Skip to content

Bump ray from 2.55.0 to 2.56.0 in /sota-implementations/expert-iteration - #4052

Open
dependabot[bot] wants to merge 1 commit into
mainfrom
dependabot/pip/sota-implementations/expert-iteration/ray-2.56.0
Open

Bump ray from 2.55.0 to 2.56.0 in /sota-implementations/expert-iteration#4052
dependabot[bot] wants to merge 1 commit into
mainfrom
dependabot/pip/sota-implementations/expert-iteration/ray-2.56.0

Conversation

@dependabot

@dependabot dependabot Bot commented on behalf of github Jul 28, 2026

Copy link
Copy Markdown
Contributor

Bumps ray from 2.55.0 to 2.56.0.

Release notes

Sourced from ray's releases.

Ray-2.56.0

Highlights

  • Ray Data Stability: In this Ray release, we've added a variety of stability improvements, including running multiple datasets in a cluster, adding automatic batch size selection to CPU-based map-batches, and default logical memory configuration to prevent OOMs. We've also tightened iter_batches stability by reducing hidden buffering and shutting down the executor when consumers exit early (#63660, #63682, #62949). This reduces object-store spilling for common training workloads
  • Ray Serve: We re-architected Ray Serve LLM by decoupling request handling from token streaming response path (#62667, #62680, #62668, #62669, #63167), resulting in significant LLM serving performance improvements. We've also introduced new routing policies such as session-sticky routing via consistent hashing with ConsistentHashRouter (#62905, #63096, #62906) and CapacityQueueRouter (#62323) which is beneficial for supply-constrained workloads.
  • Ray Core: We've added GPU-domain-aware placement groups using label locality (#61442, #61614, #62487, #62533). This enables placement groups to pack bundles onto nodes that share a ray.io/gpu-domain label instead of only packing at the single-node level. We've also added initial Kubernetes in-place pod resizing support for Autoscaler v2 (#55961, #62369, #62215), enabling Ray clusters to resize CPU and memory on existing worker pods before scaling out new pods.

Ray Data

🎉 New Features

  • Support multiple datasets per cluster via subcluster labels and resource partitioning (#63331, #63375, #63982)
  • Add Dataset.mix() public API and MixOperator for weighted dataset mixing (#63168, #62450)
  • New DataSourceV2 framework: ParquetDatasourceV2, chunked reader, predicate splitting, listing/scanner infra (#63113, #63454, #63163, #62975, #63027, #62182)
  • Add batch_size='auto' to map_batches to derive batch row count from target row batch size (#62648)
  • Implement distributed upsert for Iceberg using task-based merge algorithm, preventing performance bottleneck on driver (#63482)
  • Add include_row_hash to read_parquet (#61408)
  • Add JAX data iterator (#61630)
  • Expose flag to run read tasks on isolated worker processes via isolate_read_workers (#63490)
  • Expose flag to set default logical memory for map operators via default_map_logical_memory_enabled (#63814)
  • Support predicate pushdown for Lance format (#61400)
  • Support per-partition start_offset and end_offset for read_kafka (#61620)
  • Add obstore async download backend for download operator (#61735)
  • Support UDF retries on transient exceptions (#63023)

💫 Enhancements

  • Fix iter_batches spilling by replacing make_async_gen with iter_threaded and reducing buffered batches (#63660, #63682)
  • Gate restore_original_order in iter_batches behind preserve_order (#63792)
  • Convert drop_columns to a Project logical operator when input schema is known (#63813)
  • Make ConcatAggregation and TurbopufferDatasink use polars for sorting (#61904)
  • Boost and vectorize hash_partition with sort_indices, zero-copy slices, and pandas (#63498, #62757, #63152, #62587)
  • Enable GPU_SHUFFLE in grouped_data.py (#62410)
  • Eager StarExpr expansion, schema inference for non-black-box UDFs, and Expressions struct support (#63776, #63387, #62560)
  • Make logging configurable via RAY_DATA_LOG_LEVEL and log RAY_DATA env vars at execution start (#63487, #63380)
  • Display and track logical memory in progress bar (#63379)
  • Honor compute= in filter(expr=...) and deprecate concurrency= (#63576)
  • Enable filter pushdown through StreamingRepartition and read stage column-rename removal (#62347, #63384, #63582)
  • Cache deserialized Arrow schemas in BlockMetadataWithSchema (#63462)
  • Track scheduling-loop step duration (p50/p90/max), peak USS/object-store memory, and task block locality (#63586, #63345, #63489, #63418, #62249)
  • Replace TaskDurationStats and Timer with DistributionTracker (#63488, #63530, #63825)
  • Introduce BlockEntry on RefBundle in place of (ref, metadata) tuples (#63654)
  • Pre-resolve filesystem in threaded download to avoid IMDS herd (#62898)
  • Convert logical operators to frozen dataclasses and consolidate operator base/repr (#62593, #62568, #62400, #63137, #63140, #63108)
  • Non-blocking default autoscaling coordinator and resource-aware auto-downscaling (#62725, #62574)
  • Release pinned blocks after dataset execution and shut down executor on early DataIterator exit (#62456, #62949)
  • Optimize local shuffle with incremental index and configurable compaction threshold (#62539)
  • Speed up checkpoint filter and reduce memory usage (#60294)
  • Preserve Arrow types through pandas roundtrip and reorder block columns by name before schema ops (#63017, #63582)
  • Block pickle object columns when reading untrusted Parquet and gate unsafe WebDataset deserialization (#63470, #63469)
  • Move backpressure escape hatch across all policies (#63539)
  • Update pandas, modin, and pyarrow minimum versions (#62899)

