Understanding Callbacks and Logging in PyTorch

• Callbacks in PyTorch are functions or classes that can be used to insert custom logic at various stages of the training loop. They are useful for tasks like logging, early stopping, learning rate scheduling, and saving models. While PyTorch does not have a built-in callback system like some other frameworks (e.g., Keras), you can implement callbacks by customizing the training loop or using third-party libraries like pytorch-lightning or ignite.
• Logging is an important part of training models to keep track of metrics like loss and accuracy over time. PyTorch does not provide a built-in logging system, but you can use Python’s logging module or integrate with logging libraries such as TensorBoard or wandb (Weights and Biases).

Logging involves recording information about the training process, which can include Loss values, Accuracy scores, Time taken for each epoch or batch, Any other metric or state of interest.

Monitoring model training is crucial for understanding the performance and behavior of your machine learning models. PyTorch provides several mechanisms to facilitate this, including the use of callbacks and logging. This article will guide you through the process of using these tools effectively.