avivl

def policy_checker(name):
    """
    Check if there is a need to take an action for a policy.
    Args:
        name: policy name

    Returns:

    """
    policy = PolicyModel.query(PolicyModel.Name == name).get()

avivl

cron:
- description: "Run scheduled tasks"
  url: /tasks/schedule
  schedule: every 60 minutes synchronized
  target: zorya-backend

avivl

"""Admin view """
import flask_admin
from flask_admin.contrib import appengine
from wtforms import validators
from collections import OrderedDict
from validators import GreaterEqualThan, SmallerEqualThan


class LastUpdatedOrderedDict(OrderedDict):
    """Store items in the order the keys were last added"""

avivl

    def calc_slope(self, minuets):
        """
        Calculate the slope of available memory change.

        :param: minuets how long to go back in time
        """

        met = metrics.Metrics(self.cluster_name)
        series = met.read_timeseries('YARNMemoryAvailablePercentage', minuets)
        retlist = []

avivl

   def calc_how_many(self):
        """
        Calculate how  many new nodes of each type we need.

        :return:
        """
        # No allocated memory so we don't need any workers above the
        # bare minimum
        if self.scale_to != -1:
            self.total = self.min_instances

avivl

@app.route('/tasks/check-load')
def check_load():
    """Entry point for cron task that launches a task for each cluster
    check cluster stats"""
    clusters = settings.get_all_clusters_settings()
    for cluster in clusters.iter():
        task = taskqueue.add(queue_name='shamash',
                             url="/monitors",
                             method='GET',
                             params={'cluster_name': cluster.Cluster})

avivl

cron:
- description: "check cluster load"
  url: /tasks/check-load
  schedule: every 2 minutes

avivl

def get_tf_record(sentence):
    global words
    # tokenize the pattern
    sentence_words = nltk.word_tokenize(sentence)
    # stem each word
    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
    # bag of words
    bow = [0]*len(words)
    for s in sentence_words:
        for i, w in enumerate(words):

avivl

# shuffle our features and turn into np.array as tensorflow  takes in numpy array
random.shuffle(training)
training = np.array(training)

# trainX contains the Bag of words and train_y contains the label/ category
train_x = list(training[:, 0])
train_y = list(training[:, 1])

# reset underlying graph data
tf.reset_default_graph()

avivl

SELECT review ,(communication+price+wait+quality) as score
FROM [[aviv-playground:telegrass_reviews.reviews]
WHERE RAND() < 0.7