def setup(self, config):
model = model_creator(config)
optimizer = optimizer_creator(model, config)
train_loader, val_loader = data_creator(config)
self.model, self.optimizer, = \
self.register(
models=model, optimizers=optimizer, ddp_args={
"find_unused_parameters": True})
assert self.model.find_unused_parameters
def setup(self, config):
model = nn.Sequential(nn.Linear(1, config.get("hidden_size", 1)))
optimizer = IterableOptimizer(model.parameters(),
lr=config.get("lr", 1e-2))
criterion = nn.MSELoss()
self.model, self.optimizer, self.criterion = self.register(
models=model, optimizers=optimizer, criterion=criterion)
train_ld, val_ld = data_creator(config)
self.register_data(train_loader=train_ld, validation_loader=val_ld)
def setup(self, config):
model = model_creator(config)
optimizer = optimizer_creator(model, config)
train_loader, val_loader = data_creator(config)
scheduler = scheduler_creator(optimizer, config)
loss = nn.MSELoss()
self.model, self.optimizer, self.criterion, self.scheduler = self.register(
models=model, optimizers=optimizer, criterion=loss, schedulers=scheduler
)
self.register_data(train_loader=train_loader, validation_loader=val_loader)
def setup(self, config):
models = nn.Linear(1, 1), nn.Linear(1, 1)
opts = [
torch.optim.SGD(model.parameters(), lr=0.0001)
for model in models
]
loss = nn.MSELoss()
train_dataloader, val_dataloader = data_creator(config)
self.models, self.optimizers, self.criterion = self.register(
models=models, optimizers=opts, criterion=loss)
self.register_data(train_loader=train_dataloader,
validation_loader=val_dataloader)
def setup(self, stage):
self.train_loader, self.val_loader = data_creator(self.config)
self.loss = nn.MSELoss()
