Does it make sense to use Conda + Poetry for a Machine Learning project? Allow me to share my (novice) understanding and please correct or enlighten me:

As far as I understand, Conda and Poetry have different purposes but are largely redundant:

• Conda is primarily a environment manager (in fact not necessarily Python), but it can also manage packages and dependencies.
• Poetry is primarily a Python package manager (say, an upgrade of pip), but it can also create and manage Python environments (say, an upgrade of Pyenv).

My idea is to use both and compartmentalize their roles: let Conda be the environment manager and Poetry the package manager. My reasoning is that (it sounds like) Conda is best for managing environments and can be used for compiling and installing non-python packages, especially CUDA drivers (for GPU capability), while Poetry is more powerful than Conda as a Python package manager.

I've managed to make this work fairly easily by using Poetry within a Conda environment. The trick is to not use Poetry to manage the Python environment: I'm not using commands like poetry shell or poetry run, only poetry init, poetry install etc (after activating the Conda environment).

For full disclosure, my environment.yml file (for Conda) looks like this:

name: N

channels:
  - defaults
  - conda-forge

dependencies:
  - python=3.9
  - cudatoolkit
  - cudnn

and my poetry.toml file looks like that:

[tool.poetry]
name = "N"
authors = ["B"]

[tool.poetry.dependencies]
python = "3.9"
torch = "^1.10.1"

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

To be honest, one of the reasons I proceeded this way is that I was struggling to install CUDA (for GPU support) without Conda.

Does this project design look reasonable to you?