Skip to content

Latest commit

 

History

History
2526 lines (1838 loc) · 83.1 KB

deploy_on_foundation_v4.0.0.md

File metadata and controls

2526 lines (1838 loc) · 83.1 KB
Raw

Deploying on top of existing Foundation v.4.0.0

Overview

To deploy a simple machine learning application, you must first have a terraform-example-foundation v4.0.0 instance set up. The following steps will guide you through the additional configurations required on top of the foundation.

Requirements

Code

  • terraform-example-foundation v4.0.0 deployed until at least step 4-projects.
  • You must have role Service Account User (roles/iam.serviceAccountUser) on the Terraform Service Accounts created in the foundation Seed Project. The Terraform Service Accounts have the permissions to deploy each step of the foundation. Service Accounts:
    • sa-terraform-bootstrap@<SEED_PROJECT_ID>.iam.gserviceaccount.com.
    • sa-terraform-env@<SEED_PROJECT_ID>.iam.gserviceaccount.com
    • sa-terraform-net@<SEED_PROJECT_ID>.iam.gserviceaccount.com
    • sa-terraform-proj@<SEED_PROJECT_ID>.iam.gserviceaccount.com

Software

Install the following dependencies:

Google Cloud SDK Configuration

Terraform must have Application Default Credentials configured, to configure it run:

gcloud auth application-default login

Directory Layout and Terraform Initialization

For these instructions we assume that:

  • The foundation was deployed using Cloud Build.

  • Every repository, excluding the policies repositories, should be on the production branch and terraform init should be executed in each one.

  • The following layout should exists in your local environment since you will need to make changes in these steps. If you do not have this layout, please checkout the source repositories for the foundation steps following this layout.

    gcp-bootstrap
gcp-environments
gcp-networks
gcp-org
gcp-policies
gcp-projects

  • Also checkout the terraform-google-enterprise-genai repository at the same level.

The final layout should look like this:

gcp-bootstrap
gcp-environments
gcp-networks
gcp-org
gcp-policies
gcp-projects
terraform-google-enterprise-genai

Policies

Update gcloud terraform vet policies

the first step is to update the gcloud terraform vet policies constraints to allow usage of the APIs needed by the Blueprint and add more policies. The constraints are located in the repository:

  • gcp-policies

IMPORTANT: Please note that the steps below are assuming you are checked out on terraform-google-enterprise-genai/.

  • Copy cmek_settings.yaml from this repository to the policies repository:
cp policy-library/policies/constraints/cmek_settings.yaml ../gcp-policies/policies/constraints/cmek_settings.yaml
  • Copy network_enable_firewall_logs.yaml from this repository to the policies repository:
cp policy-library/policies/constraints/network_enable_firewall_logs.yaml ../gcp-policies/policies/constraints/network_enable_firewall_logs.yaml
  • Copy require_dnssec.yaml from this repository to the policies repository:
cp policy-library/policies/constraints/require_dnssec.yaml ../gcp-policies/policies/constraints/require_dnssec.yaml
  • On gcp-policies change serviceusage_allow_basic_apis.yaml and add the following apis:
     - "aiplatform.googleapis.com"
     - "bigquerymigration.googleapis.com"
     - "bigquerystorage.googleapis.com"
     - "containerregistry.googleapis.com"
     - "dataflow.googleapis.com"
     - "dataform.googleapis.com"
     - "deploymentmanager.googleapis.com"
     - "notebooks.googleapis.com"
     - "composer.googleapis.com"
     - "containerscanning.googleapis.com"

Add files to tracked on gcp-policies repository, commit and push the code:

cd ../gcp-policies

git add policies/constraints/*.yaml
git commit -m "Add ML policies constraints"
git push origin $(git branch --show-current)

1-org: Create Machine Learning Organization Policies and Organization Level Keys

This step corresponds to modifications made to 1-org step on foundation.

IMPORTANT: Please note that the steps below are assuming you are checked out on terraform-google-enterprise-genai/ and that gcp-org repository is checked out on production branch.

cd ../terraform-google-enterprise-genai
  • Copy Machine Learning modules from this repo to gcp-org repository.
cp -r 1-org/modules/ml_kms_keyring ../gcp-org/modules
cp -r 1-org/modules/ml-org-policies ../gcp-org/modules
  • Create ml_ops_org_policy.tf file on gcp-org/envs/shared path:
cp docs/assets/terraform/1-org/ml_ops_org_policy.tf ../gcp-org/envs/shared
  • Create ml_key_rings.tf file on gcp-org/envs/shared path:
cp docs/assets/terraform/1-org/ml_key_rings.tf ../gcp-org/envs/shared
  • Edit gcp-org/envs/shared/remote.tf and add the following value to locals:
projects_step_terraform_service_account_email = data.terraform_remote_state.bootstrap.outputs.projects_step_terraform_service_account_email
  • Edit gcp-org/envs/shared/variables.tf and add the following variables:
variable "keyring_regions" {
  description = "Regions to create keyrings in"
  type        = list(string)
  default = [
    "us-central1",
    "us-east4"
  ]
}

variable "keyring_name" {
  description = "Name to be used for KMS Keyring"
  type        = string
  default     = "ml-org-keyring"
}
  • Edit gcp-org/envs/shared/outputs.tf and add the following output:
output "key_rings" {
  description = "Keyring Names created"
  value       = module.kms_keyring.key_rings
}

Add files to git on gcp-org, commit and push code:

cd ../gcp-org

git add .

git commit -m "Add ML org policies and Org-level key"
git push origin production

2-environment: Create environment level logging keys, logging project and logging bucket

This step corresponds to modifications made to 2-environment step on foundation.

Please note that the steps below are assuming you are checked out on terraform-google-enterprise-genai/.

cd ../terraform-google-enterprise-genai

development branch

  • Go to gcp-environments repository, and check out on development branch.
cd ../gcp-environments

git checkout development
  • Return to terraform-google-enterprise-genai repo.
cd ../terraform-google-enterprise-genai
  • Copy Machine Learning modules from this repo to gcp-environments repository.
cp -r 2-environments/modules/ml_kms_keyring ../gcp-environments/modules
  • Create ml_key_rings.tf file on gcp-environments/modules/env_baseline path:
cp docs/assets/terraform/2-environments/ml_key_rings.tf ../gcp-environments/modules/env_baseline
  • Create ml_logging.tf file on gcp-environments/modules/env_baseline path:
cp docs/assets/terraform/2-environments/ml_logging.tf ../gcp-environments/modules/env_baseline
  • On gcp-environments/modules/env_baseline/variables.tf add the following variables:
variable "keyring_name" {
  description = "Name to be used for KMS Keyring"
  type        = string
  default     = "ml-env-keyring"
}

variable "keyring_regions" {
  description = "Regions to create keyrings in"
  type        = list(string)
  default = [
    "us-central1",
    "us-east4"
  ]
}

variable "kms_prevent_destroy" {
  description = "Wheter to prevent keyring and keys destruction. Must be set to false if the user wants to disable accidental terraform deletions protection."
  type        = bool
  default     = true
}

variable "gcs_bucket_prefix" {
  description = "Bucket Prefix"
  type        = string
  default     = "bkt"
}

variable "gcs_logging_bucket_location" {
  description = "Location of environment logging bucket"
  type        = string
  default     = "us-central1"
}

variable "gcs_logging_retention_period" {
  description = "Retention configuration for environment logging bucket"
  type = object({
    is_locked             = bool
    retention_period_days = number
  })
  default = null
}

variable "gcs_logging_key_rotation_period" {
  description = "Rotation period in seconds to be used for KMS Key"
  type        = string
  default     = "7776000s"
}
  • On gcp-environments/modules/env_baseline/variables.tf add the following field to project_budget specification:
logging_budget_amount                       = optional(number, 1000)
logging_alert_spent_percents                = optional(list(number), [1.2])
logging_alert_pubsub_topic                  = optional(string, null)
logging_budget_alert_spend_basis            = optional(string, "FORECASTED_SPEND")

This will result in a variable similar to the variable specified below:

variable "project_budget" {
  description = <<EOT
  Budget configuration for projects.
  budget_amount: The amount to use as the budget.
  alert_spent_percents: A list of percentages of the budget to alert on when threshold is exceeded.
  alert_pubsub_topic: The name of the Cloud Pub/Sub topic where budget related messages will be published, in the form of `projects/{project_id}/topics/{topic_id}`.
  alert_spend_basis: The type of basis used to determine if spend has passed the threshold. Possible choices are `CURRENT_SPEND` or `FORECASTED_SPEND` (default).
  EOT
  type = object({
    base_network_budget_amount                  = optional(number, 1000)
    base_network_alert_spent_percents           = optional(list(number), [1.2])
    base_network_alert_pubsub_topic             = optional(string, null)
    base_network_budget_alert_spend_basis       = optional(string, "FORECASTED_SPEND")
    restricted_network_budget_amount            = optional(number, 1000)
    restricted_network_alert_spent_percents     = optional(list(number), [1.2])
    restricted_network_alert_pubsub_topic       = optional(string, null)
    restricted_network_budget_alert_spend_basis = optional(string, "FORECASTED_SPEND")
    monitoring_budget_amount                    = optional(number, 1000)
    monitoring_alert_spent_percents             = optional(list(number), [1.2])
    monitoring_alert_pubsub_topic               = optional(string, null)
    monitoring_budget_alert_spend_basis         = optional(string, "FORECASTED_SPEND")
    secret_budget_amount                        = optional(number, 1000)
    secret_alert_spent_percents                 = optional(list(number), [1.2])
    secret_alert_pubsub_topic                   = optional(string, null)
    secret_budget_alert_spend_basis             = optional(string, "FORECASTED_SPEND")
    kms_budget_amount                           = optional(number, 1000)
    kms_alert_spent_percents                    = optional(list(number), [1.2])
    kms_alert_pubsub_topic                      = optional(string, null)
    kms_budget_alert_spend_basis                = optional(string, "FORECASTED_SPEND")
    logging_budget_amount                       = optional(number, 1000)
    logging_alert_spent_percents                = optional(list(number), [1.2])
    logging_alert_pubsub_topic                  = optional(string, null)
    logging_budget_alert_spend_basis            = optional(string, "FORECASTED_SPEND")
  })
  default = {}
}
  • On gcp-environments/modules/env_baseline/remote.tf add the following value to locals:
projects_step_terraform_service_account_email = data.terraform_remote_state.bootstrap.outputs.projects_step_terraform_service_account_email
  • On gcp-environments/envs/development/outputs.tf add the following outputs:
output "env_log_project_id" {
  description = "Project ID of the environments log project"
  value       = module.env.env_logs_project_id
}

output "env_log_project_number" {
  description = "Project Number of the environments log project"
  value       = module.env.env_logs_project_number
}

output "env_log_bucket_name" {
  description = "Name of environment log bucket"
  value       = module.env.env_log_bucket_name
}

output "env_kms_project_number" {
  description = "Project Number for environment Cloud Key Management Service (KMS)."
  value       = module.env.env_kms_project_number
}

output "key_rings" {
  description = "Keyring Names created"
  value       = module.env.key_rings
}
  • On gcp-environments/modules/env_baseline/outputs.tf add the following outputs:
output "key_rings" {
  description = "Keyring Names created"
  value       = module.kms_keyring.key_rings
}

output "env_kms_project_number" {
  description = "Project number for environment Cloud Key Management Service (KMS)."
  value       = module.env_kms.project_number
}

output "env_logs_project_id" {
  description = "Project ID for environment logging."
  value       = module.env_logs.project_id
}

output "env_logs_project_number" {
  description = "Project number for environment logging."
  value       = module.env_logs.project_number
}

output "env_log_bucket_name" {
  description = "Name of environment log bucket"
  value       = google_storage_bucket.log_bucket.name
}
  • Commit and push files to git repo.
cd ../gcp-environments

git add .

git commit -m "Create env-level keys and env-level logging"

git push origin development

nonproduction branch

  • Go to gcp-environments repository, and check out on nonproduction branch.
cd ../gcp-environments

git checkout nonproduction
  • Return to terraform-google-enterprise-genai repo.
cd ../terraform-google-enterprise-genai
  • Copy Machine Learning modules from this repo to gcp-environments repository.
cp -r 2-environments/modules/ml_kms_keyring ../gcp-environments/modules
  • Create ml_key_rings.tf file on gcp-environments/modules/env_baseline path:
cp docs/assets/terraform/2-environments/ml_key_rings.tf ../gcp-environments/modules/env_baseline
  • Create ml_logging.tf file on gcp-environments/modules/env_baseline path:
cp docs/assets/terraform/2-environments/ml_logging.tf ../gcp-environments/modules/env_baseline
  • On gcp-environments/modules/env_baseline/variables.tf add the following variables:
variable "keyring_name" {
  description = "Name to be used for KMS Keyring"
  type        = string
  default     = "ml-env-keyring"
}

variable "keyring_regions" {
  description = "Regions to create keyrings in"
  type        = list(string)
  default = [
    "us-central1",
    "us-east4"
  ]
}

variable "kms_prevent_destroy" {
  description = "Wheter to prevent keyring and keys destruction. Must be set to false if the user wants to disable accidental terraform deletions protection."
  type        = bool
  default     = true
}

variable "gcs_bucket_prefix" {
  description = "Bucket Prefix"
  type        = string
  default     = "bkt"
}

variable "gcs_logging_bucket_location" {
  description = "Location of environment logging bucket"
  type        = string
  default     = "us-central1"
}

variable "gcs_logging_retention_period" {
  description = "Retention configuration for environment logging bucket"
  type = object({
    is_locked             = bool
    retention_period_days = number
  })
  default = null
}

variable "gcs_logging_key_rotation_period" {
  description = "Rotation period in seconds to be used for KMS Key"
  type        = string
  default     = "7776000s"
}
  • On gcp-environments/modules/env_baseline/variables.tf add the following field to project_budget specification:
logging_budget_amount                       = optional(number, 1000)
logging_alert_spent_percents                = optional(list(number), [1.2])
logging_alert_pubsub_topic                  = optional(string, null)
logging_budget_alert_spend_basis            = optional(string, "FORECASTED_SPEND")

This will result in a variable similar to the variable specified below:

variable "project_budget" {
  description = <<EOT
  Budget configuration for projects.
  budget_amount: The amount to use as the budget.
  alert_spent_percents: A list of percentages of the budget to alert on when threshold is exceeded.
  alert_pubsub_topic: The name of the Cloud Pub/Sub topic where budget related messages will be published, in the form of `projects/{project_id}/topics/{topic_id}`.
  alert_spend_basis: The type of basis used to determine if spend has passed the threshold. Possible choices are `CURRENT_SPEND` or `FORECASTED_SPEND` (default).
  EOT
  type = object({
    base_network_budget_amount                  = optional(number, 1000)
    base_network_alert_spent_percents           = optional(list(number), [1.2])
    base_network_alert_pubsub_topic             = optional(string, null)
    base_network_budget_alert_spend_basis       = optional(string, "FORECASTED_SPEND")
    restricted_network_budget_amount            = optional(number, 1000)
    restricted_network_alert_spent_percents     = optional(list(number), [1.2])
    restricted_network_alert_pubsub_topic       = optional(string, null)
    restricted_network_budget_alert_spend_basis = optional(string, "FORECASTED_SPEND")
    monitoring_budget_amount                    = optional(number, 1000)
    monitoring_alert_spent_percents             = optional(list(number), [1.2])
    monitoring_alert_pubsub_topic               = optional(string, null)
    monitoring_budget_alert_spend_basis         = optional(string, "FORECASTED_SPEND")
    secret_budget_amount                        = optional(number, 1000)
    secret_alert_spent_percents                 = optional(list(number), [1.2])
    secret_alert_pubsub_topic                   = optional(string, null)
    secret_budget_alert_spend_basis             = optional(string, "FORECASTED_SPEND")
    kms_budget_amount                           = optional(number, 1000)
    kms_alert_spent_percents                    = optional(list(number), [1.2])
    kms_alert_pubsub_topic                      = optional(string, null)
    kms_budget_alert_spend_basis                = optional(string, "FORECASTED_SPEND")
    logging_budget_amount                       = optional(number, 1000)
    logging_alert_spent_percents                = optional(list(number), [1.2])
    logging_alert_pubsub_topic                  = optional(string, null)
    logging_budget_alert_spend_basis            = optional(string, "FORECASTED_SPEND")
  })
  default = {}
}
  • On gcp-environments/modules/env_baseline/remote.tf add the following value to locals:
projects_step_terraform_service_account_email = data.terraform_remote_state.bootstrap.outputs.projects_step_terraform_service_account_email
  • On gcp-environments/envs/nonproduction/outputs.tf add the following outputs:
output "env_log_project_id" {
  description = "Project ID of the environments log project"
  value       = module.env.env_logs_project_id
}

output "env_log_project_number" {
  description = "Project Number of the environments log project"
  value       = module.env.env_logs_project_number
}

output "env_log_bucket_name" {
  description = "Name of environment log bucket"
  value       = module.env.env_log_bucket_name
}

output "env_kms_project_number" {
  description = "Project Number for environment Cloud Key Management Service (KMS)."
  value       = module.env.env_kms_project_number
}

output "key_rings" {
  description = "Keyring Names created"
  value       = module.env.key_rings
}
  • On gcp-environments/modules/env_baseline/outputs.tf add the following outputs:
output "key_rings" {
  description = "Keyring Names created"
  value       = module.kms_keyring.key_rings
}

output "env_kms_project_number" {
  description = "Project number for environment Cloud Key Management Service (KMS)."
  value       = module.env_kms.project_number
}

output "env_logs_project_id" {
  description = "Project ID for environment logging."
  value       = module.env_logs.project_id
}

output "env_logs_project_number" {
  description = "Project number for environment logging."
  value       = module.env_logs.project_number
}

output "env_log_bucket_name" {
  description = "Name of environment log bucket"
  value       = google_storage_bucket.log_bucket.name
}
  • Commit and push files to git repo.
cd ../gcp-environments

git add .

