July 13–16, 2020

Training on Ray: High-performance, distributed Python applications

Dean Wampler (Anyscale)
9:00am–5:00pm Tuesday, 07/14/2020
Location: D139/140

To attend, participants must be registered for a Training Pass. Please note: 2-Day Training passholders have access to TWO 1-day training courses, ONE on Monday and ONE on Tuesday. 1-Day Training passholders have access to ONE 1-day training course on Monday OR Tuesday.

Ray is a system for scaling Python apps from single machines to large clusters. It solves common problems in scalable, distributed computing, so Python developers don’t need this expertise. Dean Wampler outlines examples that show how Ray sits between broad but inflexible frameworks, and low-level libraries that are hard to use.

What you'll learn, and how you can apply it

Attendees will learn:

  • Why scaling Python applications is needed, yet often difficult
  • How to scale these applications using Ray
  • Specific machine learning libraries that use Ray
  • How Ray works behind the simple API

Who is this presentation for?

Python developers who need to scale their applications.

Level

Intermediate

Prerequisites:

Some prior experience with Python and Jupyter notebooks will be helpful, but we'll explain most details as we go if you haven't used them before. Knowledge of basic machine learning concepts, including reinforcement learning, and principles of distributed computing are helpful, but not required.

Hardware and/or installation requirements:

Bring a laptop. You'll use a browser-based environment with cloud-hosted services to use the examples and do the exercises.

Most applications require distributed computing over a cluster to achieve scalability and resiliency goals. Whether you have the distributed computing expertise required or not, you probably prefer focusing on your application logic.

Ray enables distributed Python with relatively simple and intuitive changes to code. Ray’s design is motivated by the performance challenges of demanding applications like reinforcement learning (RL), where rapid, efficient scheduling of diverse computing tasks is required and distributed state has to be managed. This means that Ray is an excellent platform for stateful serverless computing.

Using several example applications, including several machine learning libraries, we’ll explore how Ray meets these challenges and how Ray works behind the simple API. Most of the day will be spent working with these examples using Jupyter notebooks.

About your instructor

Photo of Dean Wampler

Dean Wampler is an expert in streaming data systems, focusing on applications of ML/AI. He is Head of Evangelism at Anyscale, which is focused on distributed Python for ML/AI. Previously, he was an engineering VP at Lightbend, where he led the development of Lightbend CloudFlow, an integrated system for building and running streaming data applications with popular open source tools. Dean has written books for O’Reilly and contributed to several open source projects. He is a frequent conference speaker and tutorial teacher, and a co-organizer of several conferences and user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington.

Twitter for deanwampler

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