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graph-app-kit

★ 246 Open GitHub ↗

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!

// repository documentation

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# Welcome to graph-app-kit

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

![Screenshot](https://user-images.githubusercontent.com/4249447/92298596-8e518600-eeff-11ea-8276-069281a4af93.png)

## Why

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.

## What

* **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](https://hub.graphistry.com) 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

```bash
# Minimal core
git clone https://github.com/graphistry/graph-app-kit.git
cd graph-app-kit/src/docker

# Enable docker buildkit
# ... or run docker-compose via provided alias script `./dc`
export DOCKER_BUILDKIT=1
export COMPOSE_DOCKER_CLI_BUILD=1

# 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](docs/views.md) and relaunch:

```bash
# Add dashboards @ src/python/views/<your_custom_view>/__init__.py

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`:

```bash
git clone https://github.com/graphistry/graph-app-kit.git
cd graph-app-kit/src/docker
./dc.cpu build
...
./dc.cpu up
```

### Quick Launchers - minimal/full core

1. Quick launch options:

**Full**: [![Launch Stack](https://s3.amazonaws.com/cloudformation-examples/cloudformation-launch-stack.png)](https://console.aws.amazon.com/cloudformation/home?region=region#/stacks/new?stackName=graph_app_kit_full&templateURL=https://graph-app-kit-repo-public.s3.us-east-2.amazonaws.com/templates/latest/core/graphistry.yml)

* 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](docs/setup.md)

Admin:

```bash
# 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](https://www.graphistry.com/get-started):

**Database-specific**: [Amazon Neptune](docs/neptune.md), [TigerGraph](docs/tigergraph.md)

2. [Add views](docs/views.md)

3. [Main configurations and extensions](docs/extend.md): Database connectors, authentication, notebook-based editing, and more

### Native (Experimental)

Install dependencies, pick location of views folder, and run:

```bash
cd src/python
pip3 install -r requirements-system.txt
pip3 install -r requirements-app.txt
VIEW_PATH="`pwd`/views" streamlit run entrypoint.py 
```

## The pieces

### Core

* Prebuilt Python project structure ready for prototyping
* [Streamlit](https://www.streamlit.io/) quick self-serve dashboarding
* [Graphistry](https://www.graphistry.com/get-started) point-and-click GPU-accelerated visual graph analytics
* Data frames: Data wrangling via [Pandas](https://pandas.pydata.org/)and [Apache Arrow](https://arrow.apache.org/), 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](https://www.rapids.ai) and CUDA already setup for use if run with an Nvidia docker runtime - cudf GPU dataframes, [BlazingSQL](https://www.blazingsql.com) 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](https://aws.amazon.com/neptune/): [quick launch](docs/neptune.md) and [manual launch](docs/neptune-manual.md)
  * [TinkerPop Gremlin](https://tinkerpop.apache.org/): [query demos](https://github.com/graphistry/graph-app-kit/tree/master/src/python/views/demo_neptune_01_minimal_gremlin)
  * [TigerGraph](https://www.tigergraph.com): [setup](docs/tigergraph.md)

* Collaborations welcome!

* [Jupyter notebooks](https://jupyter.org/): Use quick launchers or [integrations guide](docs/extend.md) for web-based live editing of dashboards by sharing volume mounts between Jupyter and Streamlit

* [Caddy](https://caddyserver.com/): Reverse proxy for custom URLs, [automatic LetsEncrypt TLS certificates](http://letsencrypt.org/), multiple sites on the same domain, pluggable authentication (see [integrations guide](docs/extend.md))

* Also you can install your [custom Python Packages](docs/additional-packages.md).

## 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](DEVELOP.md) for more contributor information

graph-app-kit

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