A common example is running a Windows desktop with a Linux virtual machine. Virtual machines allow you to emulate alternative operating systems from the one running on your local machine. One solution has been the use of virtual machines.
DOCKER FOR MAC TUTORIAL SOFTWARE
These types of problems have been around since the beginning of software development. In the introduction, we briefly described the difficulty of working on teams with multiple operating systems and installing third-party libraries. By the end, you should be able to run your own local Jupyter server with the latest data science libraries.Īn overview of Docker and containerizationīefore we dive into Docker, it’s important to know some preliminary software concepts that led to the rise of technologies like Docker.
DOCKER FOR MAC TUTORIAL HOW TO
We’ll cover the basics of Docker and containerization, how to install Docker, and how to download and run Dockerized applications. In this tutorial, we’re going to show you how to set up your own Jupyter Notebook server using Docker.
Since 2013, Docker has made it fast and easy to launch multiple data science environments supporting the infrastructure needs of different projects. The one we’ll be exploring in this post is a containerization tool called Docker. For many, the setup is the biggest detractor to learning how to code.įortunately, there has been a rise of technologies that help with these development woes.
These issues are exaggerated to a higher degree when working on teams with different operating systems. Dealing with inconsistent package versions, lengthy installations that fail due to errors, and obscure setup instructions make it difficult to even start programming. Sadly, setting up your own local environment is the most frustrating experience of being a data scientist. While we provide a seemless experience to learn on our datasets, when you want to switch to your own data sets you’ll have to move to a local development environment. It allows brand new data scientists, and experienced ones, to start running code right away. This environment comes preconfigured with the latest version of Python, well known data science libraries, and a runnable code editor. At Dataquest, we provide an easy to use environment to start learning data science.
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