We introduce Equitrain, an open-source software package designed to simplify the training and fine-tuning of machine learning universal interatomic potentials (MLIPs). Equitrain addresses the challenges posed by the diverse and often complex training codes specific to each MLIP by providing a unified and efficient framework. This allows researchers to focus on model development rather than implementation details. Equitrain supports the efficient handling of large datasets with an optimized HDF5 implementation for storing and managing training data, along with seamless conversion of various data formats into HDF5 to ensure interoperability. The package also incorporates distributed training on multiple GPUs via HuggingFace Accelerate, enabling scalability for computationally intensive tasks. By standardizing training workflows, Equitrain empowers researchers to train and test a variety of ML models and contribute to advancing materials discovery. Equitrain is freely available under an open-source license, and we encourage community contributions to enhance its functionality. We envision it as an essential tool for the materials science community, enabling researchers to concentrate on the development and refinement of MLIPs for real-world applications.