Distributed Computing In Python Made Easy With Ray

Summary

Distributed computing is a powerful tool for increasing the speed and performance of your applications, but it is also a complex and difficult undertaking. While performing research for his PhD, Robert Nishihara ran up against this reality. Rather than cobbling together another single purpose system, he built what ultimately became Ray to make scaling Python projects to multiple cores and across machines easy. In this episode he explains how Ray allows you to scale your code easily, how to use it in your own projects, and his ambitions to power the next wave of distributed systems at Anyscale. If you are running into scaling limitations in your Python projects for machine learning, scientific computing, or anything else, then give this a listen and then try it out!

Announcements

• Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
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• Your host as usual is Tobias Macey and today I’m interviewing Robert Nishihara about Ray, a framework for building and running distributed applications and machine learning

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by describing what Ray is and how the project got started?
  • How did the environment of the RISE lab factor into the early design and development of Ray?
• What are some of the main use cases that you were initially targeting with Ray?
  • Now that it has been publicly available for some time, what are some of the ways that it is being used which you didn’t originally anticipate?
• What are the limitations for the types of workloads that can be run with Ray, or any edge cases that developers should be aware of?
• For someone who is building on top of ray, what is involved in either converting an existing application to take advantage of Ray’s parallelism, or creating a greenfield project with it?
• Can you describe how Ray itself is implemented and how it has evolved since you first began working on it?
• How does the clustering and task distriubtion mechanism in Ray work?
• How does the increased parallelism that Ray offers help with machine learning workloads?
  • Are there any types of ML/AI that are easier to do in this context?
• What are some of the additional layers or libraries that have been built on top of the functionality of Ray?
• What are some of the most interesting, challenging, or complex aspects of building and maintaining Ray?
• You and your co-founders recently announced the formation of Anyscale to support the future development of Ray. What is your business model and how are you approaching the governance of Ray and its ecosystem?
• What are some of the most interesting or unexpected projects that you have seen built with Ray?
• What are some cases where Ray is the wrong choice?
• What do you have planned for the future of Ray and Anyscale?

Keep In Touch

• Website
• @robertnishihara on Twitter
• robertnishihara on GitHub

Picks

• Tobias
  • D&D Castle Ravenloft board game
  • One Deck Dungeon
• Robert
  • The Everything Store

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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Links

• Ray
• Anyscale
• UC Berkeley
• RISELab
• MATLAB
• Deep Learning
• Theano
• Tensorflow
• PyTorch
  • Podcast Episode
• Philip Moritz
• Reinforcement Learning
• Hyperparameter Tuning
• IPython Parallel
• AMPLab
• Apache Spark
  • Data Engineering Podcast Episode
• Actor Model
• Horovod(?)
• Flink
  • Data Engineering Podcast Episode
• Spark Streaming
• Dask
  • Data Engineering Podcast Episode
• gRPC
• Tune
• Rust
• C++
• C
• Apache Arrow
• Wes McKinney
  • Podcast Interview
• DataBricks
• MongoDB
• Elastic
  • Data Engineering Podcast Episode
• Confluent
• Embarassingly Parallel
• Ant Financial
• Flame Graph

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA