JupyterLite demo deployed to GitHub Pages 🚀

Related tags

Deep Learningdemo
Overview

JupyterLite Demo

lite-badge

JupyterLite deployed as a static site to GitHub Pages, for demo purposes.

Try it in your browser

➡️ https://jupyterlite.github.io/demo

github-pages

Requirements

JupyterLite is being tested against modern web browsers:

  • Firefox 90+
  • Chromium 89+

Usage

This repository provides a demonstration of how to:

  • build a JupyterLite release using prebuilt JupyterLite assets that bundles a collection of pre-existing Jupyter notebooks as part of the distribution;
  • deploy the release to GitHub Pages.

The process is automated using Github Actions.

You can use this repository in two main ways:

  • generate a new repository from this template repository and build and deploy your own site to the corresponding Github Pages site;
  • build a release from a PR made to this repository and download the release from the created GitHub Actions artifact.

Using Your Own Repository to Build a Release and Deploy it to Github Pages

Requires Github account.

Click on "Use this template" to generate a repository of your own from this template:

template

From the Actions tab on your repository, ensure that workflows are enabled. When you make a commit to the main branch, a Github Action will run to build your JupoyterLite release and deploy it to the repository's Github Pages site. By default, the Github Pages site will be found at YOUR_GITHUB_USERNAME.github.io/YOUR_REPOSITORY-NAME. You can also check the URL from the Repository Settings tab Pages menu item.

If the deployment failed, go to "Settings - Actions - General", in the "Workflow permissions" section, check "Read and write permissions". Update files such as readme, and commit so that GitHub rebuids and re-deploys the project. Go to "Settings - Pages", choose "gh-pages" as the source.

Add any additional requirements as required to the requirements.txt file.

You can do this via the Github website by selecting the requirements.txt file, clicking to edit it, making the required changes then saving ("committing") the result to the main branch of your repository.

Modify the contents of the contents folder to include the notebooks you want to distribute as part of your distribution.

You can do this by clicking on the contents directory and dragging notebooks from your desktop onto the contents listing. Wait for the files to be uploaded and then save them ("commit" them) to the main branch of the repository.

Check that you have Github Pages enabled for your repository: from your repository Settings tab, select the Pages menu item and ensure that the source is set to gh-pages.

When you commit a file, an updated release will be built and published on the Github Pages site. Note that it may take a few minutes for the Github Pages site to be updated. Do a hard refresh on your Github Pages site in your web browser to see the new release.

Further Information and Updates

For more info, keep an eye on the JupyterLite documentation:

Deploy a new version of JupyterLite

To change the version of the prebuilt JupyterLite assets, update the jupyterlite package version in the requirements.txt file.

The requirements.txt file can also be used to add extra prebuilt ("federated") JupyterLab extensions to the deployed JupyterLite website.

Commit and push any changes. The site will be deployed on the next push to the main branch.

Development

Create a new environment:

mamba create -n jupyterlite-demo
conda activate jupyterlite-demo
pip install -r requirements.txt

Then follow the steps documented in the Configuring section.

Owner
JupyterLite
Wasm powered Jupyter running in the browser 💡
JupyterLite
A setup script to generate ITK Python Wheels

ITK Python Package This project provides a setup.py script to build ITK Python binary packages and infrastructure to build ITK external module Python

Insight Software Consortium 59 Dec 14, 2022
Offline Multi-Agent Reinforcement Learning Implementations: Solving Overcooked Game with Data-Driven Method

Overcooked-AI We suppose to apply traditional offline reinforcement learning technique to multi-agent algorithm. In this repository, we implemented be

Baek In-Chang 14 Sep 16, 2022
Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets" (ECCV 2020 Spotlight)

Distribution-Balanced Loss [Paper] The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (

Tong WU 304 Dec 22, 2022
CLEAR algorithm for multi-view data association

CLEAR: Consistent Lifting, Embedding, and Alignment Rectification Algorithm The Matlab, Python, and C++ implementation of the CLEAR algorithm, as desc

MIT Aerospace Controls Laboratory 30 Jan 02, 2023
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).

