nnU-Net v2 is a self-configuring deep learning framework for medical image segmentation that automates preprocessing, network architecture selection, training, and inference. It ingests raw medical images in various formats (NIfTI, TIFF, DICOM via SimpleITK) and produces segmentation masks. The system is organized as a modular monolith: experiment planning extracts dataset fingerprints to determine optimal preprocessing and network topology; a preprocessing pipeline handles resampling, normalization, and cropping; training uses a configurable base trainer with many variants (including 3D/2D U-Nets, residual encoders, and transformer-based Primus architectures); inference employs sliding-window with Gaussian weighting and test-time augmentation. Key entry points are CLI commands (nnUNetv2_plan_and_preprocess, nnUNetv2_train, nnUNetv2_predict) and the programmatic nnUNetPredictor. The codebase demonstrates high craftsmanship in its checkpoint system (safetensors with backward compatibility), pluggable image I/O, and configuration management via plans files, though it carries some technical debt from legacy .pth support and a proliferation of trainer variant subclasses.