paddlepaddle/epep

EPEP is a framework that sits on top of PaddlePaddle (a machine learning library) to make it faster and easier to build, train, and deploy AI models. Its main goal is to let you focus on just the business logic of your model, defining your inputs and your network, while the framework handles the plumbing like batching data, logging, and switching between CPU and GPU. The idea is that you only need to write about 20 lines of actual model code: one class to define and parse your inputs, and one class to define your network (the math that turns inputs into predictions). After that, you write a simple config file pointing to your data and setting hyperparameters like learning rate and number of training epochs. The framework then runs training or prediction via a single shell command, and it handles switching your model from running on a regular CPU to a GPU or even multiple GPUs without you changing your code. This is designed for strategy developers and machine learning practitioners who want to iterate quickly. The project's README notes it has been widely used inside Baidu, where it has noticeably sped up model iteration times on everything from single-machine CPU setups to multi-machine GPU clusters. It comes with example models for common natural language processing tasks like classification, regression, matching, sequence labeling, translation, and text generation. If you wanted to prototype, say, a text classifier, you could copy the provided linear regression example files, rename them, and adapt the code to your needs rather than starting from scratch. A notable design choice is how it minimizes boilerplate. You don't need to manually define every hyperparameter in code, if you add a new setting to your config file, you can reference it directly in your model. The framework also defaults to a standard optimizer (Adam) but lets you override it if you want something different. The README lists several features still in progress, including automatic hyperparameter search, a prediction server, and distributed training support.