# Ray 1.4.0 — Ray-1.4.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2021-06-07
- Version: 1.4.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-1.4.0
- Permalink: https://whatsnew.fyi/product/ray/releases/1.4.0

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** — Support Helm Chart for deploying Ray on Kubernetes
- **added** — Key Autoscaler metrics are now exported via Prometheus
- **changed** — Better error messages when a node fails to come online
- **fixed** — Stability and interface fixes for Kubernetes deployments
- **fixed** — Fixes to Azure NodeProvider
- **added** — Complete API parity with non-client mode
- **added** — Experimental ClientBuilder API
- **added** — Full Asyncio support
- **changed** — Keep Alive for Messages for long lived connections
- **changed** — Improved pickling error messages
- **fixed** — Client Disconnect can be called multiple times
- **fixed** — Client Reference Equality Check
- **added** — Namespaces support for Ray actors
- **fixed** — Support increment by arbitrary number with ray.util.metrics.Counter
- **fixed** — Various bug fixes for the placement group APIs including the GPU assignment bug
- **fixed** — Fixed a critical bug where the driver uses excessive memory usage when there are many objects in the cluster
- **fixed** — Dask on Ray and Modin can now be run with Ray client
- **added** — PyTorch multi-GPU support
- **added** — CQL TensorFlow support
- **added** — Task-settable Env/Curriculum Learning API

#### Release 1.4.0 Notes

##### Ray Autoscaler

###### 🎉 New Features:
* Support Helm Chart for deploying Ray on Kubernetes
* Key Autoscaler metrics are now exported via Prometheus!

###### 💫Enhancements
* Better error messages when a node fails to come online

###### 🔨 Fixes:
* Stability and interface fixes for Kubernetes deployments.
* Fixes to Azure NodeProvider

##### Ray Client

###### 🎉 New Features:
* Complete API parity with non-client mode
* Experimental ClientBuilder API (docs here)
* Full Asyncio support

###### 💫Enhancements
* Keep Alive for Messages for long lived connections
* Improved pickling error messages

###### 🔨 Fixes:
* Client Disconnect can be called multiple times
* Client Reference Equality Check
* Many bug fixes and tests for the complete ray API!

##### Ray Core

###### 🎉 New Features:
* Namespaces ([check out the docs](https://docs.ray.io/en/master/namespaces.html))! Note: this may be a breaking change if you’re using detached actors (set ray.init(namespace=””) for backwards compatible behavior). 

###### 🔨 Fixes:
* Support increment by arbitrary number with ray.util.metrics.Counter 
* Various bug fixes for the placement group APIs including the GPU assignment bug (#15049).

###### 🏗 Architecture refactoring:
* Increase the efficiency and robustness of resource reporting

##### Ray Data Processing

###### 🔨 Fixes:
* Various bug fixes for better stability (#16063, #14821, #15669, #15757, #15431, #15426, #15034, #15071, #15070, #15008, #15955)
* Fixed a critical bug where the driver uses excessive memory usage when there are many objects in the cluster (#14322).
* Dask on Ray and Modin can now be run with Ray client

###### 🏗 Architecture refactoring:
* Ray 100TB shuffle results: https://github.com/ray-project/ray/issues/15770
* More robust memory management subsystem is in progress (#15157, #15027)

##### RLlib

###### 🎉 New Features:
* PyTorch multi-GPU support (#14709, #15492, #15421).
* CQL TensorFlow support (#15841).
* Task-settable Env/Curriculum Learning API (#15740).
* Support for native tf.keras Models (no ModelV2 required) (#14684, #15273).
* Trainer.train() and Trainer.evaluate() can run in parallel (optional) (#15040, #15345).

###### 💫Enhancements and documentation:
* CQL: Bug fixes and confirmed MuJoCo benchmarks (#15814, #15603, #15761).
* Example for differentiable neural computer (DNC) network (#14844, 15939).
* Added support for int-Box action spaces. (#15012)
* DDPG/TD3/A[23]C/MARWIL/BC: Code cleanup and type annotations. (#14707).
* Example script for restoring 1 agent out of n 
* Examples for fractional GPU usage. (15334)
* Enhanced documentation page describing example scripts and blog posts (15763).
* Various enhancements/test coverage improvements: 15499, 15454, 15335, 14865, 15525, 15290, 15611, 14801, 14903, 15735, 15631, 

###### 🔨 Fixes:
* Memory Leak in multi-agent environment (#15815). Shoutout to Bam4d!
* DDPG PyTorch GPU bug. (#16133)
* Simple optimizer should not be used by default for tf+MA (#15365)
* Various bug fixes: #15762, 14843, 15042, 15427, 15871, 15132, 14840, 14386, 15014, 14737, 15015, 15733, 15737, 15736, 15898, 16118, 15020, 15218, 15451, 15538, 15610, 15326, 15295, 15762, 15436, 15558, 15937

###### 🏗 Architecture refactoring:
* Remove atari dependency (#15292).
* `Trainer._evaluate()` renamed to `Trainer.evaluate()` (backward compatible); `Trainer.evaluate()` can be called even w/o evaluation worker set, if `create_env_on_driver=True` (#15591).

##### Tune

###### 🎉 New Features:
* ASHA scheduler now supports save/restore. (#15438)
* Add HEBO to search algorithm shim function (#15468)
* Add SkoptSearcher/Bayesopt Searcher restore functionality (#15075)

###### 💫Enhancements:
* We now document scalability best practices (k8s, scalability thresholds). You can [find this here](https://docs.ray.io/en/master/tune/api_docs/scalability.html) (#14566)
*

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