git commit -m "Create env-level keys and env-level logging"

git push origin nonproduction

production branch

  • Go to gcp-environments repository, and check out on production branch.
cd ../gcp-environments

git checkout production
  • Return to terraform-google-enterprise-genai repo.
cd ../terraform-google-enterprise-genai
  • Copy Machine Learning modules from this repo to gcp-environments repository.
cp -r 2-environments/modules/ml_kms_keyring ../gcp-environments/modules
  • Create ml_key_rings.tf file on gcp-environments/modules/env_baseline path:
cp docs/assets/terraform/2-environments/ml_key_rings.tf ../gcp-environments/modules/env_baseline
  • Create ml_logging.tf file on gcp-environments/modules/env_baseline path:
cp docs/assets/terraform/2-environments/ml_logging.tf ../gcp-environments/modules/env_baseline
  • On gcp-environments/modules/env_baseline/variables.tf add the following variables:
variable "keyring_name" {
  description = "Name to be used for KMS Keyring"
  type        = string
  default     = "ml-env-keyring"
}

variable "keyring_regions" {
  description = "Regions to create keyrings in"
  type        = list(string)
  default = [
    "us-central1",
    "us-east4"
  ]
}

variable "kms_prevent_destroy" {
  description = "Wheter to prevent keyring and keys destruction. Must be set to false if the user wants to disable accidental terraform deletions protection."
  type        = bool
  default     = true
}

variable "gcs_bucket_prefix" {
  description = "Bucket Prefix"
  type        = string
  default     = "bkt"
}

variable "gcs_logging_bucket_location" {
  description = "Location of environment logging bucket"
  type        = string
  default     = "us-central1"
}

variable "gcs_logging_retention_period" {
  description = "Retention configuration for environment logging bucket"
  type = object({
    is_locked             = bool
    retention_period_days = number
  })
  default = null
}

variable "gcs_logging_key_rotation_period" {
  description = "Rotation period in seconds to be used for KMS Key"
  type        = string
  default     = "7776000s"
}
  • On gcp-environments/modules/env_baseline/variables.tf add the following field to project_budget specification:
logging_budget_amount                       = optional(number, 1000)
logging_alert_spent_percents                = optional(list(number), [1.2])
logging_alert_pubsub_topic                  = optional(string, null)
logging_budget_alert_spend_basis            = optional(string, "FORECASTED_SPEND")

This will result in a variable similar to the variable specified below:

variable "project_budget" {
  description = <<EOT
  Budget configuration for projects.
  budget_amount: The amount to use as the budget.
  alert_spent_percents: A list of percentages of the budget to alert on when threshold is exceeded.
  alert_pubsub_topic: The name of the Cloud Pub/Sub topic where budget related messages will be published, in the form of `projects/{project_id}/topics/{topic_id}`.
  alert_spend_basis: The type of basis used to determine if spend has passed the threshold. Possible choices are `CURRENT_SPEND` or `FORECASTED_SPEND` (default).
  EOT
  type = object({
    base_network_budget_amount                  = optional(number, 1000)
    base_network_alert_spent_percents           = optional(list(number), [1.2])
    base_network_alert_pubsub_topic             = optional(string, null)
    base_network_budget_alert_spend_basis       = optional(string, "FORECASTED_SPEND")
    restricted_network_budget_amount            = optional(number, 1000)
    restricted_network_alert_spent_percents     = optional(list(number), [1.2])
    restricted_network_alert_pubsub_topic       = optional(string, null)
    restricted_network_budget_alert_spend_basis = optional(string, "FORECASTED_SPEND")
    monitoring_budget_amount                    = optional(number, 1000)
    monitoring_alert_spent_percents             = optional(list(number), [1.2])
    monitoring_alert_pubsub_topic               = optional(string, null)
    monitoring_budget_alert_spend_basis         = optional(string, "FORECASTED_SPEND")
    secret_budget_amount                        = optional(number, 1000)
    secret_alert_spent_percents                 = optional(list(number), [1.2])
    secret_alert_pubsub_topic                   = optional(string, null)
    secret_budget_alert_spend_basis             = optional(string, "FORECASTED_SPEND")
    kms_budget_amount                           = optional(number, 1000)
    kms_alert_spent_percents                    = optional(list(number), [1.2])
    kms_alert_pubsub_topic                      = optional(string, null)
    kms_budget_alert_spend_basis                = optional(string, "FORECASTED_SPEND")
    logging_budget_amount                       = optional(number, 1000)
    logging_alert_spent_percents                = optional(list(number), [1.2])
    logging_alert_pubsub_topic                  = optional(string, null)
    logging_budget_alert_spend_basis            = optional(string, "FORECASTED_SPEND")
  })
  default = {}
}
  • On gcp-environments/modules/env_baseline/remote.tf add the following value to locals:
projects_step_terraform_service_account_email = data.terraform_remote_state.bootstrap.outputs.projects_step_terraform_service_account_email
  • On gcp-environments/envs/production/outputs.tf add the following outputs:
output "env_log_project_id" {
  description = "Project ID of the environments log project"
  value       = module.env.env_logs_project_id
}

output "env_log_project_number" {
  description = "Project Number of the environments log project"
  value       = module.env.env_logs_project_number
}

output "env_log_bucket_name" {
  description = "Name of environment log bucket"
  value       = module.env.env_log_bucket_name
}

output "env_kms_project_number" {
  description = "Project Number for environment Cloud Key Management Service (KMS)."
  value       = module.env.env_kms_project_number
}

output "key_rings" {
  description = "Keyring Names created"
  value       = module.env.key_rings
}
  • On gcp-environments/modules/env_baseline/outputs.tf add the following outputs:
output "key_rings" {
  description = "Keyring Names created"
  value       = module.kms_keyring.key_rings
}

output "env_kms_project_number" {
  description = "Project number for environment Cloud Key Management Service (KMS)."
  value       = module.env_kms.project_number
}

output "env_logs_project_id" {
  description = "Project ID for environment logging."
  value       = module.env_logs.project_id
}

output "env_logs_project_number" {
  description = "Project number for environment logging."
  value       = module.env_logs.project_number
}

output "env_log_bucket_name" {
  description = "Name of environment log bucket"
  value       = google_storage_bucket.log_bucket.name
}
  • Commit and push files to git repo.
cd ../gcp-environments

git add .

git commit -m "Create env-level keys and env-level logging"

git push origin production

N.B. Read this before continuing further

A logging project will be created in every environment (development, non-production, production) when running this code. This project contains a storage bucket for the purposes of project logging within its respective environment. This requires the [email protected] group permissions for the storage bucket. Since foundations has more restricted security measures, a domain restriction constraint is enforced. This restraint will prevent the google cloud-storage-analytics group to be added to any permissions. In order for this terraform code to execute without error, manual intervention must be made to ensure everything applies without issue.

You must disable the contraint, assign the permission on the bucket and then apply the contraint again. This step-by-step presents you with two different options (Option 1 and Option 2) and only one of them should be executed.

The first and the recommended option is making the changes by using gcloud cli, as described in Option 1.

Option 2 is an alternative to gcloud cli and relies on Google Cloud Console.

Option 1: Use gcloud cli to disable/enable organization policy constraint

You will be doing this procedure for each environment (development, non-production & production)

development environment configuration

  1. Configure the following variable below with the value of gcp-environments repository path.

    export GCP_ENVIRONMENTS_PATH=INSERT_YOUR_PATH_HERE

    Make sure your git is checked out to the development branch by running git checkout development on GCP_ENVIRONMENTS_PATH.

    (cd $GCP_ENVIRONMENTS_PATH && git checkout development)

  2. Retrieve the bucket name and project id from terraform outputs.

    export ENV_LOG_BUCKET_NAME=$(terraform -chdir="$GCP_ENVIRONMENTS_PATH/envs/development" output -raw env_log_bucket_name)
export ENV_LOG_PROJECT_ID=$(terraform -chdir="$GCP_ENVIRONMENTS_PATH/envs/development" output -raw env_log_project_id)

  3. Validate the variable values.

    echo env_log_project_id=$ENV_LOG_PROJECT_ID
echo env_log_bucket_name=$ENV_LOG_BUCKET_NAME

  4. Reset your org policy for the logging project by running the following command.

    gcloud org-policies reset iam.allowedPolicyMemberDomains --project=$ENV_LOG_PROJECT_ID

  5. Assign roles/storage.objectCreator role to [email protected] group.

    gcloud storage buckets add-iam-policy-binding gs://$ENV_LOG_BUCKET_NAME --member="group:[email protected]" --role="roles/storage.objectCreator"

    Note: you might receive an error telling you that this is against an organization policy, this can happen because of the propagation time from the change made to the organization policy (propagation time is tipically 2 minutes, but can take 7 minutes or longer). If this happens, wait some minutes and try again

  6. Delete the change made on the first step to the organization policy, this will make the project inherit parent policies.

    gcloud org-policies delete iam.allowedPolicyMemberDomains --project=$ENV_LOG_PROJECT_ID
non-production environment configuration

  1. Configure the following variable below with the value of gcp-environments repository path.

    export GCP_ENVIRONMENTS_PATH=INSERT_YOUR_PATH_HERE

    Make sure your git is checked out to the non-production branch by running git checkout nonproduction on GCP_ENVIRONMENTS_PATH.

