https://github.com/rickstaa/ai-compute-visualizer
A StreamLit-based web application to visualize GPU inventory and AI capabilities on the Livepeer network.
https://github.com/rickstaa/ai-compute-visualizer
ai data livepeer streamlit
Last synced: 12 months ago
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A StreamLit-based web application to visualize GPU inventory and AI capabilities on the Livepeer network.
- Host: GitHub
- URL: https://github.com/rickstaa/ai-compute-visualizer
- Owner: rickstaa
- Created: 2025-06-24T11:30:10.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-06-24T11:36:56.000Z (12 months ago)
- Last Synced: 2025-06-24T12:41:48.401Z (12 months ago)
- Topics: ai, data, livepeer, streamlit
- Language: Python
- Homepage:
- Size: 4.88 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Livepeer AI GPU and Capabilities Dashboard
A [Streamlit](https://streamlit.io/)-based web application to visualize GPU inventory
and AI capabilities across orchestrators in the Livepeer AI network using the Gateway
`/capabilities` endpoint.
## Features
- **GPU Type Distribution**: View the distribution of GPU types across orchestrators.
- **Orchestrator GPU Distribution**: Analyze GPU availability per orchestrator.
- **Capabilities Distribution**: Explore AI capabilities provided by the orchestrators.
- **Interactive Filters**: Filter data by GPU models and AI models.
- **Data Table**: View detailed GPU and orchestrator data in a tabular format.
## How to Run
1. Clone the repository:
```bash
git clone https://github.com/rickstaa/ai-compute-visualizer
cd ai-compute-visualizer
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Set the required environment variable:
```bash
export CAPABILITIES_DATA_URL=""
```
4. Run the application:
```bash
streamlit run gpu_dashboard.py
```
5. Open your web browser and navigate to `http://localhost:8501` to view the dashboard.
## Deployment
To deploy the application on [Streamlit Community Cloud](https://streamlit.io/cloud):
1. Push the code to a GitHub repository.
2. Link the repository to Streamlit Community Cloud.
3. Set the environment variable `CAPABILITIES_DATA_URL` in the Streamlit Cloud settings
to the appropriate gateway capabilities URL.
## Limitations
- Data represents a snapshot of orchestrators and capabilities at the time of parsing.
- Only GPUs ready to take jobs are shown.
- Data is collected from a single gateway and may not include all orchestrators and GPUs.
- Realtime AI GPUs are excluded as they are still in beta and not discoverable on chain.