class ray.data.ExecutionOptions(resource_limits: ExecutionResources | None = None, exclude_resources: ExecutionResources | None = None, locality_with_output: bool | List[str] = False, preserve_order: bool = False, actor_locality_enabled: bool = False, verbose_progress: bool | None = None)[source]#

Common options for execution.

Some options may not be supported on all executors (e.g., resource limits).


Set a soft limit on the resource usage during execution. Autodetected by default.


Amount of resources to exclude from Ray Data. Set this if you have other workloads running on the same cluster. Note, - If using Ray Data with Ray Train, training resources will be automatically excluded. - For each resource type, resource_limits and exclude_resources can not be both set.


Set this to prefer running tasks on the same node as the output node (node driving the execution). It can also be set to a list of node ids to spread the outputs across those nodes. Off by default.


Set this to preserve the ordering between blocks processed by operators. Off by default.


Whether to enable locality-aware task dispatch to actors (off by default). This parameter applies to both stateful map and streaming_split operations.


Whether to report progress individually per operator. By default, only AllToAll operators and global progress is reported. This option is useful for performance debugging. On by default.

DeveloperAPI: This API may change across minor Ray releases.


Returns True if resource_limits is the default value.

validate() None[source]#

Validate the options.