    (cd $GCP_ENVIRONMENTS_PATH && git checkout nonproduction)

  2. Retrieve the bucket name and project id from terraform outputs.

    export ENV_LOG_BUCKET_NAME=$(terraform -chdir="$GCP_ENVIRONMENTS_PATH/envs/nonproduction" output -raw env_log_bucket_name)
export ENV_LOG_PROJECT_ID=$(terraform -chdir="$GCP_ENVIRONMENTS_PATH/envs/nonproduction" output -raw env_log_project_id)

  3. Validate the variable values.

    echo env_log_project_id=$ENV_LOG_PROJECT_ID
echo env_log_bucket_name=$ENV_LOG_BUCKET_NAME

  4. Reset your org policy for the logging project by running the following command.

    gcloud org-policies reset iam.allowedPolicyMemberDomains --project=$ENV_LOG_PROJECT_ID

  5. Assign roles/storage.objectCreator role to [email protected] group.

    gcloud storage buckets add-iam-policy-binding gs://$ENV_LOG_BUCKET_NAME --member="group:[email protected]" --role="roles/storage.objectCreator"

    Note: you might receive an error telling you that this is against an organization policy, this can happen because of the propagation time from the change made to the organization policy (propagation time is tipically 2 minutes, but can take 7 minutes or longer). If this happens, wait some minutes and try again

  6. Delete the change made on the first step to the organization policy, this will make the project inherit parent policies.

    gcloud org-policies delete iam.allowedPolicyMemberDomains --project=$ENV_LOG_PROJECT_ID
production environment configuration

  1. Configure the following variable below with the value of gcp-environments repository path.

    export GCP_ENVIRONMENTS_PATH=INSERT_YOUR_PATH_HERE

    Make sure your git is checked out to the production branch by running git checkout production on GCP_ENVIRONMENTS_PATH.

    (cd $GCP_ENVIRONMENTS_PATH && git checkout production)

  2. Retrieve the bucket name and project id from terraform outputs.

    export ENV_LOG_BUCKET_NAME=$(terraform -chdir="$GCP_ENVIRONMENTS_PATH/envs/production" output -raw env_log_bucket_name)
export ENV_LOG_PROJECT_ID=$(terraform -chdir="$GCP_ENVIRONMENTS_PATH/envs/production" output -raw env_log_project_id)

  3. Validate the variable values.

    echo env_log_project_id=$ENV_LOG_PROJECT_ID
echo env_log_bucket_name=$ENV_LOG_BUCKET_NAME

  4. Reset your org policy for the logging project by running the following command.

    gcloud org-policies reset iam.allowedPolicyMemberDomains --project=$ENV_LOG_PROJECT_ID

  5. Assign roles/storage.objectCreator role to [email protected] group.

    gcloud storage buckets add-iam-policy-binding gs://$ENV_LOG_BUCKET_NAME --member="group:[email protected]" --role="roles/storage.objectCreator"

    Note: you might receive an error telling you that this is against an organization policy, this can happen because of the propagation time from the change made to the organization policy (propagation time is tipically 2 minutes, but can take 7 minutes or longer). If this happens, wait some minutes and try again

  6. Delete the change made on the first step to the organization policy, this will make the project inherit parent policies.

    gcloud org-policies delete iam.allowedPolicyMemberDomains --project=$ENV_LOG_PROJECT_ID

Option 2: Use Google Cloud Console to disable/enable organization policy constraint

Proceed with these steps only if Option 1 is not chosen.

  1. On ml_logging.tf locate the following lines and uncomment them:

    resource "google_storage_bucket_iam_member" "bucket_logging" {
  bucket = google_storage_bucket.log_bucket.name
  role   = "roles/storage.objectCreator"
  member = "group:[email protected]"
}

  2. Under IAM & Admin, select Organization Policies. Search for "Domain Restricted Sharing".

    list-policy

  3. Select 'Manage Policy'. This directs you to the Domain Restricted Sharing Edit Policy page. It will be set at 'Inherit parent's policy'. Change this to 'Google-managed default'.

    edit-policy

  4. Follow the instructions on checking out development, non-production & production branches. Once environments terraform code has successfully applied, edit the policy again and select 'Inherit parent's policy' and Click SET POLICY.

After making these modifications, you can follow the README.md procedure for 2-environment step on foundation, make sure you change the organization policy after running the steps on foundation.

3-network: Configure private DNS zone for Vertex Workbench Instances, Enable NAT and Attach projects to perimeter

This step corresponds to modifications made to 3-networks-dual-svpc step on foundation.

Please note that the steps below are assuming you are checked out on terraform-google-enterprise-genai/.

cd ../terraform-google-enterprise-genai

development branch on gcp-networks

  • Go to gcp-networks repository, and check out on development branch.
cd ../gcp-networks

git checkout development

Private DNS zone configuration (dev)

  • Return to terraform-google-enterprise-genai repo.
cd ../terraform-google-enterprise-genai
  • Copy DNS notebook network module from this repo to gcp-networks repository.
cp -r 3-networks-dual-svpc/modules/ml_dns_notebooks ../gcp-networks/modules
  • Create a file named ml_dns_notebooks.tf on path gcp-networks/modules/base_env:
cp docs/assets/terraform/3-networks-dual-svpc/ml_dns_notebooks.tf ../gcp-networks/modules/base_env

Commit and push files to git repo.

cd ../gcp-networks

git add .

git commit -m "Create DNS notebook configuration"

git push origin development

Enabling NAT, Attaching projects to Service Perimeter and Creating custom firewall rules (dev)

Create gcp-networks/modules/base_env/data.tf file with the following content:

/**
 * Copyright 2024 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */


data "google_netblock_ip_ranges" "legacy_health_checkers" {
  range_type = "legacy-health-checkers"
}

data "google_netblock_ip_ranges" "health_checkers" {
  range_type = "health-checkers"
}

// Cloud IAP's TCP forwarding netblock
data "google_netblock_ip_ranges" "iap_forwarders" {
  range_type = "iap-forwarders"
}

On gcp-networks/modules/restricted_shared_vpc/variables.tf add the following variables:

variable "perimeter_projects" {
  description = "A list of project numbers to be added to the service perimeter"
  type        = list(number)
  default     = []
}

variable "allow_all_egress_ranges" {
  description = "List of network ranges to which all egress traffic will be allowed"
  default     = null
}

variable "allow_all_ingress_ranges" {
  description = "List of network ranges from which all ingress traffic will be allowed"
  default     = null
}

On gcp-networks/modules/base_env/remote.tf:

  1. Add the env remote state, by adding the following terraform code to the file:

    data "terraform_remote_state" "env" {
  backend = "gcs"

  config = {
    bucket = var.remote_state_bucket
    prefix = "terraform/environments/${var.env}"
  }
}

  2. Edit locals and add the following fields:

    logging_env_project_number   = data.terraform_remote_state.env.outputs.env_log_project_number
kms_env_project_number       = data.terraform_remote_state.env.outputs.env_kms_project_number

  3. The final result will contain existing locals and the added ones, it should look similar to the code below:

    locals {
  restricted_project_id        = data.terraform_remote_state.org.outputs.shared_vpc_projects[var.env].restricted_shared_vpc_project_id
  restricted_project_number    = data.terraform_remote_state.org.outputs.shared_vpc_projects[var.env].restricted_shared_vpc_project_number
  base_project_id              = data.terraform_remote_state.org.outputs.shared_vpc_projects[var.env].base_shared_vpc_project_id
  interconnect_project_number  = data.terraform_remote_state.org.outputs.interconnect_project_number
  dns_hub_project_id           = data.terraform_remote_state.org.outputs.dns_hub_project_id
  organization_service_account = data.terraform_remote_state.bootstrap.outputs.organization_step_terraform_service_account_email
  networks_service_account     = data.terraform_remote_state.bootstrap.outputs.networks_step_terraform_service_account_email
  projects_service_account     = data.terraform_remote_state.bootstrap.outputs.projects_step_terraform_service_account_email
  logging_env_project_number   = data.terraform_remote_state.env.outputs.env_log_project_number
  kms_env_project_number       = data.terraform_remote_state.env.outputs.env_kms_project_number
}
Adding projects to service perimeter (dev)

On gcp-networks/modules/restricted_shared_vpc/service_control.tf, modify the terraform module called regular_service_perimeter and add the following module field to resources:

distinct(concat([var.project_number], var.perimeter_projects))

This shall result in a module similar to the code below:

module "regular_service_perimeter" {
  source  = "terraform-google-modules/vpc-service-controls/google//modules/regular_service_perimeter"
  version = "~> 4.0"

  policy         = var.access_context_manager_policy_id
  perimeter_name = local.perimeter_name
  description    = "Default VPC Service Controls perimeter"
  resources      = distinct(concat([var.project_number], var.perimeter_projects))
  access_levels  = [module.access_level_members.name]

  restricted_services     = var.restricted_services
  vpc_accessible_services = ["RESTRICTED-SERVICES"]

  ingress_policies = var.ingress_policies
  egress_policies  = var.egress_policies

  depends_on = [
    time_sleep.wait_vpc_sc_propagation
  ]
}
Creating "allow all ingress ranges" and "allow all egress ranges" firewall rules (dev)

