Deploy Docker containers in Google Cloud Platform
                     Deploy Docker containers 
in Google Cloud Platform
 Delivering complex software solutions inside a neat container is exactly what 
Docker has promised, and Machine Box has really benefited. This article shows 
you how to run Machine Box in Google Cloud Platform, but the technique 
works for any Docker image you want to deploy.
      You will need:
• Google Cloud SDK (gcloud command)
• Your favorite text editor
• A terminal
1. Create an account and sign in: Go to the Google Cloud Platform Console 
and log in. You will need a Google account to accept certain terms and 
conditions, etc.
2. Create a project: From the projects drop-down list, select Create a new 
project. For new users, you will probably be guided to do so with an assistant.

 Choose a project name and set up a billing account before clicking Create.While 
setting up your project, you can prepare it for deployment.
3. Your project files: Create a folder on your development machine, ideally 
corresponding to the name of the project you just created. Create two text 
files called app.yaml and Dockerfile.
Docker File: The Dockerfile describes the image you want to add. In the 
simplest distribution, simply specify the image you want to generate:
   FROM machinebox/facebox
In the case of Facebox, we also need to include the MB_KEY environment 
variable, which we can do in this file:
FROM machinebox/facebox
ENV MB_KEY=YOUR_KEY_HERE
app.yaml: The app.yaml file is where we will configure the deployment.
runtime: custom
env: flex
service: default
threadsafe: yes
network:
forwarded_ports:
- 80:8080
  automatic_scaling:
       min_num_instances: 1
       max_num_instances: 10
      cool_down_period_sec: 120 # default value
       cpu_utilization:
        target_utilization: 0.5
   resources:
     cpu: 1
     memory_gb: 2
    disk_size_gb: 10
# volumes:
# - name: ramdisk1
#   volume_type: tmpfs
#   size_gb: 0.5
This file describes an automatic scaling image (between 1 and 10 instances at 
a time) with a single processor, 2 GB of RAM and a 10 GB disk, 
transmitting port 8080 to 80 of the machine box (container) so that it is 
accessible on the Web.
4. Deploy: Deploy your first version with the following command line in the 
terminal:
   gcloud app deploy app.yaml -v v1
After a while, the image will be deployed and available.
Go to https://saved project name/ to access the image.
Introducing Machine Box: 
 Machine Box puts state-of-the-art machine learning capabilities in Docker 
containers so developers like you can easily integrate natural language 
processing, facial detection, object recognition, and more. in your own 
applications very quickly. The boxes are designed for scale, so when your 
app really takes off, simply add other boxes horizontally, ad infinitum and 
beyond.
   For More Information on Google Cloud 
Platform Click Here
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