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Running on Zero
Running on Zero
| """Settings contract shared by the CPU and leased GPU dispatch paths.""" | |
| import copy | |
| import math | |
| DEFAULTS={'mode':'train','clean_min_laps':2,'population':4,'generations':1,'ticks':60,'mutation':.01,'seed':42, | |
| 'mutation_end':.01,'schedule':'fixed','selection':'best','parents':1,'elite':1, | |
| 'crossover':'none','decay':0,'adaptive_mutation':False,'control_hz':60,'workers':1, | |
| 'collision':False,'idle':False,'freeze':{'layers':[],'inputs':[],'outputs':[]}, | |
| 'rewards':[{'metric':'total_score','type':'default','weight':100}]} | |
| METRICS={'total_score','total_speed','total_abs_steering','total_actual_distance', | |
| 'total_drifting_amount','total_momentum_change_frequency','total_throttle_change_frequency', | |
| 'total_braking_frequency','total_distance_from_wall','total_distance_from_center', | |
| 'network_novelty','collision_count','best_lap_performance','total_racing_line_efficiency'} | |
| def validate_freeze(value): | |
| """Check the settings envelope; the uploaded network supplies exact bounds.""" | |
| if not isinstance(value,dict) or set(value)!={'layers','inputs','outputs'}: | |
| raise ValueError('Invalid freeze selection') | |
| result={} | |
| for key,low,high in (('layers',1,10),('inputs',0,19),('outputs',0,4)): | |
| items=value[key] | |
| if (not isinstance(items,list) or len(items)>high-low+1 or | |
| any(type(item) is not int or not low<=item<=high for item in items) or | |
| items!=sorted(set(items))): | |
| raise ValueError('Invalid frozen '+key) | |
| result[key]=items[:] | |
| return result | |
| def validate_config(value=None, *, work_limit=4_000_000): | |
| if value is None:return copy.deepcopy(DEFAULTS) | |
| if not isinstance(value,dict) or set(value)-set(DEFAULTS):raise ValueError('Unexpected training settings') | |
| c={**copy.deepcopy(DEFAULTS),**value} | |
| if type(c['control_hz']) is not int or c['control_hz']!=60: | |
| raise ValueError('New training and evaluation runs require control_hz=60; historical records are not converted') | |
| bounds={'population':(1,1024,True),'generations':(1,2000,True),'ticks':(1,18000,True), | |
| 'mutation':(0,4.2,False),'mutation_end':(0,4.2,False),'seed':(0,2147483647,True), | |
| 'clean_min_laps':(1,100,True),'parents':(1,1024,True),'elite':(0,1024,True),'decay':(0,1,False),'workers':(1,4,True)} | |
| for key,(low,high,integer) in bounds.items(): | |
| v=c[key] | |
| if type(v) not in (int,float) or not math.isfinite(v) or not low<=v<=high or (integer and type(v) is not int):raise ValueError('Invalid '+key) | |
| for key,choices in {'mode':('train','evaluate'),'schedule':('fixed','linear','geometric'),'selection':('best','tournament','roulette'), | |
| 'crossover':('none','single_point','uniform'),'control_hz':(60,)}.items(): | |
| if c[key] not in choices or (key=='control_hz' and type(c[key]) is not int):raise ValueError('Invalid '+key) | |
| for key in ('adaptive_mutation','collision','idle'): | |
| if type(c[key]) is not bool:raise ValueError('Invalid '+key) | |
| c['freeze']=validate_freeze(c['freeze']) | |
| if c['mode']=='train' and (c['parents']>c['population'] or c['elite']>c['population']):raise ValueError('Parents/elites exceed population') | |
| if c['mode']=='train' and (c['mutation']<=0 or (c['schedule']!='fixed' and c['mutation_end']<=0)):raise ValueError('This training runner requires positive mutation rates, including when continuing an uploaded network') | |
| # Echo the rate actually passed to the native CLI, not an unused form value. | |
| if c['schedule']=='fixed':c['mutation_end']=c['mutation'] | |
| rewards=c['rewards'] | |
| if not isinstance(rewards,list) or not 1<=len(rewards)<=len(METRICS):raise ValueError('Use one or more distinct supported rewards') | |
| seen=set();clean=[] | |
| for r in rewards: | |
| if not isinstance(r,dict) or set(r)!= {'metric','type','weight'}:raise ValueError('Unexpected reward fields') | |
| if not isinstance(r['metric'],str) or r['metric'] not in METRICS or r['type'] not in ('default','average') or r['metric'] in seen or type(r['weight']) is not int or abs(r['weight'])>100:raise ValueError('Invalid reward') | |
| seen.add(r['metric']);clean.append(dict(r)) | |
| if not any(r['weight'] for r in clean):raise ValueError('A reward weight must be nonzero') | |
| c['rewards']=clean | |
| if c['mode']=='evaluate': | |
| c.update(population=1,generations=1,mutation=0,mutation_end=0,decay=0,adaptive_mutation=False,parents=1,elite=0,crossover='none',selection='best',schedule='fixed',freeze={'layers':[],'inputs':[],'outputs':[]}) | |
| if c['population']*c['generations']>32_768:raise ValueError('Run exceeds 32,768 car-generation analysis rows') | |
| if c['population']*c['generations']*c['ticks']>work_limit:raise ValueError('Run exceeds backend vehicle-tick limit') | |
| return c | |