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Resources

The hera.workflows.resources module provides the Resources class for setting CPU, memory and other limits.

Resources

A representation of a collection of resources that are requested to be consumed by a task for execution.

This follow the K8S definition for resources.

Attributes:

Name Type Description
cpu_request Optional[Union[float, int, str]]

The number of CPUs to request, either as a fraction (millicpu), whole number, or a string.
cpu_limit Optional[Union[float, int, str]]

The limit of CPUs to request, either as a fraction (millicpu), whole number, or a string.
memory_request Optional[str]

The amount of memory to request.
memory_limit Optional[str]

The memory limit of the pod.
ephemeral_request Optional[str]

The amount of ephemeral storage to request.
ephemeral_limit Optional[str]

The ephemeral storage limit of the pod.
gpus Optional[Union[int, str]]

The number of GPUs to request.
gpu_flag Optional[str]

The GPU flag to use for identifying how many GPUs to mount to a pod. This is dependent on the cloud provider.
custom_resources Optional[Dict]

Any custom resources to request. This is dependent on the cloud provider.
Notes

Most of the fields that support a union of int and str support either specifying a number for the resource, such as 1 CPU, 2 GPU, etc., a str representation of that numerical resource, such as ‘1’ CPU, ‘2’ GPU, etc., but also supports specifying a to be computed value, such as {{inputs.parameters.cpu_request}}. This means tasks, steps, etc., can be stitched together in a way to have a task/step that computes the resource requirements, and then outputs them to the next step/task.

Source code in src/hera/workflows/resources.py 
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@dataclass(kw_only=True)
class Resources:
    """A representation of a collection of resources that are requested to be consumed by a task for execution.

    This follow the K8S definition for resources.

    Attributes:
        cpu_request: The number of CPUs to request, either as a fraction (millicpu), whole number, or a string.
        cpu_limit: The limit of CPUs to request, either as a fraction (millicpu), whole number, or a string.
        memory_request: The amount of memory to request.
        memory_limit: The memory limit of the pod.
        ephemeral_request: The amount of ephemeral storage to request.
        ephemeral_limit: The ephemeral storage limit of the pod.
        gpus: The number of GPUs to request.
        gpu_flag: The GPU flag to use for identifying how many GPUs to mount to a pod. This is dependent on the cloud provider.
        custom_resources: Any custom resources to request. This is dependent on the cloud provider.

    Notes:
        Most of the fields that support a union of `int` and `str` support either specifying a number for the resource,
        such as 1 CPU, 2 GPU, etc., a `str` representation of that numerical resource, such as '1' CPU, '2' GPU, etc., but
        also supports specifying a *to be computed* value, such as `{{inputs.parameters.cpu_request}}`. This means tasks,
        steps, etc., can be stitched together in a way to have a task/step that *computes* the resource requirements, and
        then `outputs` them to the next step/task.
    """

    cpu_request: Optional[Union[float, int, str]] = None
    cpu_limit: Optional[Union[float, int, str]] = None
    memory_request: Optional[str] = None
    memory_limit: Optional[str] = None
    ephemeral_request: Optional[str] = None
    ephemeral_limit: Optional[str] = None
    gpus: Optional[Union[int, str]] = None
    gpu_flag: Optional[str] = "nvidia.com/gpu"
    custom_resources: Optional[Dict] = None

    def __post_init__(self):
        """Perform validation of values."""
        if self.memory_request is not None:
            validate_memory_units(self.memory_request)
        if self.memory_limit is not None:
            validate_memory_units(self.memory_limit)
            if self.memory_request is not None:
                if convert_memory_units(self.memory_request) > convert_memory_units(self.memory_limit):
                    raise ValueError("Memory request must be smaller or equal to limit")

        if self.ephemeral_request is not None:
            validate_storage_units(self.ephemeral_request)
        if self.ephemeral_limit is not None:
            validate_storage_units(self.ephemeral_limit)
            if self.ephemeral_request is not None:
                if convert_storage_units(self.ephemeral_request) > convert_storage_units(self.ephemeral_limit):
                    raise ValueError("Ephemeral request must be smaller or equal to limit")

