pyenv-virtualenv VS bioconda-recipes

Compare pyenv-virtualenv vs bioconda-recipes and see what are their differences.

pyenv-virtualenv

a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv) (by pyenv)
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pyenv-virtualenv bioconda-recipes
31 5
5,987 1,555
1.9% 0.8%
4.3 10.0
about 1 month ago 15 minutes ago
Shell Shell
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

pyenv-virtualenv

Posts with mentions or reviews of pyenv-virtualenv. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-14.
  • Integrating GPT in Your Project: Create an API for Anything Using LangChain and FastAPI
    2 projects | dev.to | 14 Jan 2024
    First of all, install the Python virtual environment from these links: 1 and 2. I developed my GPT-based API in Python version 3.8.18. Pick any Python versions >= 3.7.
  • Can't Get Any LoRA Training Repos To Work
    3 projects | /r/SDtechsupport | 28 Mar 2023
  • shell personalization- my custom setup
    3 projects | dev.to | 18 Dec 2022
    git clone https://github.com/pyenv/pyenv-virtualenv.git $(pyenv root)/plugins/pyenv-virtualenv
  • Ask HN: Programming Without a Build System?
    15 projects | news.ycombinator.com | 12 Nov 2022
    > trying to build a lifeboat for Twitter, Python works, but then modules require builds that break.

    > Alternatively, any good resources for the above?

    There are many, _unbelievably many_ writeups and tools for Python building and packaging. Some of them are really neat! But paralysis of choice is real. So is the reality that many of the new/fully integrated/cutting edge tools, however superior they may be, just won't get long term support to catch on and stay relevant.

    When getting started with Python, I very personally like to choose from a few simple options (others are likely to pipe up with their own, and that's great; mine aren't The One Right Way, just some fairly cold/mainstream takes).

    1. First pick what stack you'll be using to develop and test software. In Python this is sadly often going to be different from the stack you'll use to deploy/run it in production, but here we are. There are two sub-choices to be made here:

    1.a. How will you be running the _python interpreter_ in dev/test? "I just want to use the Python that came with my laptop" is fine to a point, but breaks down a lot sooner than folks expect (again, the reasons for this are variously reasonable and stupid, but here we are). Personally, I like pyenv (https://github.com/pyenv/pyenv) here. It's a simple tool that builds interpreters on your system and provides shell aliases to adjust pathing so they can optionally be used. At the opposite extreme from pyenv, some folks choose Python-in-Docker here (pros: reproducible, makes deployment environments very consistent with dev; cons: IDE/quick build-and-run automations get tricker). There are some other tools that wrap/automate the same stuff that pyenv does.

    1.b. How will you be isolating your project's dependencies? "I want to install dependencies globally" breaks down (or worse, breaks your laptop!) pretty quickly, yes it's a bummer. There are three options here: if you really eschew automations/wrappers/thick tools in general, you can do this yourself (i.e. via "pip install --local", optionally in a dedicated development workstation user account); you can use venv (https://docs.python.org/3/library/venv.html stdlib version of virtualenv, yes the names suck and confusing, here we are etc. etc.), which is widely standardized upon and manually use "pip install" while inside your virtualenv, and you can optionally integrate your virtualenv with pyenv so "inside your virtualenv" is easy to achieve via pyenv-virtualenv (https://github.com/pyenv/pyenv-virtualenv); or you can say "hell with this, I want maximum convenience via a wrapper that manages my whole project" and use Poetry (https://python-poetry.org/). There's no right point on that spectrum, it's up to you to decide where you fall on the "I want an integrated experience and to start prototyping quickly" versus "I want to reduce customizations/wrappers/tooling layers" spectrum.

    2. Then, pick how you'll be developing said software: what frameworks or tools you'll be using. A Twitter lifeboat sounds like a webapp, so you'll likely want a web framework. Python has a spectrum of those of varying "thickness"/batteries-included-ness. At the minimum of thickness are tools like Flask (https://flask.palletsprojects.com/en/2.2.x/) and Sanic (like Flask, but with a bias towards performance at the cost of using async and some newer Python programming techniques which tend, in Python, to be harder than the traditional Flask approach: https://sanic.dev). At the maximum of thickness are things like Django/Pyramid. With the minimally-thick frameworks you'll end up plugging together other libraries for things like e.g. database access or web content serving/templating, with the maximally-thick approach that is included but opinionated. Same as before: no right answers, but be clear on the axis (or axes) along with you're choosing.

    3. Choose how you'll be deploying/running the software, maybe after prototyping for awhile. This isn't "lock yourself into a cloud provider/hosting platform", but rather a choice about what tools you use with the hosting environment. Docker is pretty uncontentious here, if you want a generic way to run your Python app on many environments. So is "configure Linux instances to run equivalent Python/package versions to your dev/test environment". If you choose the latter, be aware that (and this is very important/often not discussed) many tools that the Python community suggests for local development or testing are very unsuitable for managing production environments (e.g. a tool based around shell state mutation is going to be extremely inconvenient to productionize).

    Yeah, that's a lot of choices, but in general there are some pretty obvious/uncontentious paths there. Pyenv-for-interpreters/Poetry-for-packaging-and-project-management/Flask-for-web-serving/Docker-for-production is not going to surprise anyone or break any assumptions. Docker/raw-venv/Django is going to be just as easy to Google your way through.

    Again, no one obvious right way (ha!) but plenty of valid options!

    Not sure if that's what you were after. If you want a "just show me how to get started"-type writeup rather than an overview on the choices involved, I'm sure folks here or some quick googling will turn up many!

