# # define timerstart=torch.cuda.Event(enable_timing=True)end=torch.cuda.Event(enable_timing=True)# # start ptstart.record()### your code here ### # Waits for everything to finish runningtorch.cuda.synchronize()end.record()# # print timeprint(start.elapsed_time(end)) 注意在记录模块时...
import time def epoch_time(start_time, end_time): elapsed_time = end_time - start_time elapsed_mins = int(elapsed_time / 60) elapsed_secs = int(elapsed_time - (elapsed_mins * 60)) return elapsed_mins, elapsed_secs 然后,我们通过多个 epoch 来训练模型,每一个 epoch 是对训练和验证集中...
self._record_sxf[name][TimerKeys.END] = time.time() self._record_sxf[name][TimerKeys.ELAPSED] = self._record_sxf[name][TimerKeys.END] - self._record_sxf[name][TimerKeys.START] return self.store(name) if store else self._record_sxf[name][TimerKeys.ELAPSED] else: print(f'>> 不存...
def epoch_time(start_time, end_time): elapsed_time = end_time - start_time elapsed_mins = int(elapsed_time / 60) elapsed_secs = int(elapsed_time - (elapsed_mins * 60)) return elapsed_mins, elapsed_secs N_EPOCHS = 5 best_valid_loss = float('inf') for epoch in range(N_EPOCHS)...
elapsed_time = end_time - start_time elapsed_mins =int(elapsed_time /60) elapsed_secs =int(elapsed_time - (elapsed_mins *60))returnelapsed_mins, elapsed_secs N_EPOCHS =2CLIP =1best_valid_loss =float('inf')forepochinrange(N_EPOCHS): ...
(TimerKeys.ELAPSED)self._record_sxf[name][TimerKeys.START]=time.time()defstop(self,name,store=True):'''计算指定name的本次耗时'''torch.cuda.current_stream(self._device).synchronize()ifself._record_sxf.get(name)and self._record_sxf[name].get(TimerKeys.START):self._record_sxf[name][...
epoch_start_time = time.time() train(model) val_loss = evaluate(model, val_data) val_ppl = math.exp(val_loss) elapsed = time.time() - epoch_start_timeprint('-'*89)print(f'| end of epoch{epoch:3d}| time:{elapsed:5.2f}s | 'f'valid loss{val_loss:5.2f}| valid ppl{val_ppl...
elapsed_mins=int(elapsed_time/60)elapsed_secs=int(elapsed_time-(elapsed_mins*60))returnelapsed_mins,elapsed_secs N_EPOCHS=10CLIP=1best_valid_loss=float('inf')forepochinrange(N_EPOCHS):start_time=time.time()train_loss=train(model,train_iterator,optimizer,criterion,CLIP)valid_loss=evaluate(mode...
duration=start_event.elapsed_time(end_event)# 设置内存分配的比例,限制进程在MPS设备上的内存分配 torch.mps.set_per_process_memory_fraction(0) 还有一些附加的API,可以在记录、等待和流逝时间等事件上进行事件管理和创建自定义计时操作。对于MPS分配器的API,如设置每个进程的内存分数,使开发人员能够更加细粒度地...
return result, start.elapsed_time(end) / 1000 # Generates random input and targets data for the model, where `b` is # batch size. def generate_data(b): return ( torch.randn(b, 3, 128, 128).to(torch.float32).cuda(), torch.randint(1000, (b,)).cuda(), ...