def select_cluster_environment(self) -> ClusterEnvironment:
if self._cluster_environment is not None:
return self._cluster_environment
if self.is_slurm_managing_tasks:
env = SLURMEnvironment()
elif TorchElasticEnvironment.is_using_torchelastic():
env = TorchElasticEnvironment()
elif KubeflowEnvironment.is_using_kubeflow():
env = KubeflowEnvironment()
else:
env = LightningEnvironment()
return env
def select_cluster_environment(self) -> ClusterEnvironment:
if self._cluster_environment is not None:
return self._cluster_environment
if self._is_slurm_managing_tasks():
env = SLURMEnvironment()
rank_zero_info("Multiprocessing is handled by SLURM.")
elif TorchElasticEnvironment.is_using_torchelastic():
env = TorchElasticEnvironment()
elif KubeflowEnvironment.is_using_kubeflow():
env = KubeflowEnvironment()
elif LSFEnvironment.is_using_lsf():
env = LSFEnvironment()
else:
env = LightningEnvironment()
return env
def select_training_type_plugin(self) -> TrainingTypePlugin:
if isinstance(
self.distributed_backend, Accelerator
) and self.distributed_backend.training_type_plugin is not None:
plugin = self.distributed_backend.training_type_plugin
elif self.use_ddp2:
plugin = DDP2Plugin(
parallel_devices=self.parallel_devices,
cluster_environment=self.cluster_environment,
)
elif self.use_ddp and self.use_deepspeed:
plugin = DeepSpeedPlugin(
cluster_environment=self.select_cluster_environment(), parallel_devices=self.parallel_devices
)
elif self.use_ddp:
use_slurm_ddp = self.use_ddp and self.is_slurm_managing_tasks
use_torchelastic_ddp = self.use_ddp and TorchElasticEnvironment.is_using_torchelastic()
use_kubeflow_ddp = self.use_ddp and KubeflowEnvironment.is_using_kubeflow()
use_ddp_spawn = self._distrib_type == DistributedType.DDP_SPAWN
use_ddp_cpu_spawn = self.use_ddp and self.on_cpu
use_tpu_spawn = self.on_tpu and self._distrib_type == DistributedType.TPU_SPAWN
use_ddp_cpu_torch_elastic = use_ddp_cpu_spawn and TorchElasticEnvironment.is_using_torchelastic()
use_ddp_cpu_kubeflow = use_ddp_cpu_spawn and KubeflowEnvironment.is_using_kubeflow()
use_ddp_cpu_slurm = use_ddp_cpu_spawn and self.is_slurm_managing_tasks
use_ddp_sharded = self._distrib_type == DistributedType.DDP_SHARDED
use_ddp_sharded_spawn = self._distrib_type == DistributedType.DDP_SHARDED_SPAWN
use_ddp_fully_sharded = self._distrib_type == DistributedType.DDP_FULLY_SHARDED
# TODO: decouple from TE
# ddp script mode uses the same flags as TE
if os.environ.get("PL_IN_DDP_SUBPROCESS", False):
use_torchelastic_ddp = False
if use_tpu_spawn:
ddp_plugin_cls = TPUSpawnPlugin
elif use_ddp_sharded:
ddp_plugin_cls = DDPShardedPlugin
elif use_ddp_sharded_spawn:
ddp_plugin_cls = DDPSpawnShardedPlugin
elif (
use_ddp_cpu_slurm or use_slurm_ddp or use_ddp_cpu_torch_elastic or use_torchelastic_ddp
or use_kubeflow_ddp or use_ddp_cpu_kubeflow
):
ddp_plugin_cls = DDPPlugin
elif use_ddp_spawn or use_ddp_cpu_spawn:
ddp_plugin_cls = DDPSpawnPlugin
elif use_ddp_fully_sharded:
ddp_plugin_cls = DDPFullyShardedPlugin
else:
ddp_plugin_cls = DDPPlugin
plugin = ddp_plugin_cls(
parallel_devices=self.parallel_devices,
cluster_environment=self.cluster_environment,
)
elif self.use_dp:
plugin = DataParallelPlugin(parallel_devices=self.parallel_devices)
elif self.use_horovod:
plugin = HorovodPlugin(parallel_devices=self.parallel_devices)
elif self.on_tpu and isinstance(self.tpu_cores, list):
plugin = SingleTPUPlugin(self.tpu_id)
elif self.on_ipu:
plugin = IPUPlugin(parallel_devices=self.parallel_devices)
else:
single_gpu_ordinal = device_parser.determine_root_gpu_device(self.parallel_device_ids)
plugin = SingleDevicePlugin(device=torch.device(f"cuda:{single_gpu_ordinal}" if self.on_gpu else "cpu"))
return plugin
def test_is_using_kubeflow():
assert KubeflowEnvironment.is_using_kubeflow()
def test_is_using_kubeflow_torchelastic():
assert not KubeflowEnvironment.is_using_kubeflow()