Local GPU Training Pipeline & Production Model Synchronization Architecture

Author: MLOps & Infrastructure Security Group

#SCP#Key-Based Auth#Model Sync#ONNX#Hot-Swap

1. Context & Problem Statement

Training deep neural networks on cloud GPU instances creates high operational costs, while training on local hardware requires a secure, automated mechanism to promote and hot-reload weights into production.

2. Decision

We engineered a secure SCP synchronization pipeline utilizing dedicated SSH keypairs to transfer validated ONNX weights and manifest metadata from local RTX 5080 workstations to production Docker volumes, triggering an in-memory watchdog hot-swap in the live ML Engine.

3. Consequences & Trade-Offs

Positive Outcomes

Over 95% reduction in cloud GPU compute expenses

Sub-minute promotion cycle from local training convergence to production inference

Zero-downtime hot-reloading without dropping active client connections

Negative / Trade-Offs

Requires maintaining secure local-to-cloud SSH keypair access

Mitigation Strategies

Restricted IP allowances and ed25519 cryptographic key insulation

Standards & References

Need ADR Governance in Your Engineering Org?

We help engineering teams establish versioned ADR repositories, C4 models, and automated CI test gates.

Learn More About Architecture Sprints