Displaying 1 to 19 from 19 results

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.

geonotebook - A Jupyter notebook extension for geospatial visualization and analysis

  •    Python

GeoNotebook is an application that provides client/server environment with interactive visualization and analysis capabilities using Jupyter, GeoJS and other open source tools. Jointly developed by Kitware and NASA Ames. Documentation for GeoNotebook can be found at http://geonotebook.readthedocs.io.

IPython - Interactive Computing

  •    Python

IPython provides a rich toolkit to help you make the most of using Python interactively. It provides a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends.

kubeflow - Machine Learning Toolkit for Kubernetes

  •    Python

Please refer to the official docs at kubeflow.org. Please refer to the Community page.




Jupyter - Web-based notebook environment for interactive computing

  •    Python

The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. It supports over 40 programming languages.

sparkmagic - Jupyter magics and kernels for working with remote Spark clusters

  •    Python

Sparkmagic is a set of tools for interactively working with remote Spark clusters through Livy, a Spark REST server, in Jupyter notebooks. The Sparkmagic project includes a set of magics for interactively running Spark code in multiple languages, as well as some kernels that you can use to turn Jupyter into an integrated Spark environment. There are two ways to use sparkmagic. Head over to the examples section for a demonstration on how to use both models of execution.

VSCodeNotebook - 📝 Use VS Code as a reliable note-taking/journal application

  •    Python

VSCode Notebook is an attempt to use VSCode as a complete note taking application. This is a VSCode port of the popular SublimeNotebook project. Because of these reasons, I had to lose my notes a number of times and was forced to start from scratch. This was frustrating, and finally, I decided to do something about it.

pyspark-notebook - Pyspark Notebook With Docker

  •    Python

Run your docker with docker-compose. It helps to keep your arguments/settings in a single file and run together in an isolated environment.


scipy-2017-cython-tutorial - Material for the SciPy 2017 Cython tutorial

  •    Python

It's the last requirement that can be a challenge, depending on your platform / OS. The standard GCC / clang compiler that is available on Linux / Mac (respectively) will work fine. Windows can be more of a challenge. In an effort to make things more uniform, we are using a docker container that bundles everything together except for the contents of this repository.

cauldron - The Unnotebook: A Production-Ready Data Environment Built for DataOps Workflows

  •    Python

which must be executed in the root project directory of your local copy of Cauldron. Cauldron can be used as either through its Command Line Interface (CLI) or with the Cauldron desktop application. For more information about the desktop application visit http://www.unnotebook.com where you can find the download links and documentation. The rest of this README describes using Cauldron directly from the command line.

easy-jupyter - Containerized Data Science Tools

  •    Python

This Dockerfile will create a Docker image which consists of data science tools written in Python. Currently, this Docker image will pull notebooks from the Github repository and will start the notebook server.

jgscm - Jupyter support for Google Cloud Storage

  •    Python

Jupyter Google Storage Contents Manager allows working with Jupyter notebooks directly in Google Cloud Storage. It aims to be a complete drop-in replacement for the stock filesystem ContentsManager. Thus JGSCM is only compatible with a relatively modern IPython/Jupyter stack (version 4 and above). The root level of the virtual file system is the list of buckets, which are presented as directories. In turn, each bucket is presented as an ordinary folder where users can create files, subdirectories and notebooks. Besides, snapshots are completely supported too.

nbformat - Reference implementation of the Jupyter Notebook format

  •    Python

nbformat contains the reference implementation of the Jupyter Notebook format, and Python APIs for working with notebooks. There is also a JSON schema for notebook format versions >= 3.

xp - A framework (comand line tool + libraries) for creating flexible compute pipelines

  •    Python

Enter xp - a utility that allows you to express and run all the computational tasks in a project. Crucially, it captures the specific parameters used for each task, the data files produced, and any dependencies that task has on other tasks. All this is captured in files called pipelines (which can even be connected to one another). Toss in some helpful comments, and you have executable documentation for your project. This may sound a lot like scientific notebook environments (e.g., Jupyter and Mathematica), but there are some key differences. Notebooks only allow linear dependencies between computational tasks - which is a tremendous simplification of even moderate-sized projects.

SublimeNotebook - 📝 Make Sublime Text your favorite note taking/journal application

  •    Python

Sublime Notebook is an attempt to use Sublime Text as a complete note taking application. Because of these reasons, I had to lose my notes a number of times and was forced to start from scratch. This was frustrating, and finally, I decided to do something about it.

bookstore - 📚 Notebook storage workflows for the masses

  •    Python

This repository provides tooling and workflow recommendations for storing, scheduling, and publishing notebooks. Every save of a notebook creates an immutable copy of the notebook on object storage.

molecular-design-toolkit - Notebook-integrated tools for molecular simulation and visualization

  •    Python

Molecular modeling without the pain - a Python library offering integrated simulation, visualization, analysis, and cloud computing. The toolkit aims to lower the barriers between you and your science by integrating mature, open source simulation packages with a readable abstract API, Jupyter notebook visualization, and native cloud computing.

cjworkbench - The data journalism platform with built in training

  •    Python

To see what Workbench does, try our public server, now in beta. Or run your own server. Workbench is a project of Columbia Journalism School, made possible through the generous support of Krishna Bharat and the Knight Foundation.

scrapbook - A library for recording and reading data in notebooks.

  •    Python

THE scrapbook library records a notebook’s data values and generated visual content as "scraps". Recorded scraps can be read at a future time. Notebook users may wish to record data produced during a notebook's execution. This recorded data, scraps, can be used at a later time or passed in a workflow to another notebook as input.