def on_epoch_end(self, epoch, logs={}): self.loss.append(logs.get('loss')) self.acc.append(logs.get('acc')) publish_data({ 'loss': self.loss, 'acc': self.acc, })
def setStatus(self, status): from ipyparallel.datapub import publish_data context = self.context context.setVars(status=status) if not self.runLocal: publish_data(dict(context=context))
def on_epoch_end(self, epoch, logs): for k in logs: self.history[k].append(logs[k]) self.history["epoch"].append(epoch) publish_data({ "status": "Ended Epoch", "epoch": epoch, "history": self.history })
def on_train_begin(self, logs): self.history = { "acc": [], "loss": [], "val_acc": [], "val_loss": [], "epoch": [] } publish_data({"status": "Begin Training", "history": self.history})
def dummyTask(key): from os import getpid from time import sleep from ipyparallel.datapub import publish_data publish_data({key: 'running'}) sleep(2 * key) publish_data({key: 'finishing'}) sleep(2) return {'key': key, 'pid': getpid()}
def runTrial(argDict): from time import sleep from random import random from ipyparallel.datapub import publish_data def randomSleep(minSleep, maxSleep): delay = minSleep + random() * (maxSleep - minSleep) sleep(delay) argDict['slept'] = '%.2f' % delay runId = argDict['runId'] publish_data({runId: 'running'}) randomSleep(10, 15) publish_data({runId: 'finishing'}) sleep(2) return argDict
def on_epoch_begin(self, epoch, logs): publish_data({ "status": "Begin Epoch", "epoch": epoch, "history": self.history })
def on_train_end(self, logs): publish_data({"status": "Ended Training", "history": self.history})
def main(): while not getattr(user_ns, 'stop_publishing', False): publish_data(get_usage()) time.sleep(interval)
def main(): while not getattr(user_ns, "stop_publishing", False): publish_data(get_usage()) time.sleep(interval)