gcloud snippets

gcloud-snippets.sh

#!/usr/bin/env bash
# Description: snippets for gcloud

# install gcloud sdk

# login

# first look
gcloud auth list
gcloud config list
gcloud config list project

# enable APIs and services:
# here for AI Platform
gcloud services enable \
  compute.googleapis.com \
  iam.googleapis.com \
  iamcredentials.googleapis.com \
  monitoring.googleapis.com \
  logging.googleapis.com \
  notebooks.googleapis.com \
  aiplatform.googleapis.com \
  bigquery.googleapis.com \
  artifactregistry.googleapis.com \
  cloudbuild.googleapis.com \
  container.googleapis.com

# Create custom service account:
SERVICE_ACCOUNT_ID=vertex-custom-training-sa

gcloud iam service-accounts create $SERVICE_ACCOUNT_ID  \
    --description="A custom service account for Vertex custom training with Tensorboard" \
    --display-name="Vertex AI Custom Training"

# Grant it access to Cloud Storage for
# writing and retrieving Tensorboard logs:
PROJECT_ID=$(gcloud config get-value core/project)
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \
    --role="roles/storage.admin"

# Grant it access to your BigQuery data source to read data
# into your TensorFlow model:

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \
    --role="roles/bigquery.admin"

# Grant it access to Vertex AI for running model training, deployment, and explanation jobs:
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \
    --role="roles/aiplatform.user"