Build: Bump ray from 2.55.1 to 2.57.0

[dependabot]

Bumps ray from 2.55.1 to 2.57.0.

Release notes

Sourced from ray's releases.

Ray-2.57.0

Highlights

• Ray Data: In this Ray release, we've enabled DataSourceV2 by default (#64821), so read_parquet and friends use the new scan/listing infrastructure with row-group-aware chunking and predicate splitting. Hash Shuffle V2 eliminates the aggregator actor pool. V1 had to provision that pool up front from an estimate of the input size, and its actors accumulated partition shards in actor heap memory, invisible to Ray and unspillable, until finalization. V2 replaces it with two stateless task-based operators, ShuffleMapOp --> ShuffleReduceOp, that pass shards through the object store, so intermediate state spills under pressure and no capacity has to be reserved in advance. The map/reduce barrier itself remains in both designs.
• HashShuffleV2 supports join (#63598, #64538, #64687). This lets shuffles reuse standard map/reduce scheduling, backpressure, and resource accounting.
• Ray Serve: The HAProxy ingress is now distributed as the ray-haproxy PyPI package instead of being compiled into images, and it is the default HAProxy binary (#64141, #64163, #64164). We've also added gRPC support to the HAProxy direct-ingress path, including streaming, metrics, and custom request IDs (#63735, #64310, #64166, #64112). For Ray Serve LLM, we've added experimental KV-cache-aware request routing that tracks replica KV state through an event plane, tokenizes before routing, and routes on prefill/decode token load (#64084, #64085, #64097, #64224, #64327, #64400). KV cache-aware routing’s complete support will land in 2.58.
• Ray Core: We've added an embedded RocksDB storage backend for GCS fault tolerance (REP-64), selectable with RAY_gcs_storage=rocksdb and RAY_gcs_storage_path (#63657). GCS fault tolerance no longer requires an external Redis instance. We've also added a public API for topology-aware scheduling (#63479, #63740).

Ray Data

🎉 New Features

• Enable DataSourceV2 by default via DataContext.use_datasource_v2 (#64821)
• New task-based hash shuffle v2 (ShuffleMapOp → ShuffleReduceOp) with join, multi-input reduce, downstream map fusion, and reducer remote args, behind an env flag (#63598, #64538, #64687, #64438, #64302, #64532, #64481)
• Add a Catalog abstraction with a UnityCatalog implementation that can be passed to read_*, and Unity Catalog write support for Parquet and Iceberg (#64193, #64519)
• Add read_zarr for Zarr datasets (#63003) and read_lerobot for LeRobot v3 datasets (#63821)
• Add PushdownCountFiles optimization to answer count() from Parquet footers (#64763)
• Add common subexpression elimination to the expression optimizer (#63974)
• Add GPU support for Aggregate (#63708)
• Make dataset iteration metrics queryable per split (#64608)
• Add custom operator stats to capture worker-side metrics during task execution (#64221)
• Refactor usage collection into an extensible UsageCallback (#64500)
• Export from_blocks from ray.data (#64127)
• Add support for rapidsmpf-26.4.0 (#64324)

💫 Enhancements

• Hide Ray Data internal frames from user-code error tracebacks (#64587)
• Run projection/predicate pushdown before limit pushdown, block optimization of non-deterministic expressions, and make fuse checks consistent between the logical and physical optimizers (#64651, #64165, #63936)
• Add a two-mode block metadata fetch behind a single MetadataFetcher interface (#64378)
• Use work stealing for file listing and a dynamic work queue for traversals (#64675, #64388)
• Speed up ExecutionResources and the reservation/budget loops on the scheduling hot path (#63964)
• Fix O(n^2) schema reconciliation in unify_schemas and avoid per-column Series materialization in tensor-column casting (#64555, #64038)
• Decouple collate and memory pinning in iter_torch_batches, add per-stage training-thread blocking attribution, bound in-flight iter_threaded items, and finalize after reordering under preserve_order (#64653, #64183, #64219, #64282)
• Add default logical memory for map operators, wire the materializing-op filter into OpResourceAllocator budgeting, and move estimate_object_store_usage into the physical op (#63814, #63665, #63961)
• Decrease the downstream backpressure ratio to 2.0 (#64352)
• Route native S3FileSystem downloads to the PyArrow threaded path (#64089)
• Size hash-shuffle aggregators via bounded online sampling and add a timeout to the batched ray.get when fetching partitions (#63929, #64256)
• Replace manual block reference accounting with Ray Core out-of-scope object callbacks (#64011, #64157, #64191)
• Default read_numpy to allow_pickle=False and make it manually configurable (#64684)
• Make write_lance(mode=CREATE) error instead of silently overwriting (#64364)
• Expand DistributionTracker with merge() and p25/p75, and add dead node counts and detected issues to usage collection (#64074, #64459, #64198)
• Remove cluster autoscaler v1, rename the subcluster label key to ray-subcluster, avoid scaling nodegroups dedicated to the head node, and quiet autoscaling coordinator logs (#64380, #64003, #63918, #63534)
• Delay the "cluster resources not enough" warning until an operator is persistently starved, and lower the high-memory warning threshold (#63969, #64124)
• Remove ExecutionPlan, _num_outputs, batch_format on AllToAllOperators, and InheritBatchFormatRule; use input_dependencies in logical operators (#63662, #64167, #64152, #64149, #64148)
• Deprecate low-level scheduling APIs (DataContext.scheduling_strategy, actor_locality_enabled, exclude_resources, local://) ahead of the actor-only rearchitecture (#64632)
• Migrate the Daft dependency from getdaft to daft (#64240)
• Migrate apply_chat_template/tokenize/detokenize callers to *_stage form, and remove PrepareImageStage while deprecating the image row column (#63590, #63570)

🔨 Fixes

• Fix TensorDtype.__from_arrow__ crash on empty tensor columns (#64767)
• Fix Arrow-backed to_pandas regressions with an opt-out flag and int/float block overflow handling (#64768)

... (truncated)

Commits

• 878455a [docker] Update latest Docker dependencies for 2.57.0 release (#65345)
• 4464bbb [release/2.57.0][Data] Propagate isolate_read_workers to DatasourceV2 (#651...
• 8778646 [Core] Fix Python 3.14 async-actor memory leak by re-anchoring stack … (#65177)
• 133ea78 [2.57.0][core][observability] Fix deadlock between metric registration and co...
• 8d36c02 [release/2.57.0][core] Import pytest lazily in test_utils to fix runtime_env_...
• d1fbb27 [cherry-pick][Observability] Add GPU uuid to the labels of GPU metrics (#6511...
• 44e5ef3 [release-2.57.0][release] Fix huggingface_accelerate release test: floor peft...
• 3971316 [cherry-pick][llm] Add Ray Serve LLM SGLang metrics dashboard (#64797) (#65083)
• 592b3f4 [release/2.57.0][Data] Add tf-keras to text_embedding pip install packages ...
• e09992f [Cherry-pick][2.57.0][release] Add hello_world_py314 smoke release test; fix ...
• Additional commits viewable in compare view

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.

