uniform data and tasks, and provides guidance on how to get started using Ray alongside existing Python libraries. Additionally, it emphasizes the importance of user feedback and offers resources for community engagement and learning about Ray.

2. Ray: Enterprise-Grade,
Distributed Python
Dean Wampler
Anyscale

3. @deanwampler
Feedback
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Don’t forget to rate and
review the sessions.

4. Agenda
Why Ray?
Demo
When to use Spark? When to use Ray?
How to get started with Ray

5. Why Ray?

6. @deanwampler
Usage%
2012 2014 2016
Ti
05
Hence, there is a pressing need for
robust, easy to use solutions for
distributed Python
Model sizes and therefore
compute requirements
outstripping Moore’s Law
Moore’s Law (2x every 18 months)
35x every 18 months!
Python growth driven by ML/AI and
other data science workloads

7. Reinforcement Learning: Motivation for Ray

8. @deanwampler
Decisions
(actions)
Consequences
(observations, rewards)
environmentagent

9. @deanwampler
Beating Lee Sedol…

10. @deanwampler
▪Observations
▪ Board state
▪Actions
▪ Where to place stones
▪Rewards
▪ 1 if win
▪ 0 otherwise
AlphaGo (Silver et al. 2016)

11. @deanwampler

12. @deanwampler
Diverse Compute Requirements Motivated Creation of Ray!
Simulator (game
engine, robot sim,
factory floor sim…)
Neural network “stuff”
And repeated play,
over and over again, to
train for achieving the
best reward
Complex agent?

13. The Ray Ecosystem

14. @deanwampler
Hyperparameter
Tuning
Training Simulation Model Serving

15. @deanwampler
Microservices (simulators, too)
REST
API Gateway
µ-service 1 µ-service 2
µ-service 3
Nice! (In theory…)

16. @deanwampler
Microservices (simulators, too)
REST
API Gateway
µ-service 1 µ-service 2
µ-service 3
Production is a pain
API GatewayAPI Gateway
µ-service 1 µ-service 2µ-service 2µ-service 2µ-service 2µ-service 2
µ-service 3µ-service 3µ-service 3
! Each microservice
has a different
number of instances
for scalability &
resiliency
! But they have to be
managed explicitly

17. @deanwampler
Ray Cluster
task/
actortask/
actor
task/
actor
task/
actortask/
actor
task/
actor
task/
actor
task/
actor
task/
actor
task/
actor
task/
actor
task/
actor
task/
actor
task/
actor
Microservices (simulators, too)
REST
API Gateway
µ-service 1 µ-service 2
µ-service 3
Back to simplicity
! Back to one
“logical”
instance
! Ray handles
scaling
transparently

18. Demo!

19. When to use Spark
When to use Ray

20. @deanwampler
Where Spark Excels
▪ Massive-scale data sets
▪ Uniform, records with a schema
▪ Efficient, parallelized transformations
▪ SQL
▪ Batch analytics
▪ Stream processing
▪ Intuitive, high-level abstractions for data science & engineering tasks

21. @deanwampler
Where Ray Excels
▪ Highly non-uniform data graphs
▪ Think typical “in-memory object models”, but distributed
▪ Handles distributed state intuitively
▪ Highly non-uniform task graphs
▪ Small to large scale tasks
▪ Intuitive API for the “90%” of cases
▪ Supports compute problems ranging from
▪ general services, games, and simulators
▪ to
▪ stochastic gradient descent, HPO, …

22. Getting started with Ray

23. @deanwampler
If you’re already using these…
▪ asyncio
▪ joblib
▪ multiprocessing.Pool
▪ Use Ray’s implementations
▪ Drop-in replacements
▪ Change import statements
▪ Break the one-node limitation!
For example, from this:
from multiprocessing.pool import Pool
To this:
from ray.util.multiprocessing.pool import Pool

24. @deanwampler
Ray Community and Resources
▪ ray.io - entry point for all things Ray
▪ Tutorials: anyscale.com/academy
▪ github.com/ray-project/ray
▪ anyscale.com/events

25. @deanwampler
Feedback
Your feedback is important to us.
Don’t forget to rate and
review the sessions.