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:
Python 3.7
git
Anaconda3
pip / pip3
setuptools
papermill
jupyter
google-api-python-client & google SDK libraries
Integration steps
Make a GCP Project Deployment Request with the Required Information information. Shortly, Boxalino will provide the project.
Your user email (as the requester) will be given the editor role.
Prepare the Required Files (project structure) and load them in a GCS bucket from the project.
Provide to Boxalino the information for the Application Launch
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:
the Compute Engine - launch more applications
the project`s BigQuery dataset (to store results, if required)
the Google Cloud Storage (GCS) to load files & store logs
the Cloud Scheduler to create events (for automatic runs)
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; |
| 2 | 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; | |
| 3 | client name | (also known as the Boxalino account name) this is to ensure the access to the views, core & reports datasets |
| 4 | optional; the labels are used as project meta-information. see Labels | |
| 5 | 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:
access the project from Google Console https://console.cloud.google.com/
add new permissions from the IAM Admin panel
create GCS bucket
Labels
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
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)
|
| 3 | commands.txt | a list of commands to be executed as part of your application run process (see comands.txt) |
| 4 | env.yml | (optional) anaconda environment file; |
| 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; |
| 5 | image-family | ubuntu-2004-lts | no | |
| 6 | boot-disk-size | 30 | no | |
| 7 | zone | europe-west1-b | no |
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 <root-dir 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
Once the Required Files are uploaded in a GCS bucket from the project, contact Boxalino.
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); the contents will be made available on the application as well. the bucket name must be unique, for this purpose - we recommend that every bucket-name starts with your project name. |
| 3 | launch date | optional; projects can be scheduled for launch at a later day (tomorrow, etc) |
BigQuery access
As a data scientist, chances are that you have been provided with a Service Account (SA) to access the client`s private projects.
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.
Go to the client`s private project IAM & Admin menu
Navigate to Service Accounts
Locate the service account with access provided for the scope of the application deployment
On top-right, click on “Show Info Panel”.
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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