# Ray 2.3.0 — Ray-2.3.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2023-02-24
- Version: 2.3.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.3.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.3.0

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- **added** — Streaming backend for Ray Datasets in Developer Preview for terabyte-scale ML inference and training workloads
- **added** — New Information Architecture for Ray dashboard organized around user personas and workflows in Beta
- **added** — Ray-on-Spark is now available in Preview to launch Ray clusters on Databricks and Spark clusters
- **added** — Add set_preprocessor method to Checkpoint
- **added** — Introduce DatasetIterator
- **added** — Add per-epoch preprocessor
- **added** — Add TorchVisionPreprocessor
- **added** — Persist Dataset statistics automatically to log file
- **added** — Add zero-copy batch API for ds.map_batches()
- **changed** — Rename Keras callback and its parameters to be more descriptive
- **changed** — Rename WandB save_checkpoints to upload_checkpoints
- **changed** — Rename DLPredictor.call_model tensor parameter to inputs
- **changed** — Lazy execution by default for Ray Datasets
- **changed** — Introduce streaming execution backend
- **changed** — Use Ray actors instead of multiprocessing for WandbLoggerCallback
- **changed** — Change default NCCL_SOCKET_IFNAME to blacklist veth
- **changed** — Change ScalingConfig to be optional for DataParallelTrainers if already in Tuner param_space
- **deprecated** — Deprecate MlflowTrainableMixin in favor of setup_mlflow() function
- **fixed** — Fix and improve support for HDFS remote storage
- **fixed** — Fix Imputer failing with categorical dtype

#### Release Highlights



* The streaming backend for Ray Datasets is in Developer Preview. It is designed to enable terabyte-scale ML inference and training workloads. Please contact us if you'd like to try it out on your workload, or you can find the preview guide here: https://docs.google.com/document/d/1BXd1cGexDnqHAIVoxTnV3BV0sklO9UXqPwSdHukExhY/edit
* New Information Architecture (**Beta**): We’ve restructured the [Ray dashboard](https://docs.ray.io/en/master/ray-core/ray-dashboard.html) to be organized around user personas and workflows instead of entities. 
* Ray-on-Spark is now available (Preview)!: You can launch Ray clusters on Databricks and Spark clusters and run Ray applications. Check out the [documentation](https://docs.ray.io/en/releases-2.3.0/cluster/vms/user-guides/community/spark.html) to learn more.

#### Ray Libraries


##### Ray AIR

💫Enhancements:



* Add `set_preprocessor` method to `Checkpoint` (#31721)
* Rename Keras callback and its parameters to be more descriptive (#31627)
* Deprecate MlflowTrainableMixin in favor of setup_mlflow() function (#31295)
* W&B
    * Have train_loop_config logged as a config (#31901)
    * Allow users to exclude config values with WandbLoggerCallback (#31624)
    * Rename WandB `save_checkpoints` to `upload_checkpoints` (#31582)
    * Add hook to get project/group for W&B integration (#31035, 31643)
    * Use Ray actors instead of multiprocessing for WandbLoggerCallback (#30847)
    * Update `WandbLoggerCallback` example (#31625)
* Predictor
    * Place predictor kwargs in object store (#30932)
    * Delegate BatchPredictor stage fusion to Datasets (#31585)
    * Rename `DLPredictor.call_model` `tensor` parameter to `inputs` (#30574)
    * Add `use_gpu` to `HuggingFacePredictor` (#30945)
* Checkpoints
    * Various `Checkpoint` improvements (#30948)
    * Implement lazy checkpointing for same-node case (#29824)
    * Automatically strip "module." from state dict (#30705)
    * Allow user to pass model to `TensorflowCheckpoint.get_model` (#31203)

🔨 Fixes:



* Fix and improve support for HDFS remote storage. (#31940)
* Use specified Preprocessor configs when using stream API. (#31725)
* Support nested Chain in BatchPredictor (#31407)

📖Documentation:



* Restructure API References (#32535)
* API Deprecations (#31777, #31867)
* Various fixes to docstrings, documentation, and examples (#30782, #30791)

🏗 Architecture refactoring:



* Use NodeAffinitySchedulingPolicy for scheduling (#32016)
* Internal resource management refactor (#30777, #30016)


##### Ray Data Processing

🎉 New Features:



* Lazy execution by default (#31286)
* Introduce streaming execution backend (#31579) 
* Introduce DatasetIterator (#31470)
* Add per-epoch preprocessor (#31739)
* Add TorchVisionPreprocessor (#30578)
* Persist Dataset statistics automatically to log file (#30557)

💫Enhancements:



* Async batch fetching for map_batches (#31576)
* Add informative progress bar names to map_batches (#31526)
* Provide an size bytes estimate for mongodb block (#31930)
* Add support for dynamic block splitting to actor pool (#31715)
* Improve str/repr of Dataset to include execution plan (#31604)
* Deal with nested Chain in BatchPredictor (#31407)
* Allow MultiHotEncoder to encode arrays (#31365)
* Allow specify batch_size when reading Parquet file (#31165)
* Add zero-copy batch API for `ds.map_batches()` (#30000)
* Text dataset should save texts in ArrowTable format (#30963)
* Return ndarray dicts for single-column tabular datasets (#30448)
* Execute randomize_block_order eagerly if it's the last stage for ds.schema() (#30804)

🔨 Fixes:



* Don't drop first dataset when peeking DatasetPipeline (#31513)
* Handle np.array(dtype=object) constructor for ragged ndarrays (#31670)
* Emit warning when starting Dataset execution with no CPU resources available (#31574)
* Fix the bug of eagerly clearing

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-2.3.0]_