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[dependabot]

Superseded by #334.

[dependabot]

Bumps ray from 2.55.1 to 2.57.0.

Release notes

Sourced from ray's releases.

Ray-2.57.0

Highlights

• Ray Data: In this Ray release, we've enabled DataSourceV2 by default (#64821), so read_parquet and friends use the new scan/listing infrastructure with row-group-aware chunking and predicate splitting. Hash Shuffle V2 eliminates the aggregator actor pool. V1 had to provision that pool up front from an estimate of the input size, and its actors accumulated partition shards in actor heap memory, invisible to Ray and unspillable, until finalization. V2 replaces it with two stateless task-based operators, ShuffleMapOp --> ShuffleReduceOp, that pass shards through the object store, so intermediate state spills under pressure and no capacity has to be reserved in advance. The map/reduce barrier itself remains in both designs.
• HashShuffleV2 supports join (#63598, #64538, #64687). This lets shuffles reuse standard map/reduce scheduling, backpressure, and resource accounting.
• Ray Serve: The HAProxy ingress is now distributed as the ray-haproxy PyPI package instead of being compiled into images, and it is the default HAProxy binary (#64141, #64163, #64164). We've also added gRPC support to the HAProxy direct-ingress path, including streaming, metrics, and custom request IDs (#63735, #64310, #64166, #64112). For Ray Serve LLM, we've added experimental KV-cache-aware request routing that tracks replica KV state through an event plane, tokenizes before routing, and routes on prefill/decode token load (#64084, #64085, #64097, #64224, #64327, #64400). KV cache-aware routing’s complete support will land in 2.58.
• Ray Core: We've added an embedded RocksDB storage backend for GCS fault tolerance (REP-64), selectable with RAY_gcs_storage=rocksdb and RAY_gcs_storage_path (#63657). GCS fault tolerance no longer requires an external Redis instance. We've also added a public API for topology-aware scheduling (#63479, #63740).

Ray Data

🎉 New Features

• Enable DataSourceV2 by default via DataContext.use_datasource_v2 (#64821)
• New task-based hash shuffle v2 (ShuffleMapOp → ShuffleReduceOp) with join, multi-input reduce, downstream map fusion, and reducer remote args, behind an env flag (#63598, #64538, #64687, #64438, #64302, #64532, #64481)
• Add a Catalog abstraction with a UnityCatalog implementation that can be passed to read_*, and Unity Catalog write support for Parquet and Iceberg (#64193, #64519)
• Add read_zarr for Zarr datasets (#63003) and read_lerobot for LeRobot v3 datasets (#63821)
• Add PushdownCountFiles optimization to answer count() from Parquet footers (#64763)
• Add common subexpression elimination to the expression optimizer (#63974)
• Add GPU support for Aggregate (#63708)
• Make dataset iteration metrics queryable per split (#64608)
• Add custom operator stats to capture worker-side metrics during task execution (#64221)
• Refactor usage collection into an extensible UsageCallback (#64500)
• Export from_blocks from ray.data (#64127)
• Add support for rapidsmpf-26.4.0 (#64324)

💫 Enhancements

• Hide Ray Data internal frames from user-code error tracebacks (#64587)
• Run projection/predicate pushdown before limit pushdown, block optimization of non-deterministic expressions, and make fuse checks consistent between the logical and physical optimizers (#64651, #64165, #63936)
• Add a two-mode block metadata fetch behind a single MetadataFetcher interface (#64378)
• Use work stealing for file listing and a dynamic work queue for traversals (#64675, #64388)
• Speed up ExecutionResources and the reservation/budget loops on the scheduling hot path (#63964)
• Fix O(n^2) schema reconciliation in unify_schemas and avoid per-column Series materialization in tensor-column casting (#64555, #64038)
• Decouple collate and memory pinning in iter_torch_batches, add per-stage training-thread blocking attribution, bound in-flight iter_threaded items, and finalize after reordering under preserve_order (#64653, #64183, #64219, #64282)
• Add default logical memory for map operators, wire the materializing-op filter into OpResourceAllocator budgeting, and move estimate_object_store_usage into the physical op (#63814, #63665, #63961)
• Decrease the downstream backpressure ratio to 2.0 (#64352)
• Route native S3FileSystem downloads to the PyArrow threaded path (#64089)
• Size hash-shuffle aggregators via bounded online sampling and add a timeout to the batched ray.get when fetching partitions (#63929, #64256)
• Replace manual block reference accounting with Ray Core out-of-scope object callbacks (#64011, #64157, #64191)
• Default read_numpy to allow_pickle=False and make it manually configurable (#64684)
• Make write_lance(mode=CREATE) error instead of silently overwriting (#64364)
• Expand DistributionTracker with merge() and p25/p75, and add dead node counts and detected issues to usage collection (#64074, #64459, #64198)
• Remove cluster autoscaler v1, rename the subcluster label key to ray-subcluster, avoid scaling nodegroups dedicated to the head node, and quiet autoscaling coordinator logs (#64380, #64003, #63918, #63534)
• Delay the "cluster resources not enough" warning until an operator is persistently starved, and lower the high-memory warning threshold (#63969, #64124)
• Remove ExecutionPlan, _num_outputs, batch_format on AllToAllOperators, and InheritBatchFormatRule; use input_dependencies in logical operators (#63662, #64167, #64152, #64149, #64148)
• Deprecate low-level scheduling APIs (DataContext.scheduling_strategy, actor_locality_enabled, exclude_resources, local://) ahead of the actor-only rearchitecture (#64632)
• Migrate the Daft dependency from getdaft to daft (#64240)
• Migrate apply_chat_template/tokenize/detokenize callers to *_stage form, and remove PrepareImageStage while deprecating the image row column (#63590, #63570)

🔨 Fixes

• Fix TensorDtype.__from_arrow__ crash on empty tensor columns (#64767)
• Fix Arrow-backed to_pandas regressions with an opt-out flag and int/float block overflow handling (#64768)

... (truncated)

Commits

• 878455a [docker] Update latest Docker dependencies for 2.57.0 release (#65345)
• 4464bbb [release/2.57.0][Data] Propagate isolate_read_workers to DatasourceV2 (#651...
• 8778646 [Core] Fix Python 3.14 async-actor memory leak by re-anchoring stack … (#65177)
• 133ea78 [2.57.0][core][observability] Fix deadlock between metric registration and co...
• 8d36c02 [release/2.57.0][core] Import pytest lazily in test_utils to fix runtime_env_...
• d1fbb27 [cherry-pick][Observability] Add GPU uuid to the labels of GPU metrics (#6511...
• 44e5ef3 [release-2.57.0][release] Fix huggingface_accelerate release test: floor peft...
• 3971316 [cherry-pick][llm] Add Ray Serve LLM SGLang metrics dashboard (#64797) (#65083)
• 592b3f4 [release/2.57.0][Data] Add tf-keras to text_embedding pip install packages ...
• e09992f [Cherry-pick][2.57.0][release] Add hello_world_py314 smoke release test; fix ...
• Additional commits viewable in compare view

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.

