Build: Bump ray from 2.55.1 to 2.58.0

[dependabot]

Bumps ray from 2.55.1 to 2.58.0.

Release notes

Sourced from ray's releases.

Ray-2.58.0

Highlights

• Ray Serve LLM: In this release we've completed KV cache and token aware request routing, which was previewed in 2.57. Tokenization now happens in-process on the LLMRouter ingress replica, the routing decision is made there, tokens are transmitted out-of-band so the engine does not re-tokenize, KV lifecycle events are broadcast to every ingress replica (#64642, #64920, #64949, #65010, #65095). KV cache and token aware routing is also aware of CPU KV caches, so offloaded KV cache blocks count toward a replica's cache hit (#65063).
• Ray Core: We enabled the capability to offload task events from. With RAY_enable_task_events_to_dashboard_head on, the task event buffer is replaced by the ray event recorder, events are exported from the aggregator agent to a task events head that keeps an in-memory store, and the state APIs and ray.timeline read from it (#64835, #65028, #65123, #65160, #65218). Enabling the feature removes task event ingestion and serving from the GCS hot path.
• Ray Data: We’ve added Databricks integrations for writing to DeltaLake, with Catalog support. We’ve also shipped a new shuffle v2 backend, featuring improved performance for joins and aggregations.
• Sandboxing: We've also added experimental Ray Sandbox, which runs task and actor code under gVisor and can run Docker-built images directly (#64964, #65570).
• TPU Support: Ray Train adds support for TorchTPU backend (#64796), and Ray Core adds SubslicePlacementGroup for gang scheduling on TPU subslices, single-host TPU support in SlicePlacementGroup, and resource accounting for tpu7x and multi-core chips (#64578, #64079, #64058). This lets TPU slices and subslices be reserved and trained on without external gang-scheduling glue.

Ray Data

🎉 New Features

• Add Dataset.with_columns for multi-column expression projection (#63858)
• Add write_delta for Delta Lake, with catalog support (#64923, #65079)
• Add Torch inference API (#65157)
• Promote hash shuffle v2 to a selectable shuffle strategy, with aggregation support, vectorized aggregation, and block splitting during aggregation (#64953, #64652, #64956, #65329, #64897)
• Add ignore_missing_paths and skip_paths to read_parquet on DatasourceV2 (#65118)
• Add delta_timestamps (temporal windows) to read_lerobot (#64877)
• Collect cluster usage metrics by sampling in background threads during execution (#64686)
• Tolerate actor deaths during init via DataContext.max_consecutive_actor_init_deaths (#64846)
• Expose RAY_DATA_HASH_SHUFFLE_MAP_TASK_TARGET_INPUT_BYTES in DataContext (#65103)

💫 Enhancements

• Generate sortable, collision-resistant dataset IDs (#65075)
• Push Limit into ReadFiles when it sits directly on it, and extract FileIndexer.list_file_infos (#65167, #65168)
• Use BlockRefCounter for object store memory estimation and remove BlockRefCounter.clear() (#64456, #64521)
• Fail execution if no operator makes progress within a timeout, instead of hanging (#65349)
• Improve BatchIterator and iter_torch_batches, and allow a custom collate_fn with a custom device (#64994, #64967)
• Skip downstream-capacity backpressure for eligible materializers (#64844)
• Shard exact download partitioning (#64066)
• Bump the sort_reduce memory multiplier to 3x and stop using estimated_input_blocks as the shuffle partition count (#65176, #65296, #65335)
• Disable cluster autoscaling when PlacementGroupSchedulingStrategy is in use (#64417)
• Propagate the operator name to shuffle tasks (#64802)
• Rename reports_custom_op_stats to should_report_custom_op_stats across all MapTransformFn variants (#64461, #64515)
• Rename allocated to reserved and add type aliases in the autoscaling coordinator (#64997, #65096)
• Deprecate ray_remote_args_fn and Dataset.zip (#64963, #65111)

🔨 Fixes

• Fix an RCE where read_lance or nested pickle objects could execute arbitrary code (#64881)
• Stop converting Arrow null columns to null[pyarrow] in to_pandas (#65187)
• Fix iter_torch_batches device resolution and typing (#65059, #64947)
• Propagate isolate_read_workers to DatasourceV2 (#65191)
• Avoid signalling epoch end for failed attempts (#65082)
• Add tf-keras to the text_embedding pip packages (#64889)

📖 Documentation

... (truncated)

Commits

• 01b49e4 [docker] Update latest Docker dependencies for 2.58.0 release (#65661)
• cec0a09 [cherry-pick][2.58.0][core][sandbox] Make Ray Sandbox run Docker-built images...
• 0fba74d [cherry-pick][2.58.0][Fix][Core] Give canceled_tasks_ its own mutex to brea...
• d8277b5 [cherry-pick][2.58.0][Docs][KubeRay] Use RayJob sample YAML for History Serve...
• d178226 [cherry-pick][2.58.0][docs] add initial documentation for Ray sandboxing (#65...
• c6061c1 [cherry-pick][2.58.0][doc][History server] Update doc for history server for ...
• 3421de4 [cherry-pick][2.58.0][Data] Check object store utilization instead of any spi...
• bc8f786 [cherry-pick][2.58.0][serve][test] Wait for controller after chat-template te...
• f87adb6 [cherry-pick][2.58.0][core] Fix ray.timeline() parity test on windows (#65401...
• 9048902 [cherry-pick][2.58.0][Fix][Core] Report shutdown from check_signals instead...
• 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]

Bumps ray from 2.55.1 to 2.58.0.

