Build: Bump ray from 2.55.1 to 2.56.1

[dependabot]

Bumps ray from 2.55.1 to 2.56.1.

Release notes

Sourced from ray's releases.

Ray-2.56.1

Highlights

• Ray Data: We added fixes for several to_pandas regressions introduced in 2.56: an opt-out flag (RAY_DATA_ENABLE_ARROW_BACKED_PANDAS_CONVERSION) for Arrow-backed conversion, an int64/double[pyarrow] overflow crash on concatenation, and a TensorDtype.__from_arrow__ crash on empty tensor columns (#64793, #64794).
• Ray Core: We added early detection for system-slice memory pressure: the memory monitor now snapshots the user and system cgroup slices together and logs an error when the system slice exceeds reserved system memory, warning users to raise --system-reserved-memory before it causes node deaths (#64492).
• Ray Serve: We added protobuf 7 compatibility and a routing fix for LLM direct streaming, so body-aware routers like PrefixCacheAffinityRouter no longer hang when RAY_SERVE_LLM_ENABLE_DIRECT_STREAMING=1 (#64592, #64488).

Ray Data

🔨 Fixes

• Fixed two Arrow-backed to_pandas regressions: added DataContext.enable_arrow_backed_pandas_conversion as an opt-out, and reconciled divergent numeric column types before concatenation to avoid int64/double[pyarrow] overflow crashes (#64793, #64768).
• Fixed a TensorDtype.__from_arrow__ crash on zero-size tensor elements by using an explicit row count instead of numpy's -1 dimension inference (#64794, #64767).
• Fixed a crash in hash partition caused by read-only hash arrays (#64584, #64552, #64559).
• Nullified _input_dependencies in _get_args so exporting operator args no longer triggers an exponential sanitize_for_struct call chain over fused operators (#64412, #64316).

Ray Serve

🔨 Fixes

• Added protobuf >=7 compatibility to _proto_to_dict by binding to FieldDescriptor.is_repeated when the deprecated label attribute is absent (#64592, #64362).

Ray LLM

🔨 Fixes

• Fixed direct-streaming routing for body-aware routers: the ingress now parses the raw request body into a SimpleNamespace over routing-key fields (messages, prompt) so choose_replica receives the message body instead of raw bytes (#64488, #64328, #64326).

Ray RLlib

🔨 Fixes

• Upgraded the ONNX example from the retired MobileNet v1 to MobileNet v3 via torchvision and pinned onnxscript in the GPU/ml-build CI dep locks, fixing ONNX export failures (#64591, #64028, #64031, #64590, #64033).

Ray Core

💫 Enhancements

• The threshold memory monitor now snapshots both the user and system cgroup slices and logs an error when system-slice usage exceeds the reserved system memory, prompting users to raise --system-reserved-memory (#64492).

🔨 Fixes

• Disabled NCCL cuMem host buffer registration in CI pytests to stabilize GPU test runs (#64580, #64146).

Documentation

📖 Documentation

• Repointed the ASHA Tune example links to the renamed README document, fixing the fail_on_warning ReadTheDocs build on the release line (#64761, #64630).
• Updated the Python 3.10 CPU pip freeze dependency list for the Ray 2.56.0 release (#64447, #64357).

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.

... (truncated)

Commits

• 936f0d7 [docker] Update latest Docker dependencies for 2.56.1 release (#64849)
• ea47a0f [Data] Enable memory-safety best practices in video object detection benchmar...
• 0672981 [Data][2.56.1] Fix Arrow-backed to_pandas regressions: opt-out flag + int/flo...
• 6d4f5e6 [Data][2.56.1] Fix TensorDtype.from_arrow crash on empty tensor columns (...
• e4063cc [Data] Enable spilling and OOM check for remaining multimodal release tests (...
• d7babce [Data] Add memory hint for reads to image_embedding_from_jsonl (#64717) (ch...
• b747a25 [doc] Repoint ASHA Tune example links to README (fixes docs build) (#64630) (...
• 3ba205c [release] Update shipped dependencies for Ray 2.56.1 (#64728)
• 851cf15 [cherry-pick] Cherry-pick 2.56.1 mobilenet 64028 64031 (#64591)
• e709266 [deps][cherry-pick] cherry-pick 2.56.1 onnxscript #64033 (#64590)
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[ebyhr]

