The biggest optimization I've found is to use mamba for everything. It's ridiculously faster than conda for package resolution. With everything cached, you're mostly just waiting for your SSD at that point.
(I suppose you could add the base env's lib path to the end of your PYTHONPATH, but that sounds like a sure way to get bitten by weird dependency/reproducibility issues down the line.)
The workflow is:
> nix flake init -t github:dialohq/flake-templates#python
> nix develop -c $SHELL
> # I'm in the shell with poetry env, I have a shell hook in the nix devenv that does poetry install and poetry activate.I feel more projects should start with that as the base instead of pinning on whatever variants. Most aren't using specialized CUDA kernels after all.
Suppose there's the answer, just pick the specific torch+CUDA base that matches the major version of the project you want to run. Then cross your fingers and hope the dependencies mesh :p.
If it's starting to get old, then this means that an LLM like Copilot should be able to do it for you, no?
Still worth the trade-off of not having to deal with dependency hell, but you start to wonder if there is a better way. All together this is many GBs of duplicated libs, wasted bandwidth and compute.
How are other people dealing with this? Is there an easy way to get multiple venvs to share like a common torch venv? I can do this manually but I'm wondering if there's a tool out there that does this.