for inputs, targets in loader:
  ...
  ```
This code is nearly identical to the file-based I/O pipeline found in the PyTorch Imagenet example: it creates a preprocessing/augmentation pipeline, instantiates a dataset using that pipeline and a data source location, and then constructs a DataLoader instance from the dataset.
WebDataset uses a fluent API for a configuration that internally builds up a processing pipeline. Without any added processing stages, In this example, WebDataset is used with the PyTorch DataLoader class, which replicates DataSet instances across multiple threads and performs both parallel I/O and parallel data augmentation.
WebDataset instances themselves just iterate through each training sample as a dictionary:
```python
load from a web server using a separate client process
sharedurl = "pipe:curl -s http://server/imagenet/imagenet-train-{000000..001281}.tar"
dataset = wds.Dataset(sharedurl)
for sample in dataset:
  # sample["jpg"] contains the raw image data