# Ray 1.12.0 — Ray-1.12.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2022-04-08
- Version: 1.12.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-1.12.0
- Permalink: https://whatsnew.fyi/product/ray/releases/1.12.0

What's New is an index, not a publisher: every entry below links to the vendor's own release notes, which are the authoritative source. Entries are labelled where they are hand-curated sample data, pre-releases, or drawn from a secondary source such as a developer blog.

Reuse: the summaries, labels and curation here are © What's New. Quote freely with attribution and a link back; wholesale republication of the corpus is not permitted — terms: https://whatsnew.fyi/terms. The vendors' own release notes remain their publishers'.

---

- **added** — Ray AI Runtime (AIR), an open-source toolkit for building end-to-end ML applications on Ray, is now in Alpha
- **changed** — Ray usage data collection is now off by default
- **added** — New RLlib algorithms: SlateQ, Bandits for recommender systems use cases, and AlphaStar for multi-agent multi-GPU with league-based self-play
- **added** — Ray Datasets lazy execution model with automatic task fusion and memory-optimizing move semantics
- **added** — Ray Datasets first-class support for Pandas DataFrame blocks
- **added** — Ray Datasets efficient random access datasets via binary search
- **added** — Ray Autoscaler support cache_stopped_nodes on Azure
- **added** — Ray Autoscaler AWS CloudWatch support
- **added** — ray.init has consistent return value in client mode and driver mode
- **changed** — Ray Client gets and puts are streamed to support arbitrary object sizes
- **added** — RuntimeEnv support setting timeout for runtime_env setup
- **added** — RuntimeEnv support setting pip_check and pip_version for runtime_env
- **added** — RuntimeEnv env_vars will take effect when the pip install command is executed
- **added** — RuntimeEnv strongly-typed API ray.runtime.RuntimeEnv to define runtime env
- **added** — RuntimeEnv introduce virtualenv to isolate the pip type runtime env
- **added** — Enable dashboard in the minimal ray installation
- **added** — Ray Datasets add add_column() utility for adding derived columns
- **added** — Ray Datasets support windowing a dataset by bytes instead of number of blocks
- **removed** — Removed support for bootstrapping with Redis

##### Highlights
- Ray AI Runtime (AIR), an open-source toolkit for building end-to-end ML applications on Ray, is now in Alpha. AIR is an effort to unify the experience of using different Ray libraries (Ray Data, Train, Tune, Serve, RLlib). You can find more information on the [docs](https://docs.ray.io/en/master/ray-air/getting-started.html) or on the [public RFC](https://github.com/ray-project/ray/issues/22488).
  - Getting involved with Ray AIR. We’ll be holding office hours, development sprints, and other activities as we get closer to the Ray AIR Beta/GA release. Want to join us? Fill out this [short form](https://forms.gle/wCCdbaQDtgErYycT6)!
- Ray [usage data collection](https://github.com/ray-project/ray/issues/20857) is now off by default. If you have any questions or concerns, please comment [on the RFC](https://github.com/ray-project/ray/issues/20857).
- New algorithms are added to RLlib: SlateQ & Bandits (for recommender systems use cases) and AlphaStar (multi-agent, multi-GPU w/ league-based self-play)
- Ray Datasets: new lazy execution model with automatic task fusion and memory-optimizing move semantics; first-class support for Pandas DataFrame blocks; efficient random access datasets.


##### Ray Autoscaler

🎉 New Features
- Support cache_stopped_nodes on Azure (#21747)
- AWS Cloudwatch support (#21523) 

💫 Enhancements
- Improved documentation and standards around built in autoscaler node providers. (#22236, 22237)
- Improved KubeRay support (#22987, #22847, #22348, #22188)
- Remove redis requirement (#22083)

🔨 Fixes
- No longer print infeasible warnings for internal placement group resources. Placement groups which cannot be satisfied by the autoscaler still trigger warnings. (#22235)
- Default ami’s per AWS region are updated/fixed. (#22506)
- GCP node termination updated (#23101)
- Retry legacy k8s operator on monitor failure (#22792)
- Cap min and max workers for manually managed on-prem clusters (#21710)
- Fix initialization artifacts (#22570)
- Ensure initial scaleup with high upscaling_speed isn't limited. (#21953)

##### Ray Client

🎉 New Features:
- ray.init has consistent return value in client mode and driver mode #21355

💫Enhancements:
- Gets and puts are streamed to support arbitrary object sizes #22100, #22327

🔨 Fixes:
- Fix ray client object ref releasing in wrong context #22025


##### Ray Core

🎉 New Features
- RuntimeEnv:
  - Support setting timeout for runtime_env setup. (#23082)
  - Support setting pip_check and pip_version for runtime_env. (#22826, #23306)
  - env_vars will take effect when the pip install command is executed. (temporarily ineffective in conda) (#22730)
  - Support strongly-typed API ray.runtime.RuntimeEnv to define runtime env. (#22522)
  - Introduce [virtualenv](https://github.com/pypa/virtualenv) to isolate the pip type runtime env. (#21801,#22309)
- Raylet shares fate with the dashboard agent. And the dashboard agent will stay alive when it catches the port conflicts. (#22382,#23024)
- Enable dashboard in the minimal ray installation (#21896)
- Add task and object reconstruction status to ray memory cli tools(#22317)

🔨 Fixes
- Report only memory usage of pinned object copies to improve scaledown. (#22020)
- Scheduler: 
  - No spreading if a node is selected for lease request due to locality. (#22015)
  - Placement group scheduling: Non-STRICT_PACK PGs should be sorted by resource priority, size (#22762)
  - Round robin during spread scheduling (#21303)
- Object store:  
  - Increment ref count when creating an ObjectRef to prevent object from going out of scope (#22120)
  - Cleanup handling for nondeterministic object size during transfer (#22639)
  - Fix bug in fusion for spilled objects (#22571)
  - Handle IO worker failures correctly (#20752)
- Improve ray stop behavior (#22159)
- Avoid warning when receiving too much logs from a different job (#22102)
- Gcs resource manager bug fix and cl

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-1.12.0]_