77 lines
2.0 KiB
Markdown
77 lines
2.0 KiB
Markdown
# OSVauco ML Layer
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ML-laget gir OPAX-agentene parallell kjøring, delt state-kontroll og strukturert
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telemetri. Implementert i tre faser.
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## Arkitektur
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```
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OPAX /run-endepunkt
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│
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▼
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build_agent_dag() ← Dask DAG (task_graph.py)
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│
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┌────┴────┬──────────────┐
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▼ ▼ ▼
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agent_A agent_B ... agent_N ← kjøres parallelt
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│ │ │
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└────┬────┴──────────────┘
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▼
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AgentStateStore ← Parameter Server state (state_store.py)
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│
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▼
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telemetry JSONL ← rådatagrunnlag for ML-2 og ML-3
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```
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## Filer
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| Fil | Ansvar |
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| `task_graph.py` | Dask DAG — parallell agent-scheduling |
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| `state_store.py` | Thread-safe PS-inspirert state store |
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| `telemetry.py` | Strukturert JSONL-logging per agent-kall |
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| `__init__.py` | Pakke-eksporter |
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## Scheduler-valg
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| Miljø | `scheduler`-parameter |
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| Dev / unit-test | `'synchronous'` |
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| Lokal multi-tråd | `'threads'` |
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| Cloud Run (lett) | `'threads'` |
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| Full skala | `'distributed'` (krever Dask cluster) |
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## Fase-oversikt
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| Fase | Innhold | Status |
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|---|---|---|
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| ML-1 | Dask DAG + PS state + telemetri | ✅ Implementert |
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| ML-2 | Inkrementell læring (dask-ml) + ASHA-tuning (Ray Tune) | 🔜 Neste |
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| ML-3 | XGBoost feature importance + PBT | 🔜 Fremtidig |
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## Bruk
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```python
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from ml import build_agent_dag, execute_dag, get_store, log_agent_call
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# Bygg og kjør DAG
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tasks = build_agent_dag([billing_agent, rag_agent], payload)
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results = execute_dag(tasks, scheduler="threads")
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# State store
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store = get_store()
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store.push("billing-agent", "last_cost_usd", 12.5)
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cost = store.pull("billing-agent", "last_cost_usd")
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# Telemetri
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log_agent_call(
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agent_id="billing-agent",
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input_payload=payload,
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output=result,
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model_used="gemini-2.0-flash",
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mode="standard",
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duration_s=0.4,
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success=True,
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)
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```
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