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First steps


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.

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.