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Version CI Publish Docker Cloud Build Status ✔️ Linux ✔️ OS X Windows (#39)

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Welcome to graph-app-kit

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



This open source effort puts together patterns the Graphistry team has reused across many graph projects as teams go from code-heavy Jupyter notebook experiments to deploying streamlined analyst tools. Whether building your first graph app, trying an idea, or wanting to check a reference, this project aims to simplify that process. It covers 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 data + library dependencies, and fast loading & visualization of large graphs.


  • Minimal core: The barebones dashboard server. In provides a StreamLit docker-compose container with PyData ecosystem libraries and examples of visualizing data from various systems. Install it, plug in credentials to various web services like cloud databases and a free Graphistry Hub visualization account, and launch. It does not have GPU ETL and GPU AI libraries.

  • Full core: Initially for AWS, the full core bundles adds to the docker-compose system: Accounts, Jupyter notebooks for authoring, serves StreamLit dashboards with both public + private zones, and runs Graphistry/RAPIDS locally on the same server. Launch with on click via the Cloud Formation template.

  • Full core + DB: DB-specific variants are the same as minimal/full, and add simpler DB-specific quick launching/connecting.

Get started

Quick (Local code) - full GPU core + third-party connectors

Note: Base image includes Nvidia RAPIDS and AI dependencies so is quite large, see CPU alternative for a lightweight alternativve

Note: Use sudo for docker-compose commands if your configuration requires it and is giving permission error

# Minimal core
git clone
cd graph-app-kit/src/docker

# Enable docker buildkit
# ... or run docker-compose via provided alias script `./dc`

# Build
docker-compose build

# Optional: Edit src/docker/.env (API accounts), docker-compose.yml: Auth, ports, ...

# Launch
docker-compose up -d
docker-compose logs -f -t --tail=100

=> http://localhost:8501/

To add views and relaunch:

# Add dashboards @ src/python/views/<your_custom_view>/

docker-compose up -d --force-recreate

Quick (Local code) - minimal CPU core + third-party connectors

Same commands as above, but use ./dc.cpu, which aliases docker-compose -f docker-compose.yml -f override/cpu.override.yml:

git clone
cd graph-app-kit/src/docker
./dc.cpu build
./dc.cpu up

Quick Launchers - minimal/full core

  1. Quick launch options:

Full: Launch Stack

  • Public + protected Streamlit dashboards, Jupyter notebooks + editing, Graphistry, RAPIDS
  • Login to web UI as admin / i-instanceid -> file uploader, notebooks, ...
  • Dashboards: /public/dash and /private/dash
  • More info


# launch logs
tail -f /var/log/cloud-init-output.log -n 1000

# app logs
sudo docker ps
sudo docker logs -f -t --tail=1 MY_CONTAINER

# restart a graphistry container
cd graphistry && sudo docker-compose restart MY_CONTAINER

# restart caddy (Caddy 1 override)
cd graphistry && sudo docker-compose -f docker-compose.gak.graphistry.yml up -d caddy

# run streamlit
cd graph-app-kit/public/graph-app-kit && docker-compose -p pub run -d --name streamlit-pub streamlit
cd graph-app-kit/private/graph-app-kit && docker-compose -p priv run -d --name streamlit-priv streamlit

Minimal: Open Streamlit, ssh to connect/add free Graphistry Hub username/pass:

Database-specific: Amazon Neptune, TigerGraph

  1. Add views

  2. Main configurations and extensions: Database connectors, authentication, notebook-based editing, and more

The pieces


  • 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 Pandasand Apache Arrow, including handling formats such as CSV, XLS, JSON, Parquet, and more

  • Standard Docker and docker-compose cross-platform deployment

GPU acceleration (optional) - Full

For non-minimal installs, 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:


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 for more contributor information


Go from graph data to a secure and interactive visual graph app in 15 minutes. Batteries-included self-hosting of graph data apps with Streamlit, Graphistry, RAPIDS, and more!