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The purpose of the GCP Deployment Request is to allow our client`s Data Science team access Boxalino datasets, for the goal of running jupyter/notebook processes in the designed anaconda environments.

Environment Details

On a deployed application, the following stack is available:

  1. Python 3.7

  2. git

  3. Anaconda3

  4. pip / pip3

  5. setuptools

  6. papermill

  7. jupyter

  8. google-api-python-client & google SDK libraries

Integration steps

  1. Make a GCP Project Deployment Request with the Required Information information. Shortly, Boxalino will provide the project.

  2. Your user email (as the requester) will be given the editor role.

  3. Prepare the Required Files (project structure) and load them in a GCS bucket from the project.

  4. Prepare the content for the Application Launch.

  5. Launch the application.

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

Further tools will be provided which you can use to update the code running in the VM.

Integration Access

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

  1. the Compute Engine - launch more applications https://console.cloud.google.com/compute/instances

  2. the project`s BigQuery dataset (to store results, if required) https://console.cloud.google.com/bigquery

  3. the Google Cloud Storage (GCS) to load files & store logs https://console.cloud.google.com/storage/browser

  4. 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:

  1. BigQuery Data Editor : <client>_lab, <client>_views

  2. BigQuery Data Viewer : <client>_core, <client>_stage, <client>_reports, <client>_intelligence

Required Information

When contacting Boxalino with a GCP project deployment request, please provide the following information:

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#BigQuery-Datasets-Access )

4

labels

optional; the labels are used as project meta-information. see Labels

5

permissions

optional; by default, the requestor will have full access and can further share with others. see Permissions

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

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

Labels (optional)

Labels are key-value pairs meant to better organize the projects.

Lets imagine the scenario when the client has multiple Data Scientists working on different projects. Labeling them will allow better structure.

team:data-science
component:<main-application-name>
environment:production

More information on labels: https://cloud.google.com/resource-manager/docs/creating-managing-labels

Permissions (optional)

The permissions are added when the project is created.

  • By default, the requestor`s email has the project editor role

  • Once the project is released, the requestor can add more emails / users to the IAM policies of the project.

Here is a sample of provided permissions (optional):

user:dana@boxalino.com:roles/editor
user:dana@boxalino.com:roles/resourcemanager.projectIamAdmin
user:dana@boxalino.com:roles/compute.osLogin
user:dana@boxalino.com:roles/compute.osAdminLogin
user:dana@boxalino.com:roles/bigquery.dataOwner
serviceAccount:service-account-from-other-projects:roles/iam.serviceAccountUser
serviceAccount:service-account-from-other-projects:roles/bigquery.dataOwner
serviceAccount:service-account-from-other-projects:roles/bigquery.dataEditor

More information on permissions: https://cloud.google.com/iam/docs/understanding-roles

Required Files

1

instance.txt

properties for the VM machine (name, size, root path, 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)

The #1-#4 files must be named as instructed. They are used as a base for the application start-up.

instance.txt, requirements.txt and commands.txt must end with an empty line (they are parsed dynamically)

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;

use a zone which is in Europe.

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.

  • If your setup has been tested locally, you can save all the packages in the file with the command
    pip freeze > requirements.txt. 
    Keep in mind that in this case, requirements.txt file will list all packages that have been installed in virtual environment, regardless of where they came from

They will be installed on the conda environment.

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.

You can also include other commands same as the application has been tested on the local environment.

chmod -R 777 <home value from instance.txt>/*
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.

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

Application Launch

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

To launch the application, complete the form in the Application Launch service https://gcp-deploy-du3do2ydza-ew.a.run.app/application.


Provide the following information:

1

project ID

the project ID is unique;

the project ID is diplayed on the dashboard of your project https://console.cloud.google.com/home/dashboard

2

GCS bucket name

the bucket name where the Required Files are located (ex: gs://<project-name>-<app-name>);

the contents will be made available on the application as well.

the bucket must be located in EUROPE; either use EU(multi-region) or europe-west-1 (singural region)

the bucket name must be unique, for this purpose - we recommend that every bucket-name starts with your project name.

3

access code

as provided by Boxalino

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

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

You can further log/ SSH on the virtual machine and check out the output or inspect the contents.

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.

In the MONITORING view of your Application, you are able to track the resources available & consumed:

  1. CPU utilization

  2. Memory Utilization

  3. Disk Space Utilization

3. GCP Logs Explorer

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

One of these tools is the Logging Client.

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

Application Delete

If you want to stop the application, you can freely delete it from your Compute Engine view, or use the form provided by Boxalino https://gcp-deploy-du3do2ydza-ew.a.run.app/instance

BigQuery access

  1. As a data scientist, chances are that you have been provided with a Service Account (SA) to access the client`s private projects.

  2. The application is run by the project's own Compute Engine Service Account (CE SA).
    Because the project is in the scope of Boxalino, it will have direct read access to the client's datasets.

In order for the Application Launch to be able to access the client's own project dataset,
the CE SA will have to "impersonate" the SA in the client's project.

  1. Go to the client`s private project IAM & Admin menu

  2. Navigate to Service Accounts

  3. Locate the service account with access provided for the scope of the application deployment

  4. On top-right, click on “Show Info Panel”.

  5. Add the CE SA and set the “Service Account User” permission

The output of the application can be stored directly in the deployed GCP project scope (BigQuery, GCS, etc)

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