args = parser.parse_args() 现在,你可以这样调用运行程序: python trainer_main.py --gpus 2 --num_nodes 2 --conda_env 'my_env' --encoder_layers 12 最后,确保按照以下方式开始训练: # 初始化trainer trainer = Trainer.from_argparse_args(args, early_stopping_callback=...) # 不像这样 trainer ...
args = parser.parse_args() main(args) 2.自动添加所有Trainer会用到的命令行参数: from argparse import ArgumentParser def main(args): model = LightningModule() trainer = Trainer.from_argparse_args(args) trainer.fit(model) if __name__ == '__main__': parser = ArgumentParser() parser = Tra...
trainer = pl.Trainer.from_argparse_args( hparams, max_epochs=10, callbacks = [ckpt_callback,early_stopping] ) if hparams.auto_scale_batch_size is not None: #搜索不发生OOM的最大batch_size max_batch_size = trainer.tuner.scale_batch_size(model,data_mnist, mode=hparams.auto_scale_batch_...
lr_monitor = LearningRateMonitor(logging_interval="step") trainer = pl.Trainer.from_argparse_args(args, callbacks=[checkpoint, lr_monitor]) trainer.fit(model, train_loader, val_loader) print("trainer.checkpoint_callback.best_model_path: ", str(trainer.checkpoint_callback.best_model_path)) val...
from argparse import ArgumentParser from lightning.pytorch.callbacks.early_stopping import EarlyStopping from pathlib import Path import lightning as L import os import random import torch import torchvision from datasets.mnist import MNISTDataModule ...
(monitor = 'val_loss', patience=3, mode = 'min') trainer = pl.Trainer.from_argparse_args( hparams, max_epochs=10, callbacks = [ckpt_callback,early_stopping] ) if hparams.auto_scale_batch_size is not None: #搜索不发生OOM的最大batch_size max_batch_size = trainer.tuner.scale_batch_...
trainer=pl.Trainer.from_argparse_args( arg_groups['pl.Trainer'], strategy=DDPPlugin(find_unused_parameters=False),logger=logger, trainer=pl.Trainer( **vars(arg_groups['Trainer']), strategy="ddp",logger=logger, log_every_n_steps=10,num_sanity_val_steps=0, ...
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes. - pytorch-lightning/src/lightning/pytorch/loggers/wandb.py at master · Lightning-AI/pytorch-lightning
) main(args) 3.混合式,既使用Trainer相关参数,又使用一些自定义参数,如各种模型超参: from argparse import ArgumentParserimport pytorch_lightning...as plfrom pytorch_lightning import LightningModule, Trainer def main(args): model = LightningModule...示例: from pytorch_lightning import Trainerfrom pytorch...
from torchmetricsimportAccuracy 代码语言:javascript 复制 classMNISTDataModule(pl.LightningDataModule):def__init__(self,data_dir:str="./minist/",batch_size:int=32,num_workers:int=4):super().__init__()self.data_dir=data_dir self.batch_size=batch_size ...