[Baypiggies] Next week's talks: Scaling AI Workloads with the Ray Ecosystem and a lightning talk on Docker and Kubernetes

Jeff Fischer jeffrey.fischer at gmail.com
Fri Feb 18 16:31:10 EST 2022

*Thursday February 24th 7:00 pm - 8:30 pm*
This month, we'll have a talk from Jules Damji about Ray, the open source
platform for distributed programming. We will also have a lightning talk
from Tyler Suard about Docker and Kubernetes. Come and join us!

Lightning talk: Docker and Kubernetes: Getting Doom to Run on Your Friends
Computer in 1993
*Speaker:* Tyler Suard

The absolute simplest approach to Docker and Kubernetes ever, with an
example from the flyest time in history: the 90’s!

*Speaker bio:*
Tyler Suard is a software engineer at Facebook. He likes cats, his
girlfriend, and inventing things.

Main Talk: Scaling AI Workloads with the Ray Ecosystem
*Speaker: *Jules S. Damji

Today, AI applications are becoming pervasive across all sectors of our
industry. Driven by a few fundamental trends, there is no indication of
slowing down. In fact, the trend continues rapidly, making distributed
computing at scale a norm and necessity.

But distributed computing is not easy. It has its challenges. Building
distributed applications today requires tons of expertise. For many
developers, it is out of reach. Current solutions to these challenges have
their shortcomings and tradeoff.

Ray aims to address these shortcomings. As a general-purpose distributed
computing framework, it makes programming a cluster of machines as easy as
programming a laptop, thereby enabling many more developers and
practitioners to take advantage of the advances in cloud computing and
scale their machine learning workloads to solve harder problems, without
needing to be experts in distributed systems. Besides a core
general-purpose distributed-compute system, Ray encompasses a collection of
state-of-the-art native libraries targeting scalable machine learning.
These include libraries for hyperparameter tuning, distributed training,
reinforcement learning, model serving, and last-mile ML data pre-processing
and ingestion for model training.

This talk will introduce Ray’s overview; survey its ecosystem of both
native and integrated ML libraries; and discuss key applications and
developments in the Ray ecosystem, drawing upon lessons from discussions
with practitioners over the years of developing Ray with the community—and
at Anyscale. In particular, we will demonstrate how you can easily scale
three common ML workloads, from your laptop to the cluster, with Ray’s
native libraries: training, hyperparameter tuning and optimization (HPO),
and large-scale batch inference.

Using the popular XGBoost for classification, we will show how you can
scale model training, hyperparameter tuning, and inference—from a laptop or
single node to a Ray cluster, with tangible performance difference when
using Ray.

The takeaways from this talk are :

Why distributed computing has become the norm and necessity, not an
Learn Ray’s architecture, core concepts, and programming primitives
Understand Ray’s ecosystem of scalable ML libraries
Easily extend or transition your laptop to a Ray cluster
Scale three ML workloads using Ray’s native libraries:
Training on a single node vs. Ray cluster, using XGBoost with/without Ray
Tuning HPO using XGBoost with Ray and Ray Tune
Inferencing at scale, using XGBoost with/without Ray

*Speaker bio:*
Jules S. Damji is a lead developer advocate at Anyscale Inc, an MLflow
contributor, and co-author of Learning Spark, 2nd Edition. He is a hands-on
developer with over 25 years of experience and has worked at leading
companies, such as Sun Microsystems, Netscape, @Home, Opsware/LoudCloud,
VeriSign, ProQuest, Hortonworks, and Databricks, building large-scale
distributed systems. He holds a B.Sc and M.Sc in computer science (from
Oregon State University and Cal State, Chico respectively), and an MA in
political advocacy and communication (from Johns Hopkins University).

Code of Conduct

Interactions online have less nuance than in-person interactions. Please be
Open, Considerate and Respectful. Also, please refrain from discussing
topics unrelated to the Python community or the technical content of the


We will conduct the meeting via Zoom meeting. Please RSVP on MeetUp at
https://www.meetup.com/BAyPIGgies/events/283390500/. When you RSVP "Yes" to
this event, the link to the Zoom meeting will become visible in MeetUp. If
you do not have a MeetUp account and would like to attend the meeting, just
contact the organizers.

2022 Call for Talks

We are looking for speakers for 2022. We are looking for technical talks of
interest to Python developers, either about the language and core libraries
itself, popular libraries/platforms using Python (for example, Pandas
andTensorFlow in Data Science, Flask and Django in web applications,
Ansible in DevOPs), or other experiences using Python. You can apply for an
online talk here: https://forms.gle/PqxrExC2t858xtfMA
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