c6b7a41bb4
1. Vectorized reward path (cwm/core.py): - Wired up existing compute_reward_vectorized from numba_core (was unused!) - Eliminates FeatureVector dict allocation + Python dict lookups on hot path - Numba path used when _HAS_NUMBA=True, Python fallback otherwise - Bit-identical: same math operations, just via numba JIT 2. Ray-based parallel eval (training/ray_eval.py): - Industrial multi-core execution via Ray (used by OpenAI/Anyscale) - ray.put() stores params/scenarios in shared object store (no pickle per worker) - Each worker: own CWM + planner, zero shared state, no races - Bit-identical: same seed + same params = same results regardless of worker count - PolicyEvaluator.evaluate_candidate: new use_ray=True parameter 3. VBT post-analysis (training/vbt_analysis.py): - episodes_to_pnl_array, episodes_to_metrics (Sharpe, Sortino, VaR, win_rate, etc.) - cross_asset_comparison, parameter_sensitivity - format_metrics for human-readable output - Analysis tool only — runs AFTER engine produces results 4. numba_core.py: added missing 'import math' for compute_reward_vectorized 13 new tests: vectorized reward bit-identity, Ray determinism, Ray result fields, VBT metrics structure, cross-asset comparison, parameter sensitivity, edge cases. Total: 1178 tests, 50 files, all green, zero regressions.
115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
"""
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Ray-based parallel episode evaluation — industrial multi-core execution.
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Uses Ray for conflict-free, bit-identical parallel execution across cores.
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Each worker gets its own CWM + planner — zero shared state, zero races.
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Bit-identity guarantee: same seed + same params + same scenarios = identical results,
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regardless of worker count. Each worker processes one scenario independently.
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Usage:
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from malkhut.training.ray_eval import RayEpisodeRunner
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runner = RayEpisodeRunner(workers=4)
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results = runner.run_episodes(params, scenarios, seed=42)
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"""
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from __future__ import annotations
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import os
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from typing import List, Optional, Sequence
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import ray
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from malkhut.cwm.core import MinimalCryptoLOBCWM
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from malkhut.state import FulfilmentPolicyParams
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from malkhut.training.cma_trainer import EpisodeResult, Scenario
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@ray.remote
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def _run_episode_remote(params_bytes: bytes, scenario_bytes: bytes,
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rng_seed: int, planner_type: str) -> EpisodeResult:
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"""Ray remote function. Each worker creates its own CWM + planner."""
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import pickle
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from malkhut.training.cma_trainer import PolicyEvaluator
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from malkhut.cwm.core import MinimalCryptoLOBCWM
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params = pickle.loads(params_bytes)
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scenario = pickle.loads(scenario_bytes)
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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return evaluator._run_episode(params, scenario, rng_seed, planner_type)
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class RayEpisodeRunner:
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"""Ray-based parallel episode evaluation.
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Conflict-free: each worker is an independent process with its own CWM.
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Bit-identical: same seed + same params = same results, regardless of workers.
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Industrial: uses Ray for multi-core scheduling, shared object store.
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"""
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def __init__(self, workers: int = 0) -> None:
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if workers <= 0:
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workers = min(os.cpu_count() or 4, 8)
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self.workers = workers
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self._initialized = False
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def _ensure_ray(self) -> None:
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if not self._initialized:
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if not ray.is_initialized():
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ray.init(
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num_cpus=self.workers,
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ignore_reinit_error=True,
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log_to_driver=False,
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)
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self._initialized = True
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def run_episodes(
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self,
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params: FulfilmentPolicyParams,
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scenarios: Sequence[Scenario],
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rng_seed: int = 0,
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planner_type: str = "sm_mcts",
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) -> List[EpisodeResult]:
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"""Run episodes in parallel via Ray. Returns results in original order."""
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if len(scenarios) <= 1:
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return self._run_sequential(params, scenarios, rng_seed, planner_type)
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import pickle
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self._ensure_ray()
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# Put shared data in Ray's object store (zero pickle per worker)
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params_ref = ray.put(pickle.dumps(params))
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scenario_refs = [ray.put(pickle.dumps(s)) for s in scenarios]
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# Launch all episodes as independent remote tasks
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futures = [
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_run_episode_remote.remote(
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params_ref, scenario_refs[i], rng_seed + i, planner_type
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)
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for i in range(len(scenarios))
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]
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# Gather results in order
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results = ray.get(futures)
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return list(results)
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def _run_sequential(
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self,
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params: FulfilmentPolicyParams,
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scenarios: Sequence[Scenario],
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rng_seed: int,
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planner_type: str,
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) -> List[EpisodeResult]:
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"""Sequential fallback for trivial cases."""
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from malkhut.training.cma_trainer import PolicyEvaluator
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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return [
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evaluator._run_episode(params, s, rng_seed + i, planner_type)
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for i, s in enumerate(scenarios)
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]
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def shutdown(self) -> None:
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"""Shutdown Ray if we initialized it."""
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if self._initialized and ray.is_initialized():
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ray.shutdown()
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self._initialized = False
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