docker-stacks - Ready-to-run Docker images containing Jupyter applications

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Jupyter Docker Stacks are a set of ready-to-run Docker images containing Jupyter applications and interactive computing tools. The two examples below may help you get started if you have Docker installed know which Docker image you want to use, and want to launch a single Jupyter Notebook server in a container.



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jupyterhub-deploy-docker - Reference deployment of JupyterHub with docker

  •    Python

jupyterhub-deploy-docker provides a reference deployment of JupyterHub, a multi-user Jupyter Notebook environment, on a single host using Docker. This deployment is NOT intended for a production environment. It is a reference implementation that does not meet traditional requirements in terms of availability nor scalability.

dockerspawner - Spawns JupyterHub single user servers in Docker containers

  •    Python

DockerSpawner enables JupyterHub to spawn single user notebook servers in Docker containers. JupyterHub 0.7 or above is required, which also means Python 3.3 or above.

binderhub - Deterministically build docker images from a git repository + commit

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BinderHub allows you to BUILD and REGISTER a Docker image using a GitHub repository, then CONNECT with JupyterHub, allowing you to create a public IP address that allows users to interact with the code and environment within a live JupyterHub instance. You can select a specific branch name, commit, or tag to serve. BinderHub is created using Python, kubernetes, tornado, and traitlets. As such, it should be a familiar technical foundation for Jupyter developers.

jupyterhub - Multi-user server for Jupyter notebooks

  •    Python

With JupyterHub you can create a multi-user Hub which spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server. Project Jupyter created JupyterHub to support many users. The Hub can offer notebook servers to a class of students, a corporate data science workgroup, a scientific research project, or a high performance computing group.

repo2docker - Turn git repositories into Jupyter enabled Docker Images

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jupyter-repo2docker takes as input a repository source, such as a GitHub repository. It then builds, runs, and/or pushes Docker images built from that source. See the repo2docker documentation for more information.

zero-to-jupyterhub-k8s - Resources for deploying JupyterHub to a Kubernetes Cluster

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This is under active development and subject to change. This repo contains resources, such as Helm charts and the Zero to JupyterHub Guide, which help you to deploy JupyterHub on Kubernetes.

gophernotes - The Go kernel for Jupyter notebooks and nteract.

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Acknowledgements - This project utilizes a Go interpreter called gomacro under the hood to evaluate Go code interactively. The gophernotes logo was designed by the brilliant Marcus Olsson and was inspired by Renee French's original Go Gopher design. Important Note - gomacro relies on the plugin package when importing third party libraries. This package works reliably on Mac OS X only with Go 1.10.2+ as long as you never execute the command strip gophernotes. If you can only compile gophernotes with Go <= 1.10.1 on Mac, consider using the Docker install and run gophernotes/Jupyter in Docker.

CarND-TensorFlow-Lab - TensorFlow Lab for Self-Driving Car ND

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We've prepared a Jupyter notebook that will guide you through the process of creating a single layer neural network in TensorFlow. If you don't have Docker already, download and install Docker from here.

book - Deep Learning 101 with PaddlePaddle

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This book you are reading is interactive -- each chapter can run as a Jupyter Notebook. We packed this book, Jupyter, PaddlePaddle, and all dependencies into a Docker image. So you don't need to install anything except Docker. If you are using Windows, please follow this installation guide. If you are running Mac, please follow this. For various Linux distros, please refer to If you are using Windows or Mac, you might want to give Docker more memory and CPUs/cores.

awesome-jupyter - A curated list of awesome Jupyter projects, libraries and resources


A curated list of awesome Jupyter projects, libraries and resources. Jupyter is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Your contributions are always welcome! Please take a look at the contribution guidelines first. - Deep Learning tutorials in jupyter notebooks.

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See here for installing on windows. 1: Refer to this Dockerfile and this for information on how the docker image was built.

oauthenticator - OAuth + JupyterHub Authenticator = OAuthenticator

  •    Python

A generic implementation, which you can use with any provider, is also available. For an example docker image using OAuthenticator, see the examples directory.

kubespawner - Kubernetes spawner for JupyterHub

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The kubespawner (also known as JupyterHub Kubernetes Spawner) enables JupyterHub to spawn single-user notebook servers on a Kubernetes cluster. You can read a list of all the spawner options available on ReadTheDocs.

nbgrader - A system for assigning and grading notebooks

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A system for assigning and grading Jupyter notebooks. Documentation can be found on Read the Docs.

pipeline - PipelineAI: Real-Time Enterprise AI Platform

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Each model is built into a separate Docker image with the appropriate Python, C++, and Java/Scala Runtime Libraries for training or prediction. Use the same Docker Image from Local Laptop to Production to avoid dependency surprises.

jupyter-vim-binding - Jupyter meets Vim. Vimmer will fall in love.

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Do you use Vim? And you need to use Jupyter Notebook? This is a Jupyter Notebook (formerly known as IPython Notebook) extension to enable Vim like environment powered by CodeMirror's Vim. I'm sure that this plugin helps to improve your QOL. While I changed my job, I don't use jupyter notebook and I can't make enough time to maintain this plugin.

cocalc - CoCalc: Collaborative Calculation in the Cloud

  •    CoffeeScript

CoCalc offers collaborative calculation in the cloud. This includes working with the full (scientific) Python stack, SageMath, Julia, R, Octave, and more. It also offers capabilities to author documents in LaTeX, R/knitr or Markdown, storing and organizing files, a web-based Linux Terminal, communication tools like a chat, course management and more. You can easily use CoCalc on your own computer for free by running a Docker image.

tensorflow-tutorial - A tutorial on TensorFlow

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These are all presented via Jupyter notebooks. To run them on your machine, you will need a working TensorFlow installation (v0.10.0RC0). Below are instructions on how to set up a TensorFlow environment using Docker.

lolviz - A simple Python data-structure visualization tool for lists of lists, lists, dictionaries; primarily for use in Jupyter notebooks / presentations

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By Terence Parr. See for more stuff. A simple Python data-structure visualization tool that started out as a List Of Lists (lol) visualizer but now handles arbitrary object graphs, including function call stacks! lolviz tries to look out for and format nicely common data structures such as lists, dictionaries, linked lists, and binary trees. This package is primarily for use in teaching and presentations with Jupyter notebooks, but could also be used for debugging data structures. Useful for devoting machine learning data structures, such as decision trees, as well.

dashboards - Jupyter Dashboards Layout Extension

  •    Jupyter

The dashboards layout extension is an add-on for Jupyter Notebook. It lets you arrange your notebook outputs (text, plots, widgets, ...) in grid- or report-like layouts. It saves information about your layouts in your notebook document. Other people with the extension can open your notebook and view your layouts. For a sample of what's possible with the dashboard layout extension, have a look at the demo dashboard-notebooks in this repository.