[D] What's the modern workflow for managing CUDA versions and packages across multiple ML projects?

Hello everyone, I'm a relatively new ML engineer and so far I've been using conda for dependency management. The best thing about conda was that it allowed me to install system-level packages like CUDA into isolated environments, which was a lifesaver since some of my projects require older CUDA versions. That said, conda has been a pain in other ways.