8af7e3bce8
launch_smoke_test.py: 10-min quick smoke. smoke_test_60min.py: 60-min full smoke with checkpoints.
350 lines
13 KiB
Python
350 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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MALKHUT Training Pipeline Launcher — 10-minute smoke test.
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Launches the full training pipeline with bounded resources:
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- CMA-ES training (generations, evals, time budget)
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- Strategy generation (genetic operators)
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- Pipeline logging (JSONL)
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- CPU/RAM monitoring
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- Results summary
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Usage:
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python -m malkhut.launch_smoke_test [--duration 600] [--evals 50]
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Naming convention for discovered strategies:
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{strategy_type}_{generation}_{timestamp}
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e.g., SM_MCTS_gen3_20260707_034500
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import resource
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import sys
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import time
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from dataclasses import dataclass, field
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from typing import Any, List, Mapping
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# ── Setup paths ──────────────────────────────────────────────────────────────
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_HERE = os.path.dirname(os.path.abspath(__file__))
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if _HERE not in sys.path:
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sys.path.insert(0, _HERE)
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from malkhut.state import FulfilmentPolicyParams
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from malkhut.training.pipeline import TrainingPipeline, PipelineConfig
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from malkhut.training.generator import StrategyGenerator, GeneratorConfig
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from malkhut.training.registry import PolicyRegistry
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from malkhut.training.cma_trainer import ScenarioFactory
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from malkhut.storage.ch_store import MalkhutCHStore
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# ── Configuration ────────────────────────────────────────────────────────────
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def _baseline() -> FulfilmentPolicyParams:
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return FulfilmentPolicyParams(
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version="baseline", ucb_c=1.414, max_sims=256, max_depth=3,
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rollout_depth=3, root_temperature=0.5, min_root_entropy=0.25,
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quote_offsets_ticks=(0, 1, 2), quote_size_fractions=(0.1, 0.25, 0.5),
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passive_ttl_ms=200, aggressive_ttl_ms=50,
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maker_edge_min_bps=0.5, cross_spread_edge_min_bps=5.0,
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adverse_toxicity_cancel_threshold=0.5, queue_churn_cancel_threshold=0.5,
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mae_tail_cut_bps=50.0, mfe_giveback_cut_fraction=0.5,
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max_time_in_loss_s=300.0, failed_recovery_cut_count=3,
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recovery_velocity_min_bps_per_s=0.0,
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max_symbol_notional_fraction=0.20, max_single_order_notional_fraction=0.05,
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reduce_when_global_up_fraction=0.30, session_profit_lock_fraction=0.02,
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w_expected_pnl=1.0, w_fill_probability=0.5, w_adverse_selection=2.0,
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w_queue_priority=0.5, w_inventory_risk=1.5, w_tail_loss=5.0,
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w_fee_quality=0.5, w_time_decay=0.3, w_policy_entropy=0.5,
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robust_tail_weight=2.0, toxic_counterparty_weight=3.0,
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low_liquidity_weight=2.0, latency_stress_weight=1.0,
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)
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@dataclass
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class SmokeTestResult:
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"""Results from a smoke test run."""
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duration_s: float
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generations_run: int
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total_evals: int
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best_score: float
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strategies_developed: int
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builtin_strategies_parsed: int
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genetic_strategies_evolved: int
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peak_cpu_pct: float
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peak_ram_mb: float
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avg_cpu_pct: float
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avg_ram_mb: float
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events_logged: int
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registry_records: int
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strategy_names: List[str] = field(default_factory=list)
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# ── CPU/RAM Monitor ──────────────────────────────────────────────────────────
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class ResourceMonitor:
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"""Track CPU and RAM usage during the run."""
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def __init__(self) -> None:
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self._samples: list[tuple[float, float]] = [] # (cpu%, ram_mb)
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self._start_time = time.time()
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def sample(self) -> tuple[float, float]:
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"""Sample current CPU% and RAM MB."""
