| from __future__ import annotations |
|
|
| import itertools |
| import random |
| from typing import Any, Dict, Optional |
|
|
| from edgeeda.agents.base import Action, Agent |
| from edgeeda.config import Config |
| from edgeeda.utils import sanitize_variant_prefix, stable_hash |
|
|
|
|
| class RandomSearchAgent(Agent): |
| def __init__(self, cfg: Config): |
| self.cfg = cfg |
| self.counter = 0 |
| self.variant_prefix = sanitize_variant_prefix(cfg.experiment.name) |
|
|
| def _sample_knobs(self) -> Dict[str, Any]: |
| out: Dict[str, Any] = {} |
| for name, spec in self.cfg.tuning.knobs.items(): |
| if spec.type == "int": |
| out[name] = random.randint(int(spec.min), int(spec.max)) |
| else: |
| out[name] = float(spec.min) + random.random() * (float(spec.max) - float(spec.min)) |
| out[name] = round(out[name], 3) |
| return out |
|
|
| def propose(self) -> Action: |
| self.counter += 1 |
| knobs = self._sample_knobs() |
| variant = f"{self.variant_prefix}_t{self.counter:05d}_{stable_hash(str(knobs))}" |
| fidelity = self.cfg.flow.fidelities[0] |
| return Action(variant=variant, fidelity=fidelity, knobs=knobs) |
|
|
| def observe(self, action: Action, ok: bool, reward: Optional[float], metrics_flat: Optional[Dict[str, Any]]) -> None: |
| |
| return |
|
|
