# Ray 0.8.5 — Ray 0.8.5

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2020-05-07
- Version: 0.8.5
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-0.8.5
- Permalink: https://whatsnew.fyi/product/ray/releases/0.8.5

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** — You can now cancel remote tasks using the ray.cancel API
- **added** — Experimental support for recovering objects that were lost from the Ray distributed memory store by setting lineage_pinning_enabled: 1 in the internal config
- **changed** — PyTorch support in RLlib has reached parity with TensorFlow
- **changed** — Improved callbacks API in RLlib
- **changed** — Enable Ray distributed reference counting in RLlib
- **changed** — Search algorithms in Tune are refactored to make them easier to extend
- **deprecated** — max_concurrent argument in Tune search algorithms is deprecated
- **fixed** — TensorboardX errors are now handled safely in Tune
- **fixed** — Bug fix in PBT checkpointing in Tune
- **added** — New ZOOpt search algorithm added to Tune
- **added** — Add delete_endpoint and delete_backend APIs to Serve
- **changed** — Use dictionary to update backend config in Serve
- **changed** — Made serve clusters tolerant to process failures
- **added** — Added tutorials for serving models in Tensorflow/Keras, PyTorch, and Scikit-Learn in Serve
- **fixed** — Fix GPU Reservations in SLURM usage in SGD
- **changed** — Update learning rate scheduler stepping parameter in SGD
- **changed** — Make serialization of data creation optional in SGD
- **changed** — Automatic DDP wrapping is now optional in SGD

Highlight
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- You can now cancel remote tasks using the `ray.cancel` API. 
- PyTorch is now a first-class citizen in RLlib! We've achieved parity between TensorFlow and PyTorch.
- Did you struggle to find good example code for Ray ML libraries? We wrote more examples for Ray SGD and Ray Serve.
   - Ray serve: [Keras/Tensorflow](https://docs.ray.io/en/master/rayserve/tutorials/tensorflow-tutorial.html), [PyTorch](https://docs.ray.io/en/master/rayserve/tutorials/pytorch-tutorial.html), [Scikit-Learn](https://docs.ray.io/en/master/rayserve/tutorials/sklearn-tutorial.html).
   - Ray SGD: New [Semantic Segmentation](https://github.com/ray-project/ray/tree/master/python/ray/util/sgd/torch/examples/segmentation) and [HuggingFace GLUE Fine-tuning](https://github.com/ray-project/ray/tree/master/python/ray/util/sgd/torch/examples/transformers) Examples. 

Core
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- Task cancellation is now available for locally submitted tasks. (#7699)
- Experimental support for recovering objects that were lost from the Ray distributed memory store. You can try this out by setting `lineage_pinning_enabled: 1` in the internal config. (#7733)

RLlib
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- PyTorch support has now reached parity with TensorFlow. (#7926, #8188, #8120, #8101, #8106, #8104, #8082, #7953, #7984, #7836, #7597, #7797)
- Improved callbacks API. (#6972)
- Enable Ray distributed reference counting. (#8037)
- Work towards customizable distributed training workflows. (#7958, #8077)

Tune
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- Documentation has improved with a new format. (#8083, #8201, #7716)
- Search algorithms are refactored to make them easier to extend, deprecating `max_concurrent` argument. (#7037, #8258, #8285)
- TensorboardX errors are now handled safely. (#8174)
- Bug fix in PBT checkpointing. (#7794)
- New ZOOpt search algorithm added. (#7960)

Serve
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- Improved APIs.
    - Add delete_endpoint and delete_backend. (#8252, #8256)
    - Use dictionary to update backend config. (#8202)
- Added overview section to the documentation.
- Added tutorials for serving models in Tensorflow/Keras, PyTorch, and Scikit-Learn.
- Made serve clusters tolerant to process failures. (#8116, #8008,#7970,#7936)

SGD
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- New Semantic Segmentation and HuggingFace GLUE Fine-tuning Examples. (#7792, #7825)
- Fix GPU Reservations in SLURM usage. (#8157)
- Update learning rate scheduler stepping parameter. (#8107)
- Make serialization of data creation optional. (#8027)
- Automatic DDP wrapping is now optional. (#7875)

Others Projects
----------------
- Progress towards the highly available and fault tolerant control plane. (#8144, #8119, #8145, #7909, #7949, #7771, #7557, #7675) 
- Progress towards the Ray streaming library. (#8044, #7827, #7955, #7961, #7348)
- Autoscaler improvement. (#8178, #8168, #7986, #7844, #7717)
- Progress towards Java support. (#8014)
- Progress towards the Window compatibility. (#8237, #8186)
- Progress towards cross language support. (#7711)


Thanks
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We thank the following contributors for their work on this release:

@simon-mo, @robertnishihara, @BalaBalaYi, @ericl, @kfstorm, @tirkarthi, @nflu, @ffbin, @chaokunyang, @ijrsvt, @pcmoritz, @mehrdadn, @sven1977, @iamhatesz, @nmatthews-asapp, @mitchellstern, @edoakes, @anabranch, @billowkiller, @eisber, @ujvl, @allenyin55, @yncxcw, @deanwampler, @DavidMChan, @ConeyLiu, @micafan, @rkooo567, @datayjz, @wizardfishball, @sumanthratna, @ashione, @marload, @stephanie-wang, @richardliaw, @jovany-wang, @MissiontoMars, @aannadi, @fyrestone, @JarnoRFB, @wumuzi520, @roireshef, @acxz, @gramhagen, @Servon-Lee, @ClarkZinzow, @mfitton, @maximsmol, @janblumenkamp, @istoica