def get_engine(self):
return APEXEngine(self._device, self._opt_level)
def get_engine(self):
return APEXEngine(self._device,
apex_kwargs=dict(opt_level=self._opt_level))
def get_available_engine(
fp16: bool = False, ddp: bool = False, amp: bool = False, apex: bool = False
) -> "IEngine":
"""Returns available engine based on given arguments.
Args:
fp16 (bool): option to use fp16 for training. Default is `False`.
ddp (bool): option to use DDP for training. Default is `False`.
amp (bool): option to use APEX for training. Default is `False`.
apex (bool): option to use APEX for training. Default is `False`.
Returns:
IEngine which match requirements.
"""
from catalyst.engines.torch import (
DataParallelEngine,
DeviceEngine,
DistributedDataParallelEngine,
)
if fp16 and not amp and not apex:
amp = SETTINGS.amp_required or (SETTINGS.amp_required and SETTINGS.apex_required)
apex = SETTINGS.apex_required and (not SETTINGS.amp_required)
if amp:
assert (
SETTINGS.amp_required
), "catalyst[amp] is not available, to install it, run `pip install catalyst[amp]`."
assert not apex, "Could not use both apex and amp engines"
from catalyst.engines.amp import (
AMPEngine,
DataParallelAMPEngine,
DistributedDataParallelAMPEngine,
)
if apex:
assert (
SETTINGS.apex_required
), "catalyst[apex] is not available, to install it, run `pip install catalyst[apex]`."
assert not amp, "Could not use both apex and amp engines"
from catalyst.engines.apex import (
APEXEngine,
DataParallelAPEXEngine,
DistributedDataParallelAPEXEngine,
)
is_multiple_gpus = NUM_CUDA_DEVICES > 1
if not IS_CUDA_AVAILABLE:
return DeviceEngine("cpu")
elif is_multiple_gpus:
if ddp:
if amp:
return DistributedDataParallelAMPEngine()
elif apex:
return DistributedDataParallelAPEXEngine()
else:
return DistributedDataParallelEngine()
else:
if amp:
return DataParallelAMPEngine()
elif apex:
return DataParallelAPEXEngine()
else:
return DataParallelEngine()
else:
if amp:
return AMPEngine()
elif apex:
return APEXEngine()
else:
return DeviceEngine("cuda")