# Ray 1.8.0 — Ray-1.8.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2021-11-02
- Version: 1.8.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-1.8.0
- Permalink: https://whatsnew.fyi/product/ray/releases/1.8.0

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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- **changed** — Ray SGD has been rebranded to Ray Train
- **added** — Ray Datasets is now in beta with new integration with Ray Train for scalable ML ingest in distributed training
- **added** — Apple Silicon (M1 Macs) is now supported
- **added** — Fake multi-node mode for autoscaler testing
- **changed** — Use async rpc for remote call and actor creation in Ray Client
- **changed** — Eagerly install job-level runtime_env in Ray Core
- **fixed** — Fixed resource demand reporting for infeasible 1-CPU tasks
- **fixed** — Fixed printing Python stack trace in Python worker
- **fixed** — Fixed macOS security popups
- **fixed** — Fixed thread safety issues for coreworker
- **fixed** — Fixed placement group performance and resource leaking issues
- **changed** — Improved unschedulable task warning messages by integrating with the autoscaler
- **changed** — Improved Windows support
- **fixed** — Fixed runtime_env issues
- **added** — Add support for repeating and rewindowing a DatasetPipeline
- **added** — Add .iter_epochs() API for iterating over epochs in a DatasetPipeline
- **added** — Add support for zipping two datasets together
- **added** — Transformation operations are now cancelled when one fails or the entire workload is killed
- **added** — Expose from_pandas()/to_pandas() APIs that accept and return plain Pandas DataFrames
- **added** — Allow n-step > 1 and prioritized replay for R2D2 and RNNSAC agents in RLlib

##### Highlights
- Ray SGD has been rebranded to Ray Train! The new documentation landing page can be found [here](https://docs.ray.io/en/master/train/train.html).
- Ray Datasets is now in beta! The beta release includes a new integration with Ray Train yielding scalable ML ingest for distributed training. Check out the docs [here](https://docs.ray.io/en/master/data/dataset.html), try it out for your ML ingest and batch inference workloads, and let us know how it goes!
- This Ray release supports Apple Silicon (M1 Macs). [Check out the installation instructions for more information!](https://docs.ray.io/en/master/installation.html#apple-silicon-support)
##### Ray Autoscaler
🎉 New Features:
- Fake multi-node mode for autoscaler testing (#18987) 

💫Enhancements: 
- Improve unschedulable task warning messages by integrating with the autoscaler (#18724)

##### Ray Client
💫Enhancements
- Use async rpc for remote call and actor creation (#18298)

##### Ray Core
💫Enhancements
- Eagerly install job-level runtime_env (#19449, #17949)
 
🔨 Fixes:
- Fixed resource demand reporting for infeasible 1-CPU tasks (#19000)
- Fixed printing Python stack trace in Python worker (#19423)
- Fixed macOS security popups (#18904)
- Fixed thread safety issues for coreworker (#18902, #18910, #18913 #19343)
- Fixed placement group performance and resource leaking issues (#19277, #19141, #19138, #19129, #18842, #18652)
- Improve unschedulable task warning messages by integrating with the autoscaler (#18724)
- Improved Windows support (#19014, #19062, #19171, #19362)
- Fix runtime_env issues (#19491, #19377, #18988)

##### Ray Data
Ray Datasets is now in beta! The beta release includes a new integration with Ray Train yielding scalable ML ingest for distributed training. It supports repeating and rewindowing pipelines, zipping two pipelines together, better cancellation of Datasets workloads, and many performance improvements. Check out the docs [here](https://docs.ray.io/en/master/data/dataset.html), try it out for your ML ingest and batch inference workloads, and let us know how it goes!

🎉 New Features:
- Ray Train integration (#17626)
- Add support for repeating and rewindowing a DatasetPipeline (#19091)
- .iter_epochs() API for iterating over epochs in a DatasetPipeline (#19217)
- Add support for zipping two datasets together (#18833)
- Transformation operations are now cancelled when one fails or the entire workload is killed (#18991)
- Expose from_pandas()/to_pandas() APIs that accept/return plain Pandas DataFrames (#18992)
- Customize compression, read/write buffer size, metadata, etc. in the IO layer (#19197)
- Add spread resource prefix for manual round-robin resource-based task load balancing

💫Enhancements:
- Minimal rows are now dropped when doing an equalized split (#18953)
- Parallelized metadata fetches when reading Parquet datasets (#19211)

🔨 Fixes:
- Tensor columns now properly support table slicing (#19534)
- Prevent Datasets tasks from being captured by Ray Tune placement groups (#19208)
- Empty datasets are properly handled in most transformations (#18983)

🏗 Architecture refactoring:
- Tensor dataset representation changed to a table with a single tensor column (#18867)

##### RLlib

🎉 New Features:
- Allow n-step > 1 and prioritized replay for R2D2 and RNNSAC agents. (18939)

🔨 Fixes:
- Fix memory leaks in TF2 eager mode. (#19198)
- Faster worker spaces inference if specified through configuration. (#18805)
- Fix bug for complex obs spaces containing Box([2D shape]) and discrete components. (#18917)
- Torch multi-GPU stats not protected against race conditions. (#18937)
- Fix SAC agent with dict space. (#19101)
- Fix A3C/IMPALA in multi-agent setting. (#19100)

🏗 Architecture refactoring:
- Unify results dictionary returned from Trainer.train() across agents regardless of (tf or pytorch, multi-agent, multi-gpu, or algos that use >1 SGD iterations, e.g. ppo

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-1.8.0]_