Ray

Patch release to bump the `log4j2` version from `2.14` to `2.16`. This resolves the security vulnerabilities https://nvd.nist.gov/vuln/detail/CVE-2021-44228 and https://nvd.nist.gov/vuln/detail/CVE-2021-45046.

No library or core changes included.

Thanks seonggwonyoon and ijrsvt for contributing the fixes!

Highlights

- Ray Train is now in beta! If you are using Ray Train, we’d love to hear your feedback [here](https://docs.google.com/forms/d/e/1FAIpQLSfI3asn-m1cQSIbdrk_cd6qYenZvt-eNTVfTwba3SVhmHcHIg/viewform)!
- Ray Docker images for multiple CUDA versions are now provided (19505)! You can specify a `-cuXXX` suffix to pick a specific version.
- `ray-ml:cpu` images are now deprecated. The `ray-ml` images are only built for GPU.
- Ray Datasets now supports groupby and aggregations! See the [groupby API](https://docs.ray.io/en/master/data/package-ref.html#ray.data.Dataset.groupby) and [GroupedDataset](https://docs.ray.io/en/master/data/package-ref.html#groupeddataset-api) docs for usage.
- We are making continuing progress in improving Ray stability and usability on Windows. We encourage you to try it out and report feedback or issues at https://github.com/ray-project/ray/issues.
- We are launching a Ray Job Submission server + CLI & SDK clients to make it easier to submit and monitor Ray applications when you don’t want an active connection using Ray Client. This is currently in alpha, so the APIs are subject to change, but please test it out and file issues / leave feedback on GitHub & discuss.ray.io!


Ray Autoscaler
💫Enhancements:
- Graceful termination of Ray nodes prior to autoscaler scale down (20013)
- Ray Clusters on AWS are colocated in one Availability Zone to reduce costs & latency (19051)

Ray Client
🔨 Fixes:
- ray.put on a list of of objects now returns a single object ref (​​19737)

Ray Core
🎉 New Features:
- Support remote file storage for runtime_env (20280, 19315)
- Added ray job submission client, cli and rest api (19567, 19657, 19765, 19845, 19851, 19843, 19860, 19995, 20094, 20164, 20170, 20192, 20204)

💫Enhancements:
- Garbage collection for runtime_env (20009, 20072)
- Improved logging and error messages for runtime_env (19897, 19888, 18893)

🔨 Fixes:
- Fix runtime_env hanging issues (19823)
- Fix specifying runtime env in ray.remote decorator with Ray Client (19626)
- Threaded actor / core worker / named actor race condition fixes (19751, 19598, 20178, 20126)

📖Documentation:
- New page “Handling Dependencies”
- New page “Ray Job Submission: Going from your laptop to production”

Ray Java
API Changes:
- Fully supported namespace APIs. ([Check out the namespace for more information.](https://docs.ray.io/en/latest/namespaces.html)) #19468 19986 20057
- Removed global named actor APIs and global placement group APIs. 20219 20135
- Added timeout parameter for `Ray.Get()` API. 20282

Note:
- Use `Ray.getActor(name, namespace)` API to get a named actor between jobs instead of `Ray.getGlobalActor(name)`.
- Use `PlacementGroup.getPlacementGroup(name, namespace)` API to get a placement group between jobs instead of `PlacementGroup.getGlobalPlacementGroup(name)`.

Ray Datasets
🎉 New Features:
- Added groupby and aggregations (19435, 19673, 20010, 20035, 20044, 20074)
- Support custom write paths (19347)

🔨 Fixes:
- Support custom CSV write options (19378)

🏗 Architecture refactoring:
- Optimized block compaction (19681)

Ray Workflow
🎉 New Features:
- Workflow right now support events (19239)
- Allow user to specify metadata for workflow and steps (19372)
- Allow in-place run a step if the resources match (19928)

🔨 Fixes:
- Fix the s3 path issue (20115)

RLlib
🏗 Architecture refactoring:
- “framework=tf2” + “eager_tracing=True” is now (almost) as fast as “framework=tf”. A check for tf2.x eager re-traces has been added making sure re-tracing does not happen outside the initial function calls. All CI learning tests (CartPole, Pendulum, FrozenLake) are now also run as framework=tf2. (19273, 19981, 20109)
- Prepare deprecation of `build_trainer`/`build_(tf_)?policy` utility functions. Instead, use sub-classing of `Trainer` or `Torch|TFPolicy`. POCs done for `PGTrainer`, `PPO[TF|Torch]Policy`. (20055, 20061)
- V-trace (APPO & IMPALA): Don’t drop last ts can be optionally switch on. The default is still to drop it, but this may be changed in a future release. (19601)
- Upgrade to gym 0.21. (19535)