---

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• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
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[dependabot]

Superseded by #334.

[dependabot]

Bumps ray from 2.55.1 to 2.57.0.

Release notes

Sourced from ray's releases.

Ray-2.57.0

Highlights

• Ray Data: In this Ray release, we've enabled DataSourceV2 by default (#64821), so read_parquet and friends use the new scan/listing infrastructure with row-group-aware chunking and predicate splitting. Hash Shuffle V2 eliminates the aggregator actor pool. V1 had to provision that pool up front from an estimate of the input size, and its actors accumulated partition shards in actor heap memory, invisible to Ray and unspillable, until finalization. V2 replaces it with two stateless task-based operators, ShuffleMapOp --> ShuffleReduceOp, that pass shards through the object store, so intermediate state spills under pressure and no capacity has to be reserved in advance. The map/reduce barrier itself remains in both designs.
• HashShuffleV2 supports join (#63598, #64538, #64687). This lets shuffles reuse standard map/reduce scheduling, backpressure, and resource accounting.
• Ray Serve: The HAProxy ingress is now distributed as the ray-haproxy PyPI package instead of being compiled into images, and it is the default HAProxy binary (#64141, #64163, #64164). We've also added gRPC support to the HAProxy direct-ingress path, including streaming, metrics, and custom request IDs (#63735, #64310, #64166, #64112). For Ray Serve LLM, we've added experimental KV-cache-aware request routing that tracks replica KV state through an event plane, tokenizes before routing, and routes on prefill/decode token load (#64084, #64085, #64097, #64224, #64327, #64400). KV cache-aware routing’s complete support will land in 2.58.
• Ray Core: We've added an embedded RocksDB storage backend for GCS fault tolerance (REP-64), selectable with RAY_gcs_storage=rocksdb and RAY_gcs_storage_path (#63657). GCS fault tolerance no longer requires an external Redis instance. We've also added a public API for topology-aware scheduling (#63479, #63740).

Ray Data

🎉 New Features

• Enable DataSourceV2 by default via DataContext.use_datasource_v2 (#64821)
• New task-based hash shuffle v2 (ShuffleMapOp → ShuffleReduceOp) with join, multi-input reduce, downstream map fusion, and reducer remote args, behind an env flag (#63598, #64538, #64687, #64438, #64302, #64532, #64481)
• Add a Catalog abstraction with a UnityCatalog implementation that can be passed to read_*, and Unity Catalog write support for Parquet and Iceberg (#64193, #64519)
• Add read_zarr for Zarr datasets (#63003) and read_lerobot for LeRobot v3 datasets (#63821)
• Add PushdownCountFiles optimization to answer count() from Parquet footers (#64763)
• Add common subexpression elimination to the expression optimizer (#63974)
• Add GPU support for Aggregate (#63708)
• Make dataset iteration metrics queryable per split (#64608)
• Add custom operator stats to capture worker-side metrics during task execution (#64221)
• Refactor usage collection into an extensible UsageCallback (#64500)
• Export from_blocks from ray.data (#64127)
• Add support for rapidsmpf-26.4.0 (#64324)

💫 Enhancements

• Hide Ray Data internal frames from user-code error tracebacks (#64587)
• Run projection/predicate pushdown before limit pushdown, block optimization of non-deterministic expressions, and make fuse checks consistent between the logical and physical optimizers (#64651, #64165, #63936)
• Add a two-mode block metadata fetch behind a single MetadataFetcher interface (#64378)
• Use work stealing for file listing and a dynamic work queue for traversals (#64675, #64388)
• Speed up ExecutionResources and the reservation/budget loops on the scheduling hot path (#63964)
• Fix O(n^2) schema reconciliation in unify_schemas and avoid per-column Series materialization in tensor-column casting (#64555, #64038)
• Decouple collate and memory pinning in iter_torch_batches, add per-stage training-thread blocking attribution, bound in-flight iter_threaded items, and finalize after reordering under preserve_order (#64653, #64183, #64219, #64282)
• Add default logical memory for map operators, wire the materializing-op filter into OpResourceAllocator budgeting, and move estimate_object_store_usage into the physical op (#63814, #63665, #63961)
• Decrease the downstream backpressure ratio to 2.0 (#64352)
• Route native S3FileSystem downloads to the PyArrow threaded path (#64089)
• Size hash-shuffle aggregators via bounded online sampling and add a timeout to the batched ray.get when fetching partitions (#63929, #64256)
• Replace manual block reference accounting with Ray Core out-of-scope object callbacks (#64011, #64157, #64191)
• Default read_numpy to allow_pickle=False and make it manually configurable (#64684)
• Make write_lance(mode=CREATE) error instead of silently overwriting (#64364)
• Expand DistributionTracker with merge() and p25/p75, and add dead node counts and detected issues to usage collection (#64074, #64459, #64198)
• Remove cluster autoscaler v1, rename the subcluster label key to ray-subcluster, avoid scaling nodegroups dedicated to the head node, and quiet autoscaling coordinator logs (#64380, #64003, #63918, #63534)
• Delay the "cluster resources not enough" warning until an operator is persistently starved, and lower the high-memory warning threshold (#63969, #64124)
• Remove ExecutionPlan, _num_outputs, batch_format on AllToAllOperators, and InheritBatchFormatRule; use input_dependencies in logical operators (#63662, #64167, #64152, #64149, #64148)
• Deprecate low-level scheduling APIs (DataContext.scheduling_strategy, actor_locality_enabled, exclude_resources, local://) ahead of the actor-only rearchitecture (#64632)
• Migrate the Daft dependency from getdaft to daft (#64240)
• Migrate apply_chat_template/tokenize/detokenize callers to *_stage form, and remove PrepareImageStage while deprecating the image row column (#63590, #63570)

🔨 Fixes

• Fix TensorDtype.__from_arrow__ crash on empty tensor columns (#64767)
• Fix Arrow-backed to_pandas regressions with an opt-out flag and int/float block overflow handling (#64768)

... (truncated)

Commits

• 878455a [docker] Update latest Docker dependencies for 2.57.0 release (#65345)
• 4464bbb [release/2.57.0][Data] Propagate isolate_read_workers to DatasourceV2 (#651...
• 8778646 [Core] Fix Python 3.14 async-actor memory leak by re-anchoring stack … (#65177)
• 133ea78 [2.57.0][core][observability] Fix deadlock between metric registration and co...
• 8d36c02 [release/2.57.0][core] Import pytest lazily in test_utils to fix runtime_env_...
• d1fbb27 [cherry-pick][Observability] Add GPU uuid to the labels of GPU metrics (#6511...
• 44e5ef3 [release-2.57.0][release] Fix huggingface_accelerate release test: floor peft...
• 3971316 [cherry-pick][llm] Add Ray Serve LLM SGLang metrics dashboard (#64797) (#65083)
• 592b3f4 [release/2.57.0][Data] Add tf-keras to text_embedding pip install packages ...
• e09992f [Cherry-pick][2.57.0][release] Add hello_world_py314 smoke release test; fix ...
• Additional commits viewable in compare view

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.

