优化器和LR调度器(configure_optimizers())当你使用Lightning时,代码不是抽象的——只是组织起来的。所有不在LightningModule中的其他代码都已由Trainer自动为您执行。net = MyLightningModuleNet() trainer = Trainer() trainer.fit(net)不需要.cuda()或.to(device)调用。Lightning已经为你做了这些。如下:...
self.validation_step_outputs.clear() def configure_optimizers(self): optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) return optimizer trainer = L.Trainer( accelerator="cuda", strategy='auto', precision="16-mixed", devices=1, max_epochs=100#, default_root_dir="./log", ) train...
import pytorch_lightning as pl # 假设你已经有一个定义好的LightningModule class MyLightningModule(pl.LightningModule): def __init__(self): super().__init__() # 你的模型初始化代码 def training_step(self, batch, batch_idx): # 训练步骤代码 pass def configure_optimizers(self): # 配置优化器...
In this specific example, the error can be fixed by reverting back to the old import style: -from lightning import LightningModule, Trainer+from pytorch_lightning import LightningModule, Trainer However, in more complex projects, e.g. with other dependencies using Lightning, it's not realistic (...
Another one could be to use the above structure, and that I configure somewhere that if thefitcommand is used and thevalidation_stepis present but the val_dataloader isNone, then skip and don't raise error - I dont know what the repercussion of these tbh and then operations like--trainer...
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes. - pytorch-lightning/src/lightning/pytorch/core/module.py at master · Lightning-AI/pytorch-lightning
But I receive an error: AttributeError: module 'logging' has no attribute 'TensorBoardLogger' To Reproduce ubuntu@ip-172-31-41-72:~$ mkdir pltest ubuntu@ip-172-31-41-72:~$cdpltest/ ubuntu@ip-172-31-41-72:~/pltest$ pipenv --python 3.7 Creating a virtualenvforthis project… Pipfile:...