# Ray 1.9.0 — Ray-1.9.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2021-12-03
- Version: 1.9.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-1.9.0
- Permalink: https://whatsnew.fyi/product/ray/releases/1.9.0

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 Train is now in beta
- **added** — Ray Docker images for multiple CUDA versions are provided with `-cuXXX` suffix to specify a version
- **deprecated** — ray-ml:cpu images are now deprecated; ray-ml images are only built for GPU
- **added** — Ray Datasets now supports groupby and aggregations
- **added** — Ray Job Submission server with CLI and SDK clients for submitting and monitoring Ray applications without active connection
- **added** — Support remote file storage for runtime_env
- **added** — Added garbage collection for runtime_env
- **added** — Graceful termination of Ray nodes prior to autoscaler scale down
- **changed** — Ray Clusters on AWS are now colocated in one Availability Zone to reduce costs and latency
- **fixed** — ray.put on a list of objects now returns a single object ref
- **fixed** — Fix runtime_env hanging issues
- **fixed** — Fix specifying runtime env in @ray.remote decorator with Ray Client
- **fixed** — Fix threaded actor, core worker, and named actor race conditions
- **added** — Ray Datasets support custom write paths
- **fixed** — Support custom CSV write options in Ray Datasets
- **added** — Workflow now supports events
- **added** — Allow user to specify metadata for workflow and steps
- **added** — Allow in-place run a step if the resources match
- **removed** — Removed global named actor APIs and global placement group APIs from Ray Java
- **added** — Added timeout parameter for Ray.Get() API in Ray Java

##### 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 `Tor

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-1.9.0]_