🔨 Fixes:
- Minor bugs/issues fixes and enhancements: 19069, 19276, 19306, 19408, 19544, 19623, 19627, 19652, 19693, 19805, 19807, 19809, 19881, 19934, 19945, 20095, 20128, 20134, 20144, 20217, 20283, 20366, 20387

📖Documentation:
- RLlib main page (“RLlib in 60sec”) overhaul. (20215, 20248, 20225, 19932, 19982)
- Major docstring cleanups in preparation for complete overhaul of API reference pages. (19784, 19783, 19808, 19759, 19829, 19758, 19830)
- Other documentation enhancements. (19908, 19672, 20390)


Tune

💫Enhancements:
- Refactored and improved experiment analysis (20197, 20181)
- Refactored cloud checkpointing API/SyncConfig (20155, 20418, 19632, 19641, 19638, 19880, 19589, 19553, 20045, 20283)
- Remove magic results (e.g. config) before calculating trial result metrics (19583)
- Removal of tech debt (19773, 19960, 19472, 17654)
- Improve testing (20016, 20031, 20263, 20210, 19730
- Various enhancements (19496, 20211)

🔨Fixes:
- Documentation fixes (20130, 19791)
- Tutorial fixes (20065, 19999)
- Drop 0 value keys from PGF (20279)
- Fix shim error message for scheduler (19642)
- Avoid looping through _live_trials twice in _get_next_trial. (19596)
- clean up legacy branch in update_avail_resources. (20071)
- fix Train/Tune integration on Client (20351)

Train

Ray Train is now in Beta! The beta version includes various usability improvements for distributed PyTorch training and checkpoint management, support for [Ray Client](https://docs.ray.io/en/master/cluster/ray-client.html), and an [integration with Ray Datasets](https://docs.ray.io/en/master/train/user_guide.html#distributed-data-ingest-ray-datasets) for distributed data ingest.

Check out the docs [here](https://docs.ray.io/en/latest/train/train.html), and the migration guide from Ray SGD to Ray Train [here](https://docs.ray.io/en/latest/train/migration-guide.html). If you are using Ray Train, we’d love to hear your feedback [here](https://docs.google.com/forms/d/e/1FAIpQLSfI3asn-m1cQSIbdrk_cd6qYenZvt-eNTVfTwba3SVhmHcHIg/viewform)!

🎉 New Features:
- New `train.torch.prepare_model(...)` and `train.torch.prepare_data_loader(...)` [API](https://docs.ray.io/en/master/train/user_guide.html#update-training-function) to automatically handle preparing your PyTorch model and DataLoader for distributed training (20254).
- Checkpoint management and support for custom checkpoint strategies (19111).
- Easily [configure](https://docs.ray.io/en/master/train/user_guide.html#configuring-checkpoints) what and how many checkpoints to save to disk.
- Support for [Ray Client](https://docs.ray.io/en/master/cluster/ray-client.html) (#20123, 20351).

💫Enhancements:
- Simplify workflow for training with a single worker (19814).
- [Ray Placement Groups](https://docs.ray.io/en/master/placement-group.html) are used for scheduling the training workers (#20091).
- `PACK` strategy is used by default but can be changed by setting the `TRAIN_ENABLE_WORKER_SPREAD` environment variable.
- Automatically unwrap Torch DDP model and convert to CPU when saving a model as checkpoint (20333).

🔨Fixes:
- Fix `HorovodBackend` to automatically detect NICs- thanks tgaddair! (19533).

📖Documentation:
- Denote public facing APIs with beta stability (20378)
- Doc updates (20271)

Serve
We would love to hear from you! Fill out the [Ray Serve survey here](https://forms.gle/zg4gDS84z8wTpKBLA).

🎉 New Features:
- New `checkpoint_path` configuration allows Serve to save its internal state to external storage (disk, S3, and GCS) and [recover upon failure](https://docs.ray.io/en/master/serve/deployment.html#failure-recovery). (19166, 19998, 20104)
- [Replica autoscaling](https://docs.ray.io/en/master/serve/core-apis.html#autoscaling) is ready for testing out! (19559, 19520)
- Native [Pipeline API for model composition](https://docs.ray.io/en/master/serve/pipeline.html) is ready for testing as well!

