How can you use tensor slicing to speed up training on large datasets for Generative AI

Question

Can you explain, using Python programming, how you can use tensor slicing to speed up training on large datasets for generative AI?

Answer 1

You can use tensor slicing to load and process smaller batches of data from a large dataset in memory-efficient chunks, speeding up training by reducing memory overhead. Here is the code you can refer to :

In the above code, we are using the following:

• Efficiency: Avoids loading the entire dataset into memory.
• Scalability: Handles larger datasets by working on slices.
• Flexibility: Enables dynamic memory usage for large-scale generative AI models.

Hence, referring to the above, you can use tensor slicing to speed up training on large datasets for Generative AI