Getting Started with Ray Clusters

Ray is a framework for developing and running parallel and distributed applications emphasizing ML tasks. Ray enables users to harness the power of distributed computing without much effort.
Whether you’re using the framework for non-ML or ML-related tasks, Ray is a fast and flexible open-source framework for distributed computing. Ray has built on top of their core framework to provide Ray AIR, a suite of end-to-end ML tools all accelerated by paralleling computing.
Today, we’ll walk you through how to send jobs using Ray to AWS from your local machine. To do this, we’ll walk through how to define and set up a Ray cluster. In addition, we’ll go through some commands that might be useful to manage your Ray cluster.
For more information, visit: www.saturncloud.io

[@fabsync]

fantastic video! Love your teaching style! It will be great to see a video on how to set ray on docker for local development.. probably chat with your pdf tutorial?

[@chineduezeofor2481]

Excellent tutorial!

[@Tbone913]

Excellent, thanks

[@arguscesus]

We’re you able to run tensorboard for ray machine learning task?

[@zahabkhan6832]

where is the blog post

[@abhishektrivedi7291]

Video was good, why do not you share the yaml file or the blogpost to get them

[@electricweegie9125]

Excellent demo! Would be great to share the code.

[@PompiBasumatary-s4g]

Takes a lot of time for the cluster to be set up .

[@faiyazhasan4797]

Anyone get any warning saying could not modify ulimit?

[@daycentmodel9086]

Thanks for a very intuitive demo! Where do I find the blog posts that you mentioned?

[@rodolphobabatounde6711]

It's really sad that I didn't share the code or the link to your blog. We need at least the yaml file

[@prashanthb6521]

Flew over my head :(