def create_lr_scheduler(self, optimizer: torch.optim.Optimizer): scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=self.context.get_hparam("num_warmup_steps"), num_training_steps=self.context.get_hparam("num_training_steps"), ) return LRScheduler(scheduler, LRScheduler.StepMode.STEP_EVERY_BATCH)
def create_lr_scheduler(self, optimizer: torch.optim.Optimizer): scheduler = torch.optim.lr_scheduler.StepLR( optimizer, self.context.get_hparam("reduce_every"), gamma=self.context.get_hparam("lr_gamma"), ) return LRScheduler(scheduler, LRScheduler.StepMode.STEP_EVERY_EPOCH)
def create_lr_scheduler(self, optimizer: torch.optim.Optimizer): self.Lr2 = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 100) self.Lr1 = torch.optim.lr_scheduler.StepLR(optimizer, 1) self.combined_lrs = MultiLRScheduler(self.Lr1, self.Lr2, optimizer) # Because we are calling .step() ourselves in our MultiLRScheduler we need to # set the StepMode to MANUAL_STEP. return LRScheduler(self.combined_lrs, step_mode=LRScheduler.StepMode.MANUAL_STEP)
def create_lr_scheduler(self, optimizer: torch.optim.Optimizer): """ Required Method to use a learning rate scheduler Returns: Determined scheduler object """ self.myLR = MyLR(optimizer, self.hparams) step_mode = LRScheduler.StepMode.MANUAL_STEP self._optimizer = self.context.get_optimizer() return LRScheduler(self.myLR, step_mode=step_mode)
def create_lr_scheduler(self, optimizer: torch.optim.Optimizer): """ Required Method to use a learning rate scheduler Returns: Determined scheduler object Determined will handle the learning rate scheduler update based on the Determined LRScheduler parameters. If step_every_batch or step_every_epoch is True, Determined will handle the .step(). If both are false, the user will be in charge of calling .step(). """ scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=self.context.get_hparam("num_warmup_steps"), num_training_steps=self.context.get_hparam("num_training_steps"), ) return LRScheduler(scheduler, LRScheduler.StepMode.STEP_EVERY_BATCH)
def create_lr_scheduler(self, optimizer: torch.optim.Optimizer): """ Required Method to use a learning rate scheduler Returns: Determined scheduler object Determined will handle the learning rate scheduler update based on the Determined LRScheduler parameters. If step_every_batch or step_every_epoch is True, Determined will handle the .step(). If both are false, the user will be in charge of calling .step(). """ self.myLR = MyLR(optimizer, self.context.get_hparams()) step_mode = LRScheduler.StepMode.MANUAL_STEP if self.context.get_hparam("step_every_batch"): step_mode = LRScheduler.StepMode.STEP_EVERY_BATCH elif self.context.get_hparam("step_every_epoch"): step_mode = LRScheduler.StepMode.STEP_EVERY_EPOCH return LRScheduler(self.myLR, step_mode=step_mode)
def create_lr_scheduler(self, optimizer): lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1) return LRScheduler(lr_scheduler, step_mode=LRScheduler.StepMode.STEP_EVERY_EPOCH)
def create_lr_scheduler(self, optimizer): self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( optimizer, self.context.get_hparam("cosine_annealing_epochs") ) step_mode = LRScheduler.StepMode.MANUAL_STEP return LRScheduler(self.scheduler, step_mode=step_mode)
def create_lr_scheduler(self, optimizer: torch.optim.Optimizer): step_mode = LRScheduler.StepMode.STEP_EVERY_BATCH scheduler = build_lr_scheduler(self.cfg, optimizer) return LRScheduler(scheduler, step_mode=step_mode)
def create_lr_scheduler(self, optimizer): self.scheduler = ModifyableLRSchedule(optimizer) return LRScheduler(self.scheduler, step_mode=LRScheduler.StepMode.MANUAL_STEP)
def create_lr_scheduler(self, optimizer): self.scheduler = ModifyableLRSchedule(optimizer) return LRScheduler(self.scheduler, step_mode=LRScheduler.StepMode.STEP_EVERY_BATCH)
def create_lr_scheduler(self, optimizer): self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( optimizer, self.context.get_hparam("train_epochs")) step_mode = LRScheduler.StepMode.STEP_EVERY_EPOCH return LRScheduler(self.scheduler, step_mode=step_mode)