Preview: [docs](https://ray--21931.org.readthedocs.build/en/21931/data/dataset.html)
The Ray Data project's docs now have a clearer structure and have partly been rewritten/modified. In particular we have
- [x] A Getting Started Guide
- [x] An explicit User / How-To Guide
- [x] A dedicated Key Concepts page
- [x] A consistent naming convention in `Ray Data` whenever is is referred to the project.
This surfaces quite clearly that, apart from the "Getting Started" sections, we really only have one real example. Once we have more, we can create an "Example" section like many other sub-projects have. This will be addressed in https://github.com/ray-project/ray/issues/21838.
I updated this version compatibility table on the release branch but didn't update it on master. This is my mistake, the process is to make a PR to master and then cherry pick that commit to the release branch.
Dask default's to a disk-based shuffle even thought we're using a distributed scheduler, which appears to be resulting in dropped data since the filesystem isn't shared across nodes. Dask Distributed manually sets the shuffle algorithm in the global config to the task-based shuffle, which the Dask-on-Ray scheduler should probably do as well.
This PR adds a Dask config helper, `enable_dask_on_ray`, that sets Dask-on-Ray as the default scheduler along with changing the default shuffle to a task-based shuffle. The shuffle method can still be overridden by the user by manually specifying `df.set_index(shuffle="disk")`.
## Why are these changes needed?
- Since broadcasting is moving to grpc, introducing the option to increase the client side thread number
- For hybrid schedule, ignore the threshold if gcs based actor scheduler is enabled
With these fixing, actor creation rate > 600actor/s vs ~ 140 actor/s
## Related issue number