Release notes

Sourced from ray's releases.

Ray-2.58.0

Highlights

• Ray Serve LLM: In this release we've completed KV cache and token aware request routing, which was previewed in 2.57. Tokenization now happens in-process on the LLMRouter ingress replica, the routing decision is made there, tokens are transmitted out-of-band so the engine does not re-tokenize, KV lifecycle events are broadcast to every ingress replica (#64642, #64920, #64949, #65010, #65095). KV cache and token aware routing is also aware of CPU KV caches, so offloaded KV cache blocks count toward a replica's cache hit (#65063).
• Ray Core: We enabled the capability to offload task events from. With RAY_enable_task_events_to_dashboard_head on, the task event buffer is replaced by the ray event recorder, events are exported from the aggregator agent to a task events head that keeps an in-memory store, and the state APIs and ray.timeline read from it (#64835, #65028, #65123, #65160, #65218). Enabling the feature removes task event ingestion and serving from the GCS hot path.
• Ray Data: We’ve added Databricks integrations for writing to DeltaLake, with Catalog support. We’ve also shipped a new shuffle v2 backend, featuring improved performance for joins and aggregations.
• Sandboxing: We've also added experimental Ray Sandbox, which runs task and actor code under gVisor and can run Docker-built images directly (#64964, #65570).
• TPU Support: Ray Train adds support for TorchTPU backend (#64796), and Ray Core adds SubslicePlacementGroup for gang scheduling on TPU subslices, single-host TPU support in SlicePlacementGroup, and resource accounting for tpu7x and multi-core chips (#64578, #64079, #64058). This lets TPU slices and subslices be reserved and trained on without external gang-scheduling glue.

Ray Data

🎉 New Features

• Add Dataset.with_columns for multi-column expression projection (#63858)
• Add write_delta for Delta Lake, with catalog support (#64923, #65079)
• Add Torch inference API (#65157)
• Promote hash shuffle v2 to a selectable shuffle strategy, with aggregation support, vectorized aggregation, and block splitting during aggregation (#64953, #64652, #64956, #65329, #64897)
• Add ignore_missing_paths and skip_paths to read_parquet on DatasourceV2 (#65118)
• Add delta_timestamps (temporal windows) to read_lerobot (#64877)
• Collect cluster usage metrics by sampling in background threads during execution (#64686)
• Tolerate actor deaths during init via DataContext.max_consecutive_actor_init_deaths (#64846)
• Expose RAY_DATA_HASH_SHUFFLE_MAP_TASK_TARGET_INPUT_BYTES in DataContext (#65103)

💫 Enhancements

• Generate sortable, collision-resistant dataset IDs (#65075)
• Push Limit into ReadFiles when it sits directly on it, and extract FileIndexer.list_file_infos (#65167, #65168)
• Use BlockRefCounter for object store memory estimation and remove BlockRefCounter.clear() (#64456, #64521)
• Fail execution if no operator makes progress within a timeout, instead of hanging (#65349)
• Improve BatchIterator and iter_torch_batches, and allow a custom collate_fn with a custom device (#64994, #64967)
• Skip downstream-capacity backpressure for eligible materializers (#64844)
• Shard exact download partitioning (#64066)
• Bump the sort_reduce memory multiplier to 3x and stop using estimated_input_blocks as the shuffle partition count (#65176, #65296, #65335)
• Disable cluster autoscaling when PlacementGroupSchedulingStrategy is in use (#64417)
• Propagate the operator name to shuffle tasks (#64802)
• Rename reports_custom_op_stats to should_report_custom_op_stats across all MapTransformFn variants (#64461, #64515)
• Rename allocated to reserved and add type aliases in the autoscaling coordinator (#64997, #65096)
• Deprecate ray_remote_args_fn and Dataset.zip (#64963, #65111)

🔨 Fixes

• Fix an RCE where read_lance or nested pickle objects could execute arbitrary code (#64881)
• Stop converting Arrow null columns to null[pyarrow] in to_pandas (#65187)
• Fix iter_torch_batches device resolution and typing (#65059, #64947)
• Propagate isolate_read_workers to DatasourceV2 (#65191)
• Avoid signalling epoch end for failed attempts (#65082)
• Add tf-keras to the text_embedding pip packages (#64889)

📖 Documentation

... (truncated)

Commits

• 01b49e4 [docker] Update latest Docker dependencies for 2.58.0 release (#65661)
• cec0a09 [cherry-pick][2.58.0][core][sandbox] Make Ray Sandbox run Docker-built images...
• 0fba74d [cherry-pick][2.58.0][Fix][Core] Give canceled_tasks_ its own mutex to brea...
• d8277b5 [cherry-pick][2.58.0][Docs][KubeRay] Use RayJob sample YAML for History Serve...
• d178226 [cherry-pick][2.58.0][docs] add initial documentation for Ray sandboxing (#65...
• c6061c1 [cherry-pick][2.58.0][doc][History server] Update doc for history server for ...
• 3421de4 [cherry-pick][2.58.0][Data] Check object store utilization instead of any spi...
• bc8f786 [cherry-pick][2.58.0][serve][test] Wait for controller after chat-template te...
• f87adb6 [cherry-pick][2.58.0][core] Fix ray.timeline() parity test on windows (#65401...
• 9048902 [cherry-pick][2.58.0][Fix][Core] Report shutdown from check_signals instead...
• 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]

Bumps ray from 2.55.1 to 2.58.0.

