Learn PyTorch #1: Typical Lifecycle of a Model

Before we begin to get hands-on with PyTorch, let’s take a look into a typical lifecycle of a model that starts with data preparation, followed by training and deployment as visualized below.

Figure 1: Typical lifecycle of a Model. Setup -> Train -> Deploy

The journey begins with the data preparation or setup phase, where we start by loading a dataset using PyTorch’s Dataset class. To efficiently handle this data, we create a DataLoader that manages important tasks like batching, shuffling, and parallel loading of data. The DataLoader allows us to control how many samples are processed at once through batch sizing, which is crucial for managing memory and computation efficiency. Moving into the model creation phase, we define a neural network by creating a custom class that inherits from nn.Module. After initializing the model with the desired architecture, we need to move it to the appropriate device - whether that’s a CPU, GPU, or MPS - to ensure optimal processing speed for the hardware setup.

The heart of the process lies in the training loop phase. Here, we first define a loss function that measures how well the model is performing, and set up an optimizer to update the model’s parameters. During each training epoch, the model goes through several critical steps: it makes predictions on batches of data during the forward pass, calculates loss by comparing these predictions with actual values, performs backpropagation to calculate gradients, and then updates its parameters to optimize the weights. Throughout this process, we validate the model’s performance on test data to ensure it’s learning effectively.

Finally, when training is complete and we are ready to deploy the model, we save the trained model’s state dictionary, load this model, and use it to make predictions on new data. The DataLoader serves as the crucial bridge between the data and the model throughout this entire lifecycle, while the continuous update of model parameters during training helps minimize the loss function and improve accuracy.

Key Concepts:

1. Dataset: A collection of data that is used to train and evaluate a model. In PyTorch, datasets are typically represented using the torch.utils.data.Dataset class.
2. DataLoader: A PyTorch class that provides an iterable over a dataset with support for batching, shuffling, and parallel data loading.
3. nn.Module: A base class for all neural network modules in PyTorch. Custom models are created by subclassing nn.Module.
4. Loss Function: A function that measures how well the model’s predictions match the actual target values. Common loss functions include Mean Squared Error (MSE) and Cross-Entropy Loss.
5. Optimizer: An algorithm or method used to adjust the model’s parameters based on the gradients computed during backpropagation. Examples include SGD (Stochastic Gradient Descent) and Adam.
6. Forward Pass: The process of passing input data through the model to obtain predictions.
7. Backward Pass: The process of computing gradients of the loss with respect to the model’s parameters using backpropagation.
8. Epoch: One complete pass through the entire training dataset.
9. State Dictionary: A Python dictionary object that maps each layer to its parameters. It is used to save and load models in PyTorch.