# First steps¶

Warning

Make sure you have read Getting started before using the library. In particular read about which extra dependencies you may need.

## Main concepts¶

pyDVL aims to be a repository of production-ready, reference implementations of algorithms for data valuation and influence functions. Even though we only briefly introduce key concepts in the documentation, the following sections should be enough to get you started.

- Basics of data valuation for key objects and usage patterns for Shapley value computation and related methods.
- Computing Influence Values for instructions on how to compute influence functions.

## Running the examples¶

If you are somewhat familiar with the concepts of data valuation, you can start by browsing our worked-out examples illustrating pyDVL's capabilities either:

- In the examples under Basics of data valuation and Computing Influence Values.
- Using binder notebooks, deployed from each example's page.
- Locally, by starting a jupyter server at the root of the project. You will have to install jupyter first manually since it's not a dependency of the library.

## Advanced usage¶

Refer to the Advanced usage page for explanations on how to enable and use parallelization and caching.