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Unfortunately, far too often: tradition. Using only „Pythons native tools“ like pip and venv simply works nowadays so good that I wonder about the purpose of m

• pininja 3y ago
  Another reason I used to use conda was for easy native Windows installation. GPU accelerated packages like OpenCV were especially difficult when I used use it 6 years ago. Now there’s Linux subsystem.. has pip support dramatically improved on Windows?
• smohare 3y ago
  [dead]
• nateglims 3y ago
  The biggest advantage for poetry I found, working with a lot of non-traditional software people, is that it does a lot of things by default like pin versions and manage virtual envs. Unfortunately, it does complicate some things.
  • Ringz 3y ago
    I can understand that well. A few articles from ByteCode! helped me to "follow my intuition" and do as much as possible with native Python tools. https://www.bitecode.dev/p/back-to-basics-with-pip-and-venv https://www.bitecode.dev/p/back-to-basics-with-pip-and-venv https://www.bitecode.dev/p/relieving-your-python-packaging-pain https://www.bitecode.dev/p/relieving-your-python-packaging-p...
    • daniel_grady 3y ago
      Those are interesting pointers; appreciate it! My own experience over the past three years has been similar. I tried using Pipenv, and then Poetry, for internal projects at my company; in both cases the tool seemed overly complicated for the problem, slow, and I had a hard time getting co-workers on board. About a year and a half ago, I saw [Boring Python: dependency management](https://www.b-list.org/weblog/2022/may/13/boring-python-dependencies/ https://www.b-list.org/weblog/2022/may/13/boring-python-depe...), which recommends using the third-party `pip-tools` library alongside the standard library’s `pip` and `venv`, and switched to that for the next project. It’s been working great. The project has involved a small team of scientists (four or five, depending) who use a mix of macOS and Windows. We do analysis and development locally and write production-facing algorithms in Python packages tracked in our repository, and publish releases to Gitlab’s PyPI. For our team, the “get up and running” instructions are “clone, create a venv, and pip install -r requirements.txt” and for the software team that manages the production systems, deploying an update just means pip installing a new version of the package. Every team’s got different constraints, of course, but this has been working very smoothly for us for over a year now, and it’s been easy, no pushback, with everyone understanding what’s going on. Really impressed with the progress of the core Python packaging infrastructure over the past several years.
• RockRobotRock 3y ago
  Has anyone else been paying attention to how hilariously hard it is to package PyTorch in poetry? https://github.com/python-poetry/poetry/issues/6409 https://github.com/python-poetry/poetry/issues/6409
• gcarvalho 3y ago
  For me it's the easiest and fastest cross-platform way to consistently install a Python version. pip and venv work fine, but you have to get them first; and that can be a struggle for unseasoned python devs, especially if you need a version that's not what your distro ships, and even more so on Windows and macOS. I use micromamba [1] specifically, which is a single binary. [1] https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html https://mamba.readthedocs.io/en/latest/user_guide/micromamba...
  • RockRobotRock 3y ago
    Maybe it's because I came into Python later, but I've almost never had the problem of pip not being installed. That's what ensurepip is for, right?