Only issue is that it'd be really space inefficient, my DS venvs clock in at 400+mb each so having one per sheet will probably quickly become unusable. Which is why I thought of some sort of smart system wide caching akin to maven/ivy. But I'd forgotten how complicated python dependencies (binaries, c code, etc.) were and how little api support pip had.
I think to do this stuff, you need hooks in Jupyter to setup and teardown the venv before it runs the kernel. (And generally Jupyter would want to clean up unused venvs to mitigate the teardown hook not firing.)
> Only issue is that it'd be really space inefficient
A venv is overkill. You can just run the kernel in a regular directory, and `pip install --target kernel_dir foo-bar-pkg` to put packages directly in it. As long as the linker sees it, third-party libraries will work. This technique is used in the serverless project[1] to bundle dependencies for use on AWS Lambda.
> Not sure if you'd need to find older versions of new direct dependencies to avoid conflicts.
Curation is a solution to this. Stackage[1] is popular in the Haskell community; they build a consistent version set of everything every night and curate stable releases periodically.
With curation, a date and a set of top-level packages is enough to pin your dependencies.
[1]: https://github.com/commercialhaskell/stackage#frequently-ask... [2]: https://github.com/UnitedIncome/serverless-python-requiremen...