ray.train.ScalingConfig

class ray.train.ScalingConfig(trainer_resources: Optional[Union[Dict, Domain, Dict[str, List]]] = None, num_workers: Optional[Union[int, Domain, Dict[str, List]]] = None, use_gpu: Union[bool, Domain, Dict[str, List]] = False, resources_per_worker: Optional[Union[Dict, Domain, Dict[str, List]]] = None, placement_strategy: Union[str, Domain, Dict[str, List]] = 'PACK')

Bases: object

Configuration for scaling training.

Parameters

• trainer_resources – Resources to allocate for the trainer. If None is provided, will default to 1 CPU.

• 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.

• 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 Placement Group Strategies for the possible options.

Example

from ray.train import ScalingConfig
scaling_config = ScalingConfig(
    # Number of distributed workers.
    num_workers=2,
    # Turn on/off GPU.
    use_gpu=True,
    # Specify resources used for trainer.
    trainer_resources={"CPU": 1},
    # Try to schedule workers on different nodes.
    placement_strategy="SPREAD",
)

PublicAPI: This API is stable across Ray releases.

Methods

as_placement_group_factory()	Returns a PlacementGroupFactory to specify resources for Tune.
from_placement_group_factory(pgf)	Create a ScalingConfig from a Tune's PlacementGroupFactory

Attributes

additional_resources_per_worker	Resources per worker, not including CPU or GPU resources.
num_cpus_per_worker	The number of CPUs to set per worker.
num_gpus_per_worker	The number of GPUs to set per worker.
num_workers
placement_strategy
resources_per_worker
total_resources	Map of total resources required for the trainer.
trainer_resources
use_gpu