90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
"""
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Lab 5.10: CI/CD Pipeline
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Create a golden dataset and automated regression gate.
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"""
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import json
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import sys
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from typing import Any
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# TODO 1: Import or define your agent
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# from my_agent import run_agent
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# TODO 2: Define the CI/CD pipeline
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class AgentCIPipeline:
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"""
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CI/CD pipeline for agents.
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Stages:
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1. CHECKOUT — latest agent config
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2. EVAL — run against golden dataset
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3. GATE — pass@k >= threshold?
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4. PROMOTE — deploy if pass
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5. ROLLBACK — revert if fail
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"""
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def __init__(self, golden_dataset_path: str, threshold: float = 0.8):
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self.golden_dataset = json.load(open(golden_dataset_path))
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self.threshold = threshold
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def run_eval(self) -> dict:
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"""
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Run agent against golden dataset.
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Returns pass@1, pass@3, pass@5 scores.
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"""
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pass # TODO
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def check_gate(self, eval_results: dict) -> bool:
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"""
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Check if eval results pass the gate.
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Returns True if pass@3 >= threshold.
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"""
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pass # TODO
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def promote(self):
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"""Promote new agent config to production."""
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# TODO: Copy new config to production path
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# TODO: Restart agent service
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# TODO: Log deployment
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pass
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def rollback(self, reason: str):
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"""Rollback to previous agent config."""
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# TODO: Restore previous config
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# TODO: Log rollback reason
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# TODO: Alert operator
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pass
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def run(self):
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"""Execute the full CI/CD pipeline."""
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print("=" * 50)
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print("Agent CI/CD Pipeline")
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print("=" * 50)
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print("\n[CHECKOUT] Loading agent config...")
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# TODO: load current config
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print("\n[EVAL] Running golden dataset...")
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results = self.run_eval()
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print(f" pass@1: {results['pass@1']:.1%}")
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print(f" pass@3: {results['pass@3']:.1%}")
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print(f"\n[GATE] Threshold: {self.threshold:.0%}")
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if self.check_gate(results):
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print(" ✓ Gate passed. Promoting to production...")
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self.promote()
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print(" ✓ Deployment complete")
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else:
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print(" ✗ Gate failed. Rolling back...")
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self.rollback(f"pass@3 {results['pass@3']:.1%} < threshold {self.threshold:.0%}")
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print(" ✓ Rollback complete")
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sys.exit(1)
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if __name__ == "__main__":
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pipeline = AgentCIPipeline("golden_dataset.json", threshold=0.8)
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pipeline.run()
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