Release notes

Sourced from ray's releases.

Ray-2.58.0

Highlights

• Ray Serve LLM: In this release we've completed KV cache and token aware request routing, which was previewed in 2.57. Tokenization now happens in-process on the LLMRouter ingress replica, the routing decision is made there, tokens are transmitted out-of-band so the engine does not re-tokenize, KV lifecycle events are broadcast to every ingress replica (#64642, #64920, #64949, #65010, #65095). KV cache and token aware routing is also aware of CPU KV caches, so offloaded KV cache blocks count toward a replica's cache hit (#65063).
• Ray Core: We enabled the capability to offload task events from. With RAY_enable_task_events_to_dashboard_head on, the task event buffer is replaced by the ray event recorder, events are exported from the aggregator agent to a task events head that keeps an in-memory store, and the state APIs and ray.timeline read from it (#64835, #65028, #65123, #65160, #65218). Enabling the feature removes task event ingestion and serving from the GCS hot path.
• Ray Data: We’ve added Databricks integrations for writing to DeltaLake, with Catalog support. We’ve also shipped a new shuffle v2 backend, featuring improved performance for joins and aggregations.
• Sandboxing: We've also added experimental Ray Sandbox, which runs task and actor code under gVisor and can run Docker-built images directly (#64964, #65570).
• TPU Support: Ray Train adds support for TorchTPU backend (#64796), and Ray Core adds SubslicePlacementGroup for gang scheduling on TPU subslices, single-host TPU support in SlicePlacementGroup, and resource accounting for tpu7x and multi-core chips (#64578, #64079, #64058). This lets TPU slices and subslices be reserved and trained on without external gang-scheduling glue.

Ray Data

🎉 New Features

• Add Dataset.with_columns for multi-column expression projection (#63858)
• Add write_delta for Delta Lake, with catalog support (#64923, #65079)
• Add Torch inference API (#65157)
• Promote hash shuffle v2 to a selectable shuffle strategy, with aggregation support, vectorized aggregation, and block splitting during aggregation (#64953, #64652, #64956, #65329, #64897)
• Add ignore_missing_paths and skip_paths to read_parquet on DatasourceV2 (#65118)
• Add delta_timestamps (temporal windows) to read_lerobot (#64877)
• Collect cluster usage metrics by sampling in background threads during execution (#64686)
• Tolerate actor deaths during init via DataContext.max_consecutive_actor_init_deaths (#64846)
• Expose RAY_DATA_HASH_SHUFFLE_MAP_TASK_TARGET_INPUT_BYTES in DataContext (#65103)

💫 Enhancements

• Generate sortable, collision-resistant dataset IDs (#65075)
• Push Limit into ReadFiles when it sits directly on it, and extract FileIndexer.list_file_infos (#65167, #65168)
• Use BlockRefCounter for object store memory estimation and remove BlockRefCounter.clear() (#64456, #64521)
• Fail execution if no operator makes progress within a timeout, instead of hanging (#65349)
• Improve BatchIterator and iter_torch_batches, and allow a custom collate_fn with a custom device (#64994, #64967)
• Skip downstream-capacity backpressure for eligible materializers (#64844)
• Shard exact download partitioning (#64066)
• Bump the sort_reduce memory multiplier to 3x and stop using estimated_input_blocks as the shuffle partition count (#65176, #65296, #65335)
• Disable cluster autoscaling when PlacementGroupSchedulingStrategy is in use (#64417)
• Propagate the operator name to shuffle tasks (#64802)
• Rename reports_custom_op_stats to should_report_custom_op_stats across all MapTransformFn variants (#64461, #64515)
• Rename allocated to reserved and add type aliases in the autoscaling coordinator (#64997, #65096)
• Deprecate ray_remote_args_fn and Dataset.zip (#64963, #65111)

🔨 Fixes

• Fix an RCE where read_lance or nested pickle objects could execute arbitrary code (#64881)
• Stop converting Arrow null columns to null[pyarrow] in to_pandas (#65187)
• Fix iter_torch_batches device resolution and typing (#65059, #64947)
• Propagate isolate_read_workers to DatasourceV2 (#65191)
• Avoid signalling epoch end for failed attempts (#65082)
• Add tf-keras to the text_embedding pip packages (#64889)

📖 Documentation

... (truncated)

Commits

• 01b49e4 [docker] Update latest Docker dependencies for 2.58.0 release (#65661)
• cec0a09 [cherry-pick][2.58.0][core][sandbox] Make Ray Sandbox run Docker-built images...
• 0fba74d [cherry-pick][2.58.0][Fix][Core] Give canceled_tasks_ its own mutex to brea...
• d8277b5 [cherry-pick][2.58.0][Docs][KubeRay] Use RayJob sample YAML for History Serve...
• d178226 [cherry-pick][2.58.0][docs] add initial documentation for Ray sandboxing (#65...
• c6061c1 [cherry-pick][2.58.0][doc][History server] Update doc for history server for ...
• 3421de4 [cherry-pick][2.58.0][Data] Check object store utilization instead of any spi...
• bc8f786 [cherry-pick][2.58.0][serve][test] Wait for controller after chat-template te...
• f87adb6 [cherry-pick][2.58.0][core] Fix ray.timeline() parity test on windows (#65401...
• 9048902 [cherry-pick][2.58.0][Fix][Core] Report shutdown from check_signals instead...
• 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
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• @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 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]

Bumps ray from 2.55.1 to 2.58.0.

