py-gen-ml is a Python library that aims to simplify the process of managing machine learning projects. It uses Protobuf schemas to define configurations and then automatically generates code based on those schemas. This can include code for 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 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 more likely to be correct and easier to understand for you, your team, your IDE and your type checker.
py-gen-ml also supports flexible YAML configurations, which can be used to define the parameters of your machine learning project. py-gen-ml provides support for advanced referencing and variable support within YAML files, as well as real-time validation in IDEs using JSON schemas.
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.
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.
It's still in an early stage, but ready for feedback!
py-gen-ml is a Python library that aims to simplify the process of managing machine learning projects. It uses Protobuf schemas to define configurations and then automatically generates code based on those schemas. This can include code for 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 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 more likely to be correct and easier to understand for you, your team, your IDE and your type checker.
py-gen-ml also supports flexible YAML configurations, which can be used to define the parameters of your machine learning project. py-gen-ml provides support for advanced referencing and variable support within YAML files, as well as real-time validation in IDEs using JSON schemas.
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.
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.
It's still in an early stage, but ready for feedback!
Check out the project here: https://jostosh.github.io/py-gen-ml