def __init__(self,
config,
world_rank,
device_ids=None,
use_gpu=False,
use_fp16=False,
use_tqdm=False,
apex_args=None,
wrap_ddp=False,
wrap_distributed_sampler=False,
add_dist_sampler=False,
scheduler_step_freq=None):
# You are not expected to override this method.
self._world_rank = world_rank
self._config = config
self._use_fp16 = use_fp16
self._device_ids = device_ids
self._use_gpu = use_gpu and torch.cuda.is_available()
self._device = torch.device("cuda" if self._use_gpu else "cpu")
if tqdm is None and use_tqdm:
raise ValueError("tqdm must be installed to use tqdm in training.")
self._use_tqdm = use_tqdm
self.global_step = 0
self._apex_args = apex_args if apex_args else {}
self._wrap_ddp = wrap_ddp
self._wrap_distributed_sampler = wrap_distributed_sampler
self._add_dist_sampler = add_dist_sampler
self._scheduler_step_freq = scheduler_step_freq
self.timers = TimerCollection()
self.setup(config)
def __init__(
self,
config,
world_rank,
local_rank,
is_distributed,
use_gpu,
device,
use_fp16=False,
use_tqdm=False,
wrap_ddp=False,
add_dist_sampler=False,
scheduler_step_freq=None,
):
# You are not expected to override this method.
self._world_rank = world_rank
self._local_rank = local_rank
self._config = config
self._is_distributed = is_distributed
self._use_fp16 = choose_amp_backend(use_fp16, amp, apex_amp)
self._device = device
self._use_gpu = use_gpu and torch.cuda.is_available()
if tqdm is None and use_tqdm:
raise ValueError("tqdm must be installed to use tqdm in training.")
self._use_tqdm = use_tqdm
self.global_step = 0
self._wrap_ddp = wrap_ddp
self._add_dist_sampler = add_dist_sampler
self._scheduler_step_freq = scheduler_step_freq
self.timers = TimerCollection()
self.setup(config)
def __init__(self,
config,
models,
optimizers,
criterion=None,
schedulers=None,
use_fp16=False):
# You are not expected to override this method.
self._models = models # List of models
assert isinstance(models, collections.Iterable), (
"Components need to be iterable. Got: {}".format(type(models)))
self._optimizers = optimizers # List of optimizers
assert isinstance(optimizers, collections.Iterable), (
"Components need to be iterable. Got: {}".format(type(optimizers)))
self._criterion = criterion
self._schedulers = schedulers
if schedulers:
assert isinstance(schedulers, collections.Iterable), (
"Components need to be iterable. Got: {}".format(
type(schedulers)))
self._config = config
self._use_fp16 = use_fp16
self.global_step = 0
if type(self) is TrainingOperator:
for component in (models, schedulers, optimizers):
if _is_multiple(component):
raise ValueError(
"Need to provide a custom operator subclassing "
"TrainingOperator if using multi-scheduler, "
"multi-model or multi-optimizer training/validation.")
self.timers = TimerCollection()
self.setup(config)
def __init__(self,
config,
models,
optimizers,
train_loader,
validation_loader,
world_rank,
criterion=None,
schedulers=None,
device_ids=None,
use_gpu=False,
use_fp16=False,
use_tqdm=False):
# You are not expected to override this method.
self._models = models # List of models
assert isinstance(
models,
Iterable), ("Components need to be iterable. Got: {}".format(
type(models)))
self._optimizers = optimizers # List of optimizers
assert isinstance(
optimizers,
Iterable), ("Components need to be iterable. Got: {}".format(
type(optimizers)))
self._train_loader = train_loader
self._validation_loader = validation_loader
self._world_rank = world_rank
self._criterion = criterion
self._schedulers = schedulers
if schedulers:
assert isinstance(
schedulers,
Iterable), ("Components need to be iterable. Got: {}".format(
type(schedulers)))
self._config = config
self._use_fp16 = use_fp16
self._device_ids = device_ids
self._use_gpu = use_gpu and torch.cuda.is_available()
self._device = torch.device("cuda" if self._use_gpu else "cpu")
if tqdm is None and use_tqdm:
raise ValueError("tqdm must be installed to use tqdm in training.")
self._use_tqdm = use_tqdm
self.global_step = 0
if type(self) is TrainingOperator:
for component in (models, schedulers, optimizers):
if _is_multiple(component):
raise ValueError(
"Need to provide a custom operator subclassing "
"TrainingOperator if using multi-scheduler, "
"multi-model or multi-optimizer training/validation.")
self.timers = TimerCollection()
self.setup(config)