Crab - A Recommendation Engine library for Python Crab is a flexible, fast recommender engine for Python that integrates classic information filtering r

python-recsys 1.2k Dec 21, 2022
PyTorch implementation of paper: HPNet: Deep Primitive Segmentation Using Hybrid Representations.

HPNet This repository contains the PyTorch implementation of paper: HPNet: Deep Primitive Segmentation Using Hybrid Representations. Installation The

Siming Yan 42 Dec 07, 2022
Text Extraction Formulation + Feedback Loop for state-of-the-art WSD (EMNLP 2021)

ConSeC is a novel approach to Word Sense Disambiguation (WSD), accepted at EMNLP 2021. It frames WSD as a text extraction task and features a feedback loop strategy that allows the disambiguation of

Sapienza NLP group 36 Dec 13, 2022
realsense d400 -> jpg + csv

Realsense-capture realsense d400 - jpg + csv Requirements RealSense sdk : Installation Python3 pyrealsense2 (RealSense SDK) Numpy OpenCV Tkinter Run

Ar-Ray 2 Mar 22, 2022
HNECV: Heterogeneous Network Embedding via Cloud model and Variational inference

HNECV This repository provides a reference implementation of HNECV as described in the paper: HNECV: Heterogeneous Network Embedding via Cloud model a

4 Jun 28, 2022
Code to reproduce the results for Statistically Robust Neural Network Classification, published in UAI 2021

Code to reproduce the results for Statistically Robust Neural Network Classification, published in UAI 2021

1 Jun 02, 2022
A quick recipe to learn all about Transformers

Transformers have accelerated the development of new techniques and models for natural language processing (NLP) tasks.

DAIR.AI 772 Dec 31, 2022
Cards Against Humanity AI

cah-ai This is a Cards Against Humanity AI implemented using a pre-trained Semantic Search model. How it works A player is described by a combination

Alex Nichol 2 Aug 22, 2022
ViDT: An Efficient and Effective Fully Transformer-based Object Detector

ViDT: An Efficient and Effective Fully Transformer-based Object Detector by Hwanjun Song1, Deqing Sun2, Sanghyuk Chun1, Varun Jampani2, Dongyoon Han1,

NAVER AI 262 Dec 27, 2022
Demo for the paper "Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation"

Streaming speaker diarization Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation by Juan Manuel Coria, Hervé

Juanma Coria 187 Jan 06, 2023
Spatial-Location-Constraint-Prototype-Loss-for-Open-Set-Recognition

Spatial Location Constraint Prototype Loss for Open Set Recognition Official PyTorch implementation of "Spatial Location Constraint Prototype Loss for

Xia Ziheng 12 Jun 24, 2022
The official GitHub repository for the Argoverse 2 dataset.

Argoverse 2 API Official GitHub repository for the Argoverse 2 family of datasets. If you have any questions or run into any problems with either the

Argo AI 156 Dec 23, 2022
CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes Implementation of CoSMA: Convolutional Semi-Regular Mesh Autoencoder arXiv p

Fraunhofer SCAI 10 Oct 11, 2022
ML powered analytics engine for outlier detection and root cause analysis.

Website • Docs • Blog • LinkedIn • Community Slack ML powered analytics engine for outlier detection and root cause analysis ✨ What is Chaos Genius? C

Chaos Genius 523 Jan 04, 2023
Official implementation of ETH-XGaze dataset baseline

ETH-XGaze baseline Official implementation of ETH-XGaze dataset baseline. ETH-XGaze dataset ETH-XGaze dataset is a gaze estimation dataset consisting

Xucong Zhang 134 Jan 03, 2023
P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

P-tuning v2 P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks An optimized prompt tuning strategy achievi

THUDM 540 Dec 30, 2022