---

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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)
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[dependabot]

Superseded by #334.

[dependabot]

Bumps ray from 2.55.1 to 2.57.0.

Release notes

Sourced from ray's releases.

Ray-2.57.0

Highlights

• Ray Data: In this Ray release, we've enabled DataSourceV2 by default (#64821), so read_parquet and friends use the new scan/listing infrastructure with row-group-aware chunking and predicate splitting. Hash Shuffle V2 eliminates the aggregator actor pool. V1 had to provision that pool up front from an estimate of the input size, and its actors accumulated partition shards in actor heap memory, invisible to Ray and unspillable, until finalization. V2 replaces it with two stateless task-based operators, ShuffleMapOp --> ShuffleReduceOp, that pass shards through the object store, so intermediate state spills under pressure and no capacity has to be reserved in advance. The map/reduce barrier itself remains in both designs.
• HashShuffleV2 supports join (#63598, #64538, #64687). This lets shuffles reuse standard map/reduce scheduling, backpressure, and resource accounting.
• Ray Serve: The HAProxy ingress is now distributed as the ray-haproxy PyPI package instead of being compiled into images, and it is the default HAProxy binary (#64141, #64163, #64164). We've also added gRPC support to the HAProxy direct-ingress path, including streaming, metrics, and custom request IDs (#63735, #64310, #64166, #64112). For Ray Serve LLM, we've added experimental KV-cache-aware request routing that tracks replica KV state through an event plane, tokenizes before routing, and routes on prefill/decode token load (#64084, #64085, #64097, #64224, #64327, #64400). KV cache-aware routing’s complete support will land in 2.58.
• Ray Core: We've added an embedded RocksDB storage backend for GCS fault tolerance (REP-64), selectable with RAY_gcs_storage=rocksdb and RAY_gcs_storage_path (#63657). GCS fault tolerance no longer requires an external Redis instance. We've also added a public API for topology-aware scheduling (#63479, #63740).

Ray Data

🎉 New Features

• Enable DataSourceV2 by default via DataContext.use_datasource_v2 (#64821)
• New task-based hash shuffle v2 (ShuffleMapOp → ShuffleReduceOp) with join, multi-input reduce, downstream map fusion, and reducer remote args, behind an env flag (#63598, #64538, #64687, #64438, #64302, #64532, #64481)
• Add a Catalog abstraction with a UnityCatalog implementation that can be passed to read_*, and Unity Catalog write support for Parquet and Iceberg (#64193, #64519)
• Add read_zarr for Zarr datasets (#63003) and read_lerobot for LeRobot v3 datasets (#63821)
• Add PushdownCountFiles optimization to answer count() from Parquet footers (#64763)
• Add common subexpression elimination to the expression optimizer (#63974)
• Add GPU support for Aggregate (#63708)
• Make dataset iteration metrics queryable per split (#64608)
• Add custom operator stats to capture worker-side metrics during task execution (#64221)
• Refactor usage collection into an extensible UsageCallback (#64500)
• Export from_blocks from ray.data (#64127)
• Add support for rapidsmpf-26.4.0 (#64324)

💫 Enhancements

• Hide Ray Data internal frames from user-code error tracebacks (#64587)
• Run projection/predicate pushdown before limit pushdown, block optimization of non-deterministic expressions, and make fuse checks consistent between the logical and physical optimizers (#64651, #64165, #63936)
• Add a two-mode block metadata fetch behind a single MetadataFetcher interface (#64378)
• Use work stealing for file listing and a dynamic work queue for traversals (#64675, #64388)
• Speed up ExecutionResources and the reservation/budget loops on the scheduling hot path (#63964)
• Fix O(n^2) schema reconciliation in unify_schemas and avoid per-column Series materialization in tensor-column casting (#64555, #64038)
• Decouple collate and memory pinning in iter_torch_batches, add per-stage training-thread blocking attribution, bound in-flight iter_threaded items, and finalize after reordering under preserve_order (#64653, #64183, #64219, #64282)
• Add default logical memory for map operators, wire the materializing-op filter into OpResourceAllocator budgeting, and move estimate_object_store_usage into the physical op (#63814, #63665, #63961)
• Decrease the downstream backpressure ratio to 2.0 (#64352)
• Route native S3FileSystem downloads to the PyArrow threaded path (#64089)
• Size hash-shuffle aggregators via bounded online sampling and add a timeout to the batched ray.get when fetching partitions (#63929, #64256)
• Replace manual block reference accounting with Ray Core out-of-scope object callbacks (#64011, #64157, #64191)
• Default read_numpy to allow_pickle=False and make it manually configurable (#64684)
• Make write_lance(mode=CREATE) error instead of silently overwriting (#64364)
• Expand DistributionTracker with merge() and p25/p75, and add dead node counts and detected issues to usage collection (#64074, #64459, #64198)
• Remove cluster autoscaler v1, rename the subcluster label key to ray-subcluster, avoid scaling nodegroups dedicated to the head node, and quiet autoscaling coordinator logs (#64380, #64003, #63918, #63534)
• Delay the "cluster resources not enough" warning until an operator is persistently starved, and lower the high-memory warning threshold (#63969, #64124)
• Remove ExecutionPlan, _num_outputs, batch_format on AllToAllOperators, and InheritBatchFormatRule; use input_dependencies in logical operators (#63662, #64167, #64152, #64149, #64148)
• Deprecate low-level scheduling APIs (DataContext.scheduling_strategy, actor_locality_enabled, exclude_resources, local://) ahead of the actor-only rearchitecture (#64632)
• Migrate the Daft dependency from getdaft to daft (#64240)
• Migrate apply_chat_template/tokenize/detokenize callers to *_stage form, and remove PrepareImageStage while deprecating the image row column (#63590, #63570)

🔨 Fixes

• Fix TensorDtype.__from_arrow__ crash on empty tensor columns (#64767)
• Fix Arrow-backed to_pandas regressions with an opt-out flag and int/float block overflow handling (#64768)

... (truncated)

Commits

• 878455a [docker] Update latest Docker dependencies for 2.57.0 release (#65345)
• 4464bbb [release/2.57.0][Data] Propagate isolate_read_workers to DatasourceV2 (#651...
• 8778646 [Core] Fix Python 3.14 async-actor memory leak by re-anchoring stack … (#65177)
• 133ea78 [2.57.0][core][observability] Fix deadlock between metric registration and co...
• 8d36c02 [release/2.57.0][core] Import pytest lazily in test_utils to fix runtime_env_...
• d1fbb27 [cherry-pick][Observability] Add GPU uuid to the labels of GPU metrics (#6511...
• 44e5ef3 [release-2.57.0][release] Fix huggingface_accelerate release test: floor peft...
• 3971316 [cherry-pick][llm] Add Ray Serve LLM SGLang metrics dashboard (#64797) (#65083)
• 592b3f4 [release/2.57.0][Data] Add tf-keras to text_embedding pip install packages ...
• e09992f [Cherry-pick][2.57.0][release] Add hello_world_py314 smoke release test; fix ...
• Additional commits viewable in compare view

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[dependabot]

Superseded by #334.

