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# Diamond CSGO AI Player - Deployment Guide
## 🚀 Deploying to Hugging Face Spaces
### Prerequisites
1. Hugging Face account
2. Model checkpoint files (`agent_epoch_00206.pt` or similar)
3. Git and Git LFS installed
### Step 1: Prepare Repository
1. **Clone/Fork this repository**
2. **Install Git LFS** (for large model files):
```bash
git lfs install
git lfs track "*.pt"
git add .gitattributes
```
3. **Add your model checkpoint**:
```bash
# Copy your trained model to the project root
cp /path/to/your/agent_epoch_00206.pt .
git add agent_epoch_00206.pt
git commit -m "Add trained model checkpoint"
```
### Step 2: Create Hugging Face Space
1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
2. Click "Create new Space"
3. Configure:
- **Space name**: `diamond-csgo-ai` (or your choice)
- **License**: Your preferred license
- **Space SDK**: `Docker`
- **Space hardware**:
- `CPU basic` (free) - for demo/testing
- `GPU T4 small` (paid) - for better performance
### Step 3: Upload Code
```bash
# Clone your new space
git clone https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
cd YOUR_SPACE_NAME
# Copy all project files
cp -r /path/to/diamond/* .
# Commit and push
git add .
git commit -m "Initial Diamond CSGO AI deployment"
git push
```
### Step 4: Configuration Files
Ensure these files are in your space root:
- `app.py` - Main FastAPI application
- `requirements.txt` - Python dependencies
- `Dockerfile` - Container configuration
- `README.md` - Space description
- `packages.txt` - System packages (if needed)
### Step 5: Model Setup
If your model is too large for Git (>100MB), use Git LFS or download from Hub:
```python
# In your app.py, add model downloading:
from huggingface_hub import hf_hub_download
def download_model():
return hf_hub_download(
repo_id="YOUR_USERNAME/YOUR_MODEL_REPO",
filename="agent_epoch_00206.pt"
)
```
## 🔧 Local Testing
Before deploying, test locally:
```bash
# Install dependencies
pip install -r requirements.txt
# Run tests
python test_web_app.py
# Start local server
python run_web_demo.py
```
Visit `http://localhost:7860` to test the interface.
## ⚙️ Configuration Options
### Hardware Requirements
| Tier | CPU | RAM | GPU | Performance |
|------|-----|-----|-----|-------------|
| Free | 2 vCPU | 16GB | None | Basic demo |
| Basic GPU | 4 vCPU | 16GB | T4 | Good performance |
| Premium | 8 vCPU | 32GB | A10G | Best experience |
### Environment Variables
Add these in your Space settings:
- `CUDA_VISIBLE_DEVICES=""` (for CPU-only)
- `PYTHONPATH="/app/src:/app"`
## 🎮 Usage Instructions
Once deployed, users can:
1. Visit your Space URL
2. Click on the game canvas
3. Use keyboard controls:
- **WASD** - Movement
- **Space** - Jump
- **Arrow keys** - Camera
- **1,2,3** - Weapons
- **R** - Reload
- **M** - Switch Human/AI mode
## 🐛 Troubleshooting
### Common Issues
1. **Model not loading**:
- Check checkpoint file exists
- Verify file size (<5GB for Spaces)
- Use Git LFS for large files
2. **Import errors**:
- Check `requirements.txt` is complete
- Verify Python path in `Dockerfile`
3. **Performance issues**:
- Use GPU hardware tier
- Reduce model complexity
- Lower frame rate
4. **WebSocket connection failed**:
- Check firewall settings
- Verify port 7860 is accessible
- Try different browser
### Debug Mode
Enable debug logging:
```python
import logging
logging.basicConfig(level=logging.DEBUG)
```
## 📊 Monitoring
Monitor your Space:
- View logs in HF Spaces interface
- Check GPU utilization
- Monitor user sessions
## 🔄 Updates
To update your deployed Space:
```bash
git pull # Get latest changes
git add .
git commit -m "Update to latest version"
git push # Automatically redeploys
```
## 💡 Tips for Success
1. **Start with CPU tier** to test basic functionality
2. **Use smaller models** for faster loading
3. **Test thoroughly locally** before deploying
4. **Monitor resource usage** to optimize costs
5. **Add usage instructions** in your Space README
## 🎯 Next Steps
After successful deployment:
- Share your Space with the community
- Collect user feedback
- Iterate on the interface
- Add new features like replay saving
- Consider multi-user support
Happy deploying! 🚀
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