超参数优化:Lightning还能和一些超参数优化库(比如Optuna)无缝集合,帮你自动找最佳超参数。 模型checkpointing:Lightning会自动保存训练过程中的最佳模型,你都不用操心。 5. 小结 PyTorch Lightning就像是给你的PyTorch代码装上了一对翅膀,让你的深度学习之旅飞得更高、更远。它简化了代码结构,自动化了训练流程,还提...
PyTorch Lightning 1.6.0dev documentationpytorch-lightning.readthedocs.io/en/latest/common/trainer.html Trainer可接受的全部参数如下 Trainer.__init__( logger=True, checkpoint_callback=None, enable_checkpointing=True, callbacks=None, default_root_dir=None, gradient_clip_val=None, gradient_clip_algor...
limit_predict_batches=None, overfit_batches=0.0, val_check_interval=None, check_val_every_n_epoch=1, num_sanity_val_steps=None, log_every_n_steps=None, enable_checkpointing=None, enable_progress_bar=None, enable_model_summary=None, accumulate_grad_batches=1, gradient_clip_val=None, gradient...
还有checkpointing, and early stopping 5 附加功能 但是Lightning以开箱即用的东西(例如TPU训练等)而闻名。 在Lightning中,可以在CPU,GPU,多个GPU或TPU上训练模型,而无需更改PyTorch代码的一行。 5.1 16位精度训练 Trainer(precision=16) 1. 5.2 多种日志记录方法 使用Tensorboard的其他5种替代方法进行记录点击查看 ...
For example, Lightning automatically saves the model checkpoint by default as compared to Pytorch which expects the developer to insert that logic for checkpointing. Lightning also provides the logs of weights summary, checkpointing, early stopping, and tensorboard logs. ...
What is the primary advantage of using PyTorch Lightning over classic PyTorch? The primary advantage of using PyTorch Lightning is that it simplifies the deep learning workflow by eliminating boilerplate code, managing training loops, and providing built-in features for logging, checkpointing, and dis...
Checkpointing checkpointing = ModelCheckpoint(monitor='val_loss') trainer = Trainer(callbacks=[checkpointing]) Export to torchscript (JIT) (production use) # torchscriptautoencoder = LitAutoEncoder() torch.jit.save(autoencoder.to_torchscript(),"model.pt") ...
Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more import lightning as L class MyCustomTrainer: def __init__(self, accelerator="auto", strategy="auto", devices="auto", precision="32-true"): self.fabric = L.Fabric(accelerator=accelerator, ...
enable_checkpointing=False, inference_mode=True, ) # Run evaluation. data_module.setup() valid_loader = data_module.val_dataloader() trainer.validate(model=model, dataloaders=valid_loader) The best validation set results are as follows:
PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. Organizing PyTorch code with Lightning enables seamless training on multiple GPUs, TPUs, CPUs, and the use of difficult to implement best practices such as checkpointing, logging, sharding, and m...