2.0.0b53

Elastic

Package: flyteplugins.pytorch

Elastic defines the configuration for running a PyTorch elastic job using torch.distributed.

            
        
class Elastic(
    nnodes: typing.Union[int, str],
    nproc_per_node: int,
    rdzv_backend: typing.Literal['c10d', 'etcd', 'etcd-v2'],
    run_policy: typing.Optional[flyteplugins.pytorch.task.RunPolicy],
    monitor_interval: int,
    max_restarts: int,
    rdzv_configs: typing.Dict[str, typing.Any],
)

Parameter	Type	Description
`nnodes`	`typing.Union[int, str]`	Number of nodes to use. Can be a fixed int or a range string (e.g., “2:4” for elastic training).
`nproc_per_node`	`int`	Number of processes to launch per node.
`rdzv_backend`	`typing.Literal['c10d', 'etcd', 'etcd-v2']`	Rendezvous backend to use. Typically “c10d”. Defaults to “c10d”.
`run_policy`	`typing.Optional[flyteplugins.pytorch.task.RunPolicy]`	Run policy applied to the job execution. Defaults to None.
`monitor_interval`	`int`	Interval (in seconds) to monitor the job’s state. Defaults to 3.
`max_restarts`	`int`	Maximum number of worker group restarts before failing the job. Defaults to 3.
`rdzv_configs`	`typing.Dict[str, typing.Any]`	Rendezvous configuration key-value pairs. Defaults to {“timeout”: 900, “join_timeout”: 900}.