Project Demeter
Machine Learning
A machine learning benchmark project demonstrating optimized ML performance across different hardware configurations. Showcases efficient GPU utilization for medical image scanning with modest compute resources.
Project Demeter was designed to provide metrics on how machine learning workloads perform across various hardware setups, with a particular focus on medical imaging applications. By optimizing the software stack and resource utilization, we were able to achieve significant performance gains even on mid-range GPU configurations.
Key Technologies
Machine Learning
AWS
NVIDIA GPUs
Docker
Kaggle Datasets
Ubuntu Linux
Key Features
- Performance comparison across multiple GPU types
- Resource utilization optimization
- Efficient ML model deployment
- Software stack consistency across platforms
- Real-world medical image processing
Results & Impact
The implementation of Project Demeter resulted in:
- 32% reduction in processing time for medical image analysis
- Successful processing of 10,000+ medical scans per day on modest hardware
- Standardized benchmarking methodology for future ML hardware evaluations
- Cost models for optimal hardware selection based on workload requirements