Prepare datasets for distributed training

You've preprocessed a dataset for a precision agriculture system to help farmers monitor crop health. Now you'll load the data by creating a DataLoader and place the data on GPUs for distributed training, if GPUs are available. Note the exercise actually uses a CPU, but the code is the same for CPUs and GPUs.

Some data has been pre-loaded:

A sample dataset with agricultural imagery
The Accelerator class from the accelerate library
The DataLoader class

Это упражнение является частью курса

Efficient AI Model Training with PyTorch

Посмотреть курс

Инструкции к упражнению

Create a dataloader for the pre-defined dataset.
Place the dataloader on available devices using the accelerator object.

Интерактивное практическое упражнение

Попробуйте выполнить это упражнение, дополнив этот пример кода.

accelerator = Accelerator()

# Create a dataloader for the pre-defined dataset
dataloader = ____(____, batch_size=32, shuffle=True)

# Place the dataloader on available devices
dataloader = accelerator.____(____)

print(accelerator.device)

Редактировать и запускать код