# Ray 2.2.0 — Ray-2.2.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2022-12-13
- Version: 2.2.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.2.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.2.0

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 Jobs API is now generally available for submitting locally developed applications to a remote Ray Cluster
- **changed** — Ray Dashboard now shows CPU flame graphs of Ray workers and new metrics for memory usage
- **changed** — Out-Of-Memory (OOM) Monitor is now enabled by default
- **changed** — Ray Data dynamic block splitting is now enabled by default to address out-of-memory and performance issues with large files
- **added** — Add NumPy first path for Torch and TensorFlow Predictors in Ray AIR
- **fixed** — Fix MLflow database integrity error in Ray AIR
- **fixed** — Fix ResourceChangingScheduler dropping PlacementGroupFactory args in Ray AIR
- **fixed** — Fix bug passing 'raise' to FailureConfig in Ray AIR
- **fixed** — Fix reserved CPU warning if no CPUs are used in Ray AIR
- **deprecated** — Deprecate Wandb mixin in Ray AIR
- **deprecated** — Deprecate Checkpoint.to_object_ref and Checkpoint.from_object_ref in Ray AIR
- **added** — Add select_columns() to select a subset of columns in Ray Data
- **added** — Add write_tfrecords() to write TFRecord files in Ray Data
- **added** — Support MongoDB data source in Ray Data
- **added** — Add from_torch() to create dataset from Torch dataset in Ray Data
- **added** — Add from_tf() to create dataset from TensorFlow dataset in Ray Data
- **added** — Allow to set batch_size in BatchMapper in Ray Data
- **added** — Support read/write from/to local node file system in Ray Data
- **fixed** — Fix iter_batches() to not return empty batch in Ray Data
- **added** — Add MosaicTrainer in Ray Train

#### Release Highlights

Ray 2.2 is a stability-focused release, featuring stability improvements across many Ray components. 

* [Ray Jobs API](https://docs.ray.io/en/releases-2.2.0/cluster/running-applications/job-submission/index.html#ray-jobs-api) is now GA. The Ray Jobs API allows you to submit locally developed applications to a remote Ray Cluster for execution. It simplifies the experience of packaging, deploying, and managing a Ray application.
* [Ray Dashboard](https://docs.ray.io/en/releases-2.2.0/ray-core/ray-dashboard.html#ray-dashboard) has received a number of improvements, such as the ability to see cpu flame graphs of your Ray workers and new metrics for memory usage.
* The [Out-Of-Memory (OOM) Monitor](https://docs.ray.io/en/releases-2.2.0/ray-core/scheduling/ray-oom-prevention.html) is now enabled by default. This will increase the stability of memory-intensive applications on top of Ray.
* [Ray Data] we’ve heard numerous users report that when files are too large, Ray Data can have out-of-memory or performance issues. In this release, we’re enabling [dynamic block splitting](https://docs.ray.io/en/releases-2.2.0/data/dataset-internals.html#execution-memory) by default, which will address the above issues by avoiding holding too much data in memory.

#### Ray Libraries

##### Ray AIR

🎉 New Features:
* Add a NumPy first path for Torch and TensorFlow Predictors (#28917)

💫Enhancements:
* Suppress "NumPy array is not writable" error in torch conversion (#29808)
* Add node rank and local world size info to session (#29919)

🔨 Fixes:
* Fix MLflow database integrity error (#29794)
* Fix ResourceChangingScheduler dropping PlacementGroupFactory args (#30304)
* Fix bug passing 'raise' to FailureConfig (#30814)
* Fix reserved CPU warning if no CPUs are used (#30598)

📖Documentation:
* Fix examples and docs to specify batch_format in BatchMapper (#30438)

🏗 Architecture refactoring:
* Deprecate Wandb mixin (#29828)
* Deprecate Checkpoint.to_object_ref and Checkpoint.from_object_ref (#30365)

##### Ray Data Processing

🎉 New Features:
* Support all PyArrow versions released by Apache Arrow (#29993, #29999)
* Add `select_columns()` to select a subset of columns (#29081)
* Add `write_tfrecords()` to write TFRecord files (#29448)
* Support MongoDB data source (#28550)
* Enable dynamic block splitting by default (#30284)
* Add `from_torch()` to create dataset from Torch dataset (#29588)
* Add `from_tf()` to create dataset from TensorFlow dataset (#29591)
* Allow to set `batch_size` in `BatchMapper` (#29193)
* Support read/write from/to local node file system (#29565)

💫Enhancements:
* Add `include_paths` in `read_images()` to return image file path (#30007)
* Print out Dataset statistics automatically after execution (#29876)
* Cast tensor extension type to opaque object dtype in `to_pandas()` and `to_dask()` (#29417)
* Encode number of dimensions in variable-shaped tensor extension type (#29281)
* Fuse AllToAllStage and OneToOneStage with compatible remote args (#29561)
* Change `read_tfrecords()` output from Pandas to Arrow format (#30390)
* Handle all Ray errors in task compute strategy (#30696)
* Allow nested Chain preprocessors (#29706)
* Warn user if missing columns and support `str` exclude in `Concatenator` (#29443)
* Raise ValueError if preprocessor column doesn't exist (#29643)

🔨 Fixes:
* Support custom resource with remote args for `random_shuffle()` (#29276)
* Support custom resource with remote args for `random_shuffle_each_window()` (#29482)
* Add PublicAPI annotation to preprocessors (#29434)
* Tensor extension column concatenation fixes (#29479)
* Fix `iter_batches()` to not return empty batch (#29638)
* Change `map_batches()` to fetch input blocks on-demand (#29289)
* Change `take_all()` to not accept limit argument (#29746)
* Convert between block and batch correctly for `map_groups()` (#30172)
* Fix `stats()` call causing Dataset

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