On gcp-networks/modules/restricted_shared_vpc/firewall.tf add the following firewall rules by adding the terraform code below to the file:

resource "google_compute_firewall" "allow_all_egress" {
  count = var.allow_all_egress_ranges != null ? 1 : 0

  name      = "fw-${var.environment_code}-shared-base-1000-e-a-all-all-all"
  network   = module.main.network_name
  project   = var.project_id
  direction = "EGRESS"
  priority  = 1000

  dynamic "log_config" {
    for_each = var.firewall_enable_logging == true ? [{
      metadata = "INCLUDE_ALL_METADATA"
    }] : []

    content {
      metadata = log_config.value.metadata
    }
  }

  allow {
    protocol = "all"
  }

  destination_ranges = var.allow_all_egress_ranges
}

resource "google_compute_firewall" "allow_all_ingress" {
  count = var.allow_all_ingress_ranges != null ? 1 : 0

  name      = "fw-${var.environment_code}-shared-base-1000-i-a-all"
  network   = module.main.network_name
  project   = var.project_id
  direction = "INGRESS"
  priority  = 1000

  dynamic "log_config" {
    for_each = var.firewall_enable_logging == true ? [{
      metadata = "INCLUDE_ALL_METADATA"
    }] : []

    content {
      metadata = log_config.value.metadata
    }
  }

  allow {
    protocol = "all"
  }

  source_ranges = var.allow_all_ingress_ranges
}
Changes to restricted shared VPC (dev)

On gcp-networks/modules/base_env/main.tf edit the terraform module named restricted_shared_vpc and add the following fields to it:

allow_all_ingress_ranges = concat(data.google_netblock_ip_ranges.health_checkers.cidr_blocks_ipv4, data.google_netblock_ip_ranges.legacy_health_checkers.cidr_blocks_ipv4, data.google_netblock_ip_ranges.iap_forwarders.cidr_blocks_ipv4)
allow_all_egress_ranges  = ["0.0.0.0/0"]

nat_enabled               = true
nat_num_addresses_region1 = 1
nat_num_addresses_region2 = 1

perimeter_projects = [local.logging_env_project_number, local.kms_env_project_number]

Commit all changes and push to the current branch.

git add .
git commit -m "Create custom fw rules, enable nat, configure dns and service perimeter"

git push origin development

nonproduction branch on gcp-networks

  • Go to gcp-networks repository, and check out on nonproduction branch.
cd ../gcp-networks

git checkout nonproduction

Private DNS zone configuration (non-production)

  • Return to terraform-google-enterprise-genai repo.
cd ../terraform-google-enterprise-genai
  • Copy DNS notebook network module from this repo to gcp-networks repository.
cp -r 3-networks-dual-svpc/modules/ml_dns_notebooks ../gcp-networks/modules
  • Create a file named ml_dns_notebooks.tf on path gcp-networks/modules/base_env:
cp docs/assets/terraform/3-networks-dual-svpc/ml_dns_notebooks.tf ../gcp-networks/modules/base_env

Commit and push files to git repo.

cd ../gcp-networks

git add .

git commit -m "Create DNS notebook configuration"

git push origin nonproduction

Enabling NAT, Attaching projects to Service Perimeter and Creating custom firewall rules (non-production)

Create gcp-networks/modules/base_env/data.tf file with the following content:

/**
 * Copyright 2024 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */


data "google_netblock_ip_ranges" "legacy_health_checkers" {
  range_type = "legacy-health-checkers"
}

data "google_netblock_ip_ranges" "health_checkers" {
  range_type = "health-checkers"
}

// Cloud IAP's TCP forwarding netblock
data "google_netblock_ip_ranges" "iap_forwarders" {
  range_type = "iap-forwarders"
}

On gcp-networks/modules/restricted_shared_vpc/variables.tf add the following variables:

variable "perimeter_projects" {
  description = "A list of project numbers to be added to the service perimeter"
  type        = list(number)
  default     = []
}

variable "allow_all_egress_ranges" {
  description = "List of network ranges to which all egress traffic will be allowed"
  default     = null
}

variable "allow_all_ingress_ranges" {
  description = "List of network ranges from which all ingress traffic will be allowed"
  default     = null
}

On gcp-networks/modules/base_env/remote.tf:

  1. Add the env remote state, by adding the following terraform code to the file:

    data "terraform_remote_state" "env" {
  backend = "gcs"

  config = {
    bucket = var.remote_state_bucket
    prefix = "terraform/environments/${var.env}"
  }
}

  2. Edit locals and add the following fields:

    logging_env_project_number   = data.terraform_remote_state.env.outputs.env_log_project_number
kms_env_project_number       = data.terraform_remote_state.env.outputs.env_kms_project_number

  3. The final result will contain existing locals and the added ones, it should look similar to the code below:

    locals {
  restricted_project_id        = data.terraform_remote_state.org.outputs.shared_vpc_projects[var.env].restricted_shared_vpc_project_id
  restricted_project_number    = data.terraform_remote_state.org.outputs.shared_vpc_projects[var.env].restricted_shared_vpc_project_number
  base_project_id              = data.terraform_remote_state.org.outputs.shared_vpc_projects[var.env].base_shared_vpc_project_id
  interconnect_project_number  = data.terraform_remote_state.org.outputs.interconnect_project_number
  dns_hub_project_id           = data.terraform_remote_state.org.outputs.dns_hub_project_id
  organization_service_account = data.terraform_remote_state.bootstrap.outputs.organization_step_terraform_service_account_email
  networks_service_account     = data.terraform_remote_state.bootstrap.outputs.networks_step_terraform_service_account_email
  projects_service_account     = data.terraform_remote_state.bootstrap.outputs.projects_step_terraform_service_account_email
  logging_env_project_number   = data.terraform_remote_state.env.outputs.env_log_project_number
  kms_env_project_number       = data.terraform_remote_state.env.outputs.env_kms_project_number
}
Adding projects to service perimeter (non-production)

On gcp-networks/modules/restricted_shared_vpc/service_control.tf, modify the terraform module called regular_service_perimeter and add the following module field to resources:

distinct(concat([var.project_number], var.perimeter_projects))

This shall result in a module similar to the code below:

module "regular_service_perimeter" {
  source  = "terraform-google-modules/vpc-service-controls/google//modules/regular_service_perimeter"
  version = "~> 4.0"

  policy         = var.access_context_manager_policy_id
  perimeter_name = local.perimeter_name
  description    = "Default VPC Service Controls perimeter"
  resources      = distinct(concat([var.project_number], var.perimeter_projects))
  access_levels  = [module.access_level_members.name]

  restricted_services     = var.restricted_services
  vpc_accessible_services = ["RESTRICTED-SERVICES"]

  ingress_policies = var.ingress_policies
  egress_policies  = var.egress_policies

  depends_on = [
    time_sleep.wait_vpc_sc_propagation
  ]
}
Creating "allow all ingress ranges" and "allow all egress ranges" firewall rules (non-production)

On gcp-networks/modules/restricted_shared_vpc/firewall.tf add the following firewall rules by adding the terraform code below to the file:

resource "google_compute_firewall" "allow_all_egress" {
  count = var.allow_all_egress_ranges != null ? 1 : 0

  name      = "fw-${var.environment_code}-shared-base-1000-e-a-all-all-all"
  network   = module.main.network_name
  project   = var.project_id
  direction = "EGRESS"
  priority  = 1000

  dynamic "log_config" {
    for_each = var.firewall_enable_logging == true ? [{
      metadata = "INCLUDE_ALL_METADATA"
    }] : []

    content {
      metadata = log_config.value.metadata
    }
  }

  allow {
    protocol = "all"
  }

  destination_ranges = var.allow_all_egress_ranges
}

resource "google_compute_firewall" "allow_all_ingress" {
  count = var.allow_all_ingress_ranges != null ? 1 : 0

  name      = "fw-${var.environment_code}-shared-base-1000-i-a-all"
  network   = module.main.network_name
  project   = var.project_id
  direction = "INGRESS"
  priority  = 1000

  dynamic "log_config" {
    for_each = var.firewall_enable_logging == true ? [{
      metadata = "INCLUDE_ALL_METADATA"
    }] : []

    content {
      metadata = log_config.value.metadata
    }
  }

  allow {
    protocol = "all"
  }

  source_ranges = var.allow_all_ingress_ranges
}
Changes to restricted shared VPC (non-production)

On gcp-networks/modules/base_env/main.tf edit the terraform module named restricted_shared_vpc and add the following fields to it:

allow_all_ingress_ranges = concat(data.google_netblock_ip_ranges.health_checkers.cidr_blocks_ipv4, data.google_netblock_ip_ranges.legacy_health_checkers.cidr_blocks_ipv4, data.google_netblock_ip_ranges.iap_forwarders.cidr_blocks_ipv4)
allow_all_egress_ranges  = ["0.0.0.0/0"]

nat_enabled               = true
nat_num_addresses_region1 = 1
nat_num_addresses_region2 = 1

perimeter_projects = [local.logging_env_project_number, local.kms_env_project_number]

Commit all changes and push to the current branch.

git add .
git commit -m "Create custom fw rules, enable nat, configure dns and service perimeter"

git push origin nonproduction

production branch on gcp-networks

  • Go to gcp-networks repository, and check out on production branch.
cd ../gcp-networks

git checkout production

Private DNS zone configuration (production)

