# Dataset Preprocessing – Axolotl

# Dataset Preprocessing

How datasets are processed

## Overview

Dataset pre-processing is the step where Axolotl takes each dataset you’ve configured alongside the dataset format and prompt strategies to:

- parse the dataset based on the dataset format
- transform the dataset to how you would interact with the model based on the prompt strategy
- tokenize the dataset based on the configured model & tokenizer
- shuffle and merge multiple datasets together if using more than one

The processing of the datasets can happen one of two ways:

1. Before kicking off training by calling `axolotl preprocess config.yaml --debug`
2. When training is started

### What are the benefits of pre-processing?

When training interactively or for sweeps (e.g. you are restarting the trainer often), processing the datasets can oftentimes be frustratingly slow. Pre-processing will cache the tokenized/formatted datasets according to a hash of dependent training parameters so that it will intelligently pull from its cache when possible.

The path of the cache is controlled by `dataset_prepared_path:` and is often left blank in example YAMLs as this leads to a more robust solution that prevents unexpectedly reusing cached data.

If `dataset_prepared_path:` is left empty, when training, the processed dataset will be cached in a default path of `./last_run_prepared/`, but will ignore anything already cached there. By explicitly setting `dataset_prepared_path: ./last_run_prepared`, the trainer will use whatever pre-processed data is in the cache.

### What are the edge cases?

Let’s say you are writing a custom prompt strategy or using a user-defined prompt template. Because the trainer cannot readily detect these changes, we cannot change the calculated hash value for the pre-processed dataset.

If you have `dataset_prepared_path: ...` set and change your prompt templating logic, it may not pick up the changes you made and you will be training over the old prompt.