# Ray 1.2.0 — Release ray-1.2.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2021-02-13
- Version: 1.2.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-1.2.0
- Permalink: https://whatsnew.fyi/product/ray/releases/1.2.0

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 client is now in beta
- **added** — XGBoost-Ray is now in beta
- **added** — Ray's C++ support is now in beta
- **added** — Object spilling is now available as an alpha feature
- **added** — New autoscaler output format in monitor.log
- **added** — Piping autoscaler events to driver logs
- **added** — Fast Attention Nets using the trajectory view API
- **added** — Attention Nets full PyTorch support
- **added** — Attention Nets support auto-wrapping around default or custom models with use_attention=True
- **added** — New Offline RL Algorithm CQL based on SAC
- **added** — MAML discrete actions support
- **added** — Support Atari framestacking via the trajectory view API
- **added** — Support for D4RL environments and benchmarks
- **added** — Preliminary work on JAX support
- **changed** — Full support of ray.autoscaler.sdk.request_resources() API
- **changed** — Placement groups now bypass max launch limit
- **changed** — Ray Tune now uses ray.cloudpickle underneath the hood allowing you to checkpoint large models over 4GB
- **changed** — Ray Serve backends now accept a Starlette request object instead of a Flask request object
- **changed** — Ray Serve backends now have the option of returning a Starlette Response object
- **added** — Ray Tune has updated and improved its integration with MLflow

#### Release v1.2.0 Notes

##### Highlights

* Ray client is now in beta! Check out more details here: https://docs.ray.io/en/master/ray-client.html
XGBoost-Ray is now in beta! Check out more details about this project at https://github.com/ray-project/xgboost_ray.
* Check out the Serve migration guide: https://docs.google.com/document/d/1CG4y5WTTc4G_MRQGyjnb_eZ7GK3G9dUX6TNLKLnKRAc/edit
* Ray’s C++ support is now in beta: https://docs.ray.io/en/master/#getting-started-with-ray
* An alpha version of object spilling is now available: https://docs.ray.io/en/master/memory-management.html#object-spilling

##### Ray Autoscaler

###### 🎉 New Features:

* A new autoscaler output format in monitor.log (#12772, #13561) 
* Piping autoscaler events to driver logs (#13434)

###### 💫Enhancements
* Full support of ray.autoscaler.sdk.request_resources() API (https://docs.ray.io/en/master/cluster/autoscaling.html?highlight=request_resources#ray.autoscaler.sdk.request_resources) .
* Make placement groups bypass max launch limit (#13089)
* [K8s] Retry getting home directory in command runner. (#12925)
* [docker] Pull if image is not present (#13136)
* [Autoscaler] Ensure ubuntu is owner of docker host mount folder (#13579)
###### 🔨 Fixes:
* Many autoscaler bug fixes (#12952, #12689, #13058, #13671, #13637, #13588, #13505, #13154, #13151, #13138, #13008, #12980, #12918, #12829, #12714, #12661, #13567, #13663, #13623, #13437, #13498, #13472, #13392, #12514, #13325, #13161, #13129, #12987, #13410, #12942, #12868, #12866, #12865, #12098, #12609)

##### RLLib
###### 🎉 New Features:
* Fast Attention Nets (using the trajectory view API) (#12753).
* Attention Nets: Full PyTorch support (#12029).
* Attention Nets: Support auto-wrapping around default- or custom models by specifying “use_attention=True” in the model’s config. * * * This works completely analogously now to “use_lstm=True”. (#11698)
* New Offline RL Algorithm: CQL (based on SAC) (#13118).
* MAML: Discrete actions support (added CartPole mass test case).
* Support Atari framestacking via the trajectory view API (#13315).
* Support for D4RL environments/benchmarks (#13550).
* Preliminary work on JAX support (#13077, #13091).

###### 💫 Enhancements:
* Rollout lengths: Allow unit to be configured as “agent_steps” in multi-agent settings (default: “env_steps”) (#12420).
* TFModelV2: Soft-deprecate register_variables and unify var names wrt TorchModelV2 (#13339, #13363).

###### 📖 Documentation:
* Added documentation on Model building API (#13260, #13261).
* Added documentation for the trajectory view API. (#12718)
* Added documentation for SlateQ (#13266).
* Readme.md documentation for almost all algorithms in rllib/agents (#12943, #13035).
* Type annotations for the “rllib/execution” folder (#12760, #13036).

###### 🔨 Fixes:
* MARWIL and BC: Add grad-clipping config option to stabilize learning (#13455).
* A3C: Solve PyTorch- and TF-eager async race condition between calling model and its value function (#13467).
* Various issues- and bug fixes (#12619, #12682, #12704, #12706, #12708, #12765, #12786, #12787, #12793, #12832, #12844, #12846, #12915, #12941, #13039, #13040, #13064, #13083, #13121, #13126, #13237, #13238, #13308, #13332, #13397, #13459, #13553).
###🏗 Architecture refactoring:
* Env directory has been cleaned up and is now divided in: Core part (rllib/env) with all basic env classes, and rllib/env/wrappers containing third-party wrapper classes (Atari, Unity3D, etc..) (#13082).

##### Tune

###### 🎉  New Features:

* Ray Tune has updated and improved its integration with MLflow. See [this blog post for details](https://medium.com/distributed-computing-with-ray/ray-mlflow-taking-distributed-machine-learning-applications-to-production-103f5505cb88) (#12840, #13301, #13533)

###### 💫 Enhancements

* Ray Tune now uses ray.cloudpickle underneath the hood, allowing you to checkpoint large models (>4GB) (#12958).
* U

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