To make our life easier when reading from the training and test sets, we use a DataLoader rather than creating one from scratch, as we did in Section 3.2. Recall that at each iteration, a DataLoader reads a minibatch of data with size batch_size each time. During training, reading data can be a significant performance bottleneck, especially when our model is simple or when our computer is fast. A handy feature of Gluonʼs DataLoader is the ability to use multiple processes to speed up data reading. For instance, we can set aside 4 processes to read the data (via num_workers). Because this feature is not currently supported on Windows the following code checks the platform to make sure that we do not saddle our Windows-using friends with error messages later on.

Just as we implemented linear regression from scratch, we believe that multiclass logistic (soft- max) regression is similarly fundamental and you ought to know the gory details of how to imple- ment it yourself. As with linear regression, after doing things by hand we will breeze through an implementation in Gluon for comparison. To begin, let us import the familiar packages.