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usage = resource.getrusage(resource.RUSAGE_SELF)
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ram_mb = usage.ru_maxrss / 1024 # KB → MB (Linux)
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# CPU% from /proc/self/stat (user + system time)
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try:
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with open("/proc/self/stat") as f:
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fields = f.read().split()
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utime = int(fields[13]) # user time (ticks)
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stime = int(fields[14]) # system time (ticks)
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total_ticks = utime + stime
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elapsed = time.time() - self._start_time
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# Approximate CPU% (ticks are ~10ms on Linux)
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cpu_pct = min(100.0, (total_ticks * 10.0) / max(elapsed * 1000.0, 1.0) * 100.0)
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except Exception:
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cpu_pct = 0.0
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self._samples.append((cpu_pct, ram_mb))
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return cpu_pct, ram_mb
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@property
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def peak_cpu(self) -> float:
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return max((s[0] for s in self._samples), default=0.0)
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@property
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def peak_ram(self) -> float:
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return max((s[1] for s in self._samples), default=0.0)
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@property
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def avg_cpu(self) -> float:
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if not self._samples:
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return 0.0
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return sum(s[0] for s in self._samples) / len(self._samples)
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@property
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def avg_ram(self) -> float:
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if not self._samples:
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return 0.0
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return sum(s[1] for s in self._samples) / len(self._samples)
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# ── Strategy Naming ──────────────────────────────────────────────────────────
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def name_strategy(
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strategy_type: str,
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generation: int,
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fitness: float,
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parent_ids: tuple[str, ...] = (),
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) -> str:
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"""
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Name a discovered strategy.
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Convention:
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{type}_gen{N}_{timestamp}
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Examples:
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SM_MCTS_gen3_20260707_034500
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UCB1_gen1_20260707_034515
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"""
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ts = time.strftime("%Y%m%d_%H%M%S")
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return f"{strategy_type}_gen{generation}_{ts}"
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# ── Main Smoke Test ──────────────────────────────────────────────────────────
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def run_smoke_test(duration_s: int = 600, max_evals: int = 50) -> SmokeTestResult:
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"""
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Run a 10-minute smoke test of the full training pipeline.
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Returns SmokeTestResult with metrics.
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"""
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print("=" * 70)
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print("MALKHUT SMOKE TEST — Training Pipeline")
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print(f"Duration: {duration_s}s | Max evals: {max_evals}")
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print("=" * 70)
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monitor = ResourceMonitor()
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t0 = time.time()
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# ── Setup ────────────────────────────────────────────────────────────────
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print("\n[1/5] Setting up infrastructure...")
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monitor.sample()
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store = MalkhutCHStore()
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store.ensure_tables()
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registry = PolicyRegistry(store=store)
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# ── Training Pipeline ────────────────────────────────────────────────────
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print("[2/5] Running training pipeline...")
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pipeline_config = PipelineConfig(
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max_generations=5,
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max_evals_per_generation=max_evals // 5,
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max_time_s=duration_s * 0.6, # 60% of time for training
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auto_promote=True,
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)
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pipeline = TrainingPipeline(
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config=pipeline_config, registry=registry,
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log_path=os.path.join(_HERE, "training.log"),
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)
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monitor.sample()
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pipeline_result = pipeline.run(
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incumbent=_baseline(),
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symbols=("BTCUSDT",),
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)
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monitor.sample()
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print(f" Generations: {pipeline_result.generations_run}")
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print(f" Evals: {pipeline_result.total_evals}")
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print(f" Best score: {pipeline_result.best_score:.4f}")
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print(f" Duration: {pipeline_result.duration_s:.1f}s")
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# ── Strategy Generation ──────────────────────────────────────────────────
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print("[3/5] Running strategy generator...")
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remaining_time = duration_s * 0.3 - pipeline_result.duration_s
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if remaining_time > 10:
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gen_config = GeneratorConfig(
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population_size=10,
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generations=2,
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tournament_size=3,
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elitism_count=2,
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)
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generator = StrategyGenerator(config=gen_config, registry=registry)
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scenarios = ScenarioFactory().build_suite(symbols=("BTCUSDT",), steps_per_scenario=5)
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gen_population = generator.evolve(_baseline(), scenarios)
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# Name discovered strategies
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strategy_names = []
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for genome in gen_population:
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name = name_strategy(
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genome.strategy_type.value,
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genome.generation,
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genome.fitness,
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)
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strategy_names.append(name)
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generator.add_to_pool(genome)
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genetic_count = len([g for g in gen_population if g.generation > 0])
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print(f" Population: {len(gen_population)} strategies")
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print(f" Genetic strategies evolved: {genetic_count}")
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else:
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gen_population = []
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strategy_names = []
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genetic_count = 0
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print(" Skipped (time budget exhausted)")
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monitor.sample()
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# ── Builtin Strategy Parsing ─────────────────────────────────────────────
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print("[4/5] Parsing builtin strategies...")