  • Pyenv and Virtualenvs Quick-start
    3 projects | dev.to | 28 Oct 2022
    For this, I will use pyenv and the pyenv-virtualenv tools.
  • I can I roll python3 back to pre 3.11 in F37 ?
    2 projects | /r/Fedora | 26 Oct 2022
    I would suggest using pyenv and the pyenv-virtualenv plugin to manage various python versions and virtualenvs
  • Will updating Python break my existing Django app?
    2 projects | /r/django | 8 Oct 2022
    To help, check out pyenv and pyenv-virtualenv (or pyenv with Poetry if you want the new hotness). You essentially can install multiple python versions and create a virtualenv that uses a specific python version. So you could have `myapp-3.7` and `myapp-3.10` each isolated with their own package versions, etc.
  • Created a CLI to manage virtual envs with pyenv-win
    4 projects | /r/Python | 28 Sep 2022
    Recently moved to Windows from Linux and was looking for a replacement for pyenv which I was using to manage multiple versions of Python. Found pyenv-win but it was missing the pyenv-virtualenv plugin which can be used to create virtualenvs for different Python versions. Frustrated with the lack of options, I decided to create my own CLI called pyenv-win-venv to do the same thing. I created it only for my personal use but later decided to open source it so it has some of the basic features of pyenv-virtualenv and I hope it is useful to other users of pyenv-win.
  • 9 shell tools for productivity
    11 projects | dev.to | 4 Sep 2022
    Pyenv lets you install different versions of python on your system simultaneously, without breaking a thing. You can switch between any of them in one command. You can also bind a specific version to a directory, which will activate every time you enter it. There is an extension that adds support for virtual environments.
  • Run Python venv from nvim
    10 projects | /r/neovim | 21 Jun 2022
    Install https://github.com/pyenv/pyenv-virtualenv

bioconda-recipes

Posts with mentions or reviews of bioconda-recipes. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-24.
  • Why should academic researchers use Rust?
    3 projects | /r/rust | 24 Feb 2023
    Rust makes distribution and maintenance near trivial. My lab develops a fairly widely-used tool, salmon, for the quantification of transcript expression from RNA-seq data. This tool is written in C++14, and has a substantial number of dependencies. The process of updating the tool (e.g. bumping dependencies) and cutting a new release is painful. To maintain widespread availability, we distribute this tool using bioconda which uses it's own CI and setup to build new releases for (in our case) Linux and MacOS. Things break all the time. For example, recently, they bumped the compiler used to build packages. This changed some default "implementation defined" behavior, causing previously functioning code to fail. We didn't find this locally, because we didn't test that specific compiler version. When we tried to release a new version, we had to go back and fix things etc. This is not just because different compilers exist, but because the C++ specification is soooo complicated and the set of undefined and implementation defined behavior is sooo broad that it's very brittle and it's easy for things to "break" via bitrot. However, the stability provided by Rust has been phenomenal so far. In our code, we only use stable Rust features, and we have benefited tremendously from the empirical guarantee that valid Rust code (except in exceptional cases like latent bugs in the language) will remain valid. While not all crates follow it religiously, there is a reasonable respect for semantic versioning. Thus, cutting a new release of one of our Rust tools is often as simple as just updating the Cargo.toml (and Cargo.lock in the case of applications), tagging a new release in GitHub, and letting the bioconda CI do it's business with the tagged artifacts. The build "scripts" are almost always trivial because the builds just work, across platforms, across CIs, etc. Now, new projects like cargo dist look like they make this process even simpler.
  • Software engineers: consider working on genomics
    6 projects | news.ycombinator.com | 19 Nov 2022
    I contribute to Nextflow core (https://nf-co.re/) It's more of a collection of pipelines than traditional software, but there are users all around the world and a good community.

    Most of the packages on bioconda (https://bioconda.github.io/) are open source. But you probably want to find a sub-field that interests you most before finding a project.

    In grad school, we also had an ex-google software engineer volunteer with us one day a week. It was very impactful for many members of the lab to learn good engineering practices, and it wasn't at all like the sentiment others in this thread are expressing where engineers were "janitors".

  • How to mix separated versions of Python in the cleanest way
    4 projects | /r/devops | 25 Sep 2021
    In my world (research science) we usually use anaconda, which is just a slightly higher-level wrapper around python virtual envs. But they also maintain more repositories of various modules that scientists need. e.g. https://bioconda.github.io/
  • Seq: A programming language for high-performance computational genomics
    9 projects | news.ycombinator.com | 15 Sep 2021
    Seems like there's a conda packaging on the works: https://github.com/bioconda/bioconda-recipes/pull/29660

What are some alternatives?

When comparing pyenv-virtualenv and bioconda-recipes you can also consider the following projects:

spaceship-prompt - :rocket::star: Minimalistic, powerful and extremely customizable Zsh prompt

pyenv-win - pyenv for Windows. pyenv is a simple python version management tool. It lets you easily switch between multiple versions of Python. It's simple, unobtrusive, and follows the UNIX tradition of single-purpose tools that do one thing well.

Biopython - Official git repository for Biopython (originally converted from CVS)

pyenv - Simple Python version management

faster-cpython - How to make CPython faster.

direnv - unclutter your .profile

peps - Python Enhancement Proposals

pyenv-virtualenvwrapper - an alternative approach to manage virtualenvs from pyenv.

adam - ADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark, and Apache Parquet. Apache 2 licensed.

ideas

TeamTeri - Bioinformatics on GCP, AWS or Azure

django-rbac