Train PyTorch with Checkpoints
Training a PyTorch model with checkpoints is an important technique to ensure that the model is saved periodically during the training process. This helps to avoid losing all of the progress made during the training if there is a power failure, hardware failure or any other unexpected problem. To train a PyTorch model with checkpoints, you can follow these steps: Import the necessary libraries and packages. import torch import torch . nn as nn import torch . optim as optim from torch . utils . data import DataLoader Define your model architecture. class MyModel ( nn . Module ) : def __init__ ( self ) : super ( MyModel , self ) . __init__ ( ) self . fc1 = nn . Linear ( 784 , 128 ) self . fc2 = nn . Linear ( 128 , 64 ) self . fc3 = nn . Linear ( 64 , 10 ) def forward ( self , x ) : x = x . view ( x . size ( 0 ) , - 1 ) x = self . fc1 ( x ) x = nn . functional . relu ...