#Docker jupyter notebook tutorial software

If you already have some experience with virtualization, itвЂ™s likely that something is starting to appear here: by means of Docker, you can run software packages in a virtualized way, in their own pseudo-OS, isolated from each other.Д«y signing up, you consent that any information you receive can include services and special offers by email.

Package-based software called containers: a design pattern where software is broken up into smaller components, packaged into its own вЂњvirtualized file systemвЂќ (such as Linux) and then ran (called a вЂњcontainerвЂќ).

Now that we understand virtualization, we can understand that it happens within the operating system (virtualization can be applied on hardware as well).

OS-level virtualization, again: virtualization at the operating system level.

#Docker jupyter notebook tutorial windows

Virtualization: running an operating system virtually, i.e., within another operating system (such as running a Linux VM on a Windows machine).

OS-level virtualization: virtualization at the operating system level.

a вЂњplaceвЂќ where вЂњthings can runвЂќ вЂ“ in this case, containers.

Platform as a Service (PaaS): a term used to describe software components that together constitute a platform, i.e.

LetвЂ™s therefore break things apart into its individual components to understand them better: Now, thatвЂ™s quite a technical text, with some interesting words that you may not be familiar with. If we take a look at the Wikipedia page for Docker, we read the following:Дocker is a set of platform as a service (PaaS) products that use OS-level virtualization to deliver software in packages called containers. Time to look at the other component of todayвЂ™s article: Docker. HereвЂ™s what a (part of) a Jupyter Notebook looks like, with some TensorFlow code:

#Docker jupyter notebook tutorial code

WhatвЂ™s more, itвЂ™s even possible to export the Notebook вЂ“ so that reuse of code is really easy! The code can also be executed right there, which makes it an ideal playground for creating a variety of data science and machine learning related code.ДЄs Python code can be created and executed within a Jupyter Notebook, it is also possible to create and train TensorFlow models from within the web application. Those notebooks are essentially digital paper sheets where code can be written. Indeed вЂ“ being widely used within the Data Science Community, a Jupyter Notebook is a web application which can be used for live code documents. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Fortunately, the Jupyter website provides clear information as to what you can expect (Project Jupyter, n.d.):

If our goal is to use a Jupyer Notebook, we must first understand what it is. Nobody installs software without knowing what it is and what it does. Installing a TensorFlow Notebook with Docker.

#Docker jupyter notebook tutorial install

Specifically, we will install a Notebook oriented to TensorFlow projects, although вЂ“ as we shall see вЂ“ there are other Notebooks specifically tailored to other use cases (such as Apache Spark). Subsequently, weвЂ™re going to install a Jupyter Notebook with Docker.

#Docker jupyter notebook tutorial how to

WeвЂ™ll therefore also cover what Docker is, briefly how it works and how to install it to your system. Fortunately, with Docker, we can remove many of those problems by abstracting away the host machine. due to different software installed on the machine, or different operating systems that are in play). This could have been problematic, as everyoneвЂ™s host machine works differently (e.g. Subsequently, we are interested in actually installing such a Notebook onto your system. What can it be used for? How can it help? This is what we will try to answer. Firstly, weвЂ™ll take a look at what a Jupyter Notebook is. ItвЂ™s widely used in the data science community and therefore deserves a more prominent role on MachineCurve and in any future article I write. Then came Python environments, then came Anaconda, but today we will cover Jupyter Notebook. In the past, this meant that everyone had to install dependencies on their own systems. In my experience, success factors of data science and machine learning projects вЂ“ or any software project in general вЂ“ include that runtime environments are shared. If youвЂ™re familiar with deployment tools, you can even deploy the model in the field, for example by means of a web service. IвЂ™d argue that very similar things are true for those who research and engineer machine learning models, as breakthroughs in the areas can directly be captured. Being a data scientist could mean that you have the sexiest job of the 21st Century, according to some business literature.