kubeflow - Machine Learning Toolkit for Kubernetes

•    Python

The Kubeflow project is dedicated to making machine learning on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to train, test, and deploy best-of-breed open-source predictive models to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run KubeFlow.This document details the steps needed to run the Kubeflow project in any environment in which Kubernetes runs.

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

•    Dockerfile

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.

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

•    Python

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.

kubeflow - Machine Learning Toolkit for Kubernetes

•    Go

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow is a platform for data scientists who want to build and experiment with ML pipelines. Kubeflow is also for ML engineers and operational teams who want to deploy ML systems to various environments for development, testing, and production-level serving.

repo2docker - Turn repositories into Jupyter-enabled Docker images

•    Python

repo2docker fetches a git repository and builds a container image based on the configuration files found in the repository. See the repo2docker documentation for more information on using repo2docker.

nbgrader - A system for assigning and grading notebooks

•    HTML

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

repo2docker - Turn git repositories into Jupyter enabled Docker Images

•    Python

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.

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.

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

•    Python

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.

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.

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.

kubespawner - Kubernetes spawner for JupyterHub

•    Python

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.

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.

best-of-jupyter - 🏆 A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools)

🏆  A ranked list of awesome Jupyter projects. Updated weekly. 🧙‍♂️  Discover other best-of lists or create your own. 📫  Subscribe to our newsletter for updates and trending projects.

marathonspawner - Spawns JupyterHub single user servers in Marathon

•    Python

A simple plugin for JupyterHub to spawn single user notebook servers on Marathon.

xsede-jetstream - Unidata on the XSEDE Jetstream Cloud

•    CSS

Unidata on the XSEDE Jetstream Cloud. A scalable solution that leverages Openstack, Kubernetes, Docker and Jupyterhub technologies for delivering a powerful tool for user training and next-generation workforce development in atmospheric sciences.

MPContribs - MP's User Contribution Framework

•    HTML

Since its start in 2011, Materials Project (MP, https://materialsproject.org/) has grown into a world-wide resource for a materials sciences community of more than 27,000 users who rely on the portal as a trusted source to accelerate their research. As a result, they wish to help with MP's efforts by contributing back, but also ask for support in sharing their experimental and computational datasets alongside MP's curated results. This provides the opportunity for researchers in both domains to validate calculations or measurements almost instantaneously and use the disseminated data for integrated materials studies. With the public announcement of our general contribution framework, MPContribs, we present a sustainable solution for well-curated data management, organization and dissemination in the context of MP. The framework serves the purpose of collectively maintaining contributions to local and MP community databases as annotations to existing MP materials. It subsequently disseminates them through a generic interactive gateway powered by Jupyter notebooks or through custom project web apps enabled by the webtzite app kit.

kubeflow-labs - 👩‍🔬 Train and Serve TensorFlow Models at Scale with Kubernetes and Kubeflow on Azure

•    Python

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com. When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

jupyterhub-kdcauthenticator - A Kerberos authenticator module for the JupyterHub platform

•    Python

KDC authenticator allows to authenticate the JuypterHub user using Kerberos protocol.