Using rocAL with PyTorch for training

rocAL improves machine learning (ML) pipeline efficiency by preprocessing data and parallelizing data loading.

PyTorch iterators and readers are provided as plugins to separate data loading from training.

You’ll need a rocAL PyTorch Docker container to run PyTorch training with rocAL.

To use rocAL with PyTorch, import the rocAL PyTorch plugin:

from amd.rocal.plugin.pytorch import ROCALClassificationIterator

Set up a training pipeline that reads data from a dataset using readers.file and uses decoders.image_slice to decode the raw images.

Call the training pipeline using ROCALClassificationIterator.

Two examples of PyTorch training using rocAL are available in the rocAL GitHub repository.