How do you manage memory-intensive datasets for efficient generative model training

Question

With the help of programming and examples, can you explain how you manage memory-intensive datasets for efficient generative model training?

Answer 1

To manage memory-intensive datasets during generative model training, you can use techniques like data streaming, batch loading, and gradient accumulation. 

Here is the code snippet you can refer to, which uses PyTorch with a DataLoader for efficient batch processing:

In the above code, we are using the following key strategies:

• DataLoader: Efficiently loads data in smaller batches.
• Gradient Accumulation: Reduces memory usage by splitting batches across multiple steps.
• Truncation & Padding: Ensures consistency in input size.

Hence, by referring to the above, you can manage memory-intensive datasets for efficient generative model training.