🔨Fixes:
- Serve deployment functions or classes can take no parameters (19708)
- Replica slow start message is improved. You can now see whether it is slow to allocate resources or slow to run constructor. (19431)
- `pip install ray[serve]` will now install `ray[default]` as well. (19570)

🏗 Architecture refactoring:
- The terminology of “backend” and “endpoint” are officially deprecated in favor of “deployment”. (20229, 20085, 20040, 20020, 19997, 19947, 19923, 19798).
- Progress towards Java API compatibility (19463).

Dashboard
- Ray Dashboard is now enabled on Windows! (19575)

Thanks
Many thanks to all those who contributed to this release!
krfricke, stefanbschneider, ericl, nikitavemuri, qicosmos, worldveil, triciasfu, AmeerHajAli, javi-redondo, architkulkarni, pdames, clay4444, mGalarnyk, liuyang-my, matthewdeng, suquark, rkooo567, mwtian, chenk008, dependabot[bot], iycheng, jiaodong, scv119, oscarknagg, Rohan138, stephanie-wang, Zyiqin-Miranda, ijrsvt, roireshef, tkaymak, simon-mo, ashione, jovany-wang, zenoengine, tgaddair, 11rohans, amogkam, zhisbug, lchu-ibm, shrekris-anyscale, pcmoritz, yiranwang52, mattip, sven1977, Yard1, DmitriGekhtman, ckw017, WangTaoTheTonic, wuisawesome, kcpevey, kfstorm, rhamnett, renos, TeoZosa, SongGuyang, clarkzinzow, avnishn, iasoon, gjoliver, jjyao, xwjiang2010, dmatrix, edoakes, czgdp1807, heng2j, sungho-joo, lixin-wei

Highlights
- Ray SGD has been rebranded to Ray Train! The new documentation landing page can be found [here](https://docs.ray.io/en/master/train/train.html).
- Ray Datasets is now in beta! The beta release includes a new integration with Ray Train yielding scalable ML ingest for distributed training. Check out the docs [here](https://docs.ray.io/en/master/data/dataset.html), try it out for your ML ingest and batch inference workloads, and let us know how it goes!
- This Ray release supports Apple Silicon (M1 Macs). [Check out the installation instructions for more information!](https://docs.ray.io/en/master/installation.html#apple-silicon-support)
Ray Autoscaler
🎉 New Features:
- Fake multi-node mode for autoscaler testing (18987)

💫Enhancements:
- Improve unschedulable task warning messages by integrating with the autoscaler (18724)

Ray Client
💫Enhancements
- Use async rpc for remote call and actor creation (18298)

Ray Core
💫Enhancements
- Eagerly install job-level runtime_env (19449, 17949)

🔨 Fixes:
- Fixed resource demand reporting for infeasible 1-CPU tasks (19000)
- Fixed printing Python stack trace in Python worker (19423)
- Fixed macOS security popups (18904)
- Fixed thread safety issues for coreworker (18902, 18910, 18913 19343)
- Fixed placement group performance and resource leaking issues (19277, 19141, 19138, 19129, 18842, 18652)
- Improve unschedulable task warning messages by integrating with the autoscaler (18724)
- Improved Windows support (19014, 19062, 19171, 19362)
- Fix runtime_env issues (19491, 19377, 18988)

Ray Data
Ray Datasets is now in beta! The beta release includes a new integration with Ray Train yielding scalable ML ingest for distributed training. It supports repeating and rewindowing pipelines, zipping two pipelines together, better cancellation of Datasets workloads, and many performance improvements. Check out the docs [here](https://docs.ray.io/en/master/data/dataset.html), try it out for your ML ingest and batch inference workloads, and let us know how it goes!

🎉 New Features:
- Ray Train integration (17626)
- Add support for repeating and rewindowing a DatasetPipeline (19091)
- .iter_epochs() API for iterating over epochs in a DatasetPipeline (19217)
- Add support for zipping two datasets together (18833)
- Transformation operations are now cancelled when one fails or the entire workload is killed (18991)
- Expose from_pandas()/to_pandas() APIs that accept/return plain Pandas DataFrames (18992)
- Customize compression, read/write buffer size, metadata, etc. in the IO layer (19197)
- Add spread resource prefix for manual round-robin resource-based task load balancing

💫Enhancements:
- Minimal rows are now dropped when doing an equalized split (18953)
- Parallelized metadata fetches when reading Parquet datasets (19211)

