Improving Your Workflow with Dev Containers
After using dev containers for the past 6 months in my daily work, I can confidently say that they have been an incredible asset in improving my workflow. In this post, I will share my personal experience of using dev containers, including what has worked well and what hasn't.
To start, dev containers are a way of creating a consistent development environment for software development. These environments are lightweight, portable, and self-contained, and can be run on a developer's local machine or in the cloud. Essentially, dev containers aim to solve the issue of "it works on my machine" by providing a uniform development environment.
One of the main advantages of using dev containers is that they can be defined using configuration files, which can be shared and reused across different projects or teams. They can also be version-controlled, making it easy to track changes and ensure that all developers are using the same version of the development environment.
It's worth noting that dev containers are not just for software development; they can also be used for machine learning and data science. In fact, one of the biggest advantages of using dev containers for machine learning and data science is that they allow you to easily run your code on powerful hardware, such as GPUs, in the cloud.
My personal experience of using dev containers has been overwhelmingly positive, particularly for machine learning projects that require a consistent and reproducible development environment. One of the most significant benefits has been the consistency provided by dev containers, which allows for easy replication and sharing of environments across multiple team members. This helps to ensure that everyone is using the same libraries, dependencies, and configuration settings, reducing the risk of compatibility issues and facilitating collaboration.
Another advantage of dev containers is the isolation they provide for development. This helps to prevent conflicts between different software versions and dependencies, which can be especially important when working with different machine learning frameworks or libraries. Additionally, dev containers are highly portable, which makes it easy to move projects between different machines or cloud instances, providing access to powerful hardware.
While there is certainly a learning curve associated with using dev containers, the benefits are well worth the initial setup and configuration. There is also the added complexity of managing multiple containers, which can be a bit of a hassle when working on multiple projects simultaneously.
If you're interested in using dev containers, I recommend starting with the official documentation, which provides a wealth of useful information and examples. Starting with a simple project and working your way up to more complex configurations is an excellent way to get started. Finally, keep an eye out for new features like Github Codespaces, which allow you to create cloud-based development environments for your code repositories, providing fully configured VS Code development environments that can be accessed from anywhere.