Developer environments — on-spot!
TL; DR: For quickly setting up your (containerised) development environment for working with Python, Julia language, Java, Scala, Rust, or Haskell, just do this:
First, install docker / docker desktop
and then:
For Linux / Windows Sub-system for Linux:curl -sSL https://raw.githubusercontent.com/onspot-tools/devlab/master/scripts/install.sh | sh
OR
For Powershell:iex ((New-Object System.Net.WebClient).DownloadString('https://raw.githubusercontent.com/onspot-tools/devlab/master/scripts/install.ps1'))
Now, what are devlabs?
Ok, your day job is a developer, and you are also a night geek. You are a polyglot. And you play your games with software, your blood is made of software.
You keep learning new languages and keep experimenting with new stuff. Today it is C#, tomorrow Haskell, and the day after Python. And then you jump into Julia. Well, anything.
And whenever you jump like a frog, and whenever you want to contribute to that ecosystem, the first thing you need to crack is:
Installing development tools and the tool ecosystem
Yes, this is the first big thing, before you even start your work on that language.
Enter devlab — a quick and on-spot development environment setup for various languages. All with a consistent look and feel, and all with a Jupyter lab/notebook for a quick test of the language features, a quick test of that algorithm, and a fast-track into your road for your next big thing.
Each devlab is a base-set of language tools for different languages, all packed into a container. All of these are available as images in the dockerhub, waiting for you to pull them, and just start!
Starting a devlab starts the juputer-lab server, and you can simply start using your language of interest — either from Jupyter itself, or starting it as a shell/terminal for command-line tools.
A taste of devlab-haskell
What I’ve done here is started devlab-haskell, and used it for explaining Haskell syntax in 60 seconds. Just like it is done for F# by fsharp for fun and profit.
Check out this:
