PYTHON ON GOOGLE CLOUD PLATFORM

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Build, deploy, and monitor Python apps at scale. Use Google's APIs to get actionable insights from your data.

  • Dynamically scale capacity up or down according to traffic
  • Build, deploy, and manage containerized applications
  • Debug and fix issues quickly
  • Provision custom virtual machines or go serverless
  • Perform data analysis or build machine learning models using powerful APIs
A Broad Set Of Python APIs and Libraries for Both Developers and Data Scientists
Store and retrieve data from Cloud Storage
Query public data using BigQuery
Analyze images with Cloud Vision API
Extract meaning from text using Cloud Natural Language API
Store and retrieve data from Cloud Storage
1
Install
pip install google-cloud-storage
2
Set up a Cloud Platform Console project
  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

  2. Set up a GCP Console project.

    Set up a project

    Click to:

    • Create or select a project.
    • Enable the Cloud Storage API for that project.
    • Create a service account.
    • Download a private key as JSON.

    You can view and manage these resources at any time in the GCP Console.

3
Write your code
from google.cloud import storage

def upload_blob(bucket_name, source_file_name, destination_blob_name):
    """Uploads a file to the bucket."""
    storage_client = storage.Client()
    bucket = storage_client.get_bucket(bucket_name)
    blob = bucket.blob(destination_blob_name)

    blob.upload_from_filename(source_file_name)

    print('File {} uploaded to {}.'.format(
        source_file_name,
        destination_blob_name))
Query public data using BigQuery
1
Install
pip install google-cloud-bigquery
2
Set up a Cloud Platform Console project
  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

  2. Set up a GCP Console project.

    Set up a project

    Click to:

    • Create or select a project.
    • Enable the BigQuery API for that project.
    • Create a service account.
    • Download a private key as JSON.

    You can view and manage these resources at any time in the GCP Console.

3
Write your code
from google.cloud import bigquery


def query_stackoverflow():
    client = bigquery.Client()
    query_job = client.query("""
        SELECT
          CONCAT(
            'https://stackoverflow.com/questions/',
            CAST(id as STRING)) as url,
          view_count
        FROM `bigquery-public-data.stackoverflow.posts_questions`
        WHERE tags like '%google-bigquery%'
        ORDER BY view_count DESC
        LIMIT 10""")

    results = query_job.result()  # Waits for job to complete.

    for row in results:
        print("{} : {} views".format(row.url, row.view_count))


if __name__ == '__main__':
    query_stackoverflow()
Analyze images with Cloud Vision API
1
Install
pip install google-cloud-vision
2
Set up a Cloud Platform Console project
  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

  2. Set up a GCP Console project.

    Set up a project

    Click to:

    • Create or select a project.
    • Enable the Cloud Vision API for that project.
    • Create a service account.
    • Download a private key as JSON.

    You can view and manage these resources at any time in the GCP Console.

3
Write your code
import io
import os

# Imports the Google Cloud client library
from google.cloud import vision
from google.cloud.vision import types

# Instantiates a client
client = vision.ImageAnnotatorClient()

# The name of the image file to annotate
file_name = os.path.join(
    os.path.dirname(__file__),
    'resources/wakeupcat.jpg')

# Loads the image into memory
with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = types.Image(content=content)

# Performs label detection on the image file
response = client.label_detection(image=image)
labels = response.label_annotations

print('Labels:')
for label in labels:
    print(label.description)
Extract meaning from text using Cloud Natural Language API
1
Install
pip install google-cloud-language
2
Set up a Cloud Platform Console project
  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

  2. Set up a GCP Console project.

    Set up a project

    Click to:

    • Create or select a project.
    • Enable the Cloud Natural Language API for that project.
    • Create a service account.
    • Download a private key as JSON.

    You can view and manage these resources at any time in the GCP Console.

3
Write your code
# Imports the Google Cloud client library
from google.cloud import language
from google.cloud.language import enums
from google.cloud.language import types

# Instantiates a client
client = language.LanguageServiceClient()

# The text to analyze
text = u'Hello, world!'
document = types.Document(
    content=text,
    type=enums.Document.Type.PLAIN_TEXT)

# Detects the sentiment of the text
sentiment = client.analyze_sentiment(document=document).document_sentiment

print('Text: {}'.format(text))
print('Sentiment: {}, {}'.format(sentiment.score, sentiment.magnitude))
PYTHON QUICK STARTS
Quickly find and Debug issues

Google Stackdriver provides powerful monitoring, logging, and diagnostics. It equips you with insight into the health, performance, and availability of cloud-powered applications, enabling you to find and fix issues faster.

Google Stackdriver
Unified monitoring, logging, and diagnostics for applications on Google Cloud Platform and AWS.
Stackdriver Error Reporting
A walk through of getting an error alert and investigating the error in the Google Cloud Console.
Stackdriver Monitor, diagnose, and fix
In this video, Aja Hammerly uses Stackdriver to find and fix some subtle errors in an example app, and you'll learn how to use Stackdriver on your own projects.
Learn more
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