Upgrade: Update ray requirement from >=2.10 to >=2.56.0

[dependabot]

Updates the requirements on ray to permit the latest version.

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)
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