Distributed GPU Training

• Local, edge & cloud GPU resource management
• Hardware capability registration & auto-discovery
• Multi-GPU & multi-node distributed training
• Dynamic resource allocation & scheduling
• Cost-aware training placement strategies

Experiment Management

• Config-driven experiment definitions (YAML/JSON)
• Hyperparameter search (grid, random, Bayesian)
• Experiment versioning & reproducibility
• Real-time training metrics visualization
• Resource utilization monitoring per experiment

Model Benchmarking

• Automated evaluation on validation & test sets
• mAP, precision, recall & F1 score tracking
• Cross-model comparison dashboards
• Inference speed benchmarking (FPS, latency)
• Hardware-specific performance profiling

Regression Testing

• Automated regression test suites per model version
• Golden dataset evaluation on every training run
• Performance threshold gates (must-pass criteria)
• Side-by-side comparison with previous best model
• Automated alerts on metric degradation