🔨 Fixes:
- Tensor columns now properly support table slicing (19534)
- Prevent Datasets tasks from being captured by Ray Tune placement groups (19208)
- Empty datasets are properly handled in most transformations (18983)

🏗 Architecture refactoring:
- Tensor dataset representation changed to a table with a single tensor column (18867)

RLlib

🎉 New Features:
- Allow n-step > 1 and prioritized replay for R2D2 and RNNSAC agents. (18939)

🔨 Fixes:
- Fix memory leaks in TF2 eager mode. (19198)
- Faster worker spaces inference if specified through configuration. (18805)
- Fix bug for complex obs spaces containing Box([2D shape]) and discrete components. (18917)
- Torch multi-GPU stats not protected against race conditions. (18937)
- Fix SAC agent with dict space. (19101)
- Fix A3C/IMPALA in multi-agent setting. (19100)

🏗 Architecture refactoring:
- Unify results dictionary returned from Trainer.train() across agents regardless of (tf or pytorch, multi-agent, multi-gpu, or algos that use >1 SGD iterations, e.g. ppo) (18879)

Ray Workflow

🎉 New Features:
- Introduce workflow.delete (19178)

🔨Fixes:
- Fix the bug which allow workflow step to be executed multiple times (19090)

🏗 Architecture refactoring:
- Object reference serialization is decoupled from workflow storage (18328)


Tune

🎉 New Features:
- PBT: Add burn-in period (19321)

💫Enhancements:
- Optional forcible trial cleanup, return default autofilled metrics even if Trainable doesn't report at least once (19144)
- Use queue to display JupyterNotebookReporter updates in Ray client (19137)
- Add resume="AUTO" and enhance resume error messages (19181)
- Provide information about resource deadlocks, early stopping in Tune docs (18947)
- Fix HEBOSearch installation docs (18861)
- OptunaSearch: check compatibility of search space with evaluated_rewards (18625)
- Add `save` and `restore` methods for searchers that were missing it & test (18760)
- Add documentation for reproducible runs (setting seeds) (18849)
- Depreciate `max_concurrent` in `TuneBOHB` (18770)
- Add `on_trial_result` to ConcurrencyLimiter (18766)
- Ensure arguments passed to tune `remote_run` match (18733)
- Only disable ipython in remote actors (18789)

🔨Fixes:
- Only try to sync driver if sync_to_driver is actually enabled (19589)
- sync_client: Fix delete template formatting (19553)
- Force no result buffering for hyperband schedulers (19140)
- Exclude trial checkpoints in experiment sync (19185)
- Fix how durable trainable is retained in global registry (19223, 19184)
- Ensure `loc` column in progress reporter is filled (19182)
- Deflake PBT Async test (19135)
- Fix `Analysis.dataframe()` documentation and enable passing of `mode=None` (18850)

Ray Train (SGD)

Ray SGD has been rebranded to Ray Train! The new documentation landing page can be found [here](https://docs.ray.io/en/master/train/train.html). Ray Train is integrated with Ray Datasets for distributed data loading while training, documentation available [here](https://docs.ray.io/en/master/train/user_guide.html#distributed-data-ingest-ray-datasets).

🎉 New Features:
- Ray Datasets Integration (17626)

🔨Fixes:
- Improved support for multi-GPU training (18824, 18958)
- Make actor creation async (19325)

📖Documentation:
- Rename Ray SGD v2 to Ray Train (19436)
- Added migration guide from Ray SGD v1 (18887)

Serve
🎉 New Features:
- Add ability to recover from a checkpoint on cluster failure (19125)
- Support kwargs to deployment constructors (19023)

🔨Fixes:
- Fix asyncio compatibility issue (19298)
- Catch spurious ConnectionErrors during shutdown (19224)
- Fix error with uris=None in runtime_env (18874)
- Fix shutdown logic with exit_forever (18820)

🏗 Architecture refactoring:
- Progress towards Serve autoscaling (18793, 19038, 19145)
- Progress towards Java support (18630)
- Simplifications for long polling (19154, 19205)

Dashboard
🎉 New Features:
- Basic support for the dashboard on Windows (19319)

🔨Fixes:
- Fix healthcheck issue causing the dashboard to crash under load (19360)
- Work around aiohttp 4.0.0+ issues (19120)

🏗 Architecture refactoring:
- Improve dashboard agent retry logic (18973)