Release notes

Sourced from ray's releases.

Ray-2.58.0

Highlights

• Ray Serve LLM: In this release we've completed KV cache and token aware request routing, which was previewed in 2.57. Tokenization now happens in-process on the LLMRouter ingress replica, the routing decision is made there, tokens are transmitted out-of-band so the engine does not re-tokenize, KV lifecycle events are broadcast to every ingress replica (#64642, #64920, #64949, #65010, #65095). KV cache and token aware routing is also aware of CPU KV caches, so offloaded KV cache blocks count toward a replica's cache hit (#65063).
• Ray Core: We enabled the capability to offload task events from. With RAY_enable_task_events_to_dashboard_head on, the task event buffer is replaced by the ray event recorder, events are exported from the aggregator agent to a task events head that keeps an in-memory store, and the state APIs and ray.timeline read from it (#64835, #65028, #65123, #65160, #65218). Enabling the feature removes task event ingestion and serving from the GCS hot path.
• Ray Data: We’ve added Databricks integrations for writing to DeltaLake, with Catalog support. We’ve also shipped a new shuffle v2 backend, featuring improved performance for joins and aggregations.
• Sandboxing: We've also added experimental Ray Sandbox, which runs task and actor code under gVisor and can run Docker-built images directly (#64964, #65570).
• TPU Support: Ray Train adds support for TorchTPU backend (#64796), and Ray Core adds SubslicePlacementGroup for gang scheduling on TPU subslices, single-host TPU support in SlicePlacementGroup, and resource accounting for tpu7x and multi-core chips (#64578, #64079, #64058). This lets TPU slices and subslices be reserved and trained on without external gang-scheduling glue.

Ray Data

🎉 New Features

• Add Dataset.with_columns for multi-column expression projection (#63858)
• Add write_delta for Delta Lake, with catalog support (#64923, #65079)
• Add Torch inference API (#65157)
• Promote hash shuffle v2 to a selectable shuffle strategy, with aggregation support, vectorized aggregation, and block splitting during aggregation (#64953, #64652, #64956, #65329, #64897)
• Add ignore_missing_paths and skip_paths to read_parquet on DatasourceV2 (#65118)
• Add delta_timestamps (temporal windows) to read_lerobot (#64877)
• Collect cluster usage metrics by sampling in background threads during execution (#64686)
• Tolerate actor deaths during init via DataContext.max_consecutive_actor_init_deaths (#64846)
• Expose RAY_DATA_HASH_SHUFFLE_MAP_TASK_TARGET_INPUT_BYTES in DataContext (#65103)

💫 Enhancements

• Generate sortable, collision-resistant dataset IDs (#65075)
• Push Limit into ReadFiles when it sits directly on it, and extract FileIndexer.list_file_infos (#65167, #65168)
• Use BlockRefCounter for object store memory estimation and remove BlockRefCounter.clear() (#64456, #64521)
• Fail execution if no operator makes progress within a timeout, instead of hanging (#65349)
• Improve BatchIterator and iter_torch_batches, and allow a custom collate_fn with a custom device (#64994, #64967)
• Skip downstream-capacity backpressure for eligible materializers (#64844)
• Shard exact download partitioning (#64066)
• Bump the sort_reduce memory multiplier to 3x and stop using estimated_input_blocks as the shuffle partition count (#65176, #65296, #65335)
• Disable cluster autoscaling when PlacementGroupSchedulingStrategy is in use (#64417)
• Propagate the operator name to shuffle tasks (#64802)
• Rename reports_custom_op_stats to should_report_custom_op_stats across all MapTransformFn variants (#64461, #64515)
• Rename allocated to reserved and add type aliases in the autoscaling coordinator (#64997, #65096)
• Deprecate ray_remote_args_fn and Dataset.zip (#64963, #65111)

🔨 Fixes

• Fix an RCE where read_lance or nested pickle objects could execute arbitrary code (#64881)
• Stop converting Arrow null columns to null[pyarrow] in to_pandas (#65187)
• Fix iter_torch_batches device resolution and typing (#65059, #64947)
• Propagate isolate_read_workers to DatasourceV2 (#65191)
• Avoid signalling epoch end for failed attempts (#65082)
• Add tf-keras to the text_embedding pip packages (#64889)

📖 Documentation

... (truncated)

Commits

• 01b49e4 [docker] Update latest Docker dependencies for 2.58.0 release (#65661)
• cec0a09 [cherry-pick][2.58.0][core][sandbox] Make Ray Sandbox run Docker-built images...
• 0fba74d [cherry-pick][2.58.0][Fix][Core] Give canceled_tasks_ its own mutex to brea...
• d8277b5 [cherry-pick][2.58.0][Docs][KubeRay] Use RayJob sample YAML for History Serve...
• d178226 [cherry-pick][2.58.0][docs] add initial documentation for Ray sandboxing (#65...
• c6061c1 [cherry-pick][2.58.0][doc][History server] Update doc for history server for ...
• 3421de4 [cherry-pick][2.58.0][Data] Check object store utilization instead of any spi...
• bc8f786 [cherry-pick][2.58.0][serve][test] Wait for controller after chat-template te...
• f87adb6 [cherry-pick][2.58.0][core] Fix ray.timeline() parity test on windows (#65401...
• 9048902 [cherry-pick][2.58.0][Fix][Core] Report shutdown from check_signals instead...
• 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.

