Credentials

Make sure to go through Setup first!

Let's check that the environment variables have been set... We'll just try one:



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GPRED_PROJECT_ID = %env GPRED_PROJECT_ID

Google Cloud Storage

Let's see if we can create a bucket with boto (using credentials, project ID, etc. specified in boto config file)...



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import datetime
now = datetime.datetime.now()
BUCKET_NAME = 'test_' + GPRED_PROJECT_ID + now.strftime("%Y-%m-%d") # lower case letters required, no upper case allowed

import boto
import gcs_oauth2_boto_plugin
project_id = %env GPRED_PROJECT_ID
header_values = {"x-goog-project-id": project_id}
boto.storage_uri(BUCKET_NAME, 'gs').create_bucket(headers=header_values)

Listing existing buckets...



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uri = boto.storage_uri('', 'gs')
# If the default project is defined, call get_all_buckets() without arguments.
for bucket in uri.get_all_buckets(headers=header_values):
  print bucket.name

Upload a file to the new bucket



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import os
os.system("echo 'hello!' > newfile")
filename = 'newfile'
boto.storage_uri(BUCKET_NAME + '/' + filename, 'gs').new_key().set_contents_from_file(open(filename))

See contents of the bucket on the web interface (URL will be outputted below)



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print "https://console.developers.google.com/project/" + project_id + "/storage/browser/" + BUCKET_NAME + "/?authuser=0"

Google Prediction

Initialize API wrapper



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import googleapiclient.gpred as gpred
oauth_file = %env GPRED_OAUTH_FILE
api = gpred.api(oauth_file)

Making predictions against a hosted model

Let's use the sample.sentiment hosted model (made publicly available by Google)



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# projectname has to be 414649711441
prediction_request = api.hostedmodels().predict(project='414649711441',
                                     hostedModelName='sample.sentiment',
                                     body={'input': {'csvInstance': ['I hate that stuff is so stupid']}})

result = prediction_request.execute()
# We can print the raw result
print result