https://github.com/boltzmannentropy/vllm-5090
vLLM-5090: Docker Container for RTX 5090 on WSL2/Windows
https://github.com/boltzmannentropy/vllm-5090
5090 cuda docker vllm
Last synced: 9 months ago
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vLLM-5090: Docker Container for RTX 5090 on WSL2/Windows
- Host: GitHub
- URL: https://github.com/boltzmannentropy/vllm-5090
- Owner: BoltzmannEntropy
- Created: 2025-10-02T05:56:26.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-02T11:12:39.000Z (9 months ago)
- Last Synced: 2025-10-02T13:25:45.864Z (9 months ago)
- Topics: 5090, cuda, docker, vllm
- Language: Python
- Homepage:
- Size: 1.21 MB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# vLLM-5090: Docker Container for RTX 5090 on WSL2/Windows
**👨💻 Author**: Shlomo Kashani
**🏫 Affiliation**: Johns Hopkins University, Maryland U.S.A.
**🏢 Organization**: QNeura.ai
[](https://docker.com)
[](https://docs.microsoft.com/en-us/windows/wsl/)
[](https://nvidia.com)
[](https://linux.org)
A pre-configured Docker environment specifically built for running vLLM on NVIDIA RTX 5090 GPUs within Windows WSL2. Includes demonstration applications showing vLLM's capabilities with vision-language models for video analysis and image processing.
## 🚀 What This Solves
Running large language models on cutting-edge GPUs like the RTX 5090 in WSL2 can be challenging due to dependency management and environment conflicts. This project provides a ready-to-use Docker container that eliminates setup headaches, allowing WSL2 users to run vLLM immediately on their 5090 GPUs.
## 🎯 Key Features
- **RTX 5090 Ready**: Containerized environment tuned for 32GB GDDR7 memory and Ada Lovelace architecture
- **WSL2 First**: Optimized Docker configuration for Windows Subsystem for Linux 2
- **Zero Setup**: Pre-built with all dependencies, CUDA 12.8, and PyTorch 2.7.0
- **Multi-Platform**: Works on both Windows WSL2 and native Linux systems
- **Vision-Language Demo**: Includes video captioning and image analysis examples to verify functionality
- **OpenAI Compatible**: Provides compatible API server for easy integration
- **High Performance**: Memory and thread optimizations for maximum throughput
## 📋 System Requirements
### Hardware Requirements
- **GPU**: NVIDIA RTX 5090 with 32GB GDDR7 VRAM
- **RAM**: Minimum 32GB system RAM (64GB recommended)
- **Storage**: 100GB+ free space for models and Docker images
### Software Requirements
#### Windows with WSL2
- Windows 11 (Build 22000 or later)
- WSL2 with Ubuntu 20.04+ or compatible distribution
- NVIDIA GPU drivers with WSL2 support
- Docker Desktop with WSL2 backend
#### Linux (Ubuntu/Debian)
- Ubuntu 20.04+ or compatible distribution
- NVIDIA GPU drivers (550+ series recommended)
- Docker CE/EE
### Dependencies
- Docker 24.0+
- NVIDIA Container Toolkit (nvidia-docker2)
- Python 3.11
- CUDA 12.8
- PyTorch 2.7.0
## 🛠️ Quick Start
### Option 1: Docker Build (Recommended)
1. **Clone and navigate to project directory:**
```bash
git clone
cd vllm-5090
```
2. **Build the optimized Docker image:**
```bash
# Windows
build.bat
# Linux/Mac
docker build -t vllm-small-5090 .
```
3. **Run the container:**
```bash
# Windows (run-d.bat)
run-d.bat
# Linux/Mac
docker run --gpus all --rm -it \
--shm-size=8gb --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
--memory=16g --env=DISPLAY \
-p 8000:8000 -p 8078:7842 -p 7861:7860 -p 8502:8501 \
-v $(pwd):/root/app \
-v ~/cache:/root/.cache \
vllm-small-5090:latest
```
### Option 2: Manual Installation
1. **Install vLLM from source (in container or host):**
```bash
# Install build dependencies
pip install -r __vllm/requirements/build.txt
pip install setuptools_scm
# Build vLLM with CUDA support
cd __vllm
python use_existing_torch.py
pip install --no-build-isolation -v -e .
