def __init__(self, context: pytorch.PyTorchTrialContext) -> None:
self.context = context
model = torch.nn.Linear(1, 1, False)
# Manually initialize the one weight to 0.
model.weight.data.fill_(0)
self.model = context.wrap_model(model)
self.lr = 0.001
opt = torch.optim.SGD(self.model.parameters(), self.lr)
self.opt = context.wrap_optimizer(opt)
self.loss_fn = torch.nn.MSELoss()
self.cls_reducer = context.wrap_reducer(TriangleLabelSum(),
name="cls_reducer")
self.fn_reducer = context.wrap_reducer(triangle_label_sum,
name="fn_reducer")
self.hparams = self.context.get_hparams()
if self.hparams.get("disable_dataset_reproducibility_checks"):
self.context.experimental.disable_dataset_reproducibility_checks()
def __init__(self, context: pytorch.PyTorchTrialContext):
self.context = context
model = nn.Linear(1, 1, False)
model.weight.data.fill_(0)
self.model = context.wrap_model(model)
opt = torch.optim.SGD(self.model.parameters(), 0.1)
self.opt = context.wrap_optimizer(opt)
def __init__(self, context: pytorch.PyTorchTrialContext) -> None:
self.context = context
model = torch.nn.Linear(1, 1, False)
model.weight.data.fill_(0)
self.model = context.wrap_model(model)
self.lr = 0.001
optimizer = torch.optim.SGD(self.model.parameters(), self.lr)
self.opt = context.wrap_optimizer(optimizer)
self.loss_fn = torch.nn.MSELoss(reduction="mean")
def __init__(self, context: pytorch.PyTorchTrialContext) -> None:
self.context = context
model = torch.nn.Linear(1, 1, False)
# Manually initialize the one weight to 0.
model.weight.data.fill_(0)
self.model = context.wrap_model(model)
self.lr = 0.001
opt = torch.optim.SGD(self.model.parameters(), self.lr)
self.opt = context.wrap_optimizer(opt)
self.loss_fn = torch.nn.MSELoss()
def setup_optimizers_schedulers(
self,
context: PyTorchTrialContext,
lightning_module: pl.LightningModule,
) -> Tuple[List[Optimizer], List[_LRScheduler]]:
"""
Wrap optimizers and lr_schedulers returned by `configure_optimizers` to
work with Determined.
Return: Wrapped `optimizers`, and `lr_schedulers` in a tuple
"""
optimizers, lr_scheduler_dicts, opt_frequencies = TrainerOptimizersMixin().init_optimizers(
lightning_module,
)
# TODO(DET-5021) support custom frequencies with the manual step.
for freq in opt_frequencies:
check.eq(freq, 1, "custom optimizer frequencies are not supported")
optimizers = cast(List[Optimizer], optimizers)
lr_scheduler_dicts = cast(List[dict], lr_scheduler_dicts)
def lightning_scheduler_dict_to_det(lrs: dict) -> _LRScheduler:
"""
input_dict = {
'scheduler': None,
'name': None, # no custom name
'interval': 'epoch', # after epoch is over
'frequency': 1, # every epoch/batch
'reduce_on_plateau': False, # most often not ReduceLROnPlateau scheduler
'monitor': monitor, # value to monitor for ReduceLROnPlateau
'strict': True, # enforce that the monitor exists for ReduceLROnPlateau
}
"""
if lrs["reduce_on_plateau"]:
raise InvalidModelException("LRScheduler reduce_on_plateaue is not supported")
if lrs["monitor"] is not None:
raise InvalidModelException("LRScheduler monitor is not supported")
step_mode = (
LRScheduler.StepMode.STEP_EVERY_EPOCH
if lrs["interval"] == "epoch"
else LRScheduler.StepMode.STEP_EVERY_BATCH
)
return context.wrap_lr_scheduler(lrs["scheduler"], step_mode)
optimizers = [context.wrap_optimizer(opt) for opt in optimizers]
lr_schedulers = [lightning_scheduler_dict_to_det(lrs) for lrs in lr_scheduler_dicts]
return optimizers, lr_schedulers
def __init__(self, context: pytorch.PyTorchTrialContext) -> None:
self.context = context
model = torch.nn.Linear(1, 1, False)
# Manually initialize the one weight to 0.
model.weight.data.fill_(0)
self.model = context.wrap_model(model)
self.lr = 0.001
opt = torch.optim.SGD(self.model.parameters(), self.lr)
self.opt = context.wrap_optimizer(opt)
self.loss_fn = torch.nn.MSELoss()
self.cls_reducer = context.wrap_reducer(TriangleLabelSum(),
name="cls_reducer")
self.fn_reducer = context.wrap_reducer(triangle_label_sum,
name="fn_reducer")