pyenv-virtualenv
Odin
Our great sponsors
pyenv-virtualenv | Odin | |
---|---|---|
31 | 84 | |
6,024 | 5,598 | |
1.6% | 4.9% | |
4.3 | 9.9 | |
13 days ago | 6 days ago | |
Shell | Odin | |
MIT License | 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.
pyenv-virtualenv
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Integrating GPT in Your Project: Create an API for Anything Using LangChain and FastAPI
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
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shell personalization- my custom setup
git clone https://github.com/pyenv/pyenv-virtualenv.git $(pyenv root)/plugins/pyenv-virtualenv
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Pyenv, poetry and other rascals - modern Python dependency and version management
Install pyenv-virtualenv plugin from repository (in never versions it's included in pyenv.run script I think)
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Ask HN: Programming Without a Build System?
> 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!
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Pyenv and Virtualenvs Quick-start
For this, I will use pyenv and the pyenv-virtualenv tools.
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I can I roll python3 back to pre 3.11 in F37 ?
I would suggest using pyenv and the pyenv-virtualenv plugin to manage various python versions and virtualenvs
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Will updating Python break my existing Django app?
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.
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Created a CLI to manage virtual envs with pyenv-win
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.
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9 shell tools for productivity
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.
Odin
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Zig, Rust, and Other Languages
There's also Odin[0] too. I tried using them all and Odin was pretty nice. Nim is also good too but a lot more features.
But - I concluded that language matters a lot less compared to APIs. Yes, the language should have enough good features to let the programmers express themselves, but overall well designed APIs matter a lot more than language. For example -tossing most of the C stdlib and following a consistent coding style (similar to one described here -[1]), with using Arenas for memory allocation, I can be just as productive in C.
[0] - https://odin-lang.org
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Odin Programming Language
I highly recommend looking at:
* The Overview: <https://odin-lang.org/docs/overview/>
* examples/demo: <https://github.com/odin-lang/Odin/blob/master/examples/demo/...>
As for the first example: a basic lexing example is probably boring, but it does show some basic ideas of what the language is about. If people want to write better examples or just reorder the current ones, please feel free to make an issue or PR on the website's GitHub page: <https://github.com/odin-lang/odin-lang.org>.
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babel tree
I use Odin primarily, it’s C-level but pascal/Go syntax and inspiration https://odin-lang.org/
- Botlib: Telegram Bots in C by Antirez
- "Odin is a general-purpose programming language with distinct typing built for high performance, modern systems and data-oriented programming."
- Austral Programming Language
- Small Joys with Odin
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Can't decide what engine/library/framework I want to master
Website: https://odin-lang.org/
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Download Odin and get started today! Includes binding to popular video game libraries
Get it from the website: https://odin-lang.org/ -- Odin includes bindings to popular gamedev libraries & APIs such as Raylib, SDL, DirectX, OpenGL and Vulkan.
- Check this odin file out for a demo of many of the language's features. It comes with the compiler inside the examples folder. I refer to it all the time when I need to figure out how to do something.
What are some alternatives?
spaceship-prompt - :rocket::star: Minimalistic, powerful and extremely customizable Zsh prompt
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
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.
v - Simple, fast, safe, compiled language for developing maintainable software. Compiles itself in <1s with zero library dependencies. Supports automatic C => V translation. https://vlang.io
pyenv - Simple Python version management
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
faster-cpython - How to make CPython faster.
rust - Empowering everyone to build reliable and efficient software.
direnv - unclutter your .profile
carbon-lang - Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)
bioconda-recipes - Conda recipes for the bioconda channel.
Beef - Beef Programming Language