---

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)

[dependabot]

Bumps ray from 2.55.1 to 2.58.0.

Release notes

Sourced from ray's releases.

Ray-2.58.0

Highlights

• Ray Serve LLM: In this release we've completed KV cache and token aware request routing, which was previewed in 2.57. Tokenization now happens in-process on the LLMRouter ingress replica, the routing decision is made there, tokens are transmitted out-of-band so the engine does not re-tokenize, KV lifecycle events are broadcast to every ingress replica (#64642, #64920, #64949, #65010, #65095). KV cache and token aware routing is also aware of CPU KV caches, so offloaded KV cache blocks count toward a replica's cache hit (#65063).
• Ray Core: We enabled the capability to offload task events from. With RAY_enable_task_events_to_dashboard_head on, the task event buffer is replaced by the ray event recorder, events are exported from the aggregator agent to a task events head that keeps an in-memory store, and the state APIs and ray.timeline read from it (#64835, #65028, #65123, #65160, #65218). Enabling the feature removes task event ingestion and serving from the GCS hot path.
• Ray Data: We’ve added Databricks integrations for writing to DeltaLake, with Catalog support. We’ve also shipped a new shuffle v2 backend, featuring improved performance for joins and aggregations.
• Sandboxing: We've also added experimental Ray Sandbox, which runs task and actor code under gVisor and can run Docker-built images directly (#64964, #65570).
• TPU Support: Ray Train adds support for TorchTPU backend (#64796), and Ray Core adds SubslicePlacementGroup for gang scheduling on TPU subslices, single-host TPU support in SlicePlacementGroup, and resource accounting for tpu7x and multi-core chips (#64578, #64079, #64058). This lets TPU slices and subslices be reserved and trained on without external gang-scheduling glue.

Ray Data

🎉 New Features

• Add Dataset.with_columns for multi-column expression projection (#63858)
• Add write_delta for Delta Lake, with catalog support (#64923, #65079)
• Add Torch inference API (#65157)
• Promote hash shuffle v2 to a selectable shuffle strategy, with aggregation support, vectorized aggregation, and block splitting during aggregation (#64953, #64652, #64956, #65329, #64897)
• Add ignore_missing_paths and skip_paths to read_parquet on DatasourceV2 (#65118)
• Add delta_timestamps (temporal windows) to read_lerobot (#64877)
• Collect cluster usage metrics by sampling in background threads during execution (#64686)
• Tolerate actor deaths during init via DataContext.max_consecutive_actor_init_deaths (#64846)
• Expose RAY_DATA_HASH_SHUFFLE_MAP_TASK_TARGET_INPUT_BYTES in DataContext (#65103)

💫 Enhancements

• Generate sortable, collision-resistant dataset IDs (#65075)
• Push Limit into ReadFiles when it sits directly on it, and extract FileIndexer.list_file_infos (#65167, #65168)
• Use BlockRefCounter for object store memory estimation and remove BlockRefCounter.clear() (#64456, #64521)
• Fail execution if no operator makes progress within a timeout, instead of hanging (#65349)
• Improve BatchIterator and iter_torch_batches, and allow a custom collate_fn with a custom device (#64994, #64967)
• Skip downstream-capacity backpressure for eligible materializers (#64844)
• Shard exact download partitioning (#64066)
• Bump the sort_reduce memory multiplier to 3x and stop using estimated_input_blocks as the shuffle partition count (#65176, #65296, #65335)
• Disable cluster autoscaling when PlacementGroupSchedulingStrategy is in use (#64417)
• Propagate the operator name to shuffle tasks (#64802)
• Rename reports_custom_op_stats to should_report_custom_op_stats across all MapTransformFn variants (#64461, #64515)
• Rename allocated to reserved and add type aliases in the autoscaling coordinator (#64997, #65096)
• Deprecate ray_remote_args_fn and Dataset.zip (#64963, #65111)

🔨 Fixes

• Fix an RCE where read_lance or nested pickle objects could execute arbitrary code (#64881)
• Stop converting Arrow null columns to null[pyarrow] in to_pandas (#65187)
• Fix iter_torch_batches device resolution and typing (#65059, #64947)
• Propagate isolate_read_workers to DatasourceV2 (#65191)
• Avoid signalling epoch end for failed attempts (#65082)
• Add tf-keras to the text_embedding pip packages (#64889)

📖 Documentation

... (truncated)

Commits

• 01b49e4 [docker] Update latest Docker dependencies for 2.58.0 release (#65661)
• cec0a09 [cherry-pick][2.58.0][core][sandbox] Make Ray Sandbox run Docker-built images...
• 0fba74d [cherry-pick][2.58.0][Fix][Core] Give canceled_tasks_ its own mutex to brea...
• d8277b5 [cherry-pick][2.58.0][Docs][KubeRay] Use RayJob sample YAML for History Serve...
• d178226 [cherry-pick][2.58.0][docs] add initial documentation for Ray sandboxing (#65...
• c6061c1 [cherry-pick][2.58.0][doc][History server] Update doc for history server for ...
• 3421de4 [cherry-pick][2.58.0][Data] Check object store utilization instead of any spi...
• bc8f786 [cherry-pick][2.58.0][serve][test] Wait for controller after chat-template te...
• f87adb6 [cherry-pick][2.58.0][core] Fix ray.timeline() parity test on windows (#65401...
• 9048902 [cherry-pick][2.58.0][Fix][Core] Report shutdown from check_signals instead...
• Additional commits viewable in compare view

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[dependabot]

Bumps ray from 2.55.1 to 2.58.0.

Release notes

Sourced from ray's releases.

Ray-2.58.0

Highlights

• Ray Serve LLM: In this release we've completed KV cache and token aware request routing, which was previewed in 2.57. Tokenization now happens in-process on the LLMRouter ingress replica, the routing decision is made there, tokens are transmitted out-of-band so the engine does not re-tokenize, KV lifecycle events are broadcast to every ingress replica (#64642, #64920, #64949, #65010, #65095). KV cache and token aware routing is also aware of CPU KV caches, so offloaded KV cache blocks count toward a replica's cache hit (#65063).
• Ray Core: We enabled the capability to offload task events from. With RAY_enable_task_events_to_dashboard_head on, the task event buffer is replaced by the ray event recorder, events are exported from the aggregator agent to a task events head that keeps an in-memory store, and the state APIs and ray.timeline read from it (#64835, #65028, #65123, #65160, #65218). Enabling the feature removes task event ingestion and serving from the GCS hot path.
• Ray Data: We’ve added Databricks integrations for writing to DeltaLake, with Catalog support. We’ve also shipped a new shuffle v2 backend, featuring improved performance for joins and aggregations.
• Sandboxing: We've also added experimental Ray Sandbox, which runs task and actor code under gVisor and can run Docker-built images directly (#64964, #65570).
• TPU Support: Ray Train adds support for TorchTPU backend (#64796), and Ray Core adds SubslicePlacementGroup for gang scheduling on TPU subslices, single-host TPU support in SlicePlacementGroup, and resource accounting for tpu7x and multi-core chips (#64578, #64079, #64058). This lets TPU slices and subslices be reserved and trained on without external gang-scheduling glue.

Ray Data

🎉 New Features

• Add Dataset.with_columns for multi-column expression projection (#63858)
• Add write_delta for Delta Lake, with catalog support (#64923, #65079)
• Add Torch inference API (#65157)
• Promote hash shuffle v2 to a selectable shuffle strategy, with aggregation support, vectorized aggregation, and block splitting during aggregation (#64953, #64652, #64956, #65329, #64897)
• Add ignore_missing_paths and skip_paths to read_parquet on DatasourceV2 (#65118)
• Add delta_timestamps (temporal windows) to read_lerobot (#64877)
• Collect cluster usage metrics by sampling in background threads during execution (#64686)
• Tolerate actor deaths during init via DataContext.max_consecutive_actor_init_deaths (#64846)
• Expose RAY_DATA_HASH_SHUFFLE_MAP_TASK_TARGET_INPUT_BYTES in DataContext (#65103)

💫 Enhancements

• Generate sortable, collision-resistant dataset IDs (#65075)
• Push Limit into ReadFiles when it sits directly on it, and extract FileIndexer.list_file_infos (#65167, #65168)
• Use BlockRefCounter for object store memory estimation and remove BlockRefCounter.clear() (#64456, #64521)
• Fail execution if no operator makes progress within a timeout, instead of hanging (#65349)
• Improve BatchIterator and iter_torch_batches, and allow a custom collate_fn with a custom device (#64994, #64967)
• Skip downstream-capacity backpressure for eligible materializers (#64844)
• Shard exact download partitioning (#64066)
• Bump the sort_reduce memory multiplier to 3x and stop using estimated_input_blocks as the shuffle partition count (#65176, #65296, #65335)
• Disable cluster autoscaling when PlacementGroupSchedulingStrategy is in use (#64417)
• Propagate the operator name to shuffle tasks (#64802)
• Rename reports_custom_op_stats to should_report_custom_op_stats across all MapTransformFn variants (#64461, #64515)
• Rename allocated to reserved and add type aliases in the autoscaling coordinator (#64997, #65096)
• Deprecate ray_remote_args_fn and Dataset.zip (#64963, #65111)

🔨 Fixes

• Fix an RCE where read_lance or nested pickle objects could execute arbitrary code (#64881)
• Stop converting Arrow null columns to null[pyarrow] in to_pandas (#65187)
• Fix iter_torch_batches device resolution and typing (#65059, #64947)
• Propagate isolate_read_workers to DatasourceV2 (#65191)
• Avoid signalling epoch end for failed attempts (#65082)
• Add tf-keras to the text_embedding pip packages (#64889)

📖 Documentation

... (truncated)

Commits

• 01b49e4 [docker] Update latest Docker dependencies for 2.58.0 release (#65661)
• cec0a09 [cherry-pick][2.58.0][core][sandbox] Make Ray Sandbox run Docker-built images...
• 0fba74d [cherry-pick][2.58.0][Fix][Core] Give canceled_tasks_ its own mutex to brea...
• d8277b5 [cherry-pick][2.58.0][Docs][KubeRay] Use RayJob sample YAML for History Serve...
• d178226 [cherry-pick][2.58.0][docs] add initial documentation for Ray sandboxing (#65...
• c6061c1 [cherry-pick][2.58.0][doc][History server] Update doc for history server for ...
• 3421de4 [cherry-pick][2.58.0][Data] Check object store utilization instead of any spi...
• bc8f786 [cherry-pick][2.58.0][serve][test] Wait for controller after chat-template te...
• f87adb6 [cherry-pick][2.58.0][core] Fix ray.timeline() parity test on windows (#65401...
• 9048902 [cherry-pick][2.58.0][Fix][Core] Report shutdown from check_signals instead...
• 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 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]

Bumps ray from 2.55.1 to 2.58.0.

