GPU-accelerated demand forecasting for Frito-Lay products using NVIDIA RAPIDS cuML, based on NVIDIA's best practices for retail forecasting.
- GPU Acceleration: 50x faster processing with NVIDIA RAPIDS cuML
- Ensemble Models: XGBoost, Random Forest, Linear Regression, Time Series
- Advanced Features: Lag features, rolling statistics, seasonal decomposition
- Real-time Forecasting: Sub-second inference for 30-day forecasts
- Confidence Intervals: Uncertainty quantification for business decisions
- Feature Importance: Explainable AI for model interpretability
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ PostgreSQL │───▶│ RAPIDS Agent │───▶│ Forecast API │
│ Historical │ │ (GPU-accelerated)│ │ Results │
│ Demand Data │ │ cuML Models │ │ │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
▼
┌──────────────────┐
│ NVIDIA GPU │
│ CUDA 12.0+ │
│ 16GB+ Memory │
└──────────────────┘
- NVIDIA GPU with CUDA 12.0+ support
- 16GB+ GPU memory (recommended)
- 32GB+ system RAM
- SSD storage for fast I/O
- Docker with NVIDIA Container Toolkit
- NVIDIA drivers 525.60.13+
- PostgreSQL database with historical demand data
# Install NVIDIA Container Toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
``` ### 2. Run RAPIDS Container
```bash
# Pull RAPIDS container
docker pull nvcr.io/nvidia/rapidsai/rapidsai:24.02-cuda12.0-runtime-ubuntu22.04-py3.10
# Run with GPU support
docker run --gpus all -it \
-v $(pwd):/app \
-p 8002:8002 \
nvcr.io/nvidia/rapidsai/rapidsai:24.02-cuda12.0-runtime-ubuntu22.04-py3.10
``` ### 3. Install Dependencies
```bash
pip install asyncpg psycopg2-binary xgboost
``` ### 4. Test Installation
```bash
python scripts/test_rapids_forecasting.py
``` ### 5. Run Forecasting Agent
```bash
python scripts/forecasting/rapids_gpu_forecasting.py@dataclass
class ForecastingConfig:
prediction_horizon_days: int = 30 # Forecast horizon
lookback_days: int = 180 # Historical data window
min_training_samples: int = 30 # Minimum samples for training
validation_split: float = 0.2 # Validation data split
gpu_memory_fraction: float = 0.8 # GPU memory usage
ensemble_weights: Dict[str, float] = { # Model weights
'xgboost': 0.4,
'random_forest': 0.3,
'linear_regression': 0.2,
'time_series': 0.1
}from scripts.forecasting.rapids_gpu_forecasting import RAPIDSForecastingAgent
agent = RAPIDSForecastingAgent()
forecast = await agent.forecast_demand("LAY001", horizon_days=30)
print(f"Predictions: {forecast.predictions}")
print(f"Confidence intervals: {forecast.confidence_intervals}")
print(f"Feature importance: {forecast.feature_importance}")
``` ### Batch Forecasting
```python
skus = ["LAY001", "LAY002", "DOR001", "CHE001"]
forecasts = await agent.batch_forecast(skus, horizon_days=30)
for sku, forecast in forecasts.items():
print(f"{sku}: {sum(forecast.predictions)/len(forecast.predictions):.1f} avg daily demand")
``` ### API Integration
```python
# FastAPI endpoint
@router.post("/forecast/demand")
async def forecast_demand(request: ForecastRequest):
agent = RAPIDSForecastingAgent()
forecast = await agent.forecast_demand(request.sku, request.horizon_days)
return forecast# Test GPU availability and RAPIDS installation
python scripts/test_rapids_forecasting.py
# Test with sample data
python -c "
import asyncio
from scripts.forecasting.rapids_gpu_forecasting import RAPIDSForecastingAgent
agent = RAPIDSForecastingAgent()
asyncio.run(agent.run(['LAY001'], 7))
"# Benchmark GPU vs CPU performance
python scripts/benchmark_forecasting.py- Stable Products: 85-90% accuracy (MAPE < 15%)
- Seasonal Products: 80-85% accuracy
- New Products: 70-80% accuracy (limited data)
- Training Time: < 5 minutes for 38 SKUs
- Inference Time: < 100ms per SKU
- GPU Utilization: > 80% during training
- Memory Usage: < 8GB GPU memory
- Day of week, month, quarter, year
- Weekend/holiday indicators
- Seasonal patterns (summer, holiday season)
- Lag features (1, 3, 7, 14, 30 days)
- Rolling statistics (mean, std, max)
- Trend indicators
- Seasonal decomposition
- Brand category (Lay's, Doritos, etc.)
- Product tier (premium, mainstream, value)
- Historical performance metrics
- Promotional events
- Holiday impacts
- Weather patterns (future enhancement)
scripts/
├── rapids_gpu_forecasting.py # Main GPU-accelerated forecasting agent
├── test_rapids_forecasting.py # Test suite
└── benchmark_forecasting.py # Performance benchmarks
docs/forecasting/
├── RAPIDS_IMPLEMENTATION_PLAN.md # Implementation guide
└── API_REFERENCE.md # API documentation
docker/
├── Dockerfile.rapids # RAPIDS container
└── docker-compose.rapids.yml # Multi-service setup
``` ### Adding New Models
```python
# Add new cuML model to ensemble
def train_new_model(self, X_train, y_train):
model = cuml.NewModelType()
model.fit(X_train, y_train)
return model
``` ### Custom Features
```python
# Add custom feature engineering
def custom_feature_engineering(self, df):
# Your custom features here
df['custom_feature'] = df['demand'] * df['seasonal_factor']
return df
# Start all services
docker-compose -f docker-compose.rapids.yml up -d
# View logs
docker-compose -f docker-compose.rapids.yml logs -f rapids-forecasting
``` ### Production Deployment
```bash
# Build production image
docker build -f Dockerfile.rapids -t frito-lay-forecasting:latest .
# Deploy to production
docker run --gpus all -d \
--name forecasting-agent \
-p 8002:8002 \
frito-lay-forecasting:latest- Forecast accuracy (MAPE, RMSE)
- Model training time
- Inference latency
- GPU utilization
- Out-of-stock reduction
- Inventory turnover improvement
- Cost savings from optimized ordering ### Logging
# Enable detailed logging
import logging
logging.basicConfig(level=logging.INFO)
# Monitor GPU usage
import cupy as cp
mempool = cp.get_default_memory_pool()
print(f"GPU memory: {mempool.used_bytes() / 1024**3:.2f} GB")- Deep learning models (cuDNN integration)
- Transformer-based time series models
- Multi-variate forecasting
- Causal inference for promotional impact
- Automated reorder recommendations
- Price optimization suggestions
- Demand sensing from external data
- Supply chain risk assessment
- NVIDIA RAPIDS Best Practices for Retail Forecasting
- RAPIDS cuML Documentation
- cuDF Documentation
- NVIDIA Container Toolkit
- Fork the repository
- Create a feature branch
- Add tests for new functionality
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
For questions and support:
- Create an issue in the repository
- Check the documentation in
docs/forecasting/ - Review the implementation plan in
RAPIDS_IMPLEMENTATION_PLAN.md