stack VS Poetry

Ah, didn't run into this issue, as I don't use vscode.

Apparently there is some work being done to improve the stack <> hls experience, but I wouldn't know how it's going and when it's being delivered: https://github.com/commercialhaskell/stack/issues/6154

this is it: # This file was automatically generated by 'stack init' # # Some commonly used options have been documented as comments in this file. # For advanced use and comprehensive documentation of the format, please see: # https://docs.haskellstack.org/en/stable/yaml\_configuration/ # Resolver to choose a 'specific' stackage snapshot or a compiler version. # A snapshot resolver dictates the compiler version and the set of packages # to be used for project dependencies. For example: # # resolver: lts-3.5 # resolver: nightly-2015-09-21 # resolver: ghc-7.10.2 # # The location of a snapshot can be provided as a file or url. Stack assumes # a snapshot provided as a file might change, whereas a url resource does not. # # resolver: ./custom-snapshot.yaml # resolver: https://example.com/snapshots/2018-01-01.yaml resolver: url: https://raw.githubusercontent.com/commercialhaskell/stackage-snapshots/master/lts/20/23.yaml # User packages to be built. # Various formats can be used as shown in the example below. # # packages: # - some-directory # - https://example.com/foo/bar/baz-0.0.2.tar.gz # subdirs: # - auto-update # - wai packages: - xmonad - xmonad-contrib # Dependency packages to be pulled from upstream that are not in the resolver. # These entries can reference officially published versions as well as # forks / in-progress versions pinned to a git hash. For example: # # extra-deps: # - acme-missiles-0.3 # - git: https://github.com/commercialhaskell/stack.git # commit: e7b331f14bcffb8367cd58fbfc8b40ec7642100a # # extra-deps: [] # Override default flag values for local packages and extra-deps # flags: {} # Extra package databases containing global packages # extra-package-dbs: [] # Control whether we use the GHC we find on the path # system-ghc: true # # Require a specific version of Stack, using version ranges # require-stack-version: -any # Default # require-stack-version: ">=2.11" # # Override the architecture used by Stack, especially useful on Windows # arch: i386 # arch: x86_64 # # Extra directories used by Stack for building # extra-include-dirs: [/path/to/dir] # extra-lib-dirs: [/path/to/dir] # # Allow a newer minor version of GHC than the snapshot specifies # compiler-check: newer-minor

Fix incorrect warning if allow-newer-deps are specified but allow-newer is false. See #6068.

You can download binaries for this pre-release from: Release rc/v2.11.0.1 (release candidate) · commercialhaskell/stack · GitHub .

Fair enough! Thanks for the suggestion, then. In fact, the non-Python language I develop most in (Haskell, with the Stack package manager) has exactly that behaviour as a default: new packages are installed to a sandboxed local directory, and it takes an explicit request to install something globally. (And even then, you can switch between different global "known good configurations" of package versions which work well together – a pretty handy feature.)

Opened a ticket on GitHub.

In YAML configuration files, the hackage-security key of the package-index key or the package-indices item can be omitted, and the Hackage Security configuration for the item will default to that for the official Hackage server. See #5870.

FYI this was solved in here: https://github.com/commercialhaskell/stack/issues/5958

Yes, that is correct. Stack's allow-newer: true configuration has always actually meant 'ignore bounds'. However, the author of the allow-newer-deps development has in mind a further development that will introduce an actual ignore-bounds key with the same expressive syntax that is used by Cabal. This is discussed at Stack #5910.

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