✔️ Linux
✔️ OS X
❌ Windows (#39)
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.
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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.
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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.
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Full core + DB: DB-specific variants are the same as minimal/full, and add simpler DB-specific quick launching/connecting.
# Minimal core
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 (API accounts), docker-compose.yml: Auth, ports, ...
# Launch
sudo docker-compose up -d
sudo docker-compose logs -f -t --tail=100
=> http://localhost:8501/
To add views and relaunch:
# Add dashboards @ src/python/views/<your_custom_view>/__init__.py
sudo docker-compose up -d --force-recreate
- Quick launch options:
- 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
Admin:
# 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
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Main configurations and extensions: Database connectors, authentication, notebook-based editing, and more
- 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
If GPUs are present, graph-app-kit
leverages GPU cloud acceleration:
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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
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GPU Visualization: Connect to an external Graphistry server or, faster, run on the same GPU server
graph-app-kit
works well with the Python data ecosystem (pandas, cudf, PySpark, SQL, ...) and we're growing the set of builtins and recipes:
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Graph databases
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Collaborations welcome!
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Jupyter notebooks: Use quick launchers or integrations guide for web-based live editing of dashboards by sharing volume mounts between Jupyter and Streamlit
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Caddy: Reverse proxy for custom URLs, automatic LetsEncrypt TLS certificates, multiple sites on the same domain, pluggable authentication (see integrations guide)
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