Class ScalingConfig

python/ray/train/v2/api/config.py:31–279

Configuration for scaling training. Args: num_workers: The number of workers (Ray actors) to launch. Each worker will reserve 1 CPU by default. The number of CPUs reserved by each worker can be overridden with the ``resources_per_worker`` argument. If

@dataclass
class ScalingConfig(ScalingConfigV1):
    """Configuration for scaling training.

    Args:
        num_workers: The number of workers (Ray actors) to launch.
            Each worker will reserve 1 CPU by default. The number of CPUs
            reserved by each worker can be overridden with the
            ``resources_per_worker`` argument. If the number of workers is 0,
            the training function will run in local mode, meaning the training
            function runs in the same process. To enable elasticity, provide a
            ``(min_workers, max_workers)`` tuple of ints.
        elastic_resize_monitor_interval_s: While the worker group is healthy,
            consider resizing the worker group every
            ``elastic_resize_monitor_interval_s`` seconds.
        use_gpu: If True, training will be done on GPUs (1 per worker).
            Defaults to False. The number of GPUs reserved by each
            worker can be overridden with the ``resources_per_worker``
            argument.
        resources_per_worker: If specified, the resources
            defined in this Dict is reserved for each worker.
            Define the ``"CPU"`` and ``"GPU"`` keys (case-sensitive) to
            override the number of CPU or GPUs used by each worker.
        placement_strategy: The placement strategy to use for the
            placement group of the Ray actors. See :ref:`Placement Group
            Strategies <pgroup-strategy>` for the possible options.
        label_selector: A list of label selectors for Ray Train worker placement.
            If a single label selector is provided, it will be applied to all Ray Train workers.
            If a list is provided, it must be the same length as the max number of Ray Train workers.
        accelerator_type: [Experimental] If specified, Ray Train will launch the
            training coordinator and workers on the nodes with the specified type
            of accelerators.
            See :ref:`the available accelerator types <accelerator_types>`.
            Ensure that your cluster has instances with the specified accelerator type
            or is able to autoscale to fulfill the request. This field is required
            when `use_tpu` is True and `num_workers` is greater than 1.
        use_tpu: [Experimental] If True, training will be done on TPUs (1 TPU VM
            per worker). Defaults to False. The number of TPUs reserved by each
            worker can be overridden with the ``resources_per_worker``
            argument. This arg enables SPMD execution of the training workload.
        topology: [Experimental] If specified, Ray Train will launch the training
            coordinator and workers on nodes with the specified topology. Topology is
            auto-detected for TPUs and added as Ray node labels. This arg enables
            SPMD execution of the training workload. This field is required
            when `use_tpu` is True and `num_workers` is greater than 1.
    """

    num_workers: Union[int, Tuple[int, int]] = 1
    trainer_resources: Optional[dict] = None
    label_selector: Optional[Union[Dict[str, str], List[Dict[str, str]]]] = None

    # Accelerator specific fields.
    use_tpu: Union[bool] = False
    topology: Optional[str] = None

    # Elasticity specific fields.
    elastic_resize_monitor_interval_s: float = 60.0

    def __post_init__(self):