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The purpose of this document is to allow our client`s Data Science team prepare and deploy their models in a Google Cloud environment.

Table of Contents

Steps

Project Deploy.
1. Make a GCP Project Deployment Request with the Required Information information.
2. a GCP project will be provided to the requestor
Billing Information
1. Set the billing account on the new project.
2. This is required in order to be able to use the GCP resources.
Application Content
1. Prepare the Required Files (application structure)
2. Load the content in a GCS bucket from the project.
Application Launch

Note

Your user email (as the requester) will be given the Editor, Owner and Project Billing Manager role.

Share access to other people who need access to the project.

Tip
The application is launched in a Virtual Machine in the project. The commands from commands.txt are executed. Additionally, you can SSH on the VM and update/check content.

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We, at Boxalino, will extend the services available for the Data Science needs (schedulers, instance management, etc). Make sure to review the documentation.

Integration Access

Because the application is launched in the scope of the project, the following Google Cloud tools can be used:

the Compute Engine - launch more applications https://console.cloud.google.com/compute/instances
the project`s BigQuery dataset (to store results, if required) https://console.cloud.google.com/bigquery
the Google Cloud Storage (GCS) to load files & store logs https://console.cloud.google.com/storage/browser
the Cloud Scheduler to create events (for automatic runs) https://console.cloud.google.com/cloudscheduler/start

BigQuery Datasets Access

Additionally, the project`s Compute Engine Default Service Account will have the following permissions on the client`s datasets in the Boxalino scope:

BigQuery Data Editor : <client>_lab, <client>_views
BigQuery Data Viewer : <client>_core, <client>_stage, <client>_reports, <client>_intelligence

If your application is meant to edit the subscriber properties, get familiar with the structure of the subscriber properties https://boxalino.atlassian.net/wiki/spaces/BPKB/pages/303825148/subscriber+properties

1. Project Deploy

Required Information

Create a project in the Boxalino ecosystem by completing the form https://gcp-deploy-du3do2ydza-ew.a.run.app/project

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1

project name

as will appear in your project's list
naming requirements: space, - and _ are allowed.

2

email

the requestor is the one managing the applications running on the project;

this email will receive messages (alert and notifications) for when the project is ready to be used;

** the email alerts for the VM / application run - is part of the instance.txt file, specific for every application launch

3

client name

(also known as the Boxalino account name) this is to ensure the access to the views, core & reports datasets (https://boxalino.atlassian.net/wiki/spaces/BPKB/pages/303792129/GCP+Project+Deployment#BigQuery303792129#BigQuery-Datasets-Access )

Once the project is created (2-3 min), the requestor will have access to it, in their Google Cloud Console.

Tip

As an editor on the project, the requestor will be able to:

access the project from Google Console https://console.cloud.google.com/
set a billing account for the project
share access to the project with other members in the IAM Admin panel
create GCS buckets and load different applications, which can be triggered

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2. Billing Information

In order to access the Google Cloud resources - a billing account must be set on the project.

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If you do not have access to a billing account, provide the Project Billing Manager role to someone who does. Use the IAM menu for this https://console.cloud.google.com/iam-admin/iam

3. Application Content

Info
In order to launch the application, the source files must be loaded in a Google Storage Bucket https://console.cloud.google.com/storage/browser

Note
The Google Storage Bucket must have an unique name. Due to this, we recommend that every bucket-name starts with your project name.

Required Files

1	instance.txt	properties for the Virtual Machine (VM) (name, size, home, etc) (see instance.txt)
2	requirements.txt	environment requirements (for pip/anaconda install) (see requirements.txt) `pip freeze > requirements.txt` - command to create the file from a tested environment
3	commands.txt	a list of commands to be executed as part of your application run process (see comands.txt) *can be left empty as well (if you chose to SSH on the VM and run your own processes from the project`s scope)
4	env.yml	(optional) anaconda environment file; if no file is provided - the environment is not created
5	your jupyter/python/application files	the content of your application (in python, jupyter notebooks, etc)

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Info

These files (and other required structures) must be uploaded in a GCS bucket.