  • Return to terraform-google-enterprise-genai repo.
cd ../terraform-google-enterprise-genai
  • Copy DNS notebook network module from this repo to gcp-networks repository.
cp -r 3-networks-dual-svpc/modules/ml_dns_notebooks ../gcp-networks/modules
  • Create a file named ml_dns_notebooks.tf on path gcp-networks/modules/base_env:
cp docs/assets/terraform/3-networks-dual-svpc/ml_dns_notebooks.tf ../gcp-networks/modules/base_env

Commit and push files to git repo.

cd ../gcp-networks

git add .

git commit -m "Create DNS notebook configuration"

git push origin production

Enabling NAT, Attaching projects to Service Perimeter and Creating custom firewall rules (production)

Create gcp-networks/modules/base_env/data.tf file with the following content:

/**
 * Copyright 2024 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */


data "google_netblock_ip_ranges" "legacy_health_checkers" {
  range_type = "legacy-health-checkers"
}

data "google_netblock_ip_ranges" "health_checkers" {
  range_type = "health-checkers"
}

// Cloud IAP's TCP forwarding netblock
data "google_netblock_ip_ranges" "iap_forwarders" {
  range_type = "iap-forwarders"
}

On gcp-networks/modules/restricted_shared_vpc/variables.tf add the following variables:

variable "perimeter_projects" {
  description = "A list of project numbers to be added to the service perimeter"
  type        = list(number)
  default     = []
}

variable "allow_all_egress_ranges" {
  description = "List of network ranges to which all egress traffic will be allowed"
  default     = null
}

variable "allow_all_ingress_ranges" {
  description = "List of network ranges from which all ingress traffic will be allowed"
  default     = null
}

On gcp-networks/modules/base_env/remote.tf:

  1. Add the env remote state, by adding the following terraform code to the file:

    data "terraform_remote_state" "env" {
  backend = "gcs"

  config = {
    bucket = var.remote_state_bucket
    prefix = "terraform/environments/${var.env}"
  }
}

  2. Edit locals and add the following fields:

    logging_env_project_number   = data.terraform_remote_state.env.outputs.env_log_project_number
kms_env_project_number       = data.terraform_remote_state.env.outputs.env_kms_project_number

  3. The final result will contain existing locals and the added ones, it should look similar to the code below:

    locals {
  restricted_project_id        = data.terraform_remote_state.org.outputs.shared_vpc_projects[var.env].restricted_shared_vpc_project_id
  restricted_project_number    = data.terraform_remote_state.org.outputs.shared_vpc_projects[var.env].restricted_shared_vpc_project_number
  base_project_id              = data.terraform_remote_state.org.outputs.shared_vpc_projects[var.env].base_shared_vpc_project_id
  interconnect_project_number  = data.terraform_remote_state.org.outputs.interconnect_project_number
  dns_hub_project_id           = data.terraform_remote_state.org.outputs.dns_hub_project_id
  organization_service_account = data.terraform_remote_state.bootstrap.outputs.organization_step_terraform_service_account_email
  networks_service_account     = data.terraform_remote_state.bootstrap.outputs.networks_step_terraform_service_account_email
  projects_service_account     = data.terraform_remote_state.bootstrap.outputs.projects_step_terraform_service_account_email
  logging_env_project_number   = data.terraform_remote_state.env.outputs.env_log_project_number
  kms_env_project_number       = data.terraform_remote_state.env.outputs.env_kms_project_number
}
Adding projects to service perimeter (production)

On gcp-networks/modules/restricted_shared_vpc/service_control.tf, modify the terraform module called regular_service_perimeter and add the following module field to resources:

distinct(concat([var.project_number], var.perimeter_projects))

This shall result in a module similar to the code below:

module "regular_service_perimeter" {
  source  = "terraform-google-modules/vpc-service-controls/google//modules/regular_service_perimeter"
  version = "~> 4.0"

  policy         = var.access_context_manager_policy_id
  perimeter_name = local.perimeter_name
  description    = "Default VPC Service Controls perimeter"
  resources      = distinct(concat([var.project_number], var.perimeter_projects))
  access_levels  = [module.access_level_members.name]

  restricted_services     = var.restricted_services
  vpc_accessible_services = ["RESTRICTED-SERVICES"]

  ingress_policies = var.ingress_policies
  egress_policies  = var.egress_policies

  depends_on = [
    time_sleep.wait_vpc_sc_propagation
  ]
}
Creating "allow all ingress ranges" and "allow all egress ranges" firewall rules (production)

On gcp-networks/modules/restricted_shared_vpc/firewall.tf add the following firewall rules by adding the terraform code below to the file:

resource "google_compute_firewall" "allow_all_egress" {
  count = var.allow_all_egress_ranges != null ? 1 : 0

  name      = "fw-${var.environment_code}-shared-base-1000-e-a-all-all-all"
  network   = module.main.network_name
  project   = var.project_id
  direction = "EGRESS"
  priority  = 1000

  dynamic "log_config" {
    for_each = var.firewall_enable_logging == true ? [{
      metadata = "INCLUDE_ALL_METADATA"
    }] : []

    content {
      metadata = log_config.value.metadata
    }
  }

  allow {
    protocol = "all"
  }

  destination_ranges = var.allow_all_egress_ranges
}

resource "google_compute_firewall" "allow_all_ingress" {
  count = var.allow_all_ingress_ranges != null ? 1 : 0

  name      = "fw-${var.environment_code}-shared-base-1000-i-a-all"
  network   = module.main.network_name
  project   = var.project_id
  direction = "INGRESS"
  priority  = 1000

  dynamic "log_config" {
    for_each = var.firewall_enable_logging == true ? [{
      metadata = "INCLUDE_ALL_METADATA"
    }] : []

    content {
      metadata = log_config.value.metadata
    }
  }

  allow {
    protocol = "all"
  }

  source_ranges = var.allow_all_ingress_ranges
}
Changes to restricted shared VPC (production)

On gcp-networks/modules/base_env/main.tf edit the terraform module named restricted_shared_vpc and add the following fields to it:

allow_all_ingress_ranges = concat(data.google_netblock_ip_ranges.health_checkers.cidr_blocks_ipv4, data.google_netblock_ip_ranges.legacy_health_checkers.cidr_blocks_ipv4, data.google_netblock_ip_ranges.iap_forwarders.cidr_blocks_ipv4)
allow_all_egress_ranges  = ["0.0.0.0/0"]

nat_enabled               = true
nat_num_addresses_region1 = 1
nat_num_addresses_region2 = 1

perimeter_projects = [local.logging_env_project_number, local.kms_env_project_number]

Commit all changes and push to the current branch.

git add .
git commit -m "Create custom fw rules, enable nat, configure dns and service perimeter"

git push origin production

4-projects: Create Service Catalog and Artifacts Shared projects and Machine Learning Projects

This step corresponds to modifications made to 4-projects step on foundation.

Please note that the steps below are assuming you are checked out on terraform-google-enterprise-genai/.

cd ../terraform-google-enterprise-genai

In this tutorial, we are using ml_business_unit as an example.

You need to manually plan and apply only once the ml_business_unit/shared.

Manually applying shared

  • Go to gcp-projects repository and checkout to plan branch.
cd ../gcp-projects

git checkout plan
  • Return to GenAI repository.
cd ../terraform-google-enterprise-genai
  • Copy ml_business_unit to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/ml_business_unit ../gcp-projects
  • Add modules to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/modules/* ../gcp-projects/modules
  • Add tfvars to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/*.example.tfvars ../gcp-projects
  • Go to gcp-projects repository.
cd ../gcp-projects
  • Update project backend by retrieving it's value from 0-bootstrap and applying it to backend.tf.
export PROJECT_BACKEND=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw projects_gcs_bucket_tfstate)

for file in $(find . -name backend.tf); do sed -i "s/UPDATE_PROJECTS_BACKEND/$PROJECT_BACKEND/" $file; done
  • Retrieve projects step service account e-mail.
export GOOGLE_IMPERSONATE_SERVICE_ACCOUNT=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw projects_step_terraform_service_account_email)
echo ${GOOGLE_IMPERSONATE_SERVICE_ACCOUNT}
  • Retrieve cloud build project id.
export CLOUD_BUILD_PROJECT_ID=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw cloudbuild_project_id)
echo ${CLOUD_BUILD_PROJECT_ID}
  • Rename auto.example.tfvars to auto.tfvars.
mv common.auto.example.tfvars common.auto.tfvars
mv shared.auto.example.tfvars shared.auto.tfvars
mv development.auto.example.tfvars development.auto.tfvars
mv non-production.auto.example.tfvars non-production.auto.tfvars
mv production.auto.example.tfvars production.auto.tfvars
  • Update REMOTE_STATE_BUCKET value.
export remote_state_bucket=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw gcs_bucket_tfstate)
echo "remote_state_bucket = ${remote_state_bucket}"

sed -i'' -e "s/REMOTE_STATE_BUCKET/${remote_state_bucket}/" ./common.auto.tfvars
  • Commit the changes.
git add .

git commit -m "Create ML Business Unit"
  • Log into gcloud using service account impersonation and then set your configuration:
gcloud auth application-default login --impersonate-service-account=${GOOGLE_IMPERSONATE_SERVICE_ACCOUNT}
  • Run init and plan and review output for environment shared.
./tf-wrapper.sh init shared
./tf-wrapper.sh plan shared
  • Run validate and check for violations.
./tf-wrapper.sh validate shared $(pwd)/../gcp-policies ${CLOUD_BUILD_PROJECT_ID}
  • Run apply shared.
./tf-wrapper.sh apply shared

This will create the artifacts and service catalog projects under common folder and configure the Machine Learning business unit infra pipeline.

