Introduction to Distributed Computing with the Ray Framework

[@Nick-tv5pu]

This is my second time going through your Ray videos, about 6mo after first watching them. I just want to say not only is this some of the most thorough and digestible Ray content I've come across, its also some of the best technical tutorial/explanation content I've come across. You have a real knack for this stuff.

[@jonathanuniversity]

@@Nick-tv5pu glad you found it helpful! Always touching to hear this from folks, really the reason I make these 🙏🏻🙏🏻🙏🏻

[@SuperOnlyP]

After going through some basic tutorial from ray-core introduction, 7:25 is really helpful to have overview of it .thanks !

[@curumo_curunir]

One of the clearest explaination about what is ray and why its useful. Kudos! Subscribed.

[@thefullbridgerectifier]

Hi Jonathan - This is your 450th subscriber speaking. Just found this series and absolutely loving it! Please continue making more content on Ray (and maybe RLLib!) Lets get you to 100k soon!

[@jonathanuniversity]

welcome! I'll be starting making more videos real soon, took a bit of a hiatus for the past *checks uploads* more than a year...

[@MSalman1]

Excellent explanation with simplicity and depth.

[@juanpabloguerra9512]

This is great. Thank you for putting this together and I look forward to check out more Ray focused videos.

[@cansucandan4124]

Thank you for the information you share! This video really helped to understand Ray :)

[@cansucandan4124]

I look forward to more theoretical and practical lessons on RAY framework

[@argmax11]

Awesome video ! Can yo cover more of the high level APIs Ray offers? And things like connecting to different - hybrid - clusters ?

[@user-wr4yl7tx3w]

This is really advanced.

[@SenthilKumar-kd2dm]

Is this applicable only to reduce time in maths computation ? why GPU is used then ? please explain

[@syedsohail1513]

Hey, when I start ray on my own system it gets started and I connect another laptop using ray start -address=‘xxxxx’ -redis-password=‘xxxxxx’ The laptop gets connected to my computer but when I run heavy task only I see my computer is using 80% memory and 80% cpu while the laptop (worker) doesn’t use much cpu and memory it’s just getting connected but not using all the resources. I wonder why Do you have any idea where am I going wrong? Thanks

[@jonathanuniversity]

it is hard to say without knowing more about the specifics of the machines and code that is running. My guess is that the task might not need more resources than the computer already has (hence only 80% utilization). So Ray might be using the resources it needs only from the single computer and since it is more efficient to avoid communication over the network it never uses the "cluster"

[@dinoscheidt]

Thank you very much! Subscribed

[@wyattholden9485]

Does a cluster have a max amount of workers it can hold?

[@jonathanuniversity]

you can specify a maximum when you launch a cluster if you want but in theory there is no bound (or you can set this to effectively be infinite)

[@wyattholden9485]

@@jonathanuniversity thanks for the quick reply! subscribed

[@jonathanuniversity]

@@wyattholden9485 if you are curious about the cluster launcher configuration, the options are listed here : docs.ray.io/en/latest/cluster/config.html

[@tljstewart]

Hmm whats the difference between Ray and MPI? 🤔

[@jonathanuniversity]

conceptually they are a little similar, but Ray has a higher level API that feels very Pythonic, has built in fault tolerance, and likely different performance characteristics for different types of jobs. From a technology standpoint, Ray is much more similar to Akka than it is to MPI.

[@101graffhead]

so is it like the akka for python

[@jonathanuniversity]

only the Actor API can be thought of as similar to Akka. The Ray project itself has many other components and is designed for ML and reinforcement learning, hence the focus on Python. So even though you could probably do similar things with Akka, they have somewhat different target audiences and use cases.