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
pyarrow
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/ application 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 dataset is shared |
| 4 | labels | optional; the labels are used as project meta-information. see Labels |
| 5 | permissions | optional; by default, the requestor will have full access. 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:
add new permissions from the IAM Admin panel
create GCS bucket
Required Files
| 1 | instance.txt | properties for the VM machine (name, size, root path, etc) (see Instance) |
| 2 | env.yml | anaconda environment file |
| 3 | requirements.txt | environment requirements (for pip/anaconda install) (see Requirements) |
| 4 | commands.txt | a list of commands to be executed as part of your application run process |
| 5 | your jupyter files | the jupyter notebook |
The #1-#4 files must be named as instructed. They are used as a base for the application run.
instance.txt, requirements.txt and commands.txt must end with an empty line (they are parsed dynamically)
Application Launch
Once the Required Files are uploaded in a GCS bucket from the project, contact Boxalino.
Provide the following information:
| 1 | project name | |
| 2 | GCS bucket path | the path where the Required Files are located (ex: gs://project-name/app); the contents will be made available on the application as well. |
| 3 | launch date | optional; projects can be scheduled for launch at a later day (tomorrow, etc) |
BigQuery access
As a data science, 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.
The output of the application can be stored directly in the deployed GCP project scope (BigQuery, GCS, etc)
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