Federated Multi-Cloud Training Plan (HF + GCP + AWS)

TL;DR

Your instinct is strong: use all three clouds, but do not do real-time gradient sync across providers. Use a federated specialization model:

1. Pick what each site does best.
2. Pick the “units” (model artifacts) each site produces.
3. Upgrade continuously with a disciplined eval-and-promote loop.

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1) Specialization Matrix (“which base trains what”)

Platform	Best Use	Output Unit	Why It Wins
HuggingFace (AutoTrain + Hub)	Fast fine-tunes, experiment velocity, central model registry	Task adapters, LoRA checkpoints, model cards	Fast iteration + collaboration + strong artifact UX
Google Cloud Vertex AI	Embeddings, structured pipeline orchestration, data quality jobs	Embedding models, retrieval/index artifacts, eval reports	Strong managed pipelines + enterprise data workflows
AWS SageMaker	Production-scale training/inference, deployment hardening, endpoint ops	Distilled production model, inference package, latency benchmarks	Mature deployment stack and production controls

Command-center rule: HuggingFace Hub remains the single source of truth for model/version metadata.

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2) Unit Production Plan (“what each facility ships”)

HF “Barracks”

• Fine-tuned adapters for instruction following and domain tone.
• Lightweight experimental branches (rapid A/B branches).
• Output naming:
  • spiralverse/textgen-lora-v{n}
  • spiralverse/policy-adapter-v{n}

GCP “Factory”

• Embedding backbone tuning and retrieval quality optimization.
• Feature extraction pipelines and dataset quality reports.
• Output naming:
  • spiralverse/embedder-v{n}
  • spiralverse/retrieval-eval-v{n}

AWS “Starport”

• Distillation and inference-optimized model packaging.
• Stress/performance and reliability benchmark artifacts.
• Output naming:
  • spiralverse/runtime-distilled-v{n}
  • spiralverse/inference-benchmark-v{n}

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3) Upgrade Loop (“upgrade upgrade upgrade”)

Promote only if all gates pass:

1. Quality gate: task accuracy / retrieval score / safety metrics beat current baseline.
2. Latency gate: p95 and cost/token not worse than threshold.
3. Safety gate: policy + adversarial prompt suites pass.
4. Compatibility gate: nodal-network fusion API contract unchanged.

Promotion cadence

• Daily experiment ingest.
• Twice-weekly federation merge candidates.
• Weekly production promotion window.

Rollback policy

• Keep last 2 stable fused releases warm.
• Automatic rollback on SLO breach.

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Nodal Network Aggregation Design (No Live Sync)

Instead of cross-cloud gradient exchange, do artifact-level federation:

1. Pull latest validated artifacts from HF/GCP/AWS.
2. Run fusion layer:
   • Router/gating logic for prompt type.
   • Optional ensemble voting for safety-critical outputs.
   • Distillation pass for a single serving model when needed.
3. Publish unified model bundle + manifest:
   • spiralverse-ai-federated-vX.Y.Z

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Operational Script (actually runs the federation step)

Use training/federated_orchestrator.py to fuse provider artifacts into one promoted manifest:

python training/federated_orchestrator.py \
  --hf-manifest training/examples/hf_manifest.json \
  --gcp-manifest training/examples/gcp_manifest.json \
  --aws-manifest training/examples/aws_manifest.json \
  --output training/examples/fused_manifest.json

This is the concrete “command center” step that applies gates and produces one unified release descriptor for the nodal network.

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Phase 1 Starter (Colab-first, low friction)

Goal (48 hours)

• Establish one reproducible path from data -> fine-tune -> eval -> publish artifact metadata to Hub.

Steps

1. Prepare curated starter dataset + split policy (train/val/test).
2. Run one AutoTrain (or transformers Trainer) fine-tune in Colab.
3. Log metrics + model card.
4. Push checkpoint metadata to HuggingFace repo.
5. Generate federation manifest (manifest.json) with:
   • artifact IDs
   • metrics
   • intended role (textgen/embed/runtime)

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Practical Guardrails

• Avoid cross-cloud data egress loops; move artifacts and metrics, not raw training data, between clouds.
• Version everything with semantic tags and immutable manifests.
• Keep one canonical eval suite used by all platforms.
• Keep one “fused release checklist” for go/no-go decisions.

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Definition of Success

You are successful when:

• each cloud has a clear specialization,
• each run emits a standard artifact unit,
• upgrades are automatic but gated,
• and the nodal network can consume all outputs through one manifest contract.