# Ray 1.3.0 — Ray-1.3.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2021-04-22
- Version: 1.3.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-1.3.0
- Permalink: https://whatsnew.fyi/product/ray/releases/1.3.0

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'.

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- **added** — Ray's scalability limits are now tested and published with each release
- **added** — Ray Client is now usable by default with any Ray cluster started by the Ray Cluster Launcher
- **added** — Ray-native Python-based collective communication primitives for Ray clusters with distributed CPUs or GPUs (alpha)
- **added** — Object spilling is turned on by default
- **added** — Dask-on-Ray and Spark-on-Ray are fully ready to use
- **added** — Dask-on-Ray is now compatible with Dask 2021.4.0
- **added** — Dask-on-Ray now works natively with dask.persist()
- **added** — R2D2 implementation for torch and tf in RLlib
- **added** — PlacementGroup support for all RLlib algorithms via Trainer.default_resource_request
- **added** — Multi-GPU support for tf-DQN/PG/A2C in RLlib
- **added** — HEBOSearcher in Tune
- **changed** — Ray is now using C++14
- **changed** — Enable Ray client server by default
- **changed** — Locality-aware scheduling is turned on by default
- **changed** — Moved from centralized GCS-based object directory protocol to decentralized owner-to-owner protocol for better cluster scalability
- **changed** — Node-type max workers defaults to infinity
- **changed** — rllib rollout runs in parallel by default via Trainer's evaluation worker set
- **fixed** — Worker nodes no longer killed on autoscaler failure
- **fixed** — Fixed high CPU breaking raylets with heartbeat missing errors
- **fixed** — Fixed high CPU issues from raylet during object transfer
- **deprecated** — lru_evict flag is now deprecated in favor of using object spilling

#### Release v1.3.0 Notes

##### Highlights
* We are now testing and publishing Ray's scalability limits with each release, see: https://github.com/ray-project/ray/tree/releases/1.3.0/benchmarks
* Ray Client is now usable by default with any Ray cluster started by the Ray Cluster Launcher. 

##### Ray Cluster Launcher

###### 💫Enhancements: 
* Observability improvements (#14816, #14608)
* Worker nodes no longer killed on autoscaler failure (#14424)
* Better validation for min_workers and max_workers (#13779)
* Auto detect memory resource for AWS and K8s (#14567)
* On autoscaler failure, propagate error message to drivers (#14219)
* Avoid launching GPU nodes when the workload only has CPU tasks (#13776)
* Autoscaler/GCS compatibility (#13970, #14046, #14050)
* Testing (#14488, #14713)
* Migration of configs to multi-node-type format (#13814, #14239)
* Better config validation (#14244, #13779)
* Node-type max workers defaults infinity (#14201)

###### 🔨 Fixes:
* AWS configuration (#14868, #13558, #14083, #13808)
* GCP configuration (#14364, #14417)
* Azure configuration (#14787, #14750, #14721)
* Kubernetes (#14712, #13920, #13720, #14773, #13756, #14567, #13705, #14024, #14499, #14593, #14655)
* Other (#14112, #14579, #14002, #13836, #14261, #14286, #14424, #13727, #13966, #14293, #14293, #14718, #14380, #14234, #14484)

##### Ray Client

###### 💫Enhancements: 
* Version checks for Python and client protocol (#13722, #13846, #13886, #13926, #14295) 
* Validate server port number (#14815) 
* Enable Ray client server by default (#13350, #13429, #13442) 
* Disconnect ray upon client deactivation (#13919)
* Convert Ray objects to Ray client objects (#13639)
* Testing (#14617, #14813, #13016, #13961, #14163, #14248, #14630, #14756, #14786)
* Documentation (#14422, #14265)

###### 🔨 Fixes: 

* Hook runtime context (#13750) 
* Fix mutual recursion (#14122)
* Set gRPC max message size (#14063)
* Monitor stream errors (#13386)
* Fix dependencies (#14654)
* Fix `ray.get` ctrl-c (#14425)
* Report error deserialization errors (#13749)
* Named actor refcounting fix (#14753)
* RayTaskError serialization (#14698) 
* Multithreading fixes (#14701)

##### Ray Core

###### 🎉 New Features:
* We are now testing and publishing Ray's scalability limits with each release. Check out https://github.com/ray-project/ray/tree/releases/1.3.0/benchmarks. 
* [alpha] Ray-native Python-based collective communication primitives for Ray clusters with distributed CPUs or GPUs. 

###### 🔨 Fixes:
* Ray is now using c++14. 
* Fixed high CPU breaking raylets with heartbeat missing errors (#13963, #14301)
* Fixed high CPU issues from raylet during object transfer (#13724)
* Improvement in placement group APIs including better Java support (#13821, #13858, #13582, #15049, #13821)

##### Ray Data Processing

###### 🎉 New Features:
* Object spilling is turned on by default. Check out the [documentation](https://docs.ray.io/en/master/memory-management.html#object-spilling).
* Dask-on-Ray and Spark-on-Ray are fully ready to use. Please [try them out](https://docs.ray.io/en/master/raydp.html) and give us feedback!
* Dask-on-Ray is now compatible with Dask 2021.4.0.
* Dask-on-Ray now works natively with [`dask.persist()`](https://docs.dask.org/en/latest/api.html#dask.persist).

###### 🔨 Fixes:
* Various improvements in object spilling and memory management layer to support large scale data processing (#13649, #14149, #13853, #13729, #14222, #13781, #13737, #14288, #14578, #15027)
* `lru_evict` flag is now deprecated. Recommended solution now is to use object spilling. 

###### 🏗 Architecture refactoring:
* Various architectural improvements in object spilling and memory management. For more details, check out the [whitepaper](https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/edit#heading=h.61xcnifjkb6v).
* Locality-aware scheduling is turned on by default.
* M

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