mamba
warehouse
mamba | warehouse | |
---|---|---|
34 | 277 | |
6,312 | 3,474 | |
3.4% | 0.6% | |
9.5 | 9.7 | |
7 days ago | 5 days ago | |
C++ | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
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).
warehouse
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Create an AI prototyping environment using Jupyter Lab IDE with Typescript, LangChain.js and Ollama for rapid AI prototyping
pip install PackageName: installs a package (you can browse the available packages in the Python Package Index)
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Smooth Packaging: Flowing from Source to PyPi with GitLab Pipelines
python3 -m pip install \ --trusted-host test.pypi.org --trusted-host test-files.pythonhosted.org \ --index-url https://test.pypi.org/simple/ \ --extra-index-url https://pypi.org/simple/ \ piper_whistle==$(python3 -m src.piper_whistle.version)
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Pickling Python in the Cloud via WebAssembly
In my experience so far, I can use a vast amount of the Python Standard Library to build Wasm-powered serverless applications. The caveat I currently understand is that Python’s implementation of TCP and UDP sockets, as well as Python libraries that use threads, processes, and signal handling behind the scenes, will not compile to Wasm. It is worth noting that a similar caveat exists with libraries that I find on The Python Package Index (PyPI) site. While these caveats might limit what can be compiled to Wasm, there are still a ton of extremely powerful libraries to leverage.
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Introducing Flama for Robust Machine Learning APIs
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.
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PyPI Packaging
From there, I needed to learn a bit about PyPi or Python Package Index, which is the home for all the wonderful packages that you know if you have ever run the handy pip install command. PyPi has a pretty quick and easy onboarding, which requires a secured account be created and, for the purposes of submitting packages from CLI, an API token be generated. This can be done in your PyPi profile. Once logg just navigate to https://pypi.org/manage/account/ and scroll down to the API tokens section. Click “Add Token” and follow the few steps to generate an API token which is your access point to uploading packages. With all this in place, I was able to use twine to handle the package upload. First I needed to install twine, again as simple as pip install twine. In order for twine to access my API token during the package upload process, it needed to read it from .pypirc file that contains the token info. For some that file may exist already, for me I was required to create it. Working in windows I simply used a text editor to create it in my home user directory ($HOME/.pypirc). The file contents had a TOML like format looked like this:
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Releasing my Python Project
I have published the package to Python Package Index, commonly called PyPi, and in this post, I'll be sharing the steps I had to follow in the process.
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Publishing my open source project to PyPI!
Register at PyPI.org
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Show HN: I mirrored all the code from PyPI to GitHub
According to the stats on the original link, there are over 25,000 identified secret ids/keys/tokens in the data. And it looks like that's just identifiable secrets, e.g. "Google API Keys" that I'm guessing are identifiable because they have a specific pattern, and may be missing other secrets that use less recognizable patterns.
I mean, sure, compared to the 478,876 Projects claimed on https://pypi.org/, that's a pretty small minority. On the other hand, I'd guess a many Python packages don't use these particular services, or even need to connect to a remote service at all, so the area for this class of mistake should be even smaller.
And mistakes do happen, but that's a pretty big thing to miss if you are knowingly publishing your code with the expectation other people will be reading it.
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Pezzo v0.5 - Dashboards, Caching, Python Client, and More!
PyPi package
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Modifying keywords in python package
Does pypi.org display the Union of all keywords, the keywords of the most recent release, the keywords of the first release or some other weird combination like the intersection?
What are some alternatives?
miniforge - A conda-forge distribution.
devpi
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
bandersnatch
pip - The Python package installer
localshop - local pypi server (custom packages and auto-mirroring of pypi)
pyenv - Simple Python version management
Poe the Poet - A task runner that works well with poetry.
conda-lock - Lightweight lockfile for conda environments
scribd-downloader
pyre-check - Performant type-checking for python.
Python Packages Project Generator - 🚀 Your next Python package needs a bleeding-edge project structure.