The Staroid node provider has been abandoned and unmaintained for quite some time now. Due to the fact that there are no active maintainers, the original contributors cannot be reached, and there is no clear interest, we are no longer officially endorsing or supporting the node provider.
Co-authored-by: Alex Wu <alex@anyscale.com>
This is a down scoped change. For the full overview picture of Tune control loop, see [`Tune control loop refactoring`](https://docs.google.com/document/d/1RDsW7SVzwMPZfA0WLOPA4YTqbRyXIHGYmBenJk33HaE/edit#heading=h.2za3bbxbs5gn)
1. Previously there are separate waits on pg ready and other events. As a result, there are quite a few timing tweaks that are inefficient, hard to understand and unit test. This PR consolidates into a single wait that is handled by TrialRunner in each step.
- A few event types are introduced, and their mapping into scenarios
* PG_READY --> Should place a trial onto it. If somehow there is no trial to be placed there, the pg will be put in _ready momentarily. This is due to historically resources is conceptualized as a pull based model.
* NO_RUNNING_TRIALS_TIME_OUT --> possibly not sufficient resources case
* TRAINING_RESULT
* SAVING_RESULT
* RESTORING_RESULT
* YIELD --> This just means that simply taking very long to train. We need to punt back to the main loop to print out status info etc.
2. Previously TrialCleanup is not very efficient and can be racing between Trainable.stop() and `return_placement_group`. This PR streamlines the Trial cleanup process by explicitly let Trainable.stop() to finish followed by `return_placement_group(pg)`. Note, graceful shutdown is needed in cases like `pause_trial` where checkpointing to memory needs to be given the time to happen before the actor is gone.
3. There are quite some env variables removed (timing tweaks), that I consider OK to proceed without deprecation cycle.
Continuing docs overhaul, tune now has:
- [x] better landing page
- [x] a getting started guide
- [x] user guide was cut down, partially merged with FAQ, and partially integrated with tutorials
- [x] the new user guide contains guides to tune features and practical integrations
- [x] we rewrote some of the feature guides for clarity
- [x] we got rid of sphinx-gallery for this sub-project (only data and core left), as it looks bad and is unnecessarily complicated anyway (plus, makes the build slower)
- [x] sphinx-gallery examples are now moved to markdown notebook, as started in #22030.
- [x] Examples are tested in the new framework, of course.
There's still a lot one can do, but this is already getting too large. Will follow up with more fine-tuning next week.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
Co-authored-by: Kai Fricke <krfricke@users.noreply.github.com>
Implement a TorchTensorboardProfilerCallback and corresponding TorchWorkerProfiler to support distributed PyTorch Profiler With TensorBoard integration.
We're introducing the usage of [MyST Notebooks](https://myst-nb.readthedocs.io/en/latest/index.html) here and demonstrate how it works by rewriting (and extending) the RLLib Serve tutorial. Benefits:
- [x] Write notebooks in markdown. Can be converted into other formats e.g. with `jupytext`
- [x] Tutorials like this have a binderhub link added to the top nav (launch button).
- [x] Notebooks get executed when docs are built, so it's impossible to have stale docs.
- [x] But locally those builds are cached so that you don't have to wait too long.
- [x] The notebook cell outputs can be shown, hidden or removed. In particular, we can now avoid adding expected code output as comments in our scripts (which might get outdated).
We're also clarifying #22022.
Old tutorial: [here](https://docs.ray.io/en/latest/serve/tutorials/rllib.html)
New tutorial (preview): [here](https://ray--22030.org.readthedocs.build/en/22030/serve/tutorials/rllib.html)
Co-authored-by: simon-mo <simon.mo@hey.com>
Previously, local files corresponding to runtime env URIs were eagerly garbage collected as soon as there were no more references to them. In this PR, we store this data in a cache instead, so when the reference count for a URI drops to zero, instead of deleting it we simple mark it as unused in the cache. When the cache exceeds its size limit (default 10 GB) it will delete unused URIs until the cache is back under the size limit or there are no more unused URIs.
Design doc: https://docs.google.com/document/d/1x1JAHg7c0ewcOYwhhclbuW0B0UC7l92WFkF4Su0T-dk/edit
- Adds unit tests for caching and integration tests for working_dir caching
This PR adds pandas block format support by implementing `PandasRow`, `PandasBlockBuilder`, `PandasBlockAccessor`.
Note that `sort_and_partition`, `combine`, `merge_sorted_blocks`, `aggregate_combined_blocks` in `PandasBlockAccessor` redirects to arrow block format implementation for now. They'll be implemented in a later PR.
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.
This PR adds a `CometLoggerCallback` to the Tune Integrations, allowing users to log runs from Ray to [Comet](https://www.comet.ml/site/).
Co-authored-by: Michael Cullan <mjcullan@gmail.com>
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
Currently, the docs have an [end-to-end tutorial](https://web.archive.org/web/20211122152843/https://docs.ray.io/en/latest/serve/tutorial.html) walking users through deploying a `Counter` function on Serve. This PR adds an end-to-end tutorial walking users through deploying an entire Hugging Face model using Serve, providing a better understanding of how to deploy an actual model via Serve.
Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
Co-authored-by: Simon Mo <simon.mo@hey.com>
This PR consolidates both #21667 and #21759 (look there for features), but improves on them in the following way:
- [x] we reverted renaming of existing projects `tune`, `rllib`, `train`, `cluster`, `serve`, `raysgd` and `data` so that links won't break. I think my consolidation efforts with the `ray-` prefix were a little overeager in that regard. It's better like this. Only the creation of `ray-core` was a necessity, and some files moved into the `rllib` folder, so that should be relatively benign.
- [x] Additionally, we added Algolia `docsearch`, screenshot below. This is _much_ better than our current search. Caveat: there's a sphinx dependency that needs to be replaced (`sphinx-tabs`) by another, newer one (`sphinx-panels`), as the former prevents loading of the `algolia.js` library. Will follow-up in the next PR (hoping this one doesn't get re-re-re-re-reverted).
This is a minimum viable product for Ray Autoscaler integration with Kuberay. It is not ready for prime time/general use, but should be enough for interested parties to get started (see the documentation in kuberay.md).
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.
This PR adds pandas block format support by implementing `PandasRow`, `PandasBlockBuilder`, `PandasBlockAccessor`.
Note that `sort_and_partition`, `combine`, `merge_sorted_blocks`, `aggregate_combined_blocks` in `PandasBlockAccessor` redirects to arrow block format implementation for now. They'll be implemented in a later PR.
Co-authored-by: Clark Zinzow <clarkzinzow@gmail.com>
Co-authored-by: Eric Liang <ekhliang@gmail.com>
Currently we install OpenSSH on the fly in fake multinode docker testing. Instead we can speed testing up a fair bit by building a Docker image which includes OpenSSH first and then run tests with this image.
