Python project dependency management can often be challenging, especially when working with both Python and non-Python packages. This issue can give rise to confusion and inefficiencies due to the juggling of multiple dependency files. UniDep, a versatile tool, was designed to simplify and streamline Python dependency management. It has proven to be significantly useful for developers, predominantly in fields like research, data science, robotics, artificial intelligence (AI), and machine learning (ML).
UniDep features an innovative method for managing Conda and Pip dependencies in one file, either requirements.yaml or pyproject.toml. This strategy removes the necessity to maintain separate files like requirements.txt and environment.yaml, thus simplifying the entire dependency landscape.
UniDep can be integrated effortlessly with Setuptools and Hatchling. This feature ensures automatic dependency management during the installation process allowing developers to set up development environments using just one command: `unidep install ./your-package`.
Moreover, UniDep’s `unidep install` command efficiently deals with Conda, Pip, and local dependencies, offering a comprehensive solution for those seeking an efficient installation process.
UniDep is beneficial for projects within a monorepo structure. It can convert multiple requirements.yaml or pyproject.toml files into a single Conda environment.yaml file, resulting in consistent global and per-subpackage conda-lock files. This feature simplifies dependency management in interconnected projects.
Recognizing a diverse range of operating systems and architectures, UniDep enables developers to outline dependencies unique to different platforms. This flexibility enhances the user experience when operating across varying environments.
UniDep can be integrated with pip-compile, allowing the convenience of creating fully pinned requirements.txt files from requirements.yaml or pyproject.toml files. Similarly, it integrates with conda-lock, enhancing the creation of fully pinned conda-lock.yml files from one or more requirements.yaml or pyproject.toml files. These integrations promote consistency in dependency versions and reproduce environments.
Developed in Python, UniDep features over 99% test coverage, full typing support, compliance with Ruff’s rules, extensibility, and minimal dependencies.
UniDep is particularly effective when a complete development environment setup with both Python and non-Python dependencies, like CUDA, compilers, etc., is required. Its one-command installation feature and support for various platforms make UniDep a valuable tool in numerous fields.
UniDep has proven to be effective in monorepos with multiple dependencies. Notably, the public project home-assistant-streamdeck-yaml showcased UniDep’s ability to manage system dependencies across platforms.
UniDep can be a gamechanger for developers looking for easier and more efficient Python dependency management. No matter the preference between Conda or Pip, UniDep can simplify the process, marking it an indispensable tool for complex development environments. Opt for UniDep now and experience a significant improvement in your development process.
