Multi-GPU Training System
Distributed training pipeline supporting 4-8 GPU setups with optimized communication and reliable checkpointing for large model training workflows.
GPU Optimization
Optimized multi-GPU training with efficient memory utilization and communication
Distributed Architecture
Scalable training system supporting 4-8 GPU configurations with fault tolerance
Performance Monitoring
Real-time training metrics and automated performance optimization
Technical Implementation
Built a production-grade distributed training system that scales efficiently across multiple GPUs with optimized communication patterns and robust fault tolerance mechanisms.
Key Features
- 85%+ GPU utilization across 4-8 GPU configurations
- Automated gradient synchronization with compressed communication
- Robust checkpointing system for long-running training jobs
- Dynamic batch size adjustment based on available memory
Performance Results
- Training throughput: 8,000+ tokens/second on 4x GPU setup
- Memory efficiency: Support for models up to 13B parameters
- Communication overhead: <8% of total training time
- Scaling efficiency: 80%+ linear scaling up to 8 GPUs
Technical Components
- PyTorch distributed training with optimized data loaders
- Custom monitoring and logging for training metrics
- Automated hyperparameter optimization
- Integration with MLOps pipeline for model versioning
- Kubernetes deployment with GPU resource management
Technologies Used
PyTorch
CUDA
Distributed
MLOps
Python
Docker
Kubernetes
Monitoring