Enables better usage with GCP.
The default behavior is that the head runs with the ray-autoscaler-sa-v1 service Account, but workers do not. Workers can run with this service account by copying & uncommenting L114->L117 from example-full
Signed-off-by: Ian <ian.rodney@gmail.com>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Page structure changes:
Deploying a Ray Cluster on Kubernetes
Getting Started -> links to jobs
Deploying a Ray Cluster on VMs
Getting started -> links to jobs
User Guides
Autoscaling (moved more content here in favor of the Getting started page)
Running Applications on Ray Clusters
Ray Jobs
Quickstart Using the Ray Jobs CLI
Python SDK
REST API
Ray Job Submission API Reference
Ray Client
Content changes:
modified "Deploying a Ray Cluster ..." quickstart pages to briefly summarize ad-hoc command execution, then link to jobs
modified Ray Jobs example to be more incremental - start with a simple example, then show long-running script, then show example with a runtime env, instead of all of them at once
center Ray Jobs quickstart around using the CLI. Made some minor changes to the Python SDK page to match it
remove "Ray Jobs Architecture"
moved "Autoscaling" content away from Kubernetes "Getting started" page into its own user guide. I think it's too complicated for "Getting Started". No content cuts.
Cut "Viewing the dashboard" and "Ray Client" from Kubernetes "Getting started" page.
Signed-off-by: Stephanie Wang <swang@cs.berkeley.edu>
