<p align="center">
<a href="https://graphbook.ai">
<img src="docs/_static/graphbook.png" alt="Logo" width=256>
</a>
<h1 align="center">Graphbook</h1>
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<p align="center">
The ML workflow framework
<br>
<a href="https://github.com/graphbookai/graphbook/issues/new?template=bug_report.md&labels=bug">Report bug</a>
·
<a href="https://github.com/graphbookai/graphbook/issues/new?template=feature_request.md&labels=enhancement">Request feature</a>
</p>
<p align="center">
<a href="#overview">Overview</a> •
<a href="#status">Status</a> •
<a href="#getting-started">Getting Started</a> •
<a href="#examples">Examples</a> •
<a href="#collaboration">Collaboration</a>
</p>
</p>
## Overview
Graphbook is a framework for building efficient, visual DAG-structured ML workflows composed of nodes written in Python. Graphbook provides common ML processing features such as multiprocessing IO and automatic batching for PyTorch tensors, and it features a web-based UI to assemble, monitor, and execute data processing workflows. It can be used to prepare training data for custom ML models, experiment with custom trained or off-the-shelf models, and to build ML-based ETL applications. Custom nodes can be built in Python, and Graphbook will behave like a framework and call lifecycle methods on those nodes.
<p align="center">
<a href="https://graphbook.ai">
<img src="https://media.githubusercontent.com/media/rsamf/public/main/docs/overview/huggingface-pipeline-demo.gif" alt="Huggingface Pipeline Demo" width="512">
</a>
<div align="center">Build, run, monitor!</div>
</p>
## Status
Graphbook is in a very early stage of development, so expect minor bugs and rapid design changes through the coming releases. If you would like to [report a bug](https://github.com/graphbookai/graphbook/issues/new?template=bug_report.md&labels=bug) or [request a feature](https://github.com/graphbookai/graphbook/issues/new?template=feature_request.md&labels=enhancement), please feel free to do so. We aim to make Graphbook serve our users in the best way possible.
### Current Features
- Graph-based visual editor to experiment and create complex ML workflows
- Caches outputs and only re-executes parts of the workflow that changes between executions
- UI monitoring components for logs and outputs per node
- Custom buildable nodes with Python via OOP and functional patterns
- Automatic batching for Pytorch tensors
- Multiprocessing I/O to and from disk and network
- Customizable multiprocessing functions
- Ability to execute entire graphs, or individual subgraphs/nodes
- Ability to execute singular batches of data
- Ability to pause graph execution
- Basic nodes for filtering, loading, and saving outputs
- Node grouping and subflows
- Autosaving and shareable serialized workflow files
- Registers node code changes without needing a restart
- Monitorable CPU and GPU resource usage
- Human-in-the-loop prompting for interactivity and manual control during DAG execution
- (BETA) Third Party Plugins *
\* We plan on adding documentation for the community to build plugins, but for now, an example can be seen at
[example_plugin](example_plugin) and
[graphbook-huggingface](https://github.com/graphbookai/graphbook-huggingface)
### Planned Features
- A `graphbook run` command to execute workflows in a CLI
- All-code workflows, so users never have to leave their IDE
- Remote subgraphs for scaling workflows on other Graphbook services
- And many optimizations for large data processing workloads
### Supported OS
The following operating systems are supported in order of most to least recommended:
- Linux
- Mac
- Windows (not recommended) *
\* There may be issues with running Graphbook on Windows. With limited resources, we can only focus testing and development on Linux.
## Getting Started
### Install from PyPI
1. `pip install graphbook`
1. `graphbook`
1. Visit http://localhost:8005
### Install with Docker
1. Pull and run the downloaded image
```bash
docker run --rm -p 8005:8005 -v $PWD/workflows:/app/workflows rsamf/graphbook:latest
```
1. Visit http://localhost:8005
### Recommended Plugins
* [Huggingface](https://github.com/graphbookai/graphbook-huggingface)
Visit the [docs](https://docs.graphbook.ai) to learn more on how to create custom nodes and workflows with Graphbook.
## Examples
We continually post examples of workflows and custom nodes in our [examples repo](https://github.com/graphbookai/graphbook-examples).
## Collaboration
Graphbook is in active development and very much welcomes contributors. This is a guide on how to run Graphbook in development mode. If you are simply using Graphbook, view the [Getting Started](#getting-started) section.
### Run Graphbook in Development Mode
You can use any other virtual environment solution, but it is highly adviced to use [poetry](https://python-poetry.org/docs/) since our dependencies are specified in poetry's format.
1. Clone the repo and `cd graphbook`
1. `poetry install --with dev`
1. `poetry shell`
1. `python graphbook/main.py`
1. `cd web`
1. `npm install`
1. `npm run dev`
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Graphbook provides common ML processing features such as multiprocessing IO and automatic batching for PyTorch tensors, and it features a web-based UI to assemble, monitor, and execute data processing workflows. It can be used to prepare training data for custom ML models, experiment with custom trained or off-the-shelf models, and to build ML-based ETL applications. Custom nodes can be built in Python, and Graphbook will behave like a framework and call lifecycle methods on those nodes.\n\n<p align=\"center\">\n <a href=\"https://graphbook.ai\">\n <img src=\"https://media.githubusercontent.com/media/rsamf/public/main/docs/overview/huggingface-pipeline-demo.gif\" alt=\"Huggingface Pipeline Demo\" width=\"512\">\n </a>\n <div align=\"center\">Build, run, monitor!</div>\n</p>\n\n## Status\nGraphbook is in a very early stage of development, so expect minor bugs and rapid design changes through the coming releases. If you would like to [report a bug](https://github.com/graphbookai/graphbook/issues/new?template=bug_report.md&labels=bug) or [request a feature](https://github.com/graphbookai/graphbook/issues/new?template=feature_request.md&labels=enhancement), please feel free to do so. We aim to make Graphbook serve our users in the best way possible.\n\n### Current Features\n- \u200b\u200bGraph-based visual editor to experiment and create complex ML workflows\n- Caches outputs and only re-executes parts of the workflow that changes between executions\n- UI monitoring components for logs and outputs per node\n- Custom buildable nodes with Python via OOP and functional patterns\n- Automatic batching for Pytorch tensors\n- Multiprocessing I/O to and from disk and network\n- Customizable multiprocessing functions\n- Ability to execute entire graphs, or individual subgraphs/nodes\n- Ability to execute singular batches of data\n- Ability to pause graph execution\n- Basic nodes for filtering, loading, and saving outputs\n- Node grouping and subflows\n- Autosaving and shareable serialized workflow files\n- Registers node code changes without needing a restart\n- Monitorable CPU and GPU resource usage\n- Human-in-the-loop prompting for interactivity and manual control during DAG execution\n- (BETA) Third Party Plugins *\n\n\\* We plan on adding documentation for the community to build plugins, but for now, an example can be seen at\n[example_plugin](example_plugin) and\n[graphbook-huggingface](https://github.com/graphbookai/graphbook-huggingface)\n\n### Planned Features\n- A `graphbook run` command to execute workflows in a CLI\n- All-code workflows, so users never have to leave their IDE\n- Remote subgraphs for scaling workflows on other Graphbook services\n- And many optimizations for large data processing workloads\n\n### Supported OS\nThe following operating systems are supported in order of most to least recommended:\n- Linux\n- Mac\n- Windows (not recommended) *\n\n\\* There may be issues with running Graphbook on Windows. 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