!ls /content/drive/MyDrive/data
!ls /content/drive/MyDrive/checkpoints

> 解決方案的核心在於創(chuàng)建功能以保存和加載模型檢查點(diǎn)。 這些功能將序列您的模型的狀態(tài)，優(yōu)化器，調(diào)度程序和其他相關(guān)信息。

保存檢查點(diǎn)函數(shù)：

import torch
import os

def save_checkpoint(epoch, model, optimizer, scheduler, loss, model_name, overwrite=True):
    checkpoint = {
        'epoch': epoch,
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'scheduler_state_dict': scheduler.state_dict(),
        'loss': loss
    }
    direc = get_checkpoint_dir(model_name) #Assumed function to construct directory path
    if overwrite:
        file_path = os.path.join(direc, 'checkpoint.pth')
    else:
        file_path = os.path.join(direc, f'epoch_{epoch}_checkpoint.pth')
    os.makedirs(direc, exist_ok=True) # Create directory if it doesn't exist
    torch.save(checkpoint, file_path)
    print(f"Checkpoint saved at epoch {epoch}")

#Example get_checkpoint_dir function (adapt to your needs)
def get_checkpoint_dir(model_name):
    return os.path.join("/content/drive/MyDrive/checkpoints", model_name)

import torch
import os

def load_checkpoint(model_name, model, optimizer, scheduler):
    direc = get_checkpoint_dir(model_name)
    if os.path.exists(direc):
        #Find checkpoint with highest epoch (adapt to your naming convention)
        checkpoints = [f for f in os.listdir(direc) if f.endswith('.pth')]
        if checkpoints:
            latest_checkpoint = max(checkpoints, key=lambda x: int(x.split('_')[-2]) if '_' in x else 0)
            file_path = os.path.join(direc, latest_checkpoint)
            checkpoint = torch.load(file_path, map_location=torch.device('cpu'))
            model.load_state_dict(checkpoint['model_state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
            scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
            epoch = checkpoint['epoch']
            loss = checkpoint['loss']
            print(f"Checkpoint loaded from epoch {epoch}")
            return epoch, loss
        else:
            print("No checkpoints found in directory.")
            return 0, None
    else:
        print(f"No checkpoint directory found for {model_name}, starting from epoch 1.")
        return 0, None

> 將這些功能集成到您的培訓(xùn)循環(huán)中。循環(huán)在開(kāi)始培訓(xùn)之前應(yīng)檢查現(xiàn)有檢查點(diǎn)。如果找到了檢查站，它將恢復(fù)從保存的時(shí)期進(jìn)行的培訓(xùn)。 >

即使Colab會(huì)話終止，這種結(jié)構(gòu)也可以無(wú)縫恢復(fù)訓(xùn)練。 請(qǐng)記住要調(diào)整

EPOCHS = 10
for exp in experiments: # Assuming 'experiments' is a list of your experiment configurations
    model, optimizer, scheduler = initialise_model_components(exp) # Your model initialization function
    train_loader, val_loader = generate_data_loaders(exp) # Your data loader function
    start_epoch, prev_loss = load_checkpoint(exp, model, optimizer, scheduler)
    for epoch in range(start_epoch, EPOCHS):
        print(f'Epoch {epoch + 1}/{EPOCHS}')
        # YOUR TRAINING CODE HERE... (training loop)
        save_checkpoint(epoch + 1, model, optimizer, scheduler, train_loss, exp) #Save after each epoch

）。

以上是培訓(xùn)語(yǔ)言模型在Google Colab上的詳細(xì)內(nèi)容。更多資訊請(qǐng)關(guān)注PHP中文網(wǎng)其他相關(guān)文章！