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)
