# Ray 1.0.1

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2020-11-10
- Version: 1.0.1
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-1.0.1
- Permalink: https://whatsnew.fyi/product/ray/releases/1.0.1

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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- **changed** — Autoscaler is now docker by default
- **added** — RLlib added documentation for Curiosity exploration module
- **added** — RLlib added RecSym environment wrapper
- **added** — RLlib added Kaggle's football environment multi-agent wrapper
- **fixed** — RLlib fixed GPU related issues for SAC
- **fixed** — RLlib fixed all example scripts to run on GPU
- **fixed** — RLlib lifted limitation on 2^31 timesteps
- **fixed** — RLlib fixed eval workers for ES and ARS
- **fixed** — RLlib fixed broken no-eager-no-workers mode
- **added** — RLlib added support for custom MultiAction distributions
- **changed** — RLlib no environment is created on driver local worker if not necessary
- **added** — RLlib added simple SampleCollector class for Trajectory View API
- **changed** — Serve will error when serve_client is serialized
- **changed** — Serve serve_client.get_handle() now gets a handle to nearest node increasing scalability in distributed mode
- **added** — SGD now supports PyTorch Lightning integration
- **added** — SGD supports num_steps continue training
- **added** — SGD callback API for SGD+Tune
- **added** — Tune added population-based bandits algorithm
- **added** — Tune added tune.with_parameters() wrapper function to pass arbitrary objects through the object store to trainables
- **changed** — Tune now errors by default if a result dict does not include the optimization metric as a key, controlled by TUNE_DISABLE_STRICT_METRIC_CHECKING

#### Ray 1.0.1

Ray 1.0.1 is now officially released!

##### Highlights

* If you're migrating from Ray < 1.0.0, be sure to check out the [1.0 Migration Guide](https://github.com/ray-project/ray/discussions/11482).
* Autoscaler is now **docker by default**.
* RLLib features multiple new environments.
* Tune supports population based bandits, checkpointing in Docker, and multiple usability improvements. 
* SGD supports PyTorch Lightning 
* All of Ray's components and libraries have improved performance, scalability, and stability.

##### Core
* [1.0 Migration Guide](https://github.com/ray-project/ray/discussions/11482).
* Many bug fixes and optimizations in GCS.
* Polishing of the Placement Group API. 
* Improved Java language support

##### RLlib 
* Added documentation for Curiosity exploration module (#11066).
* Added RecSym environment wrapper (#11205).
* Added Kaggle’s football environment (multi-agent) wrapper (#11249).
* Multiple bug fixes: GPU related fixes for SAC (#11298), MARWIL, all example scripts run on GPU (#11105), lifted limitation on 2^31 timesteps (#11301), fixed eval workers for ES and ARS (#11308), fixed broken no-eager-no-workers mode (#10745).
* Support custom MultiAction distributions (#11311).
* No environment is created on driver (local worker) if not necessary (#11307).
* Added simple SampleCollector class for Trajectory View API (#11056).
* Code cleanup: Docstrings and type annotations for Exploration classes (#11251), DQN (#10710), MB-MPO algorithm, SAC algorithm (#10825).


##### Serve
* API: Serve will error when `serve_client` is serialized. (#11181)
* Performance: `serve_client.get_handle("endpoint")` will now get a handle to nearest node, increasing scalability in distributed mode. (#11477)
* Doc: Added FAQ page and updated architecture page (#10754, #11258)
* Testing: New distributed tests and benchmarks are added (#11386)
* Testing: Serve now run on Windows (#10682)

##### SGD
* Pytorch Lightning integration is now supported (#11042)
* Support `num_steps` continue training (#11142)
* Callback API for SGD+Tune  (#11316)

##### Tune
* New Algorithm: Population-based Bandits (#11466)
* `tune.with_parameters()`, a wrapper function to pass arbitrary objects through the object store to trainables (#11504)
* Strict metric checking - by default, Tune will now error if a result dict does not include the optimization metric as a key. You can disable this with TUNE_DISABLE_STRICT_METRIC_CHECKING (#10972)
* Syncing checkpoints between multiple Docker containers on a cluster is now supported with the `DockerSyncer`  (#11035)
* Added type hints (#10806)
* Trials are now dynamically created (instead of created up front) (#10802)
* Use `tune.is_session_enabled()` in the Function API to toggle between Tune and non-tune code  (#10840)
* Support hierarchical search spaces for hyperopt (#11431)
* Tune function API now also supports `yield` and `return` statements (#10857)
* Tune now supports callbacks with `tune.run(callbacks=...` (#11001)
* By default, the experiment directory will be dated (#11104)
* Tune now supports `reuse_actors` for function API, which can largely accelerate tuning jobs.

##### Thanks

We thank all the contributors for their contribution to this release!

@acxz, @Gekho457, @allenyin55, @AnesBenmerzoug, @michaelzhiluo, @SongGuyang, @maximsmol, @WangTaoTheTonic, @Basasuya, @sumanthratna, @juliusfrost, @maxco2, @Xuxue1, @jparkerholder, @AmeerHajAli, @raulchen, @justinkterry, @herve-alanaai, @richardliaw, @raoul-khour-ts, @C-K-Loan, @mattearllongshot, @robertnishihara, @internetcoffeephone, @Servon-Lee, @clay4444, @fangyeqing, @krfricke, @ffbin, @akotlar, @rkooo567, @chaokunyang, @PidgeyBE, @kfstorm, @barakmich, @amogkam, @edoakes, @ashione, @jseppanen, @ttumiel, @desktable, @pcmoritz, @ingambe, @ConeyLiu, @wuisawesome, @fyrestone, @oliverhu, @ericl, @weepingwillowben, @rkube, @alanwguo, @architkulkarni, @lasagnaphil, @rohitrawat, @Tho

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