```
2. **Install additional dependencies:**
```bash
pip install qwen-vl-utils accelerate gradio gradio_toggle \
openai beautifulsoup4 ftfy bitsandbytes datasets optimum \
auto-gptq soundfile librosa webrtcvad
pip install -U transformers spaces modelscope
```
## 🎯 Usage
### Demo Applications
This project includes two demo applications for person detection:
1. **Direct vLLM Integration** (`app-vllm-gradio.py`): Loads vLLM directly in Python with a Gradio interface. This approach gives you full control over the model and is ideal for custom deployments.
2. **OpenAI-Compatible Server** (`app-openapi-vllm.py`): Uses the OpenAI-compatible API server. This approach is better for production deployments and allows easy integration with existing OpenAI-based applications.
### Starting the VLLM Server
#### Gemma-3 Model (Recommended for RTX 5090)
```bash
python3 -m vllm.entrypoints.openai.api_server \
--model google/gemma-3-12b-it \
--port 8000 \
--max_model_len 18000 \
--tensor-parallel-size 1 \
--gpu_memory_utilization 0.95 \
--max_num_seqs 1 \
--enforce-eager
```
#### Qwen2.5-VL Model
```bash
python3 -m vllm.entrypoints.openai.api_server \
--model Qwen/Qwen2.5-VL-7B-Instruct \
--port 8000 \
--max_model_len 24000 \
--tensor-parallel-size 1 \
--gpu_memory_utilization 0.95 \
--max_num_seqs 16 \
--enforce-eager
```
#### JoyCaption Model
```bash
python3 -m vllm.entrypoints.openai.api_server \
--model fancyfeast/llama-joycaption-beta-one-hf-llava \
--port 8000 \
--max_model_len 24000 \
--tensor-parallel-size 1 \
--gpu_memory_utilization 0.95 \
--max_num_seqs 16 \
--enforce-eager
```
### Video Captioning Web UI
1. **Start the server** as described above
2. **Launch the Gradio interface:**
```bash
python app-opernai-vid-cap-vllm.py
```
3. **Access the web interface** at `http://localhost:7860`
### API Usage Example
```python
import requests
import base64
from PIL import Image
# Server configuration
VLLM_SERVER = "http://localhost:8000"
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
# Single image analysis
image_base64 = encode_image("image.jpg")
payload = {
"model": "google/gemma-3-12b-it",
"messages": [{
"role": "user",
"content": [
{"type": "text", "text": "Describe this image in detail."},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}
]
}],
"max_tokens": 1000
}
response = requests.post(f"{VLLM_SERVER}/v1/chat/completions", json=payload)
print(response.json()['choices'][0]['message']['content'])
```
## 📖 Supported Models
| Model | Size | Context Length | Best For |
|-------|------|----------------|----------|
| `google/gemma-3-12b-it` | 12B | 18K | General purpose, optimal for 5090 |
| `Qwen/Qwen2.5-VL-7B-Instruct` | 7B | 24K | Video analysis, detailed descriptions |
| `fancyfeast/llama-joycaption-beta-one-hf-llava` | 7B | 24K | Creative image/video captioning |
## ⚙️ Configuration Options
### Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `MAX_JOBS` | `16` | Number of parallel build jobs |
| `NVCC_THREADS` | `4` | CUDA compilation threads |
| `TORCH_CUDA_ARCH_LIST` | `'12.0+PTX'` | Target CUDA architectures |
| `FLASH_ATTN_CUDA_ARCHS` | `120` | FlashAttention architectures |
| `GPU_MEMORY_UTILIZATION` | `0.95` | GPU memory usage ratio |
### Performance Tuning for RTX 5090
```bash
# Optimal settings for 32GB GDDR7 memory
--gpu_memory_utilization 0.95 \
--max_num_seqs 1 \
--enforce-eager \
--tensor-parallel-size 1
```
## 🐳 Docker Configuration
The Dockerfile is optimized for:
