packaging.python.org
mamba
packaging.python.org | mamba | |
---|---|---|
4 | 34 | |
1,363 | 6,343 | |
0.9% | 3.9% | |
9.5 | 9.5 | |
6 days ago | 6 days ago | |
Python | C++ | |
- | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
packaging.python.org
- ABI compatibility in Python: How hard could it be?
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Making Sense of pyproject.toml, setup.cfg, and setup.py
[metadata] name = sample_pkg version = 1.0.0 author = Sharon Fitzpatrick description = this is the smallest description long_description = This is a longest description url = https://github.com/pypa/packaging.python.org keywords = python,example [options] python_requires = >=3.8, <4 install_requires = numpy matplotlib [options.extras_require] test = pytest coverage [options.package_data] sample = sample_data.csv'
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How to Build a pyproject.toml File
# tells pip what build tool to use to build your package [build-system] requires = ["setuptools>=61.0"] build-backend = "setuptools.build_meta" # tells pip how to build your pypi webpage & what dependencies to install [project] name = "sample_pkg" dynamic = ["readme"] version = "0.0.30" authors = [ { name="Sharon Fitzpatrick", email="[email protected]" }] description = "A tool that performs xyz" dependencies = ["matplotlib", "numpy<1.23.0"] license = { file="LICENSE" } requires-python = ">=3.8" classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ] # (BETA) tells setuptools you will be using a readme file for the long description field for your pypi profile. [tool.setuptools.dynamic] readme = {file = ["README.md"]} # (OPTIONAL) tells pypi that these urls are where your project's source code and issue tracker reside [project.urls] "Homepage" = "https://github.com/pypa/packaging.python.org" "Bug Tracker" = "https://github.com/pypa/packaging.python.org/issues"
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Pypi.org is running a survey on the state of Python packaging
If the package is available as a wheel, you don't need to execute code to see what the requirements are; you just need to parse the "METADATA" file. However, the only way to get the METADATA for a wheel (using PyPA standard APIs, anyway) is to download the whole wheel.
For comparison, pacman (the Arch Linux package manager) packages have fairly similar ".PKGINFO" file in them; but in order to support resolving dependencies without downloading the packages, the server's repository index includes not just a listing of the (name, version) tuple for each package, it also includes each package's full .PKGINFO.
Enhancing the PyPA "Simple repository API" to allow fetching the METADATA independently of the wheel would be a relatively simple enhancement that would make a big difference.
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As I was writing this comment, I discovered that PyPA did this; adopting PEP 658 in March of this year. https://github.com/pypa/packaging.python.org/commit/1ebb57b7...
mamba
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Minimal implementation of Mamba, the new LLM architecture, in 1 file of PyTorch
>"everyone" seems to know Mamba. I never heard of Mamba
Only the "everybody who knows what mamba is" are the ones upvoting and commenting. Think of all the people who ignore it. For me, Mamba is the faster version of Conda [1], and that's why I clicked on the article.
https://github.com/mamba-org/mamba
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Towards a New SymPy
Yes, this is a big disadvantage. But have you tried Mamba that aims at implementing Anaconda more efficiently? It works really well in most cases.
https://mamba.readthedocs.io/
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Why are the bioconda bioconductor packages so slow to update?
Because conda is very slow at resolving dependencies. Mamba (https://github.com/mamba-org/mamba) is faster if that is your goal
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Is pip gaining on conda for python libs?
use mamba instead
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Real-world examples of std::expected in codebases?
We started using tl::expected in https://github.com/mamba-org/mamba/ since the beginning of this year and some other related projects like https://github.com/mamba-org/powerloader . I don't know much other big open-source codebases that use that specific lib.
- Mamba: A Drop-In Replacement for Conda Written in C++
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What's Great about Julia?
Great writeup. Minor comment about the portion of the post mentioning Conda being glacially slow: Mamba [1] is a much better drop-in replacement written in C++. Not only is it significantly faster, but error messages are much more sane and helpful.
That being said, I do agree that Pkg.jl is much more sleek and modern than Conda/Mamba.
[1]: https://github.com/mamba-org/mamba
- Mamba Reaches 1.0
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Given Rust’s rapidly growing popularity and wide range of use cases, it seems almost inevitable that it will overtake Python in the near future.
I thought that python could live a little longer when I learned about mamba. But then I found out it is written in C++? Why write a package manager for a dying language in a language that is almost dead???
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Does anyone use virtual environments (Conan's virtual env. or Conda's) for C++
Yes, I use Conda enviroments (actually I use Mamba to manage them now).
What are some alternatives?
ring-xous - Pure-Rust port of Ring for 32-bit embedded targets.
miniforge - A conda-forge distribution.
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
pip - The Python package installer
pyenv - Simple Python version management
conda-lock - Lightweight lockfile for conda environments
pyre-check - Performant type-checking for python.
quetz - The Open-Source Server for Conda Packages
pypiserver - Minimal PyPI server for uploading & downloading packages with pip/easy_install
devpi - Python PyPi staging server and packaging, testing, release tool
helm - The Kubernetes Package Manager
Poetry - Python packaging and dependency management made easy