def select_accelerator(self):
if self.trainer.accelerator_backend is not None:
return self.trainer.accelerator_backend
# SLURM ddp
use_slurm_ddp = self.trainer.use_ddp and self.trainer.is_slurm_managing_tasks
# torchelastic or general non_slurm ddp
te_flags_passed = 'WORLD_SIZE' in os.environ and (
'GROUP_RANK' in os.environ or 'NODE_RANK' in os.environ)
use_torchelastic_ddp = self.trainer.use_ddp and te_flags_passed
use_ddp_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == "ddp_spawn"
use_ddp_cpu_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == "ddp_cpu"
# choose the appropriate accelerator backend
if self.trainer.use_ddp2:
accelerator_backend = accelerators.DDP2Backend(self.trainer)
elif use_slurm_ddp:
accelerator_backend = accelerators.DDPBackend(self.trainer,
mode='slurm_ddp')
elif use_torchelastic_ddp:
accelerator_backend = accelerators.DDPBackend(
self.trainer, mode='torchelastic_ddp')
elif use_ddp_spawn:
accelerator_backend = accelerators.DDPSpawnBackend(
self.trainer, nprocs=self.trainer.num_processes)
elif use_ddp_cpu_spawn:
accelerator_backend = accelerators.DDPCPUSpawnBackend(
self.trainer, nprocs=self.trainer.num_processes)
elif self.trainer.distributed_backend == "ddp":
accelerator_backend = accelerators.DDPBackend(self.trainer,
mode='ddp')
elif self.trainer.use_dp:
accelerator_backend = accelerators.DataParallelBackend(
self.trainer)
elif self.trainer.use_horovod:
accelerator_backend = accelerators.HorovodBackend(self.trainer)
elif self.trainer.use_single_gpu:
accelerator_backend = accelerators.GPUBackend(self.trainer)
elif self.trainer.use_tpu:
accelerator_backend = accelerators.TPUBackend(self.trainer)
elif self.trainer.distributed_backend is None:
accelerator_backend = accelerators.CPUBackend(self.trainer)
else:
raise MisconfigurationException(
f'Trainer(distributed_backend={self.trainer.distributed_backend} is not a supported backend'
)
return accelerator_backend
def select_accelerator(self):
# SLURM ddp
use_slurm_ddp = self.trainer.use_ddp and self.trainer.is_slurm_managing_tasks
# torchelastic or general non_slurm ddp
te_flags_passed = 'WORLD_SIZE' in os.environ and (
'GROUP_RANK' in os.environ or 'NODE_RANK' in os.environ)
use_torchelastic_ddp = self.trainer.use_ddp and te_flags_passed
use_ddp_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == 'ddp_spawn'
use_ddp_cpu_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == 'ddp_cpu'
# choose the appropriate accelerator backend
if self.trainer.use_ddp2:
accelerator_backend = accelerators.DDP2Backend(self.trainer)
elif use_slurm_ddp:
accelerator_backend = accelerators.DDPBackend(self.trainer,
mode='slurm_ddp')
elif use_torchelastic_ddp:
accelerator_backend = accelerators.DDPBackend(
self.trainer, mode='torchelastic_ddp')
elif use_ddp_spawn:
accelerator_backend = accelerators.DDPSpawnBackend(
self.trainer, nprocs=self.trainer.num_processes)
elif use_ddp_cpu_spawn:
accelerator_backend = accelerators.DDPCPUSpawnBackend(
self.trainer, nprocs=self.trainer.num_processes)
elif self.trainer.distributed_backend == 'ddp':
accelerator_backend = accelerators.DDPBackend(self.trainer,
mode='ddp')
elif self.trainer.use_dp:
accelerator_backend = accelerators.DataParallelBackend(
self.trainer)
elif self.trainer.use_horovod:
accelerator_backend = accelerators.HorovodBackend(self.trainer)
elif self.trainer.use_single_gpu:
accelerator_backend = accelerators.GPUBackend(self.trainer)
elif self.trainer.use_tpu:
accelerator_backend = accelerators.TPUBackend(self.trainer)
else:
accelerator_backend = accelerators.CPUBackend(self.trainer)
return accelerator_backend
def select_accelerator(self):
if self.trainer.accelerator_backend is not None:
return self.trainer.accelerator_backend
# SLURM ddp
use_slurm_ddp = self.trainer.use_ddp and self.trainer.is_slurm_managing_tasks
# torchelastic or general non_slurm ddp
te_flags_passed = 'WORLD_SIZE' in os.environ and (
'GROUP_RANK' in os.environ or 'NODE_RANK' in os.environ)
use_torchelastic_ddp = self.trainer.use_ddp and te_flags_passed
use_ddp_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == "ddp_spawn"
use_ddp_cpu_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == "ddp_cpu"
use_ddp_cpu_torch_elastic = use_ddp_cpu_spawn and self._is_using_torchelastic(
)
use_ddp_cpu_slurm = use_ddp_cpu_spawn and self.trainer.is_slurm_managing_tasks
# ddp script mode uses the same flags as TE
# TODO: decouple from TE
if os.environ.get('PL_DDP_PID', False):
use_torchelastic_ddp = False
# choose the appropriate accelerator backend
if self.trainer.use_ddp2:
accelerator_backend = accelerators.DDP2Backend(self.trainer)
elif use_ddp_cpu_slurm:
accelerator_backend = accelerators.DDPCPUSLURMBackend(self.trainer)
elif use_slurm_ddp:
accelerator_backend = accelerators.DDPSLURMBackend(self.trainer)
elif use_ddp_cpu_torch_elastic:
accelerator_backend = accelerators.DDPCPUTorchElasticBackend(
self.trainer)
elif use_torchelastic_ddp:
accelerator_backend = accelerators.DDPTorchElasticBackend(
self.trainer)
elif use_ddp_spawn:
accelerator_backend = accelerators.DDPSpawnBackend(
self.trainer, nprocs=self.trainer.num_processes)
elif use_ddp_cpu_spawn:
accelerator_backend = accelerators.DDPCPUSpawnBackend(
self.trainer, nprocs=self.trainer.num_processes)
elif self.trainer.distributed_backend == "ddp":
accelerator_backend = accelerators.DDPBackend(self.trainer)
elif self.trainer.use_dp:
accelerator_backend = accelerators.DataParallelBackend(
self.trainer)
elif self.trainer.use_horovod:
accelerator_backend = accelerators.HorovodBackend(self.trainer)
elif self.trainer.use_single_gpu:
accelerator_backend = accelerators.GPUBackend(self.trainer)
elif self.trainer.use_tpu:
accelerator_backend = accelerators.TPUBackend(self.trainer)
elif self.trainer.distributed_backend is None:
accelerator_backend = accelerators.CPUBackend(self.trainer)
else:
raise MisconfigurationException(
f'Trainer(distributed_backend={self.trainer.distributed_backend} is not a supported backend'
)
return accelerator_backend