- **Base Image**: `pytorch/pytorch:2.7.0-cuda12.8-cudnn9-devel`
- **Python Version**: 3.11 (latest stable)
- **Memory Management**: Shared memory and IPC configuration for video processing
- **GPU Access**: NVIDIA Container Toolkit integration
- **Development Tools**: Includes VS Code server, Node.js, npm
### Build Customization
```dockerfile
# Add custom models or dependencies
RUN pip install your-custom-package
# Modify model configurations
ENV GPU_MEMORY_UTILIZATION=0.90
```
## 🔧 Troubleshooting
### Common Issues
**1. CUDA Out of Memory**
```bash
# Reduce memory utilization
--gpu_memory_utilization 0.8
# Use smaller batch sizes
--max_num_seqs 1
# Enable memory defragmentation
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512
```
**2. Port Conflicts**
```bash
# Change default ports
-p 8001:8000 # API server
-p 7861:7860 # Gradio UI
```
**3. WSL2 GPU Issues**
```bash
# Ensure NVIDIA drivers are installed in WSL2
nvidia-smi # Should show GPU info
# Update WSL2 kernel
wsl --update --web-download
```
**4. Model Download Issues**
```bash
# Set HuggingFace cache
export HF_HOME=/root/.cache/huggingface
# Login to HuggingFace (if needed)
huggingface-cli login
```
### Performance Optimization
- **Model Quantization**: Consider AWQ/INT4 quantization for larger models
- **Batch Size**: Start with `max_num_seqs=1` and increase gradually
- **Memory Pre-allocation**: Use `--enforce-eager` for predictable memory usage
- **Cache Management**: Mount model cache volume for faster startups
## 📊 Performance Benchmarks
### RTX 5090 Performance (Estimated)
| Model | Memory Usage | Token/s | Max Context |
|-------|--------------|---------|-------------|
| Gemma-3-12B | ~28GB | 50-80 | 18K |
| Qwen2.5-VL-7B | ~16GB | 80-120 | 24K |
| JoyCaption-7B | ~18GB | 70-100 | 24K |
*Benchmarks performed with standard video analysis prompts. Results may vary based on specific use case.*
## 🤝 Contributing
We welcome contributions! Please follow these guidelines:
1. **Fork** the repository
2. **Create** a feature branch
3. **Test** thoroughly on both WSL2 and Linux
4. **Submit** a pull request with detailed description
### Development Setup
```bash
# Install development dependencies
pip install -r __vllm/requirements/dev.txt
# Run tests
python -m pytest tests/
```
## 📄 License
This project builds upon [vLLM](https://github.com/vllm-project/vllm) and includes various open-source components. Please refer to the individual licenses:
- vLLM: Apache 2.0 License
- PyTorch: BSD-style License
- NVIDIA Components: NVIDIA Software License
## 🙋 Support & Community
- **Issues**: [GitHub Issues](https://github.com/your-repo/issues)
- **Discussions**: [GitHub Discussions](https://github.com/your-repo/discussions)
- **vLLM Community**: [Slack](https://slack.vllm.ai)
- **Documentation**: [vLLM Docs](https://docs.vllm.ai)
## 📝 Citation
If you use vLLM-5090 in your research or projects, please cite the original vLLM paper:
```bibtex
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
```
## 🔄 Changelog
### Latest Updates
- **v1.0 (2025-10-02)**: Docker container for RTX 5090 WSL2 deployment
- Ready-to-run vLLM container optimized for 5090 architecture
- Seamless WSL2 integration eliminating complex setup
- Includes video captioning demo to verify functionality
- CUDA 12.8, PyTorch 2.7.0, and complete dependency stack
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