pyenv-installer VS Poetry

To install it make sure that all prerequisites are met. Then use the pyenv-installer project like this to get the tool installed:

I tried the uninstaller from https://github.com/pyenv/pyenv-installer and I'm getting this.

After you’ve installed the build dependencies, you’re ready to install pyenv itself. I recommend using the pyenv-installer project:

For python, it's recommended to use pyenv, which allows you to install several versions of python at the same time with simple commands like this: pyenv install 3.9.13

Please don't contribute worthless and irrelevant comments like this. As you doubtless well know, piping from curl into bash is something that a large subset of respected programmers think is reasonable, and another rather tedious subset do not. For example, the entire Rust community clearly has a consensus that it's reasonable: https://rustup.rs/ As does homebrew https://brew.sh/ and pyenv https://github.com/pyenv/pyenv-installer#install to name whatever came to my mind in 30s thought.

Since the debate has such large numbers on both sides, your individual opinion on it is neither interesting nor germane.

name: Test, build and release # whenever a branch or commit is pushed on: [push] jobs: # use pytest test: # used to ensure testing is done right env: DEVELOPMENT: '1' runs-on: ubuntu-latest # to avoid using old sqlite version container: image: debian:latest options: --user root steps: # check out repo - uses: actions/checkout@v2 # prevent from asking user for input - run: export DEBIAN_FRONTEND=noninteractive # install recommended tools for building Python - run: apt -q update - run: apt -q install make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev git sqlite3 -y - run: apt -q upgrade -y # install pyenv - run: curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash - run: exec $SHELL - run: ~/.pyenv/bin/pyenv update # install and set up required Python - run: ~/.pyenv/bin/pyenv install 3.10.2 - run: ~/.pyenv/bin/pyenv virtualenv 3.10.2 npbc - run: ~/.pyenv/bin/pyenv local 3.10.2/envs/npbc # print version info (debugging) - run: ~/.pyenv/shims/python -V - run: ~/.pyenv/shims/python -c "import sqlite3; print(sqlite3.version)" # install pip packages - run: ~/.pyenv/shims/pip install -r requirements.txt pytest # run test - run: ~/.pyenv/shims/pytest -vv

curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash

This uses the pyenv-installer project to install pyenv on your system.

You can manage dependencies in Python with the package manager pip, which comes pre-installed with Python. Pip allows you to install and uninstall Python packages, and it uses a requirements.txt file to keep track of which packages your project depends on. However, pip does not have robust dependency resolution features or isolate dependencies for different projects; this is where tools like pipenv and poetry come in. These tools create a virtual environment for each project, separating the project's dependencies from the system-wide Python environment and other projects.

Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:

Poetry

The Semantify repository provides an example Astro.js project. Ensure you have poetry installed, then build the project from the root of the repository:

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

Based on this comment 5 days ago[0], it's working? I'm not sure didn't dig in too far but based on that comment it seems fair to say that it's not fully Poetry's fault because torch removed hashes (which poetry needs to be effective) for a while only recently adding it back in.

Not sure where I would stand if I fully investigated it tho.

[0] https://github.com/python-poetry/poetry/issues/6409#issuecom...

We will be running this project in Python 3.10 on Mac/Linux, and we will use Poetry to manage our dependencies. Later, we will bundle our app into a container using docker for deployment.

Here are the two main packaging issues I run into, specifically when using Poetry:

1) Lack of support for building extension modules (as mentioned by the article). There is a workaround using an undocumented feature [0], which I've tried, but ultimately decided it was not the right approach. I still use Poetry, but build the extension as a separate step in CI, rather than kludging it into Poetry.

2) Lack of support for offline installs [1], e.g. being able to download the dependencies, copy them to another machine, and perform the install from the downloaded dependencies (similar to using "pip --no-index --find-links=."). Again, you can work around this (by using "poetry export --with-credentials" and "pip download" for fetching the dependencies, then firing up pypiserver [2] to run a local PyPI server on the offline machine), but ideally this would all be a first class feature of Poetry, similar to how it is in pip.

I don't have the capacity to create Pull Requests for addressing these issues with Poetry, and I'm very grateful for the maintainers and those who do contribute. Instead, on the linked issues I share my notes on the matter, in the hope that it may at least help others and potentially get us closer to a solution.

Regardless, I'm sticking with Poetry for now. Though to be fair, the only other Python packaging tools I've used extensively are Pipenv and pip/setuptools. It's time consuming to thoroughly try out these other packaging tools, and is generally lower priority than developing features/fixing bugs, so it's helpful to read about the author's experience with these other tools, such as PDM and Hatch.

[0] https://github.com/python-poetry/poetry/issues/2740

[1] https://github.com/python-poetry/poetry/issues/2184

[2] https://pypi.org/project/pypiserver/

We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.

I started using poetry. The problem is poetry will not install if there is dependency conflict and there is no way to ignore: github