        if self.cpu_request is not None and isinstance(self.cpu_request, (int, float)):
            if self.cpu_request < 0:
                raise ValueError("CPU request must be positive")
        if self.cpu_limit is not None and isinstance(self.cpu_limit, (int, float)):
            if self.cpu_limit < 0:
                raise ValueError("CPU limit must be positive")
            if self.cpu_request is not None and isinstance(self.cpu_request, (int, float)):
                if self.cpu_request > self.cpu_limit:
                    raise ValueError("CPU request must be smaller or equal to limit")

        if self.cpu_request is not None and isinstance(self.cpu_request, str):
            validate_cpu_units(self.cpu_request)
        if self.cpu_limit is not None and isinstance(self.cpu_limit, str):
            validate_cpu_units(self.cpu_limit)
            if self.cpu_request is not None and isinstance(self.cpu_request, str):
                if convert_cpu_units(self.cpu_request) > convert_cpu_units(self.cpu_limit):
                    raise ValueError("CPU request must be smaller or equal to limit")

    def build(self) -> _ModelResourceRequirements:
        """Builds the resource requirements of the pod."""
        resources: Dict = dict()

        if self.cpu_limit is not None:
            resources = _merge_dicts(resources, dict(limits=dict(cpu=str(self.cpu_limit))))

        if self.cpu_request is not None:
            resources = _merge_dicts(resources, dict(requests=dict(cpu=str(self.cpu_request))))

        if self.memory_limit is not None:
            resources = _merge_dicts(resources, dict(limits=dict(memory=self.memory_limit)))

        if self.memory_request is not None:
            resources = _merge_dicts(resources, dict(requests=dict(memory=self.memory_request)))

        if self.ephemeral_limit is not None:
            resources = _merge_dicts(resources, dict(limits={"ephemeral-storage": self.ephemeral_limit}))

        if self.ephemeral_request is not None:
            resources = _merge_dicts(resources, dict(requests={"ephemeral-storage": self.ephemeral_request}))

        if self.gpus is not None:
            resources = _merge_dicts(resources, dict(requests={self.gpu_flag: str(self.gpus)}))
            resources = _merge_dicts(resources, dict(limits={self.gpu_flag: str(self.gpus)}))

        if self.custom_resources:
            resources = _merge_dicts(resources, self.custom_resources)

        return _ModelResourceRequirements(**resources)

cpu_limit 

cpu_limit: Optional[Union[float, int, str]] = None

cpu_request 

cpu_request: Optional[Union[float, int, str]] = None

custom_resources 

custom_resources: Optional[Dict] = None

ephemeral_limit 

ephemeral_limit: Optional[str] = None

ephemeral_request 

ephemeral_request: Optional[str] = None

gpu_flag 

gpu_flag: Optional[str] = 'nvidia.com/gpu'

gpus 

gpus: Optional[Union[int, str]] = None

memory_limit 

memory_limit: Optional[str] = None

memory_request 

memory_request: Optional[str] = None

build 

build() -> ResourceRequirements

Builds the resource requirements of the pod.

Source code in src/hera/workflows/resources.py 
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def build(self) -> _ModelResourceRequirements:
    """Builds the resource requirements of the pod."""
    resources: Dict = dict()

    if self.cpu_limit is not None:
        resources = _merge_dicts(resources, dict(limits=dict(cpu=str(self.cpu_limit))))

    if self.cpu_request is not None:
        resources = _merge_dicts(resources, dict(requests=dict(cpu=str(self.cpu_request))))

    if self.memory_limit is not None:
        resources = _merge_dicts(resources, dict(limits=dict(memory=self.memory_limit)))

    if self.memory_request is not None:
        resources = _merge_dicts(resources, dict(requests=dict(memory=self.memory_request)))

    if self.ephemeral_limit is not None:
        resources = _merge_dicts(resources, dict(limits={"ephemeral-storage": self.ephemeral_limit}))

    if self.ephemeral_request is not None:
        resources = _merge_dicts(resources, dict(requests={"ephemeral-storage": self.ephemeral_request}))

    if self.gpus is not None:
        resources = _merge_dicts(resources, dict(requests={self.gpu_flag: str(self.gpus)}))
        resources = _merge_dicts(resources, dict(limits={self.gpu_flag: str(self.gpus)}))

    if self.custom_resources:
        resources = _merge_dicts(resources, self.custom_resources)

    return _ModelResourceRequirements(**resources)

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