Release notes

Sourced from ray's releases.

Ray-2.58.0

Highlights

• Ray Serve LLM: In this release we've completed KV cache and token aware request routing, which was previewed in 2.57. Tokenization now happens in-process on the LLMRouter ingress replica, the routing decision is made there, tokens are transmitted out-of-band so the engine does not re-tokenize, KV lifecycle events are broadcast to every ingress replica (#64642, #64920, #64949, #65010, #65095). KV cache and token aware routing is also aware of CPU KV caches, so offloaded KV cache blocks count toward a replica's cache hit (#65063).
• Ray Core: We enabled the capability to offload task events from. With RAY_enable_task_events_to_dashboard_head on, the task event buffer is replaced by the ray event recorder, events are exported from the aggregator agent to a task events head that keeps an in-memory store, and the state APIs and ray.timeline read from it (#64835, #65028, #65123, #65160, #65218). Enabling the feature removes task event ingestion and serving from the GCS hot path.
• Ray Data: We’ve added Databricks integrations for writing to DeltaLake, with Catalog support. We’ve also shipped a new shuffle v2 backend, featuring improved performance for joins and aggregations.
• Sandboxing: We've also added experimental Ray Sandbox, which runs task and actor code under gVisor and can run Docker-built images directly (#64964, #65570).
• TPU Support: Ray Train adds support for TorchTPU backend (#64796), and Ray Core adds SubslicePlacementGroup for gang scheduling on TPU subslices, single-host TPU support in SlicePlacementGroup, and resource accounting for tpu7x and multi-core chips (#64578, #64079, #64058). This lets TPU slices and subslices be reserved and trained on without external gang-scheduling glue.

Ray Data

🎉 New Features

• Add Dataset.with_columns for multi-column expression projection (#63858)
• Add write_delta for Delta Lake, with catalog support (#64923, #65079)
• Add Torch inference API (#65157)
• Promote hash shuffle v2 to a selectable shuffle strategy, with aggregation support, vectorized aggregation, and block splitting during aggregation (#64953, #64652, #64956, #65329, #64897)
• Add ignore_missing_paths and skip_paths to read_parquet on DatasourceV2 (#65118)
• Add delta_timestamps (temporal windows) to read_lerobot (#64877)
• Collect cluster usage metrics by sampling in background threads during execution (#64686)
• Tolerate actor deaths during init via DataContext.max_consecutive_actor_init_deaths (#64846)
• Expose RAY_DATA_HASH_SHUFFLE_MAP_TASK_TARGET_INPUT_BYTES in DataContext (#65103)

💫 Enhancements

• Generate sortable, collision-resistant dataset IDs (#65075)
• Push Limit into ReadFiles when it sits directly on it, and extract FileIndexer.list_file_infos (#65167, #65168)
• Use BlockRefCounter for object store memory estimation and remove BlockRefCounter.clear() (#64456, #64521)
• Fail execution if no operator makes progress within a timeout, instead of hanging (#65349)
• Improve BatchIterator and iter_torch_batches, and allow a custom collate_fn with a custom device (#64994, #64967)
• Skip downstream-capacity backpressure for eligible materializers (#64844)
• Shard exact download partitioning (#64066)
• Bump the sort_reduce memory multiplier to 3x and stop using estimated_input_blocks as the shuffle partition count (#65176, #65296, #65335)
• Disable cluster autoscaling when PlacementGroupSchedulingStrategy is in use (#64417)
• Propagate the operator name to shuffle tasks (#64802)
• Rename reports_custom_op_stats to should_report_custom_op_stats across all MapTransformFn variants (#64461, #64515)
• Rename allocated to reserved and add type aliases in the autoscaling coordinator (#64997, #65096)
• Deprecate ray_remote_args_fn and Dataset.zip (#64963, #65111)

🔨 Fixes

• Fix an RCE where read_lance or nested pickle objects could execute arbitrary code (#64881)
• Stop converting Arrow null columns to null[pyarrow] in to_pandas (#65187)
• Fix iter_torch_batches device resolution and typing (#65059, #64947)
• Propagate isolate_read_workers to DatasourceV2 (#65191)
• Avoid signalling epoch end for failed attempts (#65082)
• Add tf-keras to the text_embedding pip packages (#64889)

📖 Documentation

... (truncated)

Commits

• 01b49e4 [docker] Update latest Docker dependencies for 2.58.0 release (#65661)
• cec0a09 [cherry-pick][2.58.0][core][sandbox] Make Ray Sandbox run Docker-built images...
• 0fba74d [cherry-pick][2.58.0][Fix][Core] Give canceled_tasks_ its own mutex to brea...
• d8277b5 [cherry-pick][2.58.0][Docs][KubeRay] Use RayJob sample YAML for History Serve...
• d178226 [cherry-pick][2.58.0][docs] add initial documentation for Ray sandboxing (#65...
• c6061c1 [cherry-pick][2.58.0][doc][History server] Update doc for history server for ...
• 3421de4 [cherry-pick][2.58.0][Data] Check object store utilization instead of any spi...
• bc8f786 [cherry-pick][2.58.0][serve][test] Wait for controller after chat-template te...
• f87adb6 [cherry-pick][2.58.0][core] Fix ray.timeline() parity test on windows (#65401...
• 9048902 [cherry-pick][2.58.0][Fix][Core] Report shutdown from check_signals instead...
• 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 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]

Bumps ray from 2.55.1 to 2.58.0.

