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def delete_labels(client, label_id_list): | |
res = client.execute(''' | |
mutation DeleteLabels($label_id_list: [ID!]) { | |
deleteLabels( | |
labelIds: $label_id_list | |
){ | |
id | |
deleted | |
} |
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.idea/ | |
data/ | |
model/ | |
logdir/ |
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""" | |
#TODO change | |
BigBang-DataScience.com | |
""" | |
import random | |
import tensorflow as tf | |
import dataUtils |
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apiVersion: v1 | |
kind: ServiceAccount | |
metadata: | |
labels: | |
k8s-addon: cluster-autoscaler.addons.k8s.io | |
k8s-app: cluster-autoscaler | |
name: cluster-autoscaler | |
namespace: kube-system | |
--- | |
apiVersion: rbac.authorization.k8s.io/v1beta1 |
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import tensorflow as tf | |
import numpy as np | |
import random | |
def main(): | |
label_index = { | |
'Iris-setosa': 0, | |
'Iris-versicolor': 1, | |
'Iris-virginica': 2 |
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float pulsewidth = 10; // 1/100 a second | |
long count = 0; | |
long goal = 1024; | |
void setup() { | |
pinMode(13, OUTPUT); | |
} | |
void loop() { | |
delay(pulsewidth); |
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import sys | |
import uuid | |
from apache_beam.io import iobase | |
from apache_beam.transforms import PTransform; | |
from apache_beam.utils.pipeline_options import PipelineOptions | |
from apache_beam.io.gcp.datastore.v1.datastoreio import ReadFromDatastore | |
from apache_beam.io.gcp.datastore.v1.datastoreio import WriteToDatastore | |
from apache_beam.metrics.metric import MetricsFilter | |
from apache_beam.utils.pipeline_options import GoogleCloudOptions | |
from apache_beam.utils.pipeline_options import PipelineOptions |
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import MySQLdb | |
from MySQLdb.cursors import SSCursor | |
from google.cloud import datastore | |
from google.cloud.exceptions import TooManyRequests | |
import json | |
import time | |
def chunks(l, n): | |
"""Yield successive n-sized chunks from l.""" |
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from apache_beam.metrics.metric import MetricsFilter | |
from apache_beam.utils.pipeline_options import GoogleCloudOptions | |
from apache_beam.utils.pipeline_options import PipelineOptions | |
from apache_beam.utils.pipeline_options import SetupOptions | |
from apache_beam.utils.pipeline_options import StandardOptions | |
import googledatastore | |
import apache_beam as beam | |
import json | |
import time |
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from mpl_toolkits.mplot3d import axes3d | |
import matplotlib.pyplot as plt | |
from matplotlib import cm | |
def drange(x, y, jump): | |
while x < y: | |
yield float(x) | |
x += jump |
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