Py-gen-ML – a library to generate robust ML code from a schema

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py-gen-ml ( https://jostosh.github.io/py-gen-ml ) is a Python library that aims to simplify the process of configuring machine learning projects. It uses Protobuf schemas to define configurations and then automatically generates code based on those schemas. This includes code for Pydantic base models, patch configurations, sweep configurations, command-line interfaces, and entry points. One of the key benefits of using py-gen-ml is that it reduces the amount of manual work that is required when making changes to configuration schemas and/or values. Atomic code generation guarantees consistency across Pydantic base models, YAML parsing, CLI parsing, sweeps and any other code that is generated. Another benefit of py-gen-ml is that it offers strong typing. This means that the code that is generated is guaranteed to be correctly typed and easier to understand for you, your team, your IDE, and your type checker. Similar to other config frameworks like Hydra, py-gen-ml supports flexible YAML configurations with advanced referencing and variable support within YAML files. In addition, generated JSON schemas can be used to validate YAMLs as you type. py-gen-ml can be installed using pip: > pip install py-gen-ml There is a quick start guide available to help users get started with py-gen-ml. There is also additional documentation that covers topics such as the command-line interface, parameter sweeps, generated factories, and a Cifar 10 example project. In short: using py-gen-ml can help you to manage complex ML projects more efficiently, streamline experiment running and hyperparameter tuning, and reduce the impact of configuration changes on your workflow. The project is still at an early stage, but ready for feedback!

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