Once the model is implemented, PyTorch provides the DataLoader and Dataset classes to enable using pre-loaded datasets or your own data to train the model. Dataset provides access to the data and corresponding labels and DataLoader can be used to provide iterable access to the dataset for use in training and validation loops.

With a model implemented and training and validation data available, the model can be trained to optimize the parameters, or weights, of the model. Multiple passes, or epochs, are made over the dataset to train and validate the model parameters. After completing a training loop, a validation loop is typically run to check if the model’s predictive performance is improving. In PyTorch, model parameters are kept in an internal state dictionary called state_dict. The save method can be used to save the model parameters. 
At IBM, we use PyTorch to train foundation models that power the generative AI capabilities of the IBM watsonx platform.

Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the Cloud IDE and Python runtimes that we offer as part of the IBM Skills Network Labs environment. 

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