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from malkhut.training.dsl import StrategyDSLCompiler, list_builtin_strategies
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compiler = StrategyDSLCompiler()
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builtin_count = 0
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for name in list_builtin_strategies():
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from malkhut.training.dsl import get_builtin_strategy
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text = get_builtin_strategy(name)
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if text:
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template = compiler.compile(text)
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builtin_count += 1
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print(f" Builtin strategies parsed: {builtin_count}")
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# ── Summary ──────────────────────────────────────────────────────────────
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duration = time.time() - t0
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monitor.sample()
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print("[5/5] Summary...")
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print()
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print("=" * 70)
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print("SMOKE TEST RESULTS")
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print("=" * 70)
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print(f"Duration: {duration:.1f}s")
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print(f"Generations run: {pipeline_result.generations_run}")
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print(f"Total evals: {pipeline_result.total_evals}")
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print(f"Best score: {pipeline_result.best_score:.4f}")
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print(f"Strategies developed: {len(gen_population)} (genetic: {genetic_count})")
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print(f"Builtin strategies: {builtin_count}")
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print(f"Registry records: {registry.record_count}")
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print(f"Events logged: {len(pipeline_result.events)}")
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print()
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print("RESOURCE USAGE")
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print(f"Peak CPU: {monitor.peak_cpu:.1f}%")
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print(f"Avg CPU: {monitor.avg_cpu:.1f}%")
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print(f"Peak RAM: {monitor.peak_ram:.1f} MB")
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print(f"Avg RAM: {monitor.avg_ram:.1f} MB")
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print()
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print("STRATEGY NAMING CONVENTION")
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print(" {strategy_type}_gen{generation}_{timestamp}")
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print(" Examples:")
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for name in strategy_names[:5]:
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print(f" {name}")
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if len(strategy_names) > 5:
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print(f" ... and {len(strategy_names) - 5} more")
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print()
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print("STRATEGY TYPES DISCOVERED")
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if gen_population:
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types = set(g.strategy_type.value for g in gen_population)
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for t in types:
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count = sum(1 for g in gen_population if g.strategy_type.value == t)
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print(f" {t}: {count}")
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print("=" * 70)
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return SmokeTestResult(
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duration_s=duration,
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generations_run=pipeline_result.generations_run,
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total_evals=pipeline_result.total_evals,
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best_score=pipeline_result.best_score,
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strategies_developed=len(gen_population),
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builtin_strategies_parsed=builtin_count,
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genetic_strategies_evolved=genetic_count,
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peak_cpu_pct=monitor.peak_cpu,
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peak_ram_mb=monitor.peak_ram,
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avg_cpu_pct=monitor.avg_cpu,
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avg_ram_mb=monitor.avg_ram,
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events_logged=len(pipeline_result.events),
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registry_records=registry.record_count,
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strategy_names=strategy_names,
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)
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# ── Entry Point ──────────────────────────────────────────────────────────────
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def main():
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parser = argparse.ArgumentParser(description="MALKHUT Smoke Test")
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parser.add_argument("--duration", type=int, default=600, help="Duration in seconds")
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parser.add_argument("--evals", type=int, default=50, help="Max evaluations")
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args = parser.parse_args()
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result = run_smoke_test(duration_s=args.duration, max_evals=args.evals)
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# Write results to JSON
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output = {
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"duration_s": result.duration_s,
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"generations_run": result.generations_run,
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"total_evals": result.total_evals,
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"best_score": result.best_score,
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"strategies_developed": result.strategies_developed,
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"builtin_strategies_parsed": result.builtin_strategies_parsed,
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"genetic_strategies_evolved": result.genetic_strategies_evolved,
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"peak_cpu_pct": result.peak_cpu_pct,
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"peak_ram_mb": result.peak_ram_mb,
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"avg_cpu_pct": result.avg_cpu_pct,
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"avg_ram_mb": result.avg_ram_mb,
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"events_logged": result.events_logged,
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"registry_records": result.registry_records,
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"strategy_names": result.strategy_names,
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}
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with open(os.path.join(_HERE, "smoke_test_results.json"), "w") as f:
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json.dump(output, f, indent=2)
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print(f"\nResults saved to smoke_test_results.json")
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if __name__ == "__main__":
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main()
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