# Ray 1.13.0 — Ray-1.13.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2022-06-09
- Version: 1.13.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-1.13.0
- Permalink: https://whatsnew.fyi/product/ray/releases/1.13.0

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** — Python 3.10 support is now in alpha on Linux and Mac
- **added** — Ray usage stats collection is now on by default, guarded by an opt-out prompt
- **added** — Ray Tune can now synchronize Trial data from worker nodes via the object store without rsync
- **added** — Ray Workflow comes with a new API and is integrated with Ray DAG
- **added** — Node affinity scheduling strategy
- **added** — Add metrics for disk and network I/O
- **added** — Add the ability to inject a setup hook for customization of runtime_env on init
- **added** — Add a utility to check GCS / Ray cluster health
- **added** — Datasets supports lineage serialization
- **added** — Datasets supports native Tensor views in map processing for pure-tensor datasets
- **added** — Datasets implemented push-based shuffle
- **added** — RLlib replay buffer API allowing users to define and configure custom buffers
- **added** — RLlib RolloutWorkers can be optionally recoverable after failure via recreate_failed_workers=True config flag
- **added** — RLlib POC for new TrainerConfig objects instead of python config dicts
- **changed** — Improve exponential backoff when connecting to the redis
- **changed** — Datasets aggregation computation is vectorized for performance improvement
- **changed** — Datasets bulk parquet file reading is optimized with the fast metadata provider
- **changed** — Datasets block processing uses more efficient move semantics
- **fixed** — Fix deadlock by switching to SimpleQueue on Python 3.7 and newer in async dataclient
- **deprecated** — Workflow step is deprecated

##### Highlights:

- Python 3.10 support is now in alpha.
- Ray [usage stats collection](https://docs.ray.io/en/master/cluster/usage-stats.html) is now on by default  (guarded by an opt-out prompt).
- Ray Tune can now synchronize Trial data from worker nodes via the object store (without rsync!)
- Ray Workflow comes with a new API and is integrated with Ray DAG.

##### Ray Autoscaler
💫Enhancements: 

- CI tests for KubeRay autoscaler integration (#23365, #23383, #24195) 
- Stability enhancements for KubeRay autoscaler integration (#23428)

🔨 Fixes: 

- Improved GPU support in KubeRay autoscaler integration (#23383)
- Resources scheduled with the node affinity strategy are not reported to the autoscaler (#24250)

##### Ray Client
💫Enhancements:

- Add option to configure ray.get with >2 sec timeout (#22165)
- Return `None` from internal KV for non-existent keys (#24058)

🔨 Fixes:

- Fix deadlock by switching to `SimpleQueue` on Python 3.7 and newer in async `dataclient` (#23995)

##### Ray Core
🎉 New Features:

- Ray [usage stats collection](https://docs.ray.io/en/master/cluster/usage-stats.html) is now on by default  (guarded by an opt-out prompt)
- Alpha support for python 3.10 (on Linux and Mac)
- Node affinity scheduling strategy (#23381)
- Add metrics for disk and network I/O (#23546)
- Improve exponential backoff when connecting to the redis (#24150)
- Add the ability to inject a setup hook for customization of runtime_env on init (#24036)
- Add a utility to check GCS / Ray cluster health (#23382)

🔨 Fixes:

- Fixed internal storage S3 bugs (#24167)
- Ensure "get_if_exists" takes effect in the decorator. (#24287)
- Reduce memory usage for Pubsub channels that do not require total memory cap (#23985)
- Add memory buffer limit in publisher for each subscribed entity (#23707)
- Use gRPC instead of socket for GCS client health check (#23939)
- Trim size of Reference struct (#23853)
- Enable debugging into pickle backend (#23854)

🏗 Architecture refactoring:

- Gcs storage interfaces unification (#24211)
- Cleanup pickle5 version check (#23885)
- Simplify options handling (#23882)
- Moved function and actor importer away from pubsub (#24132)
- Replace the legacy ResourceSet & SchedulingResources at Raylet (#23173)
- Unification of AddSpilledUrl and UpdateObjectLocationBatch RPCs (#23872)
- Save task spec in separate table  (#22650)


##### Ray Datasets
🎉 New Features:

- Performance improvement: the aggregation computation is vectorized (#23478)
- Performance improvement: bulk parquet file reading is optimized with the fast metadata provider (#23179)
- Performance improvement: more efficient move semantics for Datasets block processing (#24127)
- Supports Datasets lineage serialization (aka out-of-band serialization) (#23821, #23931, #23932)
- Supports native Tensor views in map processing for pure-tensor datasets (#24812)
- Implemented push-based shuffle (#24281)

🔨 Fixes:

- Documentation improvement: Getting Started page (#24860)
- Documentation improvement: FAQ (#24932)
- Documentation improvement: End to end examples (#24874)
- Documentation improvement: Feature guide - Creating Datasets (#24831)
- Documentation improvement: Feature guide - Saving Datasets (#24987)
- Documentation improvement: Feature guide - Transforming Datasets (#25033)
- Documentation improvement: Datasets APIs docstrings (#24949)
- Performance: fixed block prefetching (#23952) 
- Fixed zip() for Pandas dataset (#23532) 

🏗 Architecture refactoring:

- Refactored LazyBlockList (#23624)
- Added path-partitioning support for all content types (#23624)
- Added fast metadata provider and refactored Parquet datasource (#24094)

##### RLlib
🎉 New Features:

- Replay buffer API: First algorithms are using the new replay buffer API, allowing users to define and configure their own custom buffers or use RLlib’s built-in ones: SimpleQ, DQN (#24164, #22842, #235

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-1.13.0]_