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Removed the video, after looking at it it fits better in overview / and is possibly outdated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>
* Save work
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* Update
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* consistency
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* update
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* fixes
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* simplify
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* update
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* fix
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* update
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* wording
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
* update
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
This PR adds a user guide to AIR for using Ray Train. It provides a high level overview of the trainers and removes redundant sections.
The main file to review is here: doc/source/ray-air/trainer.rst.
Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
Signed-off-by: Richard Liaw <rliaw@berkeley.edu>
Signed-off-by: Kai Fricke <kai@anyscale.com>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Kai Fricke <kai@anyscale.com>
1. If a user reads a folder with grayscale and color images, ImageFolderDatasource errors.
2. There's no way to retain image shapes.
Co-authored-by: Clark Zinzow <clarkzinzow@gmail.com>
This PR revamps and aligns the README and Ray intro doc page:
New "What is Ray" diagram that introduces AIR vs Ray core (diagram TBD finalized, this is the working placeholder)
Update the description of Ray
Link out to the user guides for key libraries and key concepts
Remove old / broken links, as well as the inline library descriptions from the README
- Move autoscaling architecture from autoscaling page to architecture page
- Update architecture page
- Remove "Router" actor
- Update description of ServeHandle
- Update defaults about HTTPproxy (default one on each node -> default just one per cluster, on the head node)
- Add note about fault tolerance in different failure scenarios
- Assorted typos/usage nits
Co-authored-by: shrekris-anyscale <92341594+shrekris-anyscale@users.noreply.github.com>
Co-authored-by: Simon Mo <simon.mo@hey.com>
This PR migrates the old Community Supported Cluster Launcher docs to the new Ray Clusters doc structure.
Signed-off-by: Cade Daniel <cade@anyscale.com>
Removes deprecated APIs:
- serve.start()
- get_handle()
Rewrites the ServeHandle doc snippet to use the recommended workflow for ServeHandles (only access them from other deployments, pass Deployments in as input args to `.bind()`, which get resolved to ServeHandles at runtime)
Co-authored-by: shrekris-anyscale <92341594+shrekris-anyscale@users.noreply.github.com>
- Currently not all code under ray-core/doc_code is covered by CI.
- tf_example.py and torch_example.py are not used anywhere.
Signed-off-by: Jiajun Yao <jeromeyjj@gmail.com>
Signed-off-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>
This PR
adds a page of guidance on GPU deployment with Ray/K8s. This page is a modified and slightly expanded version of the existing page https://docs.ray.io/en/latest/cluster/kubernetes-gpu.html
moves managed K8s service intro links to their own page