Push plan branch to remote.

git push origin plan

development branch on gcp-projects

This will create the machine learning development environment. A Machine Learning project will be hosted under a folder.

  • Go to gcp-projects repository and checkout to plan branch.
cd ../gcp-projects

git checkout development
  • Return to GenAI repository.
cd ../terraform-google-enterprise-genai
  • Copy ml_business_unit to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/ml_business_unit ../gcp-projects
  • Add modules to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/modules/* ../gcp-projects/modules
  • Add tfvars to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/*.example.tfvars ../gcp-projects
  • Go to gcp-projects repository.
cd ../gcp-projects
  • Rename auto.example.tfvars to auto.tfvars.
mv common.auto.example.tfvars common.auto.tfvars
mv shared.auto.example.tfvars shared.auto.tfvars
mv development.auto.example.tfvars development.auto.tfvars
mv non-production.auto.example.tfvars non-production.auto.tfvars
mv production.auto.example.tfvars production.auto.tfvars
  • Update REMOTE_STATE_BUCKET value.
export remote_state_bucket=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw gcs_bucket_tfstate)
echo "remote_state_bucket = ${remote_state_bucket}"

sed -i'' -e "s/REMOTE_STATE_BUCKET/${remote_state_bucket}/" ./common.auto.tfvars
  • Update project backend by retrieving it's value from 0-bootstrap and applying it to backend.tf.
export PROJECT_BACKEND=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw projects_gcs_bucket_tfstate)

for file in $(find . -name backend.tf); do sed -i "s/UPDATE_PROJECTS_BACKEND/$PROJECT_BACKEND/" $file; done
  • Commit and push.
git add .
git commit -m "Initialize ML environment"

git push origin development

nonproduction branch on gcp-projects

This will create the machine learning nonproduction environment. A Machine Learning project will be hosted under a folder.

  • Go to gcp-projects repository and checkout to plan branch.
cd ../gcp-projects

git checkout nonproduction
  • Return to GenAI repository.
cd ../terraform-google-enterprise-genai
  • Copy ml_business_unit to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/ml_business_unit ../gcp-projects
  • Add modules to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/modules/* ../gcp-projects/modules
  • Add tfvars to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/*.example.tfvars ../gcp-projects
  • Go to gcp-projects repository.
cd ../gcp-projects
  • Rename auto.example.tfvars to auto.tfvars.
mv common.auto.example.tfvars common.auto.tfvars
mv shared.auto.example.tfvars shared.auto.tfvars
mv development.auto.example.tfvars development.auto.tfvars
mv non-production.auto.example.tfvars non-production.auto.tfvars
mv production.auto.example.tfvars production.auto.tfvars
  • Update REMOTE_STATE_BUCKET value.
export remote_state_bucket=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw gcs_bucket_tfstate)
echo "remote_state_bucket = ${remote_state_bucket}"

sed -i'' -e "s/REMOTE_STATE_BUCKET/${remote_state_bucket}/" ./common.auto.tfvars
  • Update project backend by retrieving it's value from 0-bootstrap and applying it to backend.tf.
export PROJECT_BACKEND=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw projects_gcs_bucket_tfstate)

for file in $(find . -name backend.tf); do sed -i "s/UPDATE_PROJECTS_BACKEND/$PROJECT_BACKEND/" $file; done
  • Commit and push.
git add .
git commit -m "Initialize ML environment"

git push origin nonproduction

production branch on gcp-projects

This will create the machine learning production environment. A Machine Learning project will be hosted under a folder.

  • Go to gcp-projects repository and checkout to plan branch.
cd ../gcp-projects

git checkout production
  • Return to GenAI repository.
cd ../terraform-google-enterprise-genai
  • Copy ml_business_unit to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/ml_business_unit ../gcp-projects
  • Remove shared directory on ml_business_unit on the gcp-projects repository.
rm -rf ../gcp-projects/ml_business_unit/shared
  • Add modules to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/modules/* ../gcp-projects/modules
  • Add tfvars to the gcp-projects repository.
cp -r docs/assets/terraform/4-projects/*.example.tfvars ../gcp-projects
  • Go to gcp-projects repository.
cd ../gcp-projects
  • Rename auto.example.tfvars to auto.tfvars.
mv common.auto.example.tfvars common.auto.tfvars
mv shared.auto.example.tfvars shared.auto.tfvars
mv development.auto.example.tfvars development.auto.tfvars
mv non-production.auto.example.tfvars non-production.auto.tfvars
mv production.auto.example.tfvars production.auto.tfvars
  • Update REMOTE_STATE_BUCKET value.
export remote_state_bucket=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw gcs_bucket_tfstate)
echo "remote_state_bucket = ${remote_state_bucket}"

sed -i'' -e "s/REMOTE_STATE_BUCKET/${remote_state_bucket}/" ./common.auto.tfvars
  • Update project backend by retrieving it's value from 0-bootstrap and applying it to backend.tf.
export PROJECT_BACKEND=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw projects_gcs_bucket_tfstate)

for file in $(find . -name backend.tf); do sed -i "s/UPDATE_PROJECTS_BACKEND/$PROJECT_BACKEND/" $file; done
  • Commit and push.
git add .
git commit -m "Initialize ML environment"

git push origin production

5-appinfra: Deploy Service Catalog and Artifacts Publish Applications

Update gcloud terraform vet policies for app-infra

The first step is to update the gcloud terraform vet policies constraints to allow usage of the APIs needed by the Blueprint and add more policies.

The constraints are located in the repository:

  • gcp-policies-app-infra

IMPORTANT: Please note that the steps below are assuming you are checked out on the same level as terraform-google-enterprise-genai/ and the other repos (gcp-bootstrap, gcp-org, gcp-projects...).

  • Clone the gcp-policies-app-infra repo based on the Terraform output from the 4-projects step. Clone the repo at the same level of the terraform-google-enterprise-genai folder, the following instructions assume this layout. Run terraform output cloudbuild_project_id in the 4-projects folder to get the Cloud Build Project ID.

    export INFRA_PIPELINE_PROJECT_ID=$(terraform -chdir="gcp-projects/ml_business_unit/shared/" output -raw cloudbuild_project_id)
echo ${INFRA_PIPELINE_PROJECT_ID}

gcloud source repos clone gcp-policies gcp-policies-app-infra --project=${INFRA_PIPELINE_PROJECT_ID}

    Note: gcp-policies repo has the same name as the repo created in step 1-org. In order to prevent a collision, the previous command will clone this repo in the folder gcp-policies-app-infra.

  • Navigate into the repo and copy contents of policy-library to new repo. All subsequent steps assume you are running them from the gcp-policies-app-infra directory. If you run them from another directory, adjust your copy paths accordingly.

    cd gcp-policies-app-infra/
git checkout -b main

cp -RT ../terraform-google-enterprise-genai/policy-library/ .

  • Commit changes and push your main branch to the new repo.

    git add .
git commit -m 'Initialize policy library repo'

git push --set-upstream origin main

  • Navigate out of the repo.

    cd ..

Artifacts Application

The purpose of this step is to deploy out an artifact registry to store custom docker images. A Cloud Build pipeline is also deployed out. At the time of this writing, it is configured to attach itself to a Cloud Source Repository. The Cloud Build pipeline is responsible for building out a custom image that may be used in Machine Learning Workflows. If you are in a situation where company policy requires no outside repositories to be accessed, custom images can be used to keep access to any image internally.

Since every workflow will have access to these images, it is deployed in the common folder, and keeping with the foundations structure, is listed as shared under this Business Unit. It will only need to be deployed once.

The Pipeline is connected to a Google Cloud Source Repository with a simple structure:

├── README.md
└── images
   ├── tf2-cpu.2-13:0.1
   │   └── Dockerfile
   └── tf2-gpu.2-13:0.1
      └── Dockerfile

For the purposes of this example, the pipeline is configured to monitor the main branch of this repository.

Each folder under images has the full name and tag of the image that must be built. Once a change to the main branch is pushed, the pipeline will analyse which files have changed and build that image out and place it in the artifact repository. For example, if there is a change to the Dockerfile in the tf2-cpu-13:0.1 folder, or if the folder itself has been renamed, it will build out an image and tag it based on the folder name that the Dockerfile has been housed in.

