Skip to content

iteryne

Model-Agnostic Meta-Learning (MAML) for any PyTorch nn.Module.

iteryne implements MAML (Finn, Abbeel & Levine, 2017) on top of PyTorch's native torch.func. It meta-trains a model's initial parameters so that a few gradient steps on a new task's small support set generalize to that task's query set.

Why iteryne

  • Model-agnostic. Works on any nn.Module (MLP, CNN, BatchNorm, ...) with no rewriting, via torch.func.functional_call.
  • First- and second-order. Full MAML and FOMAML share one code path; flip a single first_order flag.
  • Variants. Meta-SGD (learnable per-parameter inner learning rates) and ANIL (adapt only the head) ship in the box.
  • Two API levels. A high-level MAML wrapper plus MetaTrainer, and an exposed functional core.

Install

pip install iteryne

Requires Python >= 3.10 and PyTorch >= 2.1.

Continue to the Quickstart.