7. Workflows

Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.

Workflows are an abstraction on top of the approximator, that expose methods for training and inference in a more abstract, and therefore simplified, fashion.

7.1. BasicWorkflow

For now, BasicWorkflow is the only available workflow. In its most basic form, you provide it with four things: simulator, adapter, inference network and summary network (if you use one).

workflow = bf.BasicWorkflow(
    simulator=simulator,
    adapter=adapter,
    inference_network=inference_network,
    summary_network=summary_network,
)

You can then use the fit_online() method to run the training. After training, the you can sample from the workflow using sample().

Note that after the training, the work of the workflow is done, and you can just use the trained approximator (accessible via workflow.approximator) for downstream tasks. If you want to save you trained output, you would use the approximator as well, i.e., store it using workflow.approximator.save(filepath=filepath). You can then use sample() and related methods directly from the approximator.

BasicWorkflow is highly configurable, refer to the API reference for details. For practical usage, take a look at the Examples.