# Ray 2.33.0 — Ray-2.33.0
- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2024-07-25
- Version: 2.33.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.33.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.33.0
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** — Add last exception to error message when GCS connection fails in ray.init()
- **fixed** — Add object back to memory store when object recovery is skipped
- **fixed** — Task status should start with PENDING_ARGS_AVAIL when retry
- **fixed** — Fix ObjectFetchTimedOutError
- **fixed** — Make working_dir support files created before 1980
- **fixed** — Allow full path in conda runtime env
- **fixed** — Fix worker launch time formatting in state api
- **deprecated** — Deprecate Dataset.get_internal_block_refs()
- **added** — Add read API for reading Databricks table with Delta Sharing
- **added** — Add support for objects to Arrow blocks
- **changed** — Change offsets to int64 and change to LargeList for ArrowTensorArray
- **changed** — Prevent from_pandas from combining input blocks
- **changed** — Update Dataset.count() to avoid unnecessarily keeping BlockRefs in-memory
- **changed** — Use Set to fix inefficient iteration over Arrow table columns
- **changed** — Add AWS Error UNKNOWN to list of retried write errors
- **changed** — Always print traceback for internal exceptions
- **changed** — Allow unknown estimate of operator output bundles and ProgressBar totals
- **changed** — Improve filesystem retry coverage
- **fixed** — Replace lambda mutable default arguments in Ray Data
- **changed** — Update run status and actor status for train runs
#### Ray Libraries
#### Ray Core
💫 Enhancements:
- Add "last exception" to error message when GCS connection fails in ray.init() (#46516)
🔨 Fixes:
- Add object back to memory store when object recovery is skipped (#46460)
- Task status should start with PENDING_ARGS_AVAIL when retry (#46494)
- Fix ObjectFetchTimedOutError ([#46562](https://github.com/ray-project/ray/pull/46562))
- Make working_dir support files created before 1980 ([#46634](https://github.com/ray-project/ray/pull/46634))
- Allow full path in conda runtime env. ([#45550](https://github.com/ray-project/ray/pull/45550))
- Fix worker launch time formatting in state api ([#43516](https://github.com/ray-project/ray/pull/43516))
-
##### Ray Data
🎉 New Features:
- Deprecate Dataset.get_internal_block_refs() (#46455)
- Add read API for reading Databricks table with Delta Sharing (#46072)
- Add support for objects to Arrow blocks (#45272)
💫 Enhancements:
- Change offsets to int64 and change to LargeList for ArrowTensorArray (#45352)
- Prevent from_pandas from combining input blocks (#46363)
- Update Dataset.count() to avoid unnecessarily keeping `BlockRef`s in-memory (#46369)
- Use Set to fix inefficient iteration over Arrow table columns (#46541)
- Add AWS Error UNKNOWN to list of retried write errors (#46646)
- Always print traceback for internal exceptions (#46647)
- Allow unknown estimate of operator output bundles and `ProgressBar` totals (#46601)
- Improve filesystem retry coverage (#46685)
🔨 Fixes:
- Replace lambda mutable default arguments (#46493)
📖 Documentation:
- Auto-generate Dataset API documentation (#46557)
- Update outdated ExecutionPlan docstring (#46638)
##### Ray Train
💫 Enhancements:
- Update run status and actor status for train runs. (#46395)
🔨 Fixes:
- Replace lambda default arguments (#46576)
📖 Documentation:
- Add MNIST training using KubeRay doc page (#46123)
- Add example of pre-training Llama model on Intel Gaudi (#45459)
- Fix tensorflow example by using ScalingConfig (#46565)
##### Ray Tune
🔨 Fixes:
- Replace lambda default arguments (#46596)
##### Ray Serve
🎉 New Features:
- Fully deprecate `target_num_ongoing_requests_per_replica` and `max_concurrent_queries`, respectively replaced by `max_ongoing_requests` and `target_ongoing_requests` (#46392 and #46427)
- Configure the task launched by the controller to build an application with Serve’s logging config (#46347)
##### RLlib
💫 Enhancements:
- Moving sampling coordination for `batch_mode=complete_episodes` to `synchronous_parallel_sample`. (#46321)
- Enable complex action spaces with stateful modules. (#46468)
🏗 Architecture refactoring:
- Enable multi-learner setup for hybrid stack BC. (#46436)
- Introduce Checkpointable API for RLlib components and subcomponents. (#46376)
🔨 Fixes:
- Replace Mapping typehint with Dict: #46474
📖 Documentation:
- More example scripts for new API stack: Two separate optimizers (w/ different learning rates). (#46540) and custom loss function. (#46445)
##### Dashboard
🔨 Fixes:
- Task end time showing the incorrect time (#46439)
- Events Table rows having really bad spacing (#46701)
- UI bugs in the serve dashboard page (#46599)
#### Thanks
Many thanks to all those who contributed to this release!
@alanwguo, @hongchaodeng, @anyscalesam, @brucebismarck, @bt2513, @woshiyyya, @terraflops1048576, @lorenzoritter, @omrishiv, @davidxia, @cchen777, @nono-Sang, @jackhumphries, @aslonnie, @JoshKarpel, @zjregee, @bveeramani, @khluu, @Superskyyy, @liuxsh9, @jjyao, @ruisearch42, @sven1977, @harborn, @saihaj, @zcin, @can-anyscale, @veekaybee, @chungen04, @WeichenXu123, @GeneDer, @sergey-serebryakov, @Bye-legumes, @scottjlee, @rynewang, @kevin85421, @cristianjd, @peytondmurray, @
_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-2.33.0]_