Thanks
Many thanks to all those who contributed to this release!
rkooo567, lchu-ibm, scv119, pdames, suquark, antoine-galataud, sven1977, mvindiola1, krfricke, ijrsvt, sighingnow, marload, jmakov, clay4444, mwtian, pcmoritz, iycheng, ckw017, chenk008, jovany-wang, jjyao, hauntsaninja, franklsf95, jiaodong, wuisawesome, odp, matthewdeng, duarteocarmo, czgdp1807, gjoliver, mattip, richardliaw, max0x7ba, Jasha10, acxz, xwjiang2010, SongGuyang, simon-mo, zhisbug, ccssmnn, Yard1, hazeone, o0olele, froody, robertnishihara, amogkam, sasha-s, xychu, lixin-wei, architkulkarni, edoakes, clarkzinzow, DmitriGekhtman, avnishn, liuyang-my, stephanie-wang, Chong-Li, ericl, juliusfrost, carlogrisetti

Highlights

* [Runtime Environments](https://docs.ray.io/en/releases-1.6.0/advanced.html#runtime-environments) are ready for general use! This feature enables you to dynamically specify per-task, per-actor and per-job dependencies, including a working directory, environment variables, pip packages and conda environments. Install it with `pip install -U 'ray[default]'`.
* Ray Dataset is now in alpha! Dataset is an interchange format for distributed datasets, powered by Arrow. You can also use it for a basic Ray native data processing experience. [Check it out here. ](https://docs.ray.io/en/releases-1.6.0/data/dataset.html)
* [Ray Lightning](https://github.com/ray-project/ray_lightning) v0.1 has been released! You can install it via `pip install ray-lightning`. Ray Lightning is a library of PyTorch Lightning plugins for distributed training using Ray. Features:
* Enables quick and easy parallel training
* Supports [PyTorch DDP](https://github.com/ray-project/ray_lightning#pytorch-distributed-data-parallel-plugin-on-ray), [Horovod](https://github.com/ray-project/ray_lightning#horovod-plugin-on-ray), and [Sharded DDP with Fairscale](https://github.com/ray-project/ray_lightning#model-parallel-sharded-training-on-ray)
* Integrates with [Ray Tune for hyperparameter optimization](https://github.com/ray-project/ray_lightning#hyperparameter-tuning-with-ray-tune) and is compatible with [Ray Client](https://github.com/ray-project/ray_lightning#multi-node-training-from-your-laptop)
* `pip install ray` now has a significantly reduced set of dependencies. Features such as the dashboard, the cluster launcher, runtime environments, and observability metrics may require `pip install -U 'ray[default]'` to be enabled. Please report any issues on Github if this is an issue!

Ray Autoscaler

🎉 New Features:

* The Ray autoscaler now supports TPUs on GCP. Please refer to this example for spinning up a [simple TPU cluster](https://github.com/ray-project/ray/blob/releases/1.6.0/python/ray/autoscaler/gcp/tpu.yaml). (#17278)

💫Enhancements:

* Better AWS networking configurability (17236 17207 14080)
* Support for running autoscaler without NodeUpdaters (17194, 17328)

🔨 Fixes:

* Code clean up and corrections to downscaling policy (17352)
* Docker file sync fix (17361)

Ray Client

💫Enhancements:

* Updated docs for client server ports and ray.init(ray://) (17003, 17333)
* Better error handling for deserialization failures (17035)

🔨 Fixes:

* Fix for server proxy not working with non-default redis passwords (16885)

Ray Core

🎉 New Features:

* [Runtime Environments](https://docs.ray.io/en/releases-1.6.0/advanced.html#runtime-environments) are ready for general use!
* Specify a working directory to upload your local files to all nodes in your cluster.
* Specify different conda and pip dependencies for your tasks and actors and have them installed on the fly.

🔨 Fixes:

* Fix plasma store bugs for better data processing stability (16976, 17135, 17140, 17187, 17204, 17234, 17396, 17550)
* Fix a placement group bug where CUDA_VISIBLE_DEVICES were not properly detected (17318)
* Improved Ray stacktrace messages. (17389)
* Improved GCS stability and scalability (17456, 17373, 17334, 17238, 17072)

🏗 Architecture refactoring:

* Plasma store refactor for better testability and extensibility. (17332, 17313, 17307)

Ray Data Processing

Ray Dataset is now in alpha! Dataset is an interchange format for distributed datasets, powered by Arrow. You can also use it for a basic Ray native data processing experience. [Check it out here. ](https://docs.ray.io/en/releases-1.6.0/data/dataset.html)

RLLib

🎉 New Features:

* Support for RNN/LSTM models with SAC (new agent: "RNNSAC"). Shoutout to ddworak94! (16577)
* Support for ONNX model export (tf and torch). (16805)
* Allow Policies to be added to/removed from a Trainer on-the-fly. (17566)

🔨 Fixes:

* Fix for view requirements captured during compute actions test pass. Shoutout to Chris Bamford (15856)
* Issues: 17397, 17425, 16715, 17174\. When on driver, Torch|TFPolicy should not use `ray.get_gpu_ids()` (b/c no GPUs assigned by ray). (17444)

* Other bug fixes: 15709, 15911, 16083, 16716, 16744, 16896, 16999, 17010, 17014, 17118, 17160, 17315, 17321, 17335, 17341, 17356, 17460, 17543, 17567, 17587

🏗 Architecture refactoring:

* CV2 to Skimage dependency change (CV2 still supported). Shoutout to Vince Jankovics. (16841)
* Unify tf and torch policies wrt. multi-GPU handling: PPO-torch is now 33% faster on Atari and 1 GPU. (17371)
* Implement all policy maps inside RolloutWorkers to be LRU-caches so that a large number of policies can be added on-the-fly w/o running out of memory. (17031)
* Move all tf static-graph code into DynamicTFPolicy, such that policies can be deleted and their tf-graph is GC'd. (17169)
* Simplify multi-agent configs: In most cases, creating dummy envs (only to retrieve spaces) are no longer necessary. (16565, 17046)

📖Documentation:

* Examples scripts do-over (shoutout to Stefan Schneider for this initiative).
* Example script: League-based self-play with "open spiel" env. (17077)
* Other doc improvements: 15664 (shoutout to kk-55), 17030, 17530

Tune

🎉 New Features:

* Dynamic trial resource allocation with ResourceChangingScheduler (16787)
* It is now possible to use a define-by-run function to generate a search space with OptunaSearcher (17464)

💫Enhancements:

* String names of searchers/schedulers can now be used directly in tune.run (17517)
* Filter placement group resources if not in use (progress reporting) (16996)
* Add unit tests for flatten_dict (17241)

🔨Fixes:

* Fix HDFS sync down template (17291)
* Re-enable TensorboardX without Torch installed (17403)

📖Documentation:

* LightGBM integration (17304)
* Other documentation improvements: 17407 (shoutout to amavilla), 17441, 17539, 17503

SGD

🎉 New Features:

* We have started initial development on a new RaySGD v2! We will be rolling it out in a future version of Ray. See the documentation [here](https://docs.ray.io/en/master/raysgd/v2/raysgd.html#sgd-v2-docs). (17536, 17623, 17357, 17330, 17532, 17440, 17447, 17300, 17253)

💫Enhancements:

* Placement Group support for TorchTrainer (17037)

Serve

🎉 New Features:

* Add Ray API stability annotations to Serve, marking many `serve.\*` APIs as `Stable` (17295)
* Support `runtime_env`'s `working_dir` for Ray Serve (16480)

🔨Fixes:

* Fix FastAPI's response_model not added to class based view routes (17376)
* Replace `backend` with `deployment` in metrics & logging (17434)

🏗Stability Enhancements:

* Run Ray Serve with multi & single deployment large scale (1K+ cores) test running nightly (17310, 17411, 17368, 17026, 17277)

Thanks
Many thanks to all who contributed to this release:

suquark, xwjiang2010, clarkzinzow, kk-55, mGalarnyk, pdames, Souphis, edoakes, sasha-s, iycheng, stephanie-wang, antoine-galataud, scv119, ericl, amogkam, ckw017, wuisawesome, krfricke, vakker, qingyun-wu, Yard1, juliusfrost, DmitriGekhtman, clay4444, mwtian, corentinmarek, matthewdeng, simon-mo, pcmoritz, qicosmos, architkulkarni, rkooo567, navneet066, dependabot[bot], jovany-wang, kombuchafox, thomasjpfan, kimikuri, Ivorforce, franklsf95, MissiontoMars, lantian-xu, duburcqa, ddworak94, ijrsvt, sven1977, kira-lin, SongGuyang, kfstorm, Rohan138, jamesmishra, amavilla, fyrestone, lixin-wei, stefanbschneider, jiaodong, richardliaw, WangTaoTheTonic, chenk008, Catch-Bull, Bam4d

Cherrypick release to address RLlib issue, no library or core changes included.

Cherrypick release to address a few external integration and documentation issues, no library or core changes included.