# Ray 1.1.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2020-12-24
- Version: 1.1.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-1.1.0
- Permalink: https://whatsnew.fyi/product/ray/releases/1.1.0

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'.

---

- **added** — Progress towards supporting a Ray client
- **added** — Descendent tasks are cancelled when the calling task is cancelled
- **fixed** — Improved object broadcast robustness
- **changed** — Improved placement group support
- **added** — SUMO simulator integration
- **added** — SlateQ Algorithm added for PyTorch
- **added** — MAML extension for all Models, except recurrent ones
- **added** — Curiosity Exploration Module for tf1.x/2.x/eager
- **added** — Minimal JAXModelV2 example
- **fixed** — Fix RNN learning for tf2.x/eager
- **changed** — LSTM prev-action/prev-reward settable separately and prev-actions are now one-hot'd
- **fixed** — PyTorch LR schedule not working
- **fixed** — Various PyTorch GPU bug fixes
- **fixed** — SAC loss not using prio. replay weights in critic's loss term
- **fixed** — Fix epsilon-greedy Exploration for nested action spaces
- **changed** — Trajectory View API on by default (faster PG-type algos by ~20%)
- **added** — Loggers can now be passed as objects to tune.run with new ExperimentLogger abstraction
- **changed** — Tune verbosity refactored into four levels: 0: Silent, 1: Only experiment-level logs, 2: General trial-level logs, 3: Detailed trial-level logs
- **changed** — Docker and Kubernetes autoscaling environments are detected automatically, automatically utilizing the correct checkpoint/log syncing tools

#### Ray 1.1.0
##### Ray Core
###### 🎉 New Features:
- Progress towards supporting a Ray client
- Descendent tasks are cancelled when the calling task is cancelled
###### 🔨 Fixes:
- Improved object broadcast robustness
- Improved placement group support
###### 🏗 Architecture refactoring:
- Progress towards the new scheduler backend

##### RLlib
###### 🎉 New Features:
- SUMO simulator integration (rllib/examples/simulators/sumo/). Huge thanks to Lara Codeca! (#11710)
- SlateQ Algorithm added for PyTorch. Huge thanks to Henry Chen! (#11450)
- MAML extension for all Models, except recurrent ones. (#11337)
- Curiosity Exploration Module for tf1.x/2.x/eager. (#11945)
- Minimal JAXModelV2 example. (#12502)
###### 🔨 Fixes:
- Fix RNN learning for tf2.x/eager. (#11720)
- LSTM prev-action/prev-reward settable separately and prev-actions are now one-hot’d. (#12397)
- PyTorch LR schedule not working. (#12396)
- Various PyTorch GPU bug fixes. (#11609)
- SAC loss not using prio. replay weights in critic’s loss term. (#12394)
- Fix epsilon-greedy Exploration for nested action spaces. (#11453)
###### 🏗 Architecture refactoring:
- Trajectory View API on by default (faster PG-type algos by ~20% (e.g. PPO on Atari)). (#11717, #11826, #11747, and #11827)

##### Tune
###### 🎉 New Features:
- Loggers can now be passed as objects to tune.run. The new ExperimentLogger abstraction was introduced for all loggers, making it much easier to configure logging behavior. (#11984, #11746, #11748, #11749)
- The tune verbosity was refactored into four levels: 0: Silent, 1: Only experiment-level logs, 2: General trial-level logs, 3: Detailed trial-level logs (default) (#11767, #12132, #12571)
- Docker and Kubernetes autoscaling environments are detected automatically, automatically utilizing the correct checkpoint/log syncing tools (#12108)
- Trainables can now easily leverage Tensorflow DistributedStrategy! (#11876)

###### 💫 Enhancements
- Introduced a new serialization debugging utility (#12142)
- Added a new lightweight Pytorch-lightning example (#11497, #11585)
- The BOHB search algorithm can be seeded with a random state (#12160)
- The default anonymous metrics can be used automatically if a `mode` is set in tune.run (#12159).
- Added HDFS as Cloud Sync Client (#11524)
- Added xgboost_ray integration (#12572)
- Tune search spaces can now be passed to search algorithms on initialization, not only via tune.run (#11503)
- Refactored and added examples (#11931)
- Callable accepted for register_env (#12618)
- Tune search algorithms can handle/ignore infinite and NaN numbers (#11835)
- Improved scalability for experiment checkpointing (#12064)
- Nevergrad now supports points_to_evaluate (#12207)
- Placement group support for distributed training (#11934)

###### 🔨 Fixes:
- Fixed with_parameters behavior to avoid serializing large data in scope (#12522)
- TBX logger supports None (#12262)
- Better error when `metric` or `mode` unset in search algorithms (#11646)
- Better warnings/exceptions for fail_fast='raise' (#11842)
- Removed some bottlenecks in trialrunner (#12476)
- Fix file descriptor leak by syncer  and Tensorboard (#12590, #12425)
- Fixed validation for search metrics (#11583)
- Fixed hyperopt randint limits (#11946)

##### Serve
###### 🎉 New Features:
- You can start backends in different conda environments! See more in the [dependency management doc](https://docs.ray.io/en/master/serve/advanced.html#dependency-management). (#11743)
- You can add a optional `reconfigure` method to your Servable to allow [reconfiguring](https://docs.ray.io/en/master/serve/advanced.html#reconfiguring-backends-experimental) backend replicas at runtime. (#11709)
###### 🔨Fixes:
- Set serve.start(http_host=None) to disable HTTP servers. If you are only using ServeHandle, this option lowers resource usage. (#11627)
- Flask requests will no longer create reference cycles. This means peak memory usage

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-1.1.0]_