API Guide

There are two primary high level classes.

1. ergoExo houses the solver and handles the mlflow logging and experiment management

2. ADEPTModule is base class for the solver

If you wanted to create your own differentiable program that uses the ADEPT solvers, you could do

from adept import ergoExo

exo = ergoExo()
modules = exo.setup(cfg)

and

sol, ppo, run_id = exo(modules)

or

sol, ppo, run_id = exo.val_and_grad(modules)

This is analogous to torch.nn.Module and eqx.Module the Module workflows in general.

You can see what each of those calls does in API documentation below.

High level API:

• ergoExo
  • ergoExo
    • ergoExo.setup()
    • ergoExo.__call__()
    • ergoExo.val_and_grad()
• ADEPTModule
  • ADEPTModule
    • ADEPTModule.post_process()
    • ADEPTModule.write_units()
    • ADEPTModule.init_diffeqsolve()
    • ADEPTModule.get_derived_quantities()
    • ADEPTModule.get_solver_quantities()
    • ADEPTModule.get_save_func()
    • ADEPTModule.init_state_and_args()
    • ADEPTModule.init_modules()
    • ADEPTModule.__call__()