=========================== short test summary info ============================
FAILED tests/integration/test_reads.py::test_ray_all_types[session_catalog_hive] - AssertionError: assert False
 +  where False = equals(   longCol  intCol  ...  arrayCol                                          structCol\n0        0       0  ...       [0]  {'mapCol': [(0, Decimal('0.00'))], 'arrayCol':...\n1        1       1  ...       [1]  {'mapCol': [(1, Decimal('1.00'))], 'arrayCol':...\n2        4       4  ...       [4]  {'mapCol': [(4, Decimal('4.00'))], 'arrayCol':...\n3        2       2  ...       [2]  {'mapCol': [(2, Decimal('2.00'))], 'arrayCol':...\n4        3       3  ...       [3]  {'mapCol': [(3, Decimal('3.00'))], 'arrayCol':...\n\n[5 rows x 15 columns])
 +    where equals =    longCol  intCol  ...  arrayCol                                 structCol\n0        0       0  ...       [0]  {'mapCol': [(0, 0.00)], 'arrayCol': [0]}\n1        1       1  ...       [1]  {'mapCol': [(1, 1.00)], 'arrayCol': [1]}\n2        4       4  ...       [4]  {'mapCol': [(4, 4.00)], 'arrayCol': [4]}\n3        2       2  ...       [2]  {'mapCol': [(2, 2.00)], 'arrayCol': [2]}\n4        3       3  ...       [3]  {'mapCol': [(3, 3.00)], 'arrayCol': [3]}\n\n[5 rows x 15 columns].equals
 +    and      longCol  intCol  ...  arrayCol                                          structCol\n0        0       0  ...       [0]  {'mapCol': [(0, Decimal('0.00'))], 'arrayCol':...\n1        1       1  ...       [1]  {'mapCol': [(1, Decimal('1.00'))], 'arrayCol':...\n2        4       4  ...       [4]  {'mapCol': [(4, Decimal('4.00'))], 'arrayCol':...\n3        2       2  ...       [2]  {'mapCol': [(2, Decimal('2.00'))], 'arrayCol':...\n4        3       3  ...       [3]  {'mapCol': [(3, Decimal('3.00'))], 'arrayCol':...\n\n[5 rows x 15 columns] = to_pandas()
 +      where to_pandas = shape: (5, 15)\n╭─────────┬────────┬──────────┬───────────┬─────────────┬───────────────────────────┬────────────┬────────────┬─────────────────────────────────────┬─────────┬───────────────────┬──────────┬───────────────────────────────┬──────────────────────┬──────────────────────────────────────────╮\n│ longCol ┆ intCol ┆ floatCol ┆ doubleCol ┆ dateCol     ┆ timestampCol              ┆ stringCol  ┆ booleanCol ┆ binaryCol                           ┆ byteCol ┆ decimalCol        ┆ shortCol ┆ mapCol                        ┆ arrayCol             ┆ structCol                                │\n│ ---     ┆ ---    ┆ ---      ┆ ---       ┆ ---         ┆ ---                       ┆ ---        ┆ ---        ┆ ---                                 ┆ ---     ┆ ---               ┆ ---      ┆ ---                           ┆ ---                  ┆ ---                                      │\n│ int64   ┆ int32  ┆ float    ┆ double    ┆ date32[day] ┆ timestamp[us, tz=UTC]     ┆ string     ┆ bool       ┆ binary                              ┆ int32   ┆ decimal128(10, 2) ┆ int32    ┆ map<int64, decimal128(10, 2)> ┆ list<element: int64> ┆ struct<mapCol: map<int64, decimal128(10… │\n╞═════════╪════════╪═══════...al('1.00'))], 'arr… │\n│ 4       ┆ 4      ┆ 4.0      ┆ 4.0       ┆ 2026-07-25  ┆ 2026-07-25 00:00:00+00:00 ┆ 2026-07-25 ┆ False      ┆ b'\x00\x00\x00\x00\x00\x00\x00\x04' ┆ 4       ┆ 4.00              ┆ 4        ┆ [(4, Decimal('4.00'))]        ┆ [4]                  ┆ {'mapCol': [(4, Decimal('4.00'))], 'arr… │\n│ 2       ┆ 2      ┆ 2.0      ┆ 2.0       ┆ 2026-07-25  ┆ 2026-07-25 00:00:00+00:00 ┆ 2026-07-25 ┆ False      ┆ b'\x00\x00\x00\x00\x00\x00\x00\x02' ┆ 2       ┆ 2.00              ┆ 2        ┆ [(2, Decimal('2.00'))]        ┆ [2]                  ┆ {'mapCol': [(2, Decimal('2.00'))], 'arr… │\n│ 3       ┆ 3      ┆ 3.0      ┆ 3.0       ┆ 2026-07-25  ┆ 2026-07-25 00:00:00+00:00 ┆ 2026-07-25 ┆ False      ┆ b'\x00\x00\x00\x00\x00\x00\x00\x03' ┆ 3       ┆ 3.00              ┆ 3        ┆ [(3, Decimal('3.00'))]        ┆ [3]                  ┆ {'mapCol': [(3, Decimal('3.00'))], 'arr… │\n╰─────────┴────────┴──────────┴───────────┴─────────────┴───────────────────────────┴────────────┴────────────┴─────────────────────────────────────┴─────────┴───────────────────┴──────────┴───────────────────────────────┴──────────────────────┴──────────────────────────────────────────╯\n(Showing 5 of 5 rows).to_pandas
!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!!
==== 1 failed, 442 passed, 70 skipped, 3922 deselected in 119.42s (0:01:59) ====