The GCS bucket name is provided for the Application Launch Request

instance.txt

Property

Default

Required

Description

1

instance-name

project name

yes

the instance name is the VM name as appears in the Compute Engine view

2

machine-type

e2-micro

yes

the value depends on what the application needs: more CPU or more RAM? for options, please check the Google Cloud documentation

3

email-to

yes

the email is used once to receive an alert for when the VM is ready.

4

home

\/home\/project-name

no

the path on the server where the content of the GCS bucket is uploaded;
this is also used for the commands from the commands.txt file in order to launch/trigger your application execution.

alternatives: \/home\/<your-gcs-bucket> , \/srv\/app

_{when you SSH in the machine (ex: your email is}_{data-science-guru@boxalino-client.com}_{) , the VM creates a directory /home/data-science-guru (this is default for any server) so this is your local path;}

5

image-family

ubuntu-2004-lts

no

6

boot-disk-size

30

no

7

zone

europe-west1-b

no

this property can be left empty;

Note
use a zone which is in Europe.

8

accelerator-type

no

define this property if your VM requires GPU;
https://cloud.google.com/sdk/gcloud/reference/compute/accelerator-types

9

accelerator-count

no

define this property if your VM requires GPU

Code Block
instance-name:application-name machine-type:e2-micro email-to:data-science-guru@boxalino-client.com home:\/home\/project-name image-family:ubuntu-2004-lts boot-disk-size:30 zone:europe-west1-b

requirements.txt

This is a typical requirements.txt file for a jupyter/python application.

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Code Block
google-api-core==1.20.0 google-api-python-client==1.9.3 google-auth==1.17.2 google-auth-httplib2==0.0.3 [..] google-cloud-core==1.3.0 google-cloud-storage==1.29.0 google-pasta==0.2.0

commands.txt

You can include the commands to run the jupyter process on application launch.

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Code Block

chmod -R 777 <home value from instance.txt>/*
python3 <home value from instance.txt>/my-python-application.py
papermill <home value from instance.txt>/process.ipynb <home value from instance.txt>/process-output.ipynb

env.yml

The env.yml file is used in the setup step in order to create the anaconda environment.

Code Block

name: gcp-application-name
channels:
  - defaults
dependencies:
  - ca-certificates=2020.1.1=0
  - <a list of dependencies>
  - pip:
      - google-api-core==1.22.2
      - google-api-python-client==1.9.3
      - google-auth==1.17.2
      - <more-libraries required for the application>
prefix: /opt/conda/envs/gcp-application-env

4. Application Launch

Note
Before launching the application, make sure that the Required Files are uploaded in a GCS bucket.

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Tip
Once the application has been launched, a script will initialize the environment and load all your content from the GCS bucket.

Environment Details

On a deployed application, the following stack is available:

Python 3.7
git
Anaconda3
pip / pip3
setuptools
papermill
jupyter
google-api-python-client & google SDK libraries

Checking out the application state

1. SSH on the application VM

Go to your project Compute Engine page: https://console.cloud.google.com/compute/instances

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Code Block
tail -f -n 2000 /var/log/syslog

2. Monitoring tools from GCP

Go to your project Compute Engine page (https://console.cloud.google.com/compute/instances) and click on the running application instance. Switch to the Monitoring tab.

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CPU utilization
Memory Utilization
Disk Space Utilization

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3. GCP Logs Explorer

Google Cloud Platform provides a series of tools for monitoring the project resources.

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Go to your Logs Explorer https://console.cloud.google.com/logs/query? and review the application logs.

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Updating the application

Restart the application

Upon application restart (stop and start) - the GCS bucket content is being re-syncrhonized:

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Note
Restarting the application will not update the instance properties (the ones defined in instance.txt) - unless you manually edit them.

Resyncrhonize the bucket

If you do not want to delete & re-run the application launch, you can re-syncrhonize the content of the GCS bucket with your application home directory:

Code Block
sudo gsutil rsync -r gs://<BUCKET>/ <APPLICATION-PATH>