feat: add more

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Sun-ZhenXing
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# GPUStack
[English](./README.md) | [中文](./README.zh.md)
This service deploys GPUStack, an open-source GPU cluster manager for running large language models (LLMs).
## Services
- `gpustack`: GPUStack server with built-in worker
## Environment Variables
| Variable Name | Description | Default Value |
| --------------------------- | -------------------------------------- | ------------- |
| GPUSTACK_VERSION | GPUStack image version | `v0.5.3` |
| GPUSTACK_HOST | Host to bind the server to | `0.0.0.0` |
| GPUSTACK_PORT | Port to bind the server to | `80` |
| GPUSTACK_DEBUG | Enable debug mode | `false` |
| GPUSTACK_BOOTSTRAP_PASSWORD | Password for the bootstrap admin user | `admin` |
| GPUSTACK_TOKEN | Token for worker registration | (auto) |
| HF_TOKEN | Hugging Face token for model downloads | `""` |
| GPUSTACK_PORT_OVERRIDE | Host port mapping | `80` |
Please modify the `.env` file as needed for your use case.
## Volumes
- `gpustack_data`: Data directory for GPUStack
## GPU Support
### NVIDIA GPU
Uncomment the GPU-related configuration in `docker-compose.yaml`:
```yaml
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
runtime: nvidia
```
### AMD GPU (ROCm)
Use the ROCm-specific image:
```yaml
image: gpustack/gpustack:v0.5.3-rocm
```
## Usage
### Start GPUStack
```bash
docker compose up -d
```
### Access
- Web UI: <http://localhost:80>
- Default credentials: `admin` / `admin` (configured via `GPUSTACK_BOOTSTRAP_PASSWORD`)
### Deploy a Model
1. Log in to the web UI
2. Navigate to Models
3. Click "Deploy Model"
4. Select a model from the catalog or add a custom model
5. Configure the model parameters
6. Click "Deploy"
### Add Worker Nodes
To add more GPU nodes to the cluster:
1. Get the registration token from the server:
```bash
docker exec gpustack cat /var/lib/gpustack/token
```
2. Start a worker on another node:
```bash
docker run -d --name gpustack-worker \
--gpus all \
--network host \
--ipc host \
-v gpustack-data:/var/lib/gpustack \
gpustack/gpustack:v0.5.3 \
--server-url http://your-server-ip:80 \
--token YOUR_TOKEN
```
## Features
- **Model Management**: Deploy and manage LLM models from Hugging Face, ModelScope, or custom sources
- **GPU Scheduling**: Automatic GPU allocation and scheduling
- **Multi-Backend**: Supports llama-box, vLLM, and other backends
- **API Compatible**: OpenAI-compatible API endpoint
- **Web UI**: User-friendly web interface for management
- **Monitoring**: Resource usage and model metrics
## API Usage
GPUStack provides an OpenAI-compatible API:
```bash
curl http://localhost:80/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "llama-3.2-3b-instruct",
"messages": [{"role": "user", "content": "Hello!"}]
}'
```
## Notes
- For production use, change the default password
- GPU support requires NVIDIA Docker runtime or AMD ROCm support
- Model downloads can be large (several GB), ensure sufficient disk space
- First model deployment may take time as it downloads the model files
## Security
- Change default admin password after first login
- Use strong passwords for API keys
- Consider using TLS for production deployments
- Restrict network access to trusted sources
## License
GPUStack is licensed under Apache License 2.0. See [GPUStack GitHub](https://github.com/gpustack/gpustack) for more information.