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# MONAI
* **apps**: high level medical domain specific deep learning applications.
* **auto3dseg**: automated machine learning (AutoML) components for volumetric image analysis.
* **bundle**: components to build the portable self-descriptive model bundle.
* **config**: for system configuration and diagnostic output.
* **csrc**: for C++/CUDA extensions.
* **data**: for the datasets, readers/writers, and synthetic data.
* **engines**: engine-derived classes for extending Ignite behaviour.
* **fl**: federated learning components to allow pipeline integration with any federated learning framework.
* **handlers**: defines handlers for implementing functionality at various stages in the training process.
* **inferers**: defines model inference methods.
* **losses**: classes defining loss functions, which follow the pattern of `torch.nn.modules.loss`.
* **metrics**: defines metric tracking types.
* **networks**: contains network definitions, component definitions, and Pytorch specific utilities.
* **optimizers**: classes defining optimizers, which follow the pattern of `torch.optim`.
* **transforms**: defines data transforms for preprocessing and postprocessing.
* **utils**: generic utilities intended to be implemented in pure Python or using Numpy,
and not with Pytorch, such as namespace aliasing, auto module loading.
* **visualize**: utilities for data visualization.
* **_extensions**: C++/CUDA extensions to be loaded in a just-in-time manner using `torch.utils.cpp_extension.load`.
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