Vertex AI: Qwik Start

# Task 1. Enable Google Cloud services In Cloud Shell, use gcloud to enable the services used in the lab: ``` 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 ``` # Task 2. Create Vertex AI custom service account for Vertex Tensorboard integration 1. Create custom service account: ``` SERVICE_ACCOUNT_ID=vertex-custom-training-sa gcloud iam service-accounts create $SERVICE_ACCOUNT_ID \ --description="A custom service account for Vertex custom training with Tensorboard" \ --display-name="Vertex AI Custom Training" ``` 2. Grant it access to Cloud Storage for writing and retrieving Tensorboard logs: ``` PROJECT_ID=$(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" ``` 3. 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" ``` 4. 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" ``` # Task 3. Launch Vertex AI Workbench notebook To create and launch a Vertex AI Workbench notebook: In the Navigation Menu Navigation menu icon, click Vertex AI > Workbench. On the Workbench page, click Enable Notebooks API (if it isn't enabled yet). Click on User-Managed Notebooks tab then, click Create New. In the New instance menu, choose the latest version of TensorFlow Enterprise 2.x (with LTS) in Environment. Name the notebook. Set Region to us-east4 and Zone to any zone within the designated region. Click Advanced Options to edit the instance properties. Click Machine type and then select e2-standard-2 for Machine type. Leave the remaining fields at their default and click Create. After a few minutes, the Workbench page lists your instance, followed by Open JupyterLab. Click Open JupyterLab to open JupyterLab in a new tab. If you get a message saying beatrix jupyterlab needs to be included in the build, just ignore it. # Task 4. Clone the lab ``` git clone https://github.com/GoogleCloudPlatform/training-data-analyst ``` # Task 5. Install lab dependencies ``` cd training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart pip3 install --user -r requirements.txt ```