What is PyTorch?

PyTorch is an open-source Machine Learning Library that works on the dynamic computation graph. In the static computation approach, the models are predefined before the execution. But in dynamic computation which PyTorch follows, the structure of the graph in the Neural Network can change during the execution based on the input data. Hence, It allows to creation and training the Neural Networks to extract hidden patterns from the data.

You might think what a Neural Network is. So in simple words, a Neural Network is a collection of layers containing Nodes. These layers are interconnected with each other in which one Node processes the data and passes it to the other Node. Hence, the entire Neural Network learns and extracts the insights from the data.

It often happens that we need to use the already-trained models to perform some operations in our development environment. In this case, would you create the model again and again? Or, you will save the model somewhere else and load it as per the requirement. You would definitely choose the second option. So in this article, we will see how to implement the concept of saving and loading the models using PyTorch.