More people should start using Dev Containers in Visual Studio Code

Underfitted · Beginner ·📐 ML Fundamentals ·1y ago

Key Takeaways

The video discusses the use of Dev Containers in Visual Studio Code to simplify the development process for machine learning systems, allowing for easy replication of environments and reduction of setup time.

Full Transcript

so let me show you what I did for one of the repositories that I'm currently working on this here is an end to end machine Learning System there is a lot going on here and running it on any computer that is not this one is super painful there is systems I need to set up environment variables that I need to create tools that I need to install it's a lot and I've been dealing with this for a long time so I was in class A few weeks ago and I'm talking to my students and telling them that I really need to find a way to help them install this system in an easier way I don't want them to spend a ton of time dealing with tools and systems and configs I want to basically take the time that it takes going from cloning the repo to running the system I want to take that time and squeeze it as much as possible I want to get rid of this Gap here now of course I knew I needed some sort of like Docker container to do all of these and then it hit me that's what development containers are for solution for these has existed forever here is an explanation on how to use development containers in Visual Studio code I'm going to leave a link down below if you want to go deeper into this topic but here is a 10,000 ft view of how development containers work and how you can use them first you need the de containers Visual Studio code extension and Docker of course you need to have Docker installed on your computer this extension works on Visual Studio code it works on cursor that's the one I'm using it works on wind surve like anything that's based on code that will support the extension if you are the type of developer that prefers something like jet brains you're fine because jet brains also supports the development containers specification So Def containers also work there pretty much any decent IDE will support development containers I have that specification here and here is what it says actually this is the opening line and I'm rephrasing it a little bit it says the purpose of this specification is to provide a simple way to develop inside a container that's exactly what I need the second step is to create a de container that Json file inside your project here is my file here I specify the docker file that will Define my environment here is the docker file where I can just go and prepare that environment I can install tools like I don't know SQL I 3 or just or UV or the AWS CLI anything I want to configure for that environment I can do in this file in the definition of my container I can also specify the ports that I want to forward with my local computer so now if I access port 5,000 from my local computer that will be mapped to the internal container I can also set environment variables that I want to initialize here and this is cool I can also specify the set of extensions that I want Visual Studio code to automatically install so anyone using this container when they run their project inside this container their Visual Studio code will automatically install all of these extensions that's super awesome now there is a lot more that you can configure here the ultimate goal is to allow people to just open your repo click a button and have everything configured for them automatically the final step is to build that container and connect to it using your IDE now this is the easy part whenever you try to open a repository that contains a def container. Json file Visual Studio code will automatically ask you if you want to open the repo inside that container so you click that button and visual studio code will create the container for you and will open the repo inside that container so you don't need to do anything else when that's done everything you do will happen inside that container not on your computer so you can go and install tools delete things and nothing will be reflected on your computer you will maintain you will keep full isolation between the container and the rest of your computer which is great and on top of that anyone can literally click a button and get a fully configured setup to run the system on their computers if you're working on a team this is gold if you're working alone if you don't work as part of a team but you switch computers let's say between your office computer and your home computer this this is also awesome this is going to be very helpful I implemented this and now every one of my students is using development containers and the number of issues the number of headaches is down to zero because now everyone clicks a button and they're working on exactly the same system that I built for them anyway I hope this is helpful if you've used Dev containers before leave your best tips and tricks in the comments below stay awesome keep building cool things and I'll see you in the next one bye-bye

Original Description

Here are the links I discussed during the video: • Developing inside a Container: https://code.visualstudio.com/docs/devcontainers/containers • Development Container Specification: https://containers.dev/implementors/spec/ I teach a live, interactive program that'll help you build production-ready Machine Learning systems from the ground up. Check it out here: https://www.ml.school To keep up with my content: • Twitter/X: https://www.twitter.com/svpino • LinkedIn: https://www.linkedin.com/in/svpino 🔔 Subscribe for more stories: https://www.youtube.com/@underfitted?sub_confirmation=1
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The video teaches how to use Dev Containers in Visual Studio Code to simplify machine learning development, allowing for easy environment replication and reduced setup time. It covers the basics of Dev Containers, how to create a Dev Container JSON file, and how to build and connect to the container using Visual Studio Code.

Key Takeaways
  1. Install the Dev Containers Visual Studio Code extension
  2. Create a Dev Container JSON file
  3. Specify the Docker file and environment variables
  4. Build the container and connect to it using Visual Studio Code
  5. Use the container to develop and test machine learning models
💡 Dev Containers allow for easy replication of development environments, reducing setup time and increasing productivity for machine learning development.

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