[dependabot]

Bumps ray from 2.55.1 to 2.57.0.

Release notes

Sourced from ray's releases.

Ray-2.57.0

Highlights

• Ray Data: In this Ray release, we've enabled DataSourceV2 by default (#64821), so read_parquet and friends use the new scan/listing infrastructure with row-group-aware chunking and predicate splitting. Hash Shuffle V2 eliminates the aggregator actor pool. V1 had to provision that pool up front from an estimate of the input size, and its actors accumulated partition shards in actor heap memory, invisible to Ray and unspillable, until finalization. V2 replaces it with two stateless task-based operators, ShuffleMapOp --> ShuffleReduceOp, that pass shards through the object store, so intermediate state spills under pressure and no capacity has to be reserved in advance. The map/reduce barrier itself remains in both designs.
• HashShuffleV2 supports join (#63598, #64538, #64687). This lets shuffles reuse standard map/reduce scheduling, backpressure, and resource accounting.
• Ray Serve: The HAProxy ingress is now distributed as the ray-haproxy PyPI package instead of being compiled into images, and it is the default HAProxy binary (#64141, #64163, #64164). We've also added gRPC support to the HAProxy direct-ingress path, including streaming, metrics, and custom request IDs (#63735, #64310, #64166, #64112). For Ray Serve LLM, we've added experimental KV-cache-aware request routing that tracks replica KV state through an event plane, tokenizes before routing, and routes on prefill/decode token load (#64084, #64085, #64097, #64224, #64327, #64400). KV cache-aware routing’s complete support will land in 2.58.
• Ray Core: We've added an embedded RocksDB storage backend for GCS fault tolerance (REP-64), selectable with RAY_gcs_storage=rocksdb and RAY_gcs_storage_path (#63657). GCS fault tolerance no longer requires an external Redis instance. We've also added a public API for topology-aware scheduling (#63479, #63740).

Ray Data

🎉 New Features

• Enable DataSourceV2 by default via DataContext.use_datasource_v2 (#64821)
• New task-based hash shuffle v2 (ShuffleMapOp → ShuffleReduceOp) with join, multi-input reduce, downstream map fusion, and reducer remote args, behind an env flag (#63598, #64538, #64687, #64438, #64302, #64532, #64481)
• Add a Catalog abstraction with a UnityCatalog implementation that can be passed to read_*, and Unity Catalog write support for Parquet and Iceberg (#64193, #64519)
• Add read_zarr for Zarr datasets (#63003) and read_lerobot for LeRobot v3 datasets (#63821)
• Add PushdownCountFiles optimization to answer count() from Parquet footers (#64763)
• Add common subexpression elimination to the expression optimizer (#63974)
• Add GPU support for Aggregate (#63708)
• Make dataset iteration metrics queryable per split (#64608)
• Add custom operator stats to capture worker-side metrics during task execution (#64221)
• Refactor usage collection into an extensible UsageCallback (#64500)
• Export from_blocks from ray.data (#64127)
• Add support for rapidsmpf-26.4.0 (#64324)

💫 Enhancements

• Hide Ray Data internal frames from user-code error tracebacks (#64587)
• Run projection/predicate pushdown before limit pushdown, block optimization of non-deterministic expressions, and make fuse checks consistent between the logical and physical optimizers (#64651, #64165, #63936)
• Add a two-mode block metadata fetch behind a single MetadataFetcher interface (#64378)
• Use work stealing for file listing and a dynamic work queue for traversals (#64675, #64388)
• Speed up ExecutionResources and the reservation/budget loops on the scheduling hot path (#63964)
• Fix O(n^2) schema reconciliation in unify_schemas and avoid per-column Series materialization in tensor-column casting (#64555, #64038)
• Decouple collate and memory pinning in iter_torch_batches, add per-stage training-thread blocking attribution, bound in-flight iter_threaded items, and finalize after reordering under preserve_order (#64653, #64183, #64219, #64282)
• Add default logical memory for map operators, wire the materializing-op filter into OpResourceAllocator budgeting, and move estimate_object_store_usage into the physical op (#63814, #63665, #63961)
• Decrease the downstream backpressure ratio to 2.0 (#64352)
• Route native S3FileSystem downloads to the PyArrow threaded path (#64089)
• Size hash-shuffle aggregators via bounded online sampling and add a timeout to the batched ray.get when fetching partitions (#63929, #64256)
• Replace manual block reference accounting with Ray Core out-of-scope object callbacks (#64011, #64157, #64191)
• Default read_numpy to allow_pickle=False and make it manually configurable (#64684)
• Make write_lance(mode=CREATE) error instead of silently overwriting (#64364)
• Expand DistributionTracker with merge() and p25/p75, and add dead node counts and detected issues to usage collection (#64074, #64459, #64198)
• Remove cluster autoscaler v1, rename the subcluster label key to ray-subcluster, avoid scaling nodegroups dedicated to the head node, and quiet autoscaling coordinator logs (#64380, #64003, #63918, #63534)
• Delay the "cluster resources not enough" warning until an operator is persistently starved, and lower the high-memory warning threshold (#63969, #64124)
• Remove ExecutionPlan, _num_outputs, batch_format on AllToAllOperators, and InheritBatchFormatRule; use input_dependencies in logical operators (#63662, #64167, #64152, #64149, #64148)
• Deprecate low-level scheduling APIs (DataContext.scheduling_strategy, actor_locality_enabled, exclude_resources, local://) ahead of the actor-only rearchitecture (#64632)
• Migrate the Daft dependency from getdaft to daft (#64240)
• Migrate apply_chat_template/tokenize/detokenize callers to *_stage form, and remove PrepareImageStage while deprecating the image row column (#63590, #63570)

🔨 Fixes

• Fix TensorDtype.__from_arrow__ crash on empty tensor columns (#64767)
• Fix Arrow-backed to_pandas regressions with an opt-out flag and int/float block overflow handling (#64768)

... (truncated)

Commits

• 878455a [docker] Update latest Docker dependencies for 2.57.0 release (#65345)
• 4464bbb [release/2.57.0][Data] Propagate isolate_read_workers to DatasourceV2 (#651...
• 8778646 [Core] Fix Python 3.14 async-actor memory leak by re-anchoring stack … (#65177)
• 133ea78 [2.57.0][core][observability] Fix deadlock between metric registration and co...
• 8d36c02 [release/2.57.0][core] Import pytest lazily in test_utils to fix runtime_env_...
• d1fbb27 [cherry-pick][Observability] Add GPU uuid to the labels of GPU metrics (#6511...
• 44e5ef3 [release-2.57.0][release] Fix huggingface_accelerate release test: floor peft...
• 3971316 [cherry-pick][llm] Add Ray Serve LLM SGLang metrics dashboard (#64797) (#65083)
• 592b3f4 [release/2.57.0][Data] Add tf-keras to text_embedding pip install packages ...
• e09992f [Cherry-pick][2.57.0][release] Add hello_world_py314 smoke release test; fix ...
• Additional commits viewable in compare view

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.

---

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[dependabot]

Superseded by #334.

[dependabot]

Bumps ray from 2.55.1 to 2.57.0.

Release notes

Sourced from ray's releases.

Ray-2.57.0

Highlights

• Ray Data: In this Ray release, we've enabled DataSourceV2 by default (#64821), so read_parquet and friends use the new scan/listing infrastructure with row-group-aware chunking and predicate splitting. Hash Shuffle V2 eliminates the aggregator actor pool. V1 had to provision that pool up front from an estimate of the input size, and its actors accumulated partition shards in actor heap memory, invisible to Ray and unspillable, until finalization. V2 replaces it with two stateless task-based operators, ShuffleMapOp --> ShuffleReduceOp, that pass shards through the object store, so intermediate state spills under pressure and no capacity has to be reserved in advance. The map/reduce barrier itself remains in both designs.
• HashShuffleV2 supports join (#63598, #64538, #64687). This lets shuffles reuse standard map/reduce scheduling, backpressure, and resource accounting.
• Ray Serve: The HAProxy ingress is now distributed as the ray-haproxy PyPI package instead of being compiled into images, and it is the default HAProxy binary (#64141, #64163, #64164). We've also added gRPC support to the HAProxy direct-ingress path, including streaming, metrics, and custom request IDs (#63735, #64310, #64166, #64112). For Ray Serve LLM, we've added experimental KV-cache-aware request routing that tracks replica KV state through an event plane, tokenizes before routing, and routes on prefill/decode token load (#64084, #64085, #64097, #64224, #64327, #64400). KV cache-aware routing’s complete support will land in 2.58.
• Ray Core: We've added an embedded RocksDB storage backend for GCS fault tolerance (REP-64), selectable with RAY_gcs_storage=rocksdb and RAY_gcs_storage_path (#63657). GCS fault tolerance no longer requires an external Redis instance. We've also added a public API for topology-aware scheduling (#63479, #63740).

Ray Data

🎉 New Features

• Enable DataSourceV2 by default via DataContext.use_datasource_v2 (#64821)
• New task-based hash shuffle v2 (ShuffleMapOp → ShuffleReduceOp) with join, multi-input reduce, downstream map fusion, and reducer remote args, behind an env flag (#63598, #64538, #64687, #64438, #64302, #64532, #64481)
• Add a Catalog abstraction with a UnityCatalog implementation that can be passed to read_*, and Unity Catalog write support for Parquet and Iceberg (#64193, #64519)
• Add read_zarr for Zarr datasets (#63003) and read_lerobot for LeRobot v3 datasets (#63821)
• Add PushdownCountFiles optimization to answer count() from Parquet footers (#64763)
• Add common subexpression elimination to the expression optimizer (#63974)
• Add GPU support for Aggregate (#63708)
• Make dataset iteration metrics queryable per split (#64608)
• Add custom operator stats to capture worker-side metrics during task execution (#64221)
• Refactor usage collection into an extensible UsageCallback (#64500)
• Export from_blocks from ray.data (#64127)
• Add support for rapidsmpf-26.4.0 (#64324)

💫 Enhancements

• Hide Ray Data internal frames from user-code error tracebacks (#64587)
• Run projection/predicate pushdown before limit pushdown, block optimization of non-deterministic expressions, and make fuse checks consistent between the logical and physical optimizers (#64651, #64165, #63936)
• Add a two-mode block metadata fetch behind a single MetadataFetcher interface (#64378)
• Use work stealing for file listing and a dynamic work queue for traversals (#64675, #64388)
• Speed up ExecutionResources and the reservation/budget loops on the scheduling hot path (#63964)
• Fix O(n^2) schema reconciliation in unify_schemas and avoid per-column Series materialization in tensor-column casting (#64555, #64038)
• Decouple collate and memory pinning in iter_torch_batches, add per-stage training-thread blocking attribution, bound in-flight iter_threaded items, and finalize after reordering under preserve_order (#64653, #64183, #64219, #64282)
• Add default logical memory for map operators, wire the materializing-op filter into OpResourceAllocator budgeting, and move estimate_object_store_usage into the physical op (#63814, #63665, #63961)
• Decrease the downstream backpressure ratio to 2.0 (#64352)
• Route native S3FileSystem downloads to the PyArrow threaded path (#64089)
• Size hash-shuffle aggregators via bounded online sampling and add a timeout to the batched ray.get when fetching partitions (#63929, #64256)
• Replace manual block reference accounting with Ray Core out-of-scope object callbacks (#64011, #64157, #64191)
• Default read_numpy to allow_pickle=False and make it manually configurable (#64684)
• Make write_lance(mode=CREATE) error instead of silently overwriting (#64364)
• Expand DistributionTracker with merge() and p25/p75, and add dead node counts and detected issues to usage collection (#64074, #64459, #64198)
• Remove cluster autoscaler v1, rename the subcluster label key to ray-subcluster, avoid scaling nodegroups dedicated to the head node, and quiet autoscaling coordinator logs (#64380, #64003, #63918, #63534)
• Delay the "cluster resources not enough" warning until an operator is persistently starved, and lower the high-memory warning threshold (#63969, #64124)
• Remove ExecutionPlan, _num_outputs, batch_format on AllToAllOperators, and InheritBatchFormatRule; use input_dependencies in logical operators (#63662, #64167, #64152, #64149, #64148)
• Deprecate low-level scheduling APIs (DataContext.scheduling_strategy, actor_locality_enabled, exclude_resources, local://) ahead of the actor-only rearchitecture (#64632)
• Migrate the Daft dependency from getdaft to daft (#64240)
• Migrate apply_chat_template/tokenize/detokenize callers to *_stage form, and remove PrepareImageStage while deprecating the image row column (#63590, #63570)

🔨 Fixes

• Fix TensorDtype.__from_arrow__ crash on empty tensor columns (#64767)
• Fix Arrow-backed to_pandas regressions with an opt-out flag and int/float block overflow handling (#64768)

... (truncated)

Commits

• 878455a [docker] Update latest Docker dependencies for 2.57.0 release (#65345)
• 4464bbb [release/2.57.0][Data] Propagate isolate_read_workers to DatasourceV2 (#651...
• 8778646 [Core] Fix Python 3.14 async-actor memory leak by re-anchoring stack … (#65177)
• 133ea78 [2.57.0][core][observability] Fix deadlock between metric registration and co...
• 8d36c02 [release/2.57.0][core] Import pytest lazily in test_utils to fix runtime_env_...
• d1fbb27 [cherry-pick][Observability] Add GPU uuid to the labels of GPU metrics (#6511...
• 44e5ef3 [release-2.57.0][release] Fix huggingface_accelerate release test: floor peft...
• 3971316 [cherry-pick][llm] Add Ray Serve LLM SGLang metrics dashboard (#64797) (#65083)
• 592b3f4 [release/2.57.0][Data] Add tf-keras to text_embedding pip install packages ...
• e09992f [Cherry-pick][2.57.0][release] Add hello_world_py314 smoke release test; fix ...
• Additional commits viewable in compare view

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.

