This paper describes a deep-SDM framework, MALPOLON. Written in Python and built upon the PyTorch library, this framework aims to facilitate training and inferences of deep species distribution models (deep-SDM) and sharing for users with only general Python language skills (e.g., modeling ecologists) who are interested in testing deep learning approaches to build new SDMs. More advanced users can also benefit from the framework's modularity to run more specific experiments by overriding existing classes while taking advantage of press-button examples to train neural networks on multiple classification tasks using custom or provided raw and pre-processed datasets. The framework is open-sourced on GitHub and PyPi along with extensive documentation and examples of use in various scenarios. MALPOLON offers straightforward installation, YAML-based configuration, parallel computing, multi-GPU utilization, baseline and foundational models for benchmarking, and extensive tutorials/documentation, aiming to enhance accessibility and performance scalability for ecologists and researchers.

翻译：本文介绍了一个深度物种分布建模框架MALPOLON。该框架采用Python语言编写，基于PyTorch库构建，旨在促进深度物种分布模型的训练、推断与共享，尤其适用于仅具备基础Python编程能力（例如从事建模的生态学家）且希望尝试利用深度学习构建新型物种分布模型的用户。更高级的用户亦可借助该框架的模块化特性，通过重写现有类来开展更专门的实验，同时利用一键式示例，使用自定义或提供的原始与预处理数据集在多种分类任务上训练神经网络。该框架已在GitHub和PyPi平台开源，附有详尽的文档说明及多场景应用示例。MALPOLON提供简易安装流程、基于YAML的配置方式、并行计算支持、多GPU利用、用于基准测试的基础模型与骨干模型，以及全面的教程与文档，致力于提升生态学研究者使用的便捷性与性能可扩展性。