Dear RG users,
I noticed that several toolboxes developed for python are compatible with some specific releases and not with others and in general there is a precise set of tools defining the working configuration.
What is in your experience the best way to handle these different dependencies and configurations?
E.g., suppose that I have a toolbox A working with python 3.5 and matplotlib 3.2 and a toolbox B working with python 3.7 and matplotlib 3.1 (just a speculative example)
What's the cleanest way to handle these dependencies on a single machine? Using Virtual machines? Docker containers? or any other option that I don't know yet?
Thanks for any suggestion!
In the case of Python, it is probably easiest to use Anaconda: With it, you can create different environments without much effort and adapt them to your specific tasks (e.g. different package or Python versions, etc.).
In the case of Python, it is probably easiest to use Anaconda: With it, you can create different environments without much effort and adapt them to your specific tasks (e.g. different package or Python versions, etc.).
In the case of Python, I would choose the virtual environments to handle diferentes packages versions.
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