---

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• @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)
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[dependabot]

Superseded by #334.

[dependabot]

Bumps ray from 2.55.1 to 2.57.0.

Release notes

Sourced from ray's releases.

Ray-2.57.0

Highlights

• Ray Data: In this Ray release, we've enabled DataSourceV2 by default (#64821), so read_parquet and friends use the new scan/listing infrastructure with row-group-aware chunking and predicate splitting. Hash Shuffle V2 eliminates the aggregator actor pool. V1 had to provision that pool up front from an estimate of the input size, and its actors accumulated partition shards in actor heap memory, invisible to Ray and unspillable, until finalization. V2 replaces it with two stateless task-based operators, ShuffleMapOp --> ShuffleReduceOp, that pass shards through the object store, so intermediate state spills under pressure and no capacity has to be reserved in advance. The map/reduce barrier itself remains in both designs.
• HashShuffleV2 supports join (#63598, #64538, #64687). This lets shuffles reuse standard map/reduce scheduling, backpressure, and resource accounting.
• Ray Serve: The HAProxy ingress is now distributed as the ray-haproxy PyPI package instead of being compiled into images, and it is the default HAProxy binary (#64141, #64163, #64164). We've also added gRPC support to the HAProxy direct-ingress path, including streaming, metrics, and custom request IDs (#63735, #64310, #64166, #64112). For Ray Serve LLM, we've added experimental KV-cache-aware request routing that tracks replica KV state through an event plane, tokenizes before routing, and routes on prefill/decode token load (#64084, #64085, #64097, #64224, #64327, #64400). KV cache-aware routing’s complete support will land in 2.58.
• Ray Core: We've added an embedded RocksDB storage backend for GCS fault tolerance (REP-64), selectable with RAY_gcs_storage=rocksdb and RAY_gcs_storage_path (#63657). GCS fault tolerance no longer requires an external Redis instance. We've also added a public API for topology-aware scheduling (#63479, #63740).

Ray Data

🎉 New Features

• Enable DataSourceV2 by default via DataContext.use_datasource_v2 (#64821)
• New task-based hash shuffle v2 (ShuffleMapOp → ShuffleReduceOp) with join, multi-input reduce, downstream map fusion, and reducer remote args, behind an env flag (#63598, #64538, #64687, #64438, #64302, #64532, #64481)
• Add a Catalog abstraction with a UnityCatalog implementation that can be passed to read_*, and Unity Catalog write support for Parquet and Iceberg (#64193, #64519)
• Add read_zarr for Zarr datasets (#63003) and read_lerobot for LeRobot v3 datasets (#63821)
• Add PushdownCountFiles optimization to answer count() from Parquet footers (#64763)
• Add common subexpression elimination to the expression optimizer (#63974)
• Add GPU support for Aggregate (#63708)
• Make dataset iteration metrics queryable per split (#64608)
• Add custom operator stats to capture worker-side metrics during task execution (#64221)
• Refactor usage collection into an extensible UsageCallback (#64500)
• Export from_blocks from ray.data (#64127)
• Add support for rapidsmpf-26.4.0 (#64324)

💫 Enhancements

• Hide Ray Data internal frames from user-code error tracebacks (#64587)
• Run projection/predicate pushdown before limit pushdown, block optimization of non-deterministic expressions, and make fuse checks consistent between the logical and physical optimizers (#64651, #64165, #63936)
• Add a two-mode block metadata fetch behind a single MetadataFetcher interface (#64378)
• Use work stealing for file listing and a dynamic work queue for traversals (#64675, #64388)
• Speed up ExecutionResources and the reservation/budget loops on the scheduling hot path (#63964)
• Fix O(n^2) schema reconciliation in unify_schemas and avoid per-column Series materialization in tensor-column casting (#64555, #64038)
• Decouple collate and memory pinning in iter_torch_batches, add per-stage training-thread blocking attribution, bound in-flight iter_threaded items, and finalize after reordering under preserve_order (#64653, #64183, #64219, #64282)
• Add default logical memory for map operators, wire the materializing-op filter into OpResourceAllocator budgeting, and move estimate_object_store_usage into the physical op (#63814, #63665, #63961)
• Decrease the downstream backpressure ratio to 2.0 (#64352)
• Route native S3FileSystem downloads to the PyArrow threaded path (#64089)
• Size hash-shuffle aggregators via bounded online sampling and add a timeout to the batched ray.get when fetching partitions (#63929, #64256)
• Replace manual block reference accounting with Ray Core out-of-scope object callbacks (#64011, #64157, #64191)
• Default read_numpy to allow_pickle=False and make it manually configurable (#64684)
• Make write_lance(mode=CREATE) error instead of silently overwriting (#64364)
• Expand DistributionTracker with merge() and p25/p75, and add dead node counts and detected issues to usage collection (#64074, #64459, #64198)
• Remove cluster autoscaler v1, rename the subcluster label key to ray-subcluster, avoid scaling nodegroups dedicated to the head node, and quiet autoscaling coordinator logs (#64380, #64003, #63918, #63534)
• Delay the "cluster resources not enough" warning until an operator is persistently starved, and lower the high-memory warning threshold (#63969, #64124)
• Remove ExecutionPlan, _num_outputs, batch_format on AllToAllOperators, and InheritBatchFormatRule; use input_dependencies in logical operators (#63662, #64167, #64152, #64149, #64148)
• Deprecate low-level scheduling APIs (DataContext.scheduling_strategy, actor_locality_enabled, exclude_resources, local://) ahead of the actor-only rearchitecture (#64632)
• Migrate the Daft dependency from getdaft to daft (#64240)
• Migrate apply_chat_template/tokenize/detokenize callers to *_stage form, and remove PrepareImageStage while deprecating the image row column (#63590, #63570)

🔨 Fixes

• Fix TensorDtype.__from_arrow__ crash on empty tensor columns (#64767)
• Fix Arrow-backed to_pandas regressions with an opt-out flag and int/float block overflow handling (#64768)

... (truncated)

Commits

• 878455a [docker] Update latest Docker dependencies for 2.57.0 release (#65345)
• 4464bbb [release/2.57.0][Data] Propagate isolate_read_workers to DatasourceV2 (#651...
• 8778646 [Core] Fix Python 3.14 async-actor memory leak by re-anchoring stack … (#65177)
• 133ea78 [2.57.0][core][observability] Fix deadlock between metric registration and co...
• 8d36c02 [release/2.57.0][core] Import pytest lazily in test_utils to fix runtime_env_...
• d1fbb27 [cherry-pick][Observability] Add GPU uuid to the labels of GPU metrics (#6511...
• 44e5ef3 [release-2.57.0][release] Fix huggingface_accelerate release test: floor peft...
• 3971316 [cherry-pick][llm] Add Ray Serve LLM SGLang metrics dashboard (#64797) (#65083)
• 592b3f4 [release/2.57.0][Data] Add tf-keras to text_embedding pip install packages ...
• e09992f [Cherry-pick][2.57.0][release] Add hello_world_py314 smoke release test; fix ...
• Additional commits viewable in compare view

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.

