What is the difference between PyTorch inference and training?

What is the difference between PyTorch inference and training?

In PyTorch, training refers to the process of updating model weights using backpropagation, while inference is when you use the trained model to make predictions.

Can you explain how the training process works in PyTorch?

Sure! During training, you feed the model input data, compute the loss, and then adjust the weights using an optimizer based on the gradients.

What are some common use cases for inference in PyTorch?

Common use cases include deploying models for image classification, natural language processing, and real-time predictions in applications.

Are there any performance considerations for inference?

Yes, optimizing model size, using batch processing, and leveraging hardware accelerators like GPUs can significantly improve inference speed.