jupyterlab-prodigy 4.0.0

A Jupyterlab extension for annotating machine learning training sets using Prodigy

JupyterLab extension for the Prodigy annotation tool ✨

This repo contains a JupyterLab extension for Prodigy, our scriptable annotation tool for creating training data for machine learning models. It lets you run Prodigy within a JupyterLab tab, and annotate as you develop your models and applications. In order to use this extension, you'll need a license for Prodigy – see this page for more details. For questions, please use the Prodigy Support Forum. If you've found a bug, feel free to submit a pull request.

🙏 Special thanks to Jupyter core dev Grant Nestor for helping us build this extension!

⌛️ Installation

To use this extension, you need JupyterLab >= 3.0.0 and Prodigy.

pip install jupyterlab>=3.0.0

To install the extension, run:

pip install jupyterlab-prodigy

Ensure that the extension is installed and enabled:

jupyter labextension list

Uninstall

To remove the extension, run:

pip uninstall jupyterlab-prodigy

Compatibility

This extension is compatible with Jupyterlab 3.0.0 and above. If you're using Jupyterlab with versions >=2.0.0 and <3.0.0, then you should install the 3.0.0 version of jupyterlab-prodigy

jupyter labextension install jupyterlab-prodigy@3.0.0

📋 Usage

Start a Prodigy session in a terminal, e.g.:

$ prodigy ner.manual my_set blank:en notebooks/news_headlines.jsonl --label PERSON,ORG,PRODUCT

In another terminal session, start JupyterLab:

$ jupyter lab

Then, inside of JupyterLab, open the Commands on the left sidebar, and search/type:

Open Prodigy

Execute it, you will have a new Prodigy panel on the side.

⚙ Configuration

If your Prodigy is being served at a URL different than the default (e.g. behind a reverse proxy) you can configure the URL to use in the settings.

Open the Settings menu, go to Advanced Settings Editor, select the settings for Prodigy Jupyter Extension, and there you can add your custom URL, e.g.:

{
    "prodigyConfig": {
        "url": "https://prodigy.example.com"
    }
}

👩‍💻 Develop

Note: You will need NodeJS to build the extension package. It is also highly-recommended that you work in a virtual environment when developing.

The jlpm command is JupyterLab's pinned version of yarn that is installed with JupyterLab. You may use yarn or npm in lieu of jlpm below.

# Clone the repo to your local environment
# Change directory to the jupyterlab-prodigy directory
# Install dev requirements
pip install -r requirements-dev.txt
# Install package in development mode
pip install -e .
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Rebuild extension Typescript source after making changes
jlpm run build

You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm run watch
# Run JupyterLab in another terminal
jupyter lab

With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).

By default, the jlpm run build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

jupyter lab build --minimize=False

Uninstall

pip uninstall jupyterlab-prodigy

Packaging the extension

See RELEASE