dataset_preprocessing.qmd

---
title: Dataset Preprocessing
description: How datasets are processed
---

Dataset pre-processing is the step where Axolotl takes each dataset you've configured alongside
the (dataset format)[../dataset-formats/] 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 `python -m axolotl.cli.preprocess /path/to/your.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.