Once pushed, the pipeline build logs can be accessed by navigating to the artifacts project name created in step-4:

terraform -chdir="gcp-projects/ml_business_unit/shared/" output -raw common_artifacts_project_id

  • Clone the ml-artifact-publish repo.

    gcloud source repos clone ml-artifact-publish --project=${INFRA_PIPELINE_PROJECT_ID}

  • Navigate into the repo, change to non-main branch and copy contents of GenAI to the new repo. Subsequent steps assume you are running them from the ml-artifact-publish directory.

    cd ml-artifact-publish/
git checkout -b plan

cp -RT ../terraform-google-enterprise-genai/5-app-infra/projects/artifact-publish/ .
cp -R ../terraform-google-enterprise-genai/5-app-infra/modules/ ./modules
cp ../terraform-google-enterprise-genai/build/cloudbuild-tf-* .
cp ../terraform-google-enterprise-genai/build/tf-wrapper.sh .
chmod 755 ./tf-wrapper.sh

  • Rename common.auto.example.tfvars to common.auto.tfvars.

    mv common.auto.example.tfvars common.auto.tfvars

  • Update the file with values from your environment and 0-bootstrap. See any of the business unit 1 envs folders README.md files for additional information on the values in the common.auto.tfvars file.

    export remote_state_bucket=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw projects_gcs_bucket_tfstate)
echo "remote_state_bucket = ${remote_state_bucket}"
sed -i "s/REMOTE_STATE_BUCKET/${remote_state_bucket}/" ./common.auto.tfvars

  • Update backend.tf with your bucket from the infra pipeline output.

    export backend_bucket=$(terraform -chdir="../gcp-projects/ml_business_unit/shared/" output -json state_buckets | jq '."ml-artifact-publish"' --raw-output)
echo "backend_bucket = ${backend_bucket}"

for i in `find -name 'backend.tf'`; do sed -i "s/UPDATE_APP_INFRA_BUCKET/${backend_bucket}/" $i; done

  • Commit changes.

    git add .
git commit -m 'Initialize repo'

  • Push your plan branch to trigger a plan for all environments. Because the plan branch is not a named environment branch, pushing your plan branch triggers terraform plan but not terraform apply. Review the plan output in your Cloud Build project https://console.cloud.google.com/cloud-build/builds;region=DEFAULT_REGION?project=YOUR_INFRA_PIPELINE_PROJECT_ID.

    git push --set-upstream origin plan

  • Merge changes to shared. Because this is a named environment branch, pushing to this branch triggers both terraform plan and terraform apply. Review the apply output in your Cloud Build project https://console.cloud.google.com/cloud-build/builds;region=DEFAULT_REGION?project=YOUR_INFRA_PIPELINE_PROJECT_ID. Before proceeding further, make sure that the build applied successfully.

    git checkout -b production
git push origin production

  • cd out of the ml-artifacts-publish repository.

    cd ..

Configuring Cloud Source Repository of Artifact Application

The series of steps below will trigger the custom artifacts pipeline.

  • Grab the Artifact Project ID

    export ARTIFACT_PROJECT_ID=$(terraform -chdir="gcp-projects/ml_business_unit/shared" output -raw common_artifacts_project_id)
echo ${ARTIFACT_PROJECT_ID}

  • Clone the freshly minted Cloud Source Repository that was created for this project.

    gcloud source repos clone publish-artifacts --project=${ARTIFACT_PROJECT_ID}

  • Enter the repo folder and copy over the example files from the folder on GenAI repository.

    cd publish-artifacts
git checkout -b main

git commit -m "Initialize Repository" --allow-empty
cp -RT ../terraform-google-enterprise-genai/5-app-infra/source_repos/artifact-publish/ .

  • Commit changes and push your main branch to the new repo.

    git add .
git commit -m 'Build Images'

git push --set-upstream origin main

  • cd out of the publish-artifacts repository.

    cd ..

Service Catalog Pipeline Configuration

This step has two main purposes:

  1. To deploy a pipeline and a bucket which is linked to a Google Cloud Repository that houses terraform modules for the use in Service Catalog. Although Service Catalog itself must be manually deployed, the modules which will be used can still be automated.

  2. To deploy infrastructure for operational environments (ie. non-production & production.)

The resoning behind utilizing one repository with two deployment methodologies is due to how close interactive (development) and operational environments are.

The repository has the structure (truncated for brevity):

ml_business_unit
├── development
├── non-production
├── production
modules
├── bucket
│   ├── README.md
│   ├── data.tf
│   ├── main.tf
│   ├── outputs.tf
│   ├── provider.tf
│   └── variables.tf
├── composer
│   ├── README.md
│   ├── data.tf
│   ├── iam.roles.tf
│   ├── iam.users.tf
│   ├── locals.tf
│   ├── main.tf
│   ├── outputs.tf
│   ├── provider.tf
│   ├── terraform.tfvars.example
│   ├── variables.tf
│   └── vpc.tf
├── cryptography
│   ├── README.md
│   ├── crypto_key
│   │   ├── main.tf
│   │   ├── outputs.tf
│   │   └── variables.tf
│   └── key_ring
│       ├── main.tf
│       ├── outputs.tf
│       └── variables.tf

Each folder under modules represents a terraform module. When there is a change in any of the terraform module folders, the pipeline will find whichever module has been changed since the last push, tar.gz that file and place it in a bucket for Service Catalog to access.

This pipeline is listening to the main branch of this repository for changes in order for the modules to be uploaded to service catalog.

The pipeline also listens for changes made to plan, development, non-production & production branches, this is used for deploying infrastructure to each project.

  • Clone the ml-service-catalog repo.

    gcloud source repos clone ml-service-catalog --project=${INFRA_PIPELINE_PROJECT_ID}

  • Navigate into the repo, change to non-main branch and copy contents of foundation to new repo. All subsequent steps assume you are running them from the ml-service-catalog directory. If you run them from another directory, adjust your copy paths accordingly.

    cd ml-service-catalog
git checkout -b plan

cp -RT ../terraform-google-enterprise-genai/5-app-infra/projects/service-catalog/ .
cp -R ../terraform-google-enterprise-genai/5-app-infra/modules/ ./modules
cp ../terraform-google-enterprise-genai/build/cloudbuild-tf-* .
cp ../terraform-google-enterprise-genai/build/tf-wrapper.sh .
chmod 755 ./tf-wrapper.sh

  • Rename common.auto.example.tfvars to common.auto.tfvars.

    mv common.auto.example.tfvars common.auto.tfvars

  • Update the file with values from your environment and 0-bootstrap. See any of the business unit 1 envs folders README.md files for additional information on the values in the common.auto.tfvars file.

    export remote_state_bucket=$(terraform -chdir="../gcp-bootstrap/envs/shared" output -raw projects_gcs_bucket_tfstate)
echo "remote_state_bucket = ${remote_state_bucket}"
sed -i "s/REMOTE_STATE_BUCKET/${remote_state_bucket}/" ./common.auto.tfvars

  • Update the log_bucket variable with the value of the logs_export_storage_bucket_name.

     export log_bucket=$(terraform -chdir="../gcp-org/envs/shared" output -raw logs_export_storage_bucket_name)
 echo "log_bucket = ${log_bucket}"
 sed -i "s/REPLACE_LOG_BUCKET/${log_bucket}/" ./common.auto.tfvars

  • Update backend.tf with your bucket from the infra pipeline output.

    export backend_bucket=$(terraform -chdir="../gcp-projects/ml_business_unit/shared/" output -json state_buckets | jq '."ml-service-catalog"' --raw-output)
echo "backend_bucket = ${backend_bucket}"

for i in `find -name 'backend.tf'`; do sed -i "s/UPDATE_APP_INFRA_BUCKET/${backend_bucket}/" $i; done

  • Commit changes.

    git add .
git commit -m 'Initialize repo'

  • Push your plan branch to trigger a plan for all environments. Because the plan branch is not a named environment branch, pushing your plan branch triggers terraform plan but not terraform apply. Review the plan output in your Cloud Build project https://console.cloud.google.com/cloud-build/builds;region=DEFAULT_REGION?project=YOUR_INFRA_PIPELINE_PROJECT_ID.

    git push --set-upstream origin plan

  • Merge changes to production. Because this is a named environment branch, pushing to this branch triggers both terraform plan and terraform apply. Review the apply output in your Cloud Build project https://console.cloud.google.com/cloud-build/builds;region=DEFAULT_REGION?project=YOUR_INFRA_PIPELINE_PROJECT_ID. Before proceeding further, make sure that the build applied successfully.

    git checkout -b production
git push origin production

  • cd out of the ml-service-catalog repository.

    cd ..

Configuring Cloud Source Repository of Service Catalog Solutions Pipeline

The series of steps below will trigger the custom Service Catalog Pipeline.

  • Grab the Service Catalogs ID

    export SERVICE_CATALOG_PROJECT_ID=$(terraform -chdir="gcp-projects/ml_business_unit/shared" output -raw service_catalog_project_id)
echo ${SERVICE_CATALOG_PROJECT_ID}

  • Clone the freshly minted Cloud Source Repository that was created for this project.

    gcloud source repos clone service-catalog --project=${SERVICE_CATALOG_PROJECT_ID}

  • Enter the repo folder and copy over the service catalogs files from 5-app-infra/source_repos/service-catalog folder.

    cd service-catalog/
cp -RT ../terraform-google-enterprise-genai/5-app-infra/source_repos/service-catalog/ .
git add img
git commit -m "Add img directory"

  • Commit changes and push main branch to the new repo.

    git add modules
git commit -m 'Initialize Service Catalog Build Repo'

git push --set-upstream origin main

  • cd out of the service_catalog repository.

    cd ..

  • Navigate to the project that was output from ${SERVICE_CATALOG_PROJECT_ID} in Google's Cloud Console to view the first run of images being built.