... (truncated)

Commits
  • 637fd06 Revert "[Data] Remove safe_round from ExecutionResources hot path (#6… (#64309)
  • f211d8a [Core] Compute per component memory usage in MiB (#63932) (#64042)
  • f9476b7 [Dashboard] Implement Frontend UI for Platform Events (#63332) (#64026)
  • 6a8600a [dashboard] Fix TPU metrics (#63998) (#64025)
  • 7a07ec3 [data] Rename subcluster label key from subcluster to ray-subcluster (#63...
  • 5fe0aa1 [Data][Cherry-pick] Add default logical memory for map operators (#63814) (#6...
  • 45194d5 bumping ray version
  • 5d2c4e7 [data] Support multiple datasets in a cluster (2/2): partition cluster resour...
  • 7b18437 [ci][train] adding torchft flaky test job (#63854)
  • 5c3e900 [Autoscaler] Remove ray.autoscaler docstring ignores from pydoclint (#63571)
  • Additional commits viewable in compare view

Dependabot compatibility score

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

  • @dependabot rebase will rebase this PR
  • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
  • @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
  • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
  • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
  • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    You can disable automated security fix PRs for this repo from the Security Alerts page.

@dependabot
Bump ray from 2.55.0 to 2.56.0 in /sota-implementations/expert-iteration
a8e374b 
Bumps [ray](https://github.com/ray-project/ray) from 2.55.0 to 2.56.0.
- [Release notes](https://github.com/ray-project/ray/releases)
- [Commits](ray-project/ray@ray-2.55.0...ray-2.56.0)

---
updated-dependencies:
- dependency-name: ray
  dependency-version: 2.56.0
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot Bot added Dependencies Pull requests that update a dependency file python Pull requests that update python code labels Jul 28, 2026
@pytorch-bot

pytorch-bot Bot commented Jul 28, 2026 •
edited
Loading

Copy link
Copy Markdown

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/rl/4052

Note: Links to docs will display an error until the docs builds have been completed.

❌ 38 New Failures, 19 Unrelated Failures, 1 Unclassified Failure

As of commit a8e374b with merge base ae421b9 (image):

NEW FAILURES - The following jobs have failed:

UNCLASSIFIED FAILURE - DrCI could not classify the following job because the workflow did not run on the merge base. The failure may be pre-existing on trunk or introduced by this PR:

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

This comment was automatically generated by Dr. CI and updates every 15 minutes.

@meta-cla meta-cla Bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Jul 28, 2026
@github-actions

github-actions Bot commented Jul 28, 2026

Copy link
Copy Markdown
Contributor

⚠️ PR Title Label Error

PR title must start with a label prefix in brackets (e.g., [BugFix]).

Current title: Bump ray from 2.55.0 to 2.56.0 in /sota-implementations/expert-iteration

Supported Prefixes (case-sensitive)

Your PR title must start with exactly one of these prefixes:

Prefix Label Applied Example
[Algorithm] new algo [Algorithm] Add new RL objective
[BE] BE [BE] Improve error messages
[Benchmark] or [Benchmarks] Benchmarks [Benchmark] Add collector benchmark
[BugFix] BugFix [BugFix] Fix memory leak in collector
[Example] or [Examples] Examples [Example] Add training script
[Feature] Feature [Feature] Add new optimizer
[Doc] or [Docs] Documentation [Doc] Update installation guide
[Refactor] Refactoring [Refactor] Clean up module imports
[CI] CI [CI] Fix workflow permissions
[Test] or [Tests] Tests [Tests] Add unit tests for buffer
[Trainer] or [Trainers] Trainers [Trainer] Add trainer config
[Environment] or [Environments] Environments [Environments] Add Gymnasium support
[Data] Data [Data] Fix replay buffer sampling
[LLM] llm/ [LLM] Add reward model integration
[Minor] small change [Minor] Fix typo in error message
[Performance] or [Perf] Performance [Performance] Optimize tensor ops
[BC-Breaking] bc breaking [BC-Breaking] Remove deprecated API
[Deprecation] Deprecation [Deprecation] Mark old function
[Algorithm] or [Algorithms] new algo [Algorithm] Add new objective
[Quality] Quality [Quality] Fix typos and add codespell
[Versioning] versioning [Versioning] Bump release version
[WIP] WIP [WIP] Draft implementation

Note: Common variations like singular/plural are supported (e.g., [Doc] or [Docs]).

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Reviewers

No reviews

Assignees

No one assigned

Labels

CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. Dependencies Pull requests that update a dependency file python Pull requests that update python code sota-implementations/

Projects

None yet

Milestone

No milestone

Development

Successfully merging this pull request may close these issues.

0 participants