Enterprise Automated GPU Training & Model Lifecycle Orchestration

Author: AI/ML Research & High-Performance Compute Division

#CUDA#PyTorch#RTX 5080#Model Registry#MLOps

1. Context & Problem Statement

Model decay in non-stationary financial markets requires frequent retraining. Manual GPU execution introduces human delays, inconsistent hyperparameter tracking, and production promotion risks.

2. Decision

We engineered an automated GPU training daemon on local high-performance CUDA workstations (NVIDIA RTX 5080) running rolling walk-forward training cycles, four-tier validation gating (Sharpe >= 1.5, CVaR bounds), ONNX compilation, and automatic model registry versioning.

3. Consequences & Trade-Offs

Positive Outcomes

Continuous model adaptation to evolving market regimes without manual intervention

Mathematically validated model promotion ensuring only alpha-generating models reach production

Standardized ONNX export enabling ultra-fast CPU inference on production VPS servers

Negative / Trade-Offs

Intense local GPU utilization requiring temperature and VRAM monitoring daemons

Mitigation Strategies

Adaptive batch sizing and non-blocking background process scheduling

Standards & References

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