Welcome to graph-app-kit

Turn your graph data into a secure and interactive visual graph app in 15 minutes!

Why

This open source effort puts together patterns the Graphistry team has reused across many graph projects as teams went from Jupyter notebook experiments to deployed analyst tools. Whether building your first graph app, trying an idea, or wanting to check a reference, this project aims to simplify the process. This means covering pieces like: Easy code editing and deployment, a project stucture ready for teams, built-in authentication, no need for custom JS/CSS at the start, batteries-included dependencies, and fast loading & visualization of large graphs.

Get started

Quick

git clone https://github.com/graphistry/graph-app-kit.git
cd graph-app-kit/src/docker
sudo docker-compose build
# Optional: edit src/docker/.env, docker-compose.yml: Auth, ports, ...
sudo docker-compose up -d
sudo docker-compose logs -f -t --tail=100
# Add src/python/views/your_custom_view/__init__.py

Guides

Quick launch: Preintegrated Docker, Graphistry, Streamlit, Jupyter, RAPIDS.ai (GPU)

Variants: manual launch, Amazon Neptune

Add views
Main configurations and extensions: Database connectors, authentication, notebook-based editing, and more

The pieces

Core

Prebuilt Python project structure ready for prototyping
Streamlit quick self-serve dashboarding
Graphistry point-and-click GPU-accelerated visual graph analytics
Data frames: Data wrangling via Pandas, Apache Arrow, RAPIDS (ex: cuDF), including handling formats such as CSV, XLS, JSON, Parquet, and more
Standard Docker and docker-compose cross-platform deployment

GPU acceleration (optional)

If GPUs are present, graph-app-kit leverages GPU cloud acceleration:

GPU Analytics: RAPIDS and CUDA already setup for use if run with an Nvidia docker runtime - cudf GPU dataframes, BlazingSQL GPU SQL, cuGraph GPU graph algorithms, cuML libraries, and more
GPU Visualization: Connect to an external Graphistry server or, faster, run on the same GPU server

Prebuilt integrations & recipes

graph-app-kit works well with the Python data ecosystem (pandas, cudf, PySpark, SQL, ...) and we're growing the set of builtins and recipes:

Graph databases
- AWS Neptune: quick launch and manual launch
- TinkerPop Gremlin: query demos
- Collaborations welcome!
Jupyter notebooks: Use quick launchers or integrations guide for web-based live editing of dashboards by sharing volume mounts between Jupyter and Streamlit
Caddy: Reverse proxy for custom URLs, automatic LetsEncrypt TLS certificates, multiple sites on the same domain, pluggable authentication (see integrations guide)

Contribute

We welcome all sorts of help!

Deployment: Docker, cloud runners, ...
Dependencies: Common graph packages
Connectors: Examples for common databases and how to get a lot of data out
Demos!

See develop.md for more contributor information

sevickson/graph-app-kit