---

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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)

[dependabot]

Superseded by #334.

[dependabot]

Bumps ray from 2.55.1 to 2.57.0.

Release notes

Sourced from ray's releases.

Ray-2.57.0

Highlights

• Ray Data: In this Ray release, we've enabled DataSourceV2 by default (#64821), so read_parquet and friends use the new scan/listing infrastructure with row-group-aware chunking and predicate splitting. Hash Shuffle V2 eliminates the aggregator actor pool. V1 had to provision that pool up front from an estimate of the input size, and its actors accumulated partition shards in actor heap memory, invisible to Ray and unspillable, until finalization. V2 replaces it with two stateless task-based operators, ShuffleMapOp --> ShuffleReduceOp, that pass shards through the object store, so intermediate state spills under pressure and no capacity has to be reserved in advance. The map/reduce barrier itself remains in both designs.
• HashShuffleV2 supports join (#63598, #64538, #64687). This lets shuffles reuse standard map/reduce scheduling, backpressure, and resource accounting.
• Ray Serve: The HAProxy ingress is now distributed as the ray-haproxy PyPI package instead of being compiled into images, and it is the default HAProxy binary (#64141, #64163, #64164). We've also added gRPC support to the HAProxy direct-ingress path, including streaming, metrics, and custom request IDs (#63735, #64310, #64166, #64112). For Ray Serve LLM, we've added experimental KV-cache-aware request routing that tracks replica KV state through an event plane, tokenizes before routing, and routes on prefill/decode token load (#64084, #64085, #64097, #64224, #64327, #64400). KV cache-aware routing’s complete support will land in 2.58.
• Ray Core: We've added an embedded RocksDB storage backend for GCS fault tolerance (REP-64), selectable with RAY_gcs_storage=rocksdb and RAY_gcs_storage_path (#63657). GCS fault tolerance no longer requires an external Redis instance. We've also added a public API for topology-aware scheduling (#63479, #63740).

Ray Data

🎉 New Features

• Enable DataSourceV2 by default via DataContext.use_datasource_v2 (#64821)
• New task-based hash shuffle v2 (ShuffleMapOp → ShuffleReduceOp) with join, multi-input reduce, downstream map fusion, and reducer remote args, behind an env flag (#63598, #64538, #64687, #64438, #64302, #64532, #64481)
• Add a Catalog abstraction with a UnityCatalog implementation that can be passed to read_*, and Unity Catalog write support for Parquet and Iceberg (#64193, #64519)
• Add read_zarr for Zarr datasets (#63003) and read_lerobot for LeRobot v3 datasets (#63821)
• Add PushdownCountFiles optimization to answer count() from Parquet footers (#64763)
• Add common subexpression elimination to the expression optimizer (#63974)
• Add GPU support for Aggregate (#63708)
• Make dataset iteration metrics queryable per split (#64608)
• Add custom operator stats to capture worker-side metrics during task execution (#64221)
• Refactor usage collection into an extensible UsageCallback (#64500)
• Export from_blocks from ray.data (#64127)
• Add support for rapidsmpf-26.4.0 (#64324)

💫 Enhancements

• Hide Ray Data internal frames from user-code error tracebacks (#64587)
• Run projection/predicate pushdown before limit pushdown, block optimization of non-deterministic expressions, and make fuse checks consistent between the logical and physical optimizers (#64651, #64165, #63936)
• Add a two-mode block metadata fetch behind a single MetadataFetcher interface (#64378)
• Use work stealing for file listing and a dynamic work queue for traversals (#64675, #64388)
• Speed up ExecutionResources and the reservation/budget loops on the scheduling hot path (#63964)
• Fix O(n^2) schema reconciliation in unify_schemas and avoid per-column Series materialization in tensor-column casting (#64555, #64038)
• Decouple collate and memory pinning in iter_torch_batches, add per-stage training-thread blocking attribution, bound in-flight iter_threaded items, and finalize after reordering under preserve_order (#64653, #64183, #64219, #64282)
• Add default logical memory for map operators, wire the materializing-op filter into OpResourceAllocator budgeting, and move estimate_object_store_usage into the physical op (#63814, #63665, #63961)
• Decrease the downstream backpressure ratio to 2.0 (#64352)
• Route native S3FileSystem downloads to the PyArrow threaded path (#64089)
• Size hash-shuffle aggregators via bounded online sampling and add a timeout to the batched ray.get when fetching partitions (#63929, #64256)
• Replace manual block reference accounting with Ray Core out-of-scope object callbacks (#64011, #64157, #64191)
• Default read_numpy to allow_pickle=False and make it manually configurable (#64684)
• Make write_lance(mode=CREATE) error instead of silently overwriting (#64364)
• Expand DistributionTracker with merge() and p25/p75, and add dead node counts and detected issues to usage collection (#64074, #64459, #64198)
• Remove cluster autoscaler v1, rename the subcluster label key to ray-subcluster, avoid scaling nodegroups dedicated to the head node, and quiet autoscaling coordinator logs (#64380, #64003, #63918, #63534)
• Delay the "cluster resources not enough" warning until an operator is persistently starved, and lower the high-memory warning threshold (#63969, #64124)
• Remove ExecutionPlan, _num_outputs, batch_format on AllToAllOperators, and InheritBatchFormatRule; use input_dependencies in logical operators (#63662, #64167, #64152, #64149, #64148)
• Deprecate low-level scheduling APIs (DataContext.scheduling_strategy, actor_locality_enabled, exclude_resources, local://) ahead of the actor-only rearchitecture (#64632)
• Migrate the Daft dependency from getdaft to daft (#64240)
• Migrate apply_chat_template/tokenize/detokenize callers to *_stage form, and remove PrepareImageStage while deprecating the image row column (#63590, #63570)

🔨 Fixes

• Fix TensorDtype.__from_arrow__ crash on empty tensor columns (#64767)
• Fix Arrow-backed to_pandas regressions with an opt-out flag and int/float block overflow handling (#64768)

... (truncated)

Commits

• 878455a [docker] Update latest Docker dependencies for 2.57.0 release (#65345)
• 4464bbb [release/2.57.0][Data] Propagate isolate_read_workers to DatasourceV2 (#651...
• 8778646 [Core] Fix Python 3.14 async-actor memory leak by re-anchoring stack … (#65177)
• 133ea78 [2.57.0][core][observability] Fix deadlock between metric registration and co...
• 8d36c02 [release/2.57.0][core] Import pytest lazily in test_utils to fix runtime_env_...
• d1fbb27 [cherry-pick][Observability] Add GPU uuid to the labels of GPU metrics (#6511...
• 44e5ef3 [release-2.57.0][release] Fix huggingface_accelerate release test: floor peft...
• 3971316 [cherry-pick][llm] Add Ray Serve LLM SGLang metrics dashboard (#64797) (#65083)
• 592b3f4 [release/2.57.0][Data] Add tf-keras to text_embedding pip install packages ...
• e09992f [Cherry-pick][2.57.0][release] Add hello_world_py314 smoke release test; fix ...
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Superseded by #334.