# Ray 2.1.0 — Ray-2.1.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2022-11-08
- Version: 2.1.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.1.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.1.0

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'.

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- **added** — Ray Datasets read_images() API for loading image data
- **added** — Ray Datasets read_tfrecords() API to read TFRecord files
- **added** — Numpy-based API for user-defined functions in Preprocessor
- **added** — Ray Serve support for gRPC endpoint (alpha release)
- **added** — RLlib decision transformer (DT) algorithm
- **added** — RLlib on_episode_created() callback hook
- **added** — Learning rate schedule to SimpleQ and PG algorithms
- **added** — Ray OOM prevention (alpha release)
- **added** — Support dynamic generators as task return values
- **added** — Dashboard time series metrics support
- **added** — Dashboard export configuration files for Prometheus or Grafana
- **added** — Dashboard new progress bar in job detail view
- **added** — NumPy batch format to Preprocessor and BatchMapper
- **added** — Ragged tensor extension type
- **added** — KBinsDiscretizer Preprocessor
- **added** — FullyShardedDataParallel support to TorchTrainer
- **added** — Tuner.get_results() to retrieve results after restore
- **changed** — Auto-enable GPU for Predictors
- **changed** — Deprecate Checkpoint.to_object_ref and Checkpoint.from_object_ref
- **changed** — Deprecate legacy train/tune functions in favor of Session

#### Release Highlights

* Ray AI Runtime (AIR)
  * Better support for Image-based workloads.
    * Ray Datasets `read_images()` API for loading data.
    * Numpy-based API for user-defined functions in Preprocessor.
  * Ability to read TFRecord input.
    * Ray Datasets `read_tfrecords()` API to read TFRecord files.
* Ray Serve: 
  * Add support for gRPC endpoint (alpha release). Instead of using an HTTP server, Ray Serve supports gRPC protocol and users can bring their own schema for their use case.
* RLlib:
  * Introduce decision transformer (DT) algorithm.
  * New hook for callbacks with `on_episode_created()`.
  * Learning rate schedule to SimpleQ and PG.
* Ray Core:
  * Ray [OOM prevention](https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html) (alpha release).
  * Support dynamic generators as task return values.
* Dashboard:
  * Time series metrics support.
  * Export configuration files can be used in Prometheus or Grafana instances.
  * New progress bar in job detail view.

#### Ray Libraries

##### Ray AIR

💫Enhancements:
* Improve readability of training failure output (#27946,  #28333, #29143)
* Auto-enable GPU for Predictors (#26549)
* Add ability to create TorchCheckpoint from state dict (#27970)
* Add ability to create TensorflowCheckpoint from saved model/h5 format (#28474)
* Add attribute to retrieve URI from Checkpoint (#28731)
* Add all allowable types to WandB Callback (#28888)

🔨 Fixes:
* Handle nested metrics properly as scoring attribute (#27715)
* Fix serializability of Checkpoints (#28387, #28895, #28935)

📖Documentation:
* Miscellaneous updates to documentation and examples (#28067, #28002, #28189, #28306, #28361, #28364, #28631, #28800)

🏗 Architecture refactoring:
* Deprecate Checkpoint.to_object_ref and Checkpoint.from_object_ref (#28318)
* Deprecate legacy train/tune functions in favor of Session (#28856)

##### Ray Data Processing

🎉 New Features:
* Add read_images (#29177)
* Add read_tfrecords (#28430)
* Add NumPy batch format to Preprocessor and `BatchMapper` (#28418)
* Ragged tensor extension type (#27625)
* Add KBinsDiscretizer Preprocessor (#28389)

💫Enhancements:
* Simplify to_tf interface (#29028)
* Add metadata override and inference in `Dataset.to_dask()` (#28625)
* Prune unused columns before aggregate (#28556)
* Add Dataset.default_batch_format (#28434)
* Add partitioning parameter to read_ functions (#28413)
* Deprecate "native" batch format in favor of "default" (#28489)
* Support None partition field name (#28417)
* Re-enable Parquet sampling and add progress bar (#28021)
* Cap the number of stats kept in StatsActor and purge in FIFO order if the limit exceeded (#27964)
* Customized serializer for Arrow JSON ParseOptions in read_json (#27911)
* Optimize groupby/mapgroups performance (#27805)
* Improve size estimation of image folder data source (#27219)
* Use detached lifetime for stats actor (#25271)
* Pin _StatsActor to the driver node (#27765)
* Better error message for partition filtering if no file found (#27353)
* Make Concatenator deterministic (#27575)
* Change FeatureHasher input schema to expect token counts (#27523)
* Avoid unnecessary reads when truncating a dataset with `ds.limit()` (#27343)
* Hide tensor extension from UDFs (#27019)
* Add __repr__ to AIR classes (#27006)

🔨 Fixes:
* Add upper bound to pyarrow version check (#29674) (#29744)
* Fix map_groups to work with different output type (#29184)
* read_csv not filter out files by default (#29032)
* Check columns when adding rows to TableBlockBuilder (#29020)
* Fix the peak memory usage calculation (#28419)
* Change sampling to use same API as read Parquet (#28258)
* Fix column assignment in Concatenator for Pandas 1.2. (#27531)
* Doing partition filtering in reader constructor (#27156)
* Fix split ownership (#27149)

📖Documentation:
* Clarify dataset transformation. (#28482)
* Update map_batches do

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