semvec/pypi-packages huggingface dataset:
jupytery-react 0.0.6
Jupytery React
Requires Python: >=3.8

[![Datalayer](https://raw.githubusercontent.com/datalayer/datalayer/main/res/logo/datalayer-25.svg?sanitize=true)](https://datalayer.io)

# ⚛️ Jupytery React

> Jupytery React, React.js for Jupyter.

This folder curates techniques to use [React.js](https://reactjs.org) inside the [Jupyter](https://jupyter.org) ecosystem, with a primary focus on wrapping [JupyterLab](https://github.com/jupyterlab/jupyterlab) components into a React.js DOM tree. The JupyterLab UI is built on the [Lumino](https://github.com/jupyterlab/lumino) widgets.

Please open [Issues](https://github.com/datalayer/datalayer/issues) for questions, feature requests, bug reports... We also welcome [Pull Requests](https://github.com/datalayer/datalayer/pulls).

You can find more details on the [documentation website](https://docs.datalayer.io/docs/jupyter/react).

## Develop

You can launch a local storybook and hack the source code. The changes will be automatically built and displayed in your browser.

[![lumino-in-react](./docs/images/lumino-in-react.png)](https://jupytery-react.datalayer.io)

```bash
# Setup your development environment.
conda deactivate && \
  make env-rm # If you want to reset your env.
make env && \
  conda activate jupytery-react
```

```bash
# Clean, install and build.
make clean
make install
make build
```

```bash
# Start the storybook.
echo open http://localhost:9005
make storybook
# To execute code from the storybook, start a separated jupyterlab server.
make jupyterlab
```

To build a static storybook website, run `yarn build-storybook`. The generated website will be available under the `storybook/.out` folder.

```bash
yarn build-storybook && \
  open storybook/.out/index.html
```

To deep clean your local repository, run `make clean`.

## Examples

```bash
# Start a Lumino example.
echo open http://localhost:8080
yarn example:lumino
```

```bash
# Start a JupyterLab React example.
echo open http://localhost:8080
yarn example:jupyter:widgets
```

## Deploy

You can deploy the storybook in your [Vercel](https://vercel.com) (former Now.js) account. 

Test the build on your local environment.

```bash
# This will open storybook/.out/index.html in your browser.
make storybook-build
```

Ccnfigure a vercel `datalayer-jupyter-storybook` project with:

- Build command: `yarn build:vercel`
- Output directory: `storybook/.out`.

Then run the following command to deploy in production.

```bash 
# Deploy the storybook (if you have karma for).
# open https://datalayer-jupyter.datalayer.io
make storybook-prod
```
jupytery-content 0.0.5
Jupytery Content
Requires Python: >=3.8

[![Datalayer](https://raw.githubusercontent.com/datalayer/datalayer/main/res/logo/datalayer-25.svg?sanitize=true)](https://datalayer.io)

# Jupytery Content

## Jupytery Content Google Cloud

```bash
# Get to https://console.cloud.google.com/billing and copy the Billing Account ID value.
ACCOUNT_ID=<YOUR_ACCOUNT_ID>
# Create a new project (project id must be <=30 characters)
# RAND=$RANDOM
RAND=78026
PROJECT_ID="jupytery-content-$RAND"
# Create a GCloud Project
gcloud projects create $PROJECT_ID --enable-cloud-apis # [--organization $ORGANIZATION_ID]
gcloud config set project $PROJECT_ID
# or, record the PROJECT_ID value of an existing project
# PROJECT_ID=$(gcloud projects list --filter NAME=<YOUR_PROJECT_NAME> --format="value(PROJECT_ID)")
# Link billing to the new project.
gcloud beta billing projects link $PROJECT_ID --billing-account=$ACCOUNT_ID
# Enable GCloud Services on your Project
# Enable service on the new project.
gcloud services enable storage.googleapis.com --project $PROJECT_ID # enable Compute API
# Create a GCloud Service Account
SERVICE_ACCOUNT_NAME="example-$RAND"
SERVICE_ACCOUNT="${SERVICE_ACCOUNT_NAME}@${PROJECT_ID}.iam.gserviceaccount.com"
gcloud iam service-accounts create $SERVICE_ACCOUNT_NAME --project $PROJECT_ID --display-name "Jupytery Content Example"
# Assign GCloud Roles to your service account.
gcloud projects add-iam-policy-binding $PROJECT_ID --member "serviceAccount:$SERVICE_ACCOUNT" --role="roles/storage.admin"
open https://console.cloud.google.com/iam-admin/serviceaccounts?project=jupytery-content-78026
```

```bash
# Install dependencies.
make install
# Check env in the Makefile and get a key.
make key
# Run the tests.
make test
```

```bash
# Start a jupyterlab example configured with a content bucket.
make jlab-gcp
```
jupytery-admin 0.0.5
Jupyter Admin
Requires Python: >=3.8

[![Datalayer](https://raw.githubusercontent.com/datalayer/datalayer/main/res/logo/datalayer-25.svg?sanitize=true)](https://datalayer.io)

# 🛂 Jupyter Admin

> Jupyter Admin is a UI (Web User Interface) and CLI (Command Line Interface) to manage the Datalayer Jupyter components.

```bash
# Setup your development environment.
conda deactivate && \
  make env-rm # If you want to reset your env.
make env && \
  conda activate jupytery-admin
```

```bash
# Install a `rbac` enabled jupyterhub.
make jupyterhub-rbac
```

```bash
# Clean and install and build.
make clean
make install
make build
```

```bash
# Build and copy the javascript to the shared jupyterhub folder.
make jupyterhub-js
```

```bash
# Create an access token for your current ${USER}.
# !!! Update in `./public/index.html` the `window.jupyterhub_api_token = <token>` with the returned token value.
make jupyterhub-token
```

```bash
# Start the frontend webpack server and jupyterhub server.
echo open http://localhost:2003
echo open http://localhost:8686/hub/jupytery-admin
make start
```

```bash
# Connect to the jupyterhub local sqlite database and run e.g.
# .schema
# .tables
# .schema api_tokens
# select * from api_tokens;
make  jupyterhub-sqlite
```