This adds the structure described here, namely adding a new section under Ray Clusters which is focused on running applications on Ray clusters.
Signed-off-by: Cade Daniel <cade@anyscale.com>
Co-authored-by: Stephanie Wang <swang@cs.berkeley.edu>
*This PR:
Copies the existing clusters API reference to the new structure. The reference docs are split out into Ray Clusters (common between vms and k8s) and Ray Clusters on VMs (specific to vms). Notably, there is also a reference section for k8s, but not in this PR.
Move the three job submission user guides back into a single one. Jules had suggested that we break them out into rest/sdk/cli, but that's not P0 right now.
Fix some bugs in the left navigation bar. There should be less duplication of TOC entries. I'll keep working on related fixes in a different PR.
Signed-off-by: Cade Daniel <cade@anyscale.com>
This PR is an edit pass on the Performance Tuning page after reading it with fresh eyes. None of the content was out of date so it's mostly nits and rewording some parts that were slightly confusing.
This PR
Adds notes and example on logging for Ray/K8s.
Implements an API Reference paging pointing to the configuration guide and the RayCluster CR definition.
Takes managed K8s services out of the tabbed structure, to make that page look less sad.
Adds a comparison of the KubeRay operator and legacy K8s operator
Adds an architecture diagram for the autoscaling sections
Fixes some other minor items
Adds some info about networking to the configuration guide, removes the previously planned networking page
Signed-off-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>
The tensor extension import is a bit expensive since it will go through Arrow's and Pandas' extension type registration logic. This PR delays the tensor extension type import until Parquet reading, which is the only case in which we need to explicitly register the type.
I have confirmed that the Parquet reading in doc/source/data/doc_code/tensor.py passes with this change.
The original link doesn't exist. https://docs.ray.io/en/master/_images/air-ecosystem.svg
I fixed it by linking the raw github file link. This should have the exactly same flow as before. I tried finding a link to this image file, but I couldn't. I also couldn't find an easy way to add only a link (without embedding an image). Please lmk if you prefer other option
Adds the following to install instructions:
Tip
If you are only editing Python files, follow instructions for Building Ray (Python Only) to avoid long build times.
If you already followed the instructions in Building Ray (Python Only) and want to switch to the Full build in this section, you will need to first delete the symlinks and uninstall Ray.
Update autoscaler configuration docs for VM stack.
Removed the video, after looking at it it fits better in overview / and is possibly outdated
Co-authored-by: Eric Liang <ekhliang@gmail.com>
This reverts commit cf7305a, and unreverts #25896.
This was reverted due to a failing Windows test: #26287
We can merge once the failing Windows test (and all other relevant tests) pass.
This PR adds a guide on RayCluster configuration and a page of discussion about autoscaling.
Signed-off-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>
Integration between Ray Serve and Gradio. Users of Gradio can wrap their Gradio app in a Serve deployment by using `GradioIngress`, and scale it up through more replicas or more CPU/GPU resources.
- Add a calculating pi example to getting started page.
- Move installing ray c++ to the installation page.
Signed-off-by: Jiajun Yao <jeromeyjj@gmail.com>