Release notes

Sourced from ray's releases.

Ray-2.58.0

Highlights

• Ray Serve LLM: In this release we've completed KV cache and token aware request routing, which was previewed in 2.57. Tokenization now happens in-process on the LLMRouter ingress replica, the routing decision is made there, tokens are transmitted out-of-band so the engine does not re-tokenize, KV lifecycle events are broadcast to every ingress replica (#64642, #64920, #64949, #65010, #65095). KV cache and token aware routing is also aware of CPU KV caches, so offloaded KV cache blocks count toward a replica's cache hit (#65063).
• Ray Core: We enabled the capability to offload task events from. With RAY_enable_task_events_to_dashboard_head on, the task event buffer is replaced by the ray event recorder, events are exported from the aggregator agent to a task events head that keeps an in-memory store, and the state APIs and ray.timeline read from it (#64835, #65028, #65123, #65160, #65218). Enabling the feature removes task event ingestion and serving from the GCS hot path.
• Ray Data: We’ve added Databricks integrations for writing to DeltaLake, with Catalog support. We’ve also shipped a new shuffle v2 backend, featuring improved performance for joins and aggregations.
• Sandboxing: We've also added experimental Ray Sandbox, which runs task and actor code under gVisor and can run Docker-built images directly (#64964, #65570).
• TPU Support: Ray Train adds support for TorchTPU backend (#64796), and Ray Core adds SubslicePlacementGroup for gang scheduling on TPU subslices, single-host TPU support in SlicePlacementGroup, and resource accounting for tpu7x and multi-core chips (#64578, #64079, #64058). This lets TPU slices and subslices be reserved and trained on without external gang-scheduling glue.

Ray Data

🎉 New Features

• Add Dataset.with_columns for multi-column expression projection (#63858)
• Add write_delta for Delta Lake, with catalog support (#64923, #65079)
• Add Torch inference API (#65157)
• Promote hash shuffle v2 to a selectable shuffle strategy, with aggregation support, vectorized aggregation, and block splitting during aggregation (#64953, #64652, #64956, #65329, #64897)
• Add ignore_missing_paths and skip_paths to read_parquet on DatasourceV2 (#65118)
• Add delta_timestamps (temporal windows) to read_lerobot (#64877)
• Collect cluster usage metrics by sampling in background threads during execution (#64686)
• Tolerate actor deaths during init via DataContext.max_consecutive_actor_init_deaths (#64846)
• Expose RAY_DATA_HASH_SHUFFLE_MAP_TASK_TARGET_INPUT_BYTES in DataContext (#65103)

💫 Enhancements

• Generate sortable, collision-resistant dataset IDs (#65075)
• Push Limit into ReadFiles when it sits directly on it, and extract FileIndexer.list_file_infos (#65167, #65168)
• Use BlockRefCounter for object store memory estimation and remove BlockRefCounter.clear() (#64456, #64521)
• Fail execution if no operator makes progress within a timeout, instead of hanging (#65349)
• Improve BatchIterator and iter_torch_batches, and allow a custom collate_fn with a custom device (#64994, #64967)
• Skip downstream-capacity backpressure for eligible materializers (#64844)
• Shard exact download partitioning (#64066)
• Bump the sort_reduce memory multiplier to 3x and stop using estimated_input_blocks as the shuffle partition count (#65176, #65296, #65335)
• Disable cluster autoscaling when PlacementGroupSchedulingStrategy is in use (#64417)
• Propagate the operator name to shuffle tasks (#64802)
• Rename reports_custom_op_stats to should_report_custom_op_stats across all MapTransformFn variants (#64461, #64515)
• Rename allocated to reserved and add type aliases in the autoscaling coordinator (#64997, #65096)
• Deprecate ray_remote_args_fn and Dataset.zip (#64963, #65111)

🔨 Fixes

• Fix an RCE where read_lance or nested pickle objects could execute arbitrary code (#64881)
• Stop converting Arrow null columns to null[pyarrow] in to_pandas (#65187)
• Fix iter_torch_batches device resolution and typing (#65059, #64947)
• Propagate isolate_read_workers to DatasourceV2 (#65191)
• Avoid signalling epoch end for failed attempts (#65082)
• Add tf-keras to the text_embedding pip packages (#64889)

📖 Documentation

... (truncated)

Commits

• 01b49e4 [docker] Update latest Docker dependencies for 2.58.0 release (#65661)
• cec0a09 [cherry-pick][2.58.0][core][sandbox] Make Ray Sandbox run Docker-built images...
• 0fba74d [cherry-pick][2.58.0][Fix][Core] Give canceled_tasks_ its own mutex to brea...
• d8277b5 [cherry-pick][2.58.0][Docs][KubeRay] Use RayJob sample YAML for History Serve...
• d178226 [cherry-pick][2.58.0][docs] add initial documentation for Ray sandboxing (#65...
• c6061c1 [cherry-pick][2.58.0][doc][History server] Update doc for history server for ...
• 3421de4 [cherry-pick][2.58.0][Data] Check object store utilization instead of any spi...
• bc8f786 [cherry-pick][2.58.0][serve][test] Wait for controller after chat-template te...
• f87adb6 [cherry-pick][2.58.0][core] Fix ray.timeline() parity test on windows (#65401...
• 9048902 [cherry-pick][2.58.0][Fix][Core] Report shutdown from check_signals instead...
• 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)