How can you preprocess large datasets for generative AI tasks using Dask

Question

Can you tell me how you can preprocess large datasets for generative AI tasks using Dask?

Answer 1

You can preprocess large datasets for generative AI tasks using Dask, which enables parallel and distributed data processing. Dask's DataFrame or Bag APIs handle large-scale data efficiently by splitting computations across multiple cores or machines.

Here is the code snippet which you can refer to:

In the above code, we are using the following:

• Dask DataFrame:

  • Works like Pandas but processes data in chunks to handle datasets larger than memory.

• Preprocessing Function:

  • Define custom preprocessing (e.g., cleaning text, tokenization, or transformation).

• Parallel Execution:

  • Operations like maps are applied in parallel across the dataset.

The Output would be:

• Save the preprocessed data back to disk in a scalable manner.

Hence, this approach is efficient for preparing datasets for tasks like training generative.