I don't think "base" works like that (while it can be a fallback for some dependencies, afaik, Python packages are isolated/not in path). But even if you could, don't do it. Different packages usually have different pytorch dependencies (often CUDA as well) and it will definitely bite you.

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.)

Thank you! First time I come across. Looks very promising