This rule raises an issue when a torch.utils.data.Dataloader is instantiated without specifying the num_workers parameter.

Why is this an issue?

In the PyTorch library, the data loaders are used to provide an interface where common operations such as batching can be implemented. It is also possible to parallelize the data loading process by using multiple worker processes. This can improve performance by increasing the number of batches being fetched in parallel, at the cost of higher memory usage. This performance increase can also be attributed to avoiding the Global Interpreter Lock (GIL) in the Python interpreter.

How to fix it

Specify the num_workers parameter when instantiating the torch.utils.data.Dataloader object.

The default value of 0 will use the main process to load the data, and might be faster for small datasets that can fit completely in memory.

For larger datasets, it is recommended to use a value of 1 or higher to parallelize the data loading process.

Code examples

Noncompliant code example

from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor

train_dataset = datasets.MNIST(root='data', train=True, transform=ToTensor())
train_data_loader = DataLoader(train_dataset, batch_size=32)# Noncompliant: the num_workers parameter is not specified

Compliant solution

from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor

train_dataset = datasets.MNIST(root='data', train=True, transform=ToTensor())
train_data_loader = DataLoader(train_dataset, batch_size=32, num_workers=4)

Resources

Documentation