bgweber

# load pandas, sklearn, and pyspark types and functions
import pandas as pd
from sklearn.linear_model import LogisticRegression
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.types import *

bgweber

import pandas as pd
from sklearn.linear_model import LogisticRegression
import flask

df = pd.read_csv("https://github.com/bgweber/Twitch/raw/master/Recommendations/games-expand.csv")
model = LogisticRegression()
model.fit(df.drop(['label'], axis=1), df['label'])

app = flask.Flask(__name__)

bgweber

# connect to the monitoring service 
from google.cloud import monitoring_v3
from google.oauth2 import service_account
import time

credentials = service_account.Credentials.from_service_account_file('serving.json')
client = monitoring_v3.MetricServiceClient(credentials = credentials)
project_name = client.project_path('serving-268422')

# create a custom metric

bgweber

# connect to the monitoring service 
from google.cloud import logging
logging_client = logging.Client(project = 'serving-268422', credentials = credentials)
logger = logging_client.logger('model_service')

# log a message to stack driver 
logger.log_text('Hello World!')

bgweber

import requests

result = requests.post("http://localhost:5000", \
      json = { 'G1':'1', 'G2':'0', 'G3':'0', 'G4':'0', 'G5':'0', \
               'G6':'0', 'G7':'0', 'G8':'0', 'G9':'0', 'G10':'0'}) 
print(result)
print(result.json())

bgweber

FROM ubuntu:latest
MAINTAINER Ben Weber

RUN apt-get update \  
  && apt-get install -y python3-pip python3-dev \  
  && cd /usr/local/bin \  
  && ln -s /usr/bin/python3 python
  
RUN pip3 install flask 
RUN pip3 install pandas 

bgweber

cat serving.json | sudo docker login -u _json_key --password-stdin https://us.gcr.io

sudo docker tag model_service us.gcr.io/serving-268422/model_service
sudo docker push us.gcr.io/serving-268422/model_service

bgweber

from google.cloud import monitoring_v3
from google.oauth2 import service_account
from google.cloud import logging
import socket    
import random
import time
import pandas as pd
from sklearn.linear_model import LogisticRegression
import flask
from multiprocessing import Value

bgweber

import pandas as pd
from sklearn.linear_model import LogisticRegression
import flask

df = pd.read_csv("https://github.com/bgweber/Twitch/raw/master/Recommendations/games-expand.csv")
model = LogisticRegression()
model.fit(df.drop(['label'], axis=1), df['label'])

app = flask.Flask(__name__)

bgweber

import requests

result = requests.post("http://localhost", \
      json = { 'G1':'1', 'G2':'0', 'G3':'0', 'G4':'0', 'G5':'0', \
               'G6':'0', 'G7':'0', 'G8':'0', 'G9':'0', 'G10':'0'})
print(result)
print(result.json())