Sudo docker logs -f -t -tail=1 MY_CONTAINERĬd graphistry & sudo docker-compose restart MY_CONTAINERĬd graphistry & sudo docker-compose -f up -d caddyĬd graph-app-kit/public/graph-app-kit & docker-compose -p pub run -d -name streamlit-pub streamlitĬd graph-app-kit/private/graph-app-kit & docker-compose -p priv run -d -name streamlit-priv streamlit Tail -f /var/log/cloud-init-output.log -n 1000 Dashboards: /public/dash and /private/dash.Login to web UI as admin / i-instanceid -> file uploader, notebooks.Public + protected Streamlit dashboards, Jupyter notebooks + editing, Graphistry, RAPIDS.dc.cpu, which aliases docker-compose -f docker-compose.yml -f override/: git clone Quick (Local code) - minimal CPU core + third-party connectors => To add views and relaunch: # Add dashboards src/python/views//_init_.py # Optional: Edit src/docker/.env (API accounts), docker-compose.yml: Auth, ports. or run docker-compose via provided alias script `./dc` Note: Use sudo for docker-compose commands if your configuration requires it and is giving permission error # Minimal core Note: Base image includes Nvidia RAPIDS and AI dependencies so is quite large, see CPU alternative for a lightweight alternativve
#DATAGRAPH APP FULL#
Get started Quick (Local code) - full GPU core + third-party connectors Launch with on click via the Cloud Formation template.įull core + DB: DB-specific variants are the same as minimal/full, and add simpler DB-specific quick launching/connecting. It does not have GPU ETL and GPU AI libraries.įull 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.
#DATAGRAPH APP INSTALL#
Install it, plug in credentials to various web services like cloud databases and a free Graphistry Hub visualization account, and launch.
In provides a StreamLit docker-compose container with PyData ecosystem libraries and examples of visualizing data from various systems. Minimal core: The barebones dashboard server.
#DATAGRAPH APP CODE#
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. Whether building your first graph app, trying an idea, or wanting to check a reference, this project aims to simplify that process. 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. Turn your graph data into a secure and interactive visual graph app in 15 minutes!