ray.data.ExecutionResources#

class ray.data.ExecutionResources(cpu: float | None = None, gpu: float | None = None, object_store_memory: float | None = None, memory: float | None = None)[source]#

Bases: object

Specifies resources usage or resource limits for execution.

By default this class represents resource usage. Use for_limits or set default_to_inf to True to create an object that represents resource limits.

classmethod from_resource_dict(resource_dict: Dict[str, float])[source]#: Create an ExecutionResources object from a resource dict.

to_resource_dict() → Dict[str, float][source]#

Convert this ExecutionResources object to a resource dict.

Values are quantized to Ray Core’s fractional-resource granularity (5 decimal digits for cpu/gpu, integer bytes for memory) so the output is suitable for passing back to Ray Core via .options(...).

classmethod for_limits(cpu: float | None = None, gpu: float | None = None, object_store_memory: float | None = None, memory: float | None = None) → ExecutionResources[source]#

Create an ExecutionResources object that represents resource limits.

Parameters:

cpu – Amount of logical CPU slots.
gpu – Amount of logical GPU slots.
object_store_memory – Amount of object store memory.
memory – Amount of logical memory in bytes.

Returns:

An ExecutionResources with the given limits (defaulting to infinity for any unspecified field).

to_limits() → ExecutionResources[source]#

Return a copy of this object interpreted as resource limits.

Fields left unspecified (None) become unlimited (inf) rather than 0, so a partially-specified value like ExecutionResources(cpu=4) caps only CPU and leaves the other resources unbounded.

classmethod zero() → ExecutionResources[source]#

Returns an ExecutionResources object with zero resources.

Returns a cached, shared singleton (safe because instances are immutable in practice).

classmethod inf() → ExecutionResources[source]#

Returns an ExecutionResources object with infinite resources.

Returns a cached, shared singleton (safe because instances are immutable in practice).

is_zero() → bool[source]#: Returns True if all resources are zero.

is_non_negative() → bool[source]#: Returns True if all resources are non-negative.

object_store_memory_str() → str[source]#: Returns a human-readable string for the object store memory field.

memory_str() → str[source]#: Returns a human-readable string for the memory field.

copy(cpu: float | None = None, gpu: float | None = None, memory: float | None = None, object_store_memory: float | None = None) → ExecutionResources[source]#: Returns a copy of this ExecutionResources object allowing to override specific resources as necessary

add(other: ExecutionResources) → ExecutionResources[source]#

Adds execution resources.

Parameters:: other – The other ExecutionResources to add to this one.
Returns:: A new ExecutionResource object with summed resources.

subtract(other: ExecutionResources) → ExecutionResources[source]#

Subtracts execution resources.

Parameters:: other – The other ExecutionResources to subtract from this one.
Returns:: A new ExecutionResource object with subtracted resources.

max(other: ExecutionResources) → ExecutionResources[source]#: Returns the maximum for each resource type.

min(other: ExecutionResources) → ExecutionResources[source]#: Returns the minimum for each resource type.

satisfies_limit(limit: ExecutionResources, *, ignore_object_store_memory: bool = False) → bool[source]#

Return if this resource struct meets the specified limits.

Note that None for a field means no limit.

Parameters:

limit – The resource limits to check against.
ignore_object_store_memory – If True, ignore the object store memory limit when checking if this resource struct meets the limits.

Returns:

True if every resource is within the corresponding limit.

scale(f: float) → ExecutionResources[source]#: Return copy with all set values scaled by f.

floordiv(other: ExecutionResources) → ExecutionResources[source]#: Returns the floor division of resources.