UniDep simplifies Python dependency management by unifying Conda and Pip packages in a single system. With a one-command installation, it seamlessly handles dependencies, integrates with build systems, supports monorepos, and provides platform-specific and pip-compile integration. Developed in Python, UniDep is a valuable asset for developers in research, data science, robotics, AI, and ML projects.
Introducing UniDep: Simplifying Python Dependency Management
Unified Dependency File
Unified Dependency File
Handling dependencies in Python can be challenging. UniDep introduces a unified approach to managing Conda and Pip dependencies in a single file, streamlining the entire dependency landscape.
Build System Integration
UniDep seamlessly integrates with Setuptools and Hatchling, simplifying the installation process with just a single command: `unidep install ./your-package`.
One-Command Installation
UniDep’s `unidep install` command effortlessly handles Conda, Pip, and local dependencies, providing a comprehensive solution for hassle-free installation.
Monorepo-Friendly
UniDep excels in rendering multiple files into a single Conda environment.yaml file, simplifying dependency management across interconnected projects.
Platform-Specific Support
UniDep allows developers to specify dependencies tailored to different platforms, ensuring a smooth experience across various environments.
pip-compile Integration
UniDep integrates with pip-compile, promoting environment reproducibility and stability.
Integration with conda-lock
UniDep allows the generation of fully pinned conda-lock.yml files from requirements.yaml or pyproject.toml files, ensuring consistency in dependency versions for reproducible environments.
Real-World Application
UniDep shines in monorepos with multiple dependent projects and showcases efficiency in handling system dependencies across different platforms.
Value Proposition
UniDep streamlines the dependency management process, making it an essential tool for anyone dealing with complex development environments. Its one-command installation and support for various platforms make it a valuable asset in fields like research, data science, robotics, AI, and ML.
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