Answered by AI - You have hit on the exact reason developers sometimes get frustrated with virtual environments: storage space . Machine learning packages like PyTorch, TensorFlow, and Anaconda are notoriously huge. If you create 5 different projects and install a 2GB machine learning library in every single virtual environment, you will quickly lose 10GB+ of storage space for duplicate files. Fortunately, professional Python developers have optimized solutions to fix this. Two excellent tools allow you to keep your projects isolated while sharing identical packages globally behind the scenes: Conda (Miniconda) and uv . The Two Smart Solutions for Storage Solution A: The Modern Choice — uv (Fastest & Lightest) Created by Astral, uv is a modern replacement for pip and venv . It completely solves the duplicate storage issue using a global content-addressable cache . How it works: When you use uv to create virtual environments, it downloads the ML package (like Tens...