The recursive grep in the banned words check can get really messy when running locally depending on each person's directory structure or where the format script is being called from.
Separates the banned words check as a separate script so that it's not called by default in ./format.sh. Also adds this to the documentation
In the [docs contributing page](https://docs.ray.io/en/master/ray-contribute/docs.html), the links to other docs pages point to master/ instead of latest/, which can be a bit confusing since this is not the live version of the docs that people will be used to seeing.
I added a couple additional clarifications and fixed a typo as well. I also mentioned the need for an image and linked to the image directory (though some subprojects have their own image directories as well, which I did not mention).
Clean up the ci/ directory. This means getting rid of the travis/ path completely and moving the files into sensible subdirectories.
Details:
- Moves everything under ci/travis into subdirectories, e.g. ci/build, ci/lint, etc.
- Minor adjustments to some scripts (variable renames)
- Removes the outdated (unused) asan tests
What: This class adds a generic BatchPredictor class that offers an interface to run batch inference on Ray datasets. It takes a Predictor class and checkpoint as an input, and provides a predict(dataset) method to run scalable scoring inference.
Why: Currently users have to implement scorers themselves. This is mostly boilerplate and prone to errors, so we should provide a simple solution instead.
Note that this predictor also implements the Predictor interface.
Instead of relying on the node-ip custom resource for static task-to-node placement, this PR introduces an explicit NodeAffinitySchedulingStrategy with the following benefits:
1. Specify node using id instead of ip since ip may not be unique for each node.
2. Support soft constraint so the task can be tolerant to node failures.
After this PR, the node-ip custom resource can be deprecated.
`ray.data.from_numpy()` currently expects to be given a list of ndarray futures, instead of handling concrete ndarrays, as expected (and as allowed by other `from_*` APIs, e.g. `from_pandas`). This PR renames the existing `from_numpy` API to `from_numpy_refs`, and exposes `ray.data.from_numpy`, which takes concrete ndarrays (not object references).
1. Dataset pipeline is advanced usage of Ray Dataset, which should not jam into the Getting Started page
2. We already have a separate/dedicated page called Pipelining Compute to cover the same content
Adding a FAQ page. Currently has some basic questions that have come up in the past.
Explaining how to use Matplotlib due to threading in the distributed training function.
`api.py` has accumulated classes and functions that aren't purely public APIs, causing circular dependencies. This change pulls `Deployment` and deployment graph-related features out of `api.py` and puts them in two new files: `deployment.py` and `deployment_graph.py`.
This PR fixes the issue of diverging documentation between Ray Docs and ecosystem library readmes which live in separate repos (eg. xgboost_ray). This is achieved by adding an extra step before the docs build process starts that downloads the readmes of specified ecosystem libraries from their GitHub repositories. The files are then preprocessed by a very simple parser to allow for differences between GitHub and Docs markdowns.
In summary, this makes the markdown files in ecosystem library repositories single sources of truth and removes the need to manually keep the doc pages up to date, all the while allowing for differences between what's rendered on GitHub and in the Docs.
See ray-project/xgboost_ray#204 & https://ray--23505.org.readthedocs.build/en/23505/ray-more-libs/xgboost-ray.html for an example.
Needs ray-project/xgboost_ray#204 and ray-project/lightgbm_ray#30 to be merged first.
The `serve status` command allows users to get their deployments' status info through the CLI. This change adds a tip to the health-checking documentation to inform users about `serve status`.
Added ensemble model examples to the Documentation. That was needed, due to a user request and there was no methodology outlining the creation of higher level ensemble models.
Co-authored-by: Jiao Dong <sophchess@gmail.com>
Getting or creating a named actor is a common pattern, however it is somewhat esoteric in how to achieve this. Add a utility function and test that it doesn't cause any scary error messages.
Actor.options(name="my_singleton", get_if_exists=True).remote(args)
Algolia search now does not overflow on mobile devices anymore, making the nav scrollable again.
Signed-off-by: Max Pumperla <max.pumperla@googlemail.com>
