We've been fine-ish with classic setup.py/setup.cfg + gha for publishing to pypi. But as we do OSS data science (gpu graph ai + viz), where conda is typical nowadays...
... Have to admit: We recently ended up contracting conda packaging out because it was nowhere near clear enough to make sense for our core team to untangle. Would love to see a similar tutorial on a github flow packaging & publishing to conda. Still no convinced we're doing it right for subtleties like optional dependencies: equivalent of `pip install graphistry` vs `pip install graphistry[ai]` vs `graphistry[umap-learn]`, etc.
... Have to admit: We recently ended up contracting conda packaging out because it was nowhere near clear enough to make sense for our core team to untangle. Would love to see a similar tutorial on a github flow packaging & publishing to conda. Still no convinced we're doing it right for subtleties like optional dependencies: equivalent of `pip install graphistry` vs `pip install graphistry[ai]` vs `graphistry[umap-learn]`, etc.