Docker Jupyter Notebook Mac OS X

Consistent across platforms. Python packages are cross-platform, but some behave differently on Windows vs Linux, and some have dependencies that cant be installed on Windows.

This environment comes preconfigured with the latest version of Python, well known data science libraries, and a runnable code editor.

It allows brand new data scientists, and experienced ones, to start running code right away.

While we provide a seemless experience to learn on our datasets, when you want to switch to your own data sets youll have to move to a local development environment.

Dealing with inconsistent package versions, lengthy installations that fail due to errors, and obscure setup instructions make it difficult to even start programming.

These issues are exaggerated to a higher degree when working on teams with different operating systems.

For many, the setup is the biggest detractor to learning how to code.

The one well be exploring in this post is a containerization tool called Docker.

Since 2013, Docker has made it fast and easy to launch multiple data science environments supporting the infrastructure needs of different projects.

Well cover the basics of Docker and containerization, how to install Docker, and how to download and run Dockerized applications.

In the introduction, we briefly described the difficulty of working on teams with multiple operating systems and installing third-party libraries.

These types of problems have been around since the beginning of software development.

Virtual machines allow you to emulate alternative operating systems from the one running on your local machine.

A common example is running a Windows desktop with a Linux virtual machine.

A virtual machine is essentially a fully isolated operating system with applications that are run independent of your own system.

This is extremely helpful in a team of developers as every member can run the exact same system regardless of the OS on their machine.

They are difficult to set up, require significant system resources to run, and take a long time to boot.

Containers are similar to virtual machines as they also run applications in an isolated environment.

However, instead of running a complete operating system with all of its libraries, a containerized environment is a lightweight process that runs on top of a container engine.

This makes a running container MBs in size versus virtual machines that can be tens of GBs or more.

It allows us to download and run images which contain a preconfigured lightweight OS, set of libraries, and application specific packages.

Similar to virtual machines, systems built on different technologies (e.g.

Docker Jupyter Notebook Mac OS X

Mac OS X vs. Microsoft Windows) can deploy completely identical containers.

There are multiple advantages to using containers over virtual environments.

You dont need to wait for packages to install when you just want to jump in and start doing analysis.

Consistent across platforms.

Python packages are cross-platform, but some behave differently on Windows vs Linux, and some have dependencies that cant be installed on Windows.

Docker For Mac Jupyter Notebook