vertex-ai-project-example

Welcome! This is an example project for demonstrating how to build an extensible, production-ready project on Vertex AI for image use cases. It is built specifically for the Mineral team but has some ideas that might be useful to share.

How to use

Folder structure

• Data labelling
• Data processing
• Training
• Workspace

Workspace

Here is where you create your datasets and training pipelines.

Concepts

• Pipelines as extensible Python classes
• Reusable pipeline factories
• Automated training via Cloud Build

Out-of-scope

MLOps, such as model monitoring, feature store, etc

Setup

Automation

Cloud Build

Cloud Build packages your code and runs it on Google Cloud servers.

Note: Instead of using gcloud, you can do all the below using the Google Cloud web console UI or even with client libraries.

1. Run all pipelines

The file to determine what commands are run are in the .cloud-build/run_pipelines_cloudbuild.yaml file.

To run this on the cloud, run the following command:

gcloud builds submit --config=.cloud-build/run_pipelines_cloudbuild.yaml --substitutions=_GCP_BUCKET_NAME="gs://MY_BUCKET_NAME/pipeline_staging"

This will error out as you haven't given the correct permissions to the service account yet. To find out the service account, go the the Cloud Build logs listed in the terminal output for the command and find it under the "EXECUTION DETAILS" tab.

Alternatively, this command may give you the correct one:

gcloud projects get-iam-policy MY_PROJECT | grep cloudbuild

2. The Cloud Build service account needs access to the aiplatform.pipelineJobs.create permission. Grant it access to the roles/aiplatform.user role which has this permission.

gcloud projects add-iam-policy-binding MY_PROJECT \
      --member='serviceAccount:[email protected]' \
      --role='roles/aiplatform.user'

3. Also grant it the ability to use another service account by giving it the roles/iam.serviceAccountUser role:

gcloud projects add-iam-policy-binding MY_PROJECT \
      --member='serviceAccount:[email protected]' \
      --role='roles/iam.serviceAccountUser'

4. Now submit the build again. This time it should work.

gcloud builds submit --config=.cloud-build/run_pipelines_cloudbuild.yaml --substitutions=_GCP_BUCKET_NAME="gs://MY_BUCKET_NAME/pipeline_staging"

5. (Optional) Set up triggers

Instead of running the Cloud Build command from your local machine, you can set up triggers to automatically run your Cloud Build jobs.

For this repo, you might want to retrain all pipelines on the following conditions:

• Scheduled weekly
• Whenever someone creates a pull-request on your repositoy
• Whenever code is merged to a certain branch

Here are some examples:

1. Create a trigger to run pipelines whenever new code is merged to the 'main' branch:

gcloud beta builds triggers create github --name="run-pipelines" \
    --repo-name vertex-ai-project-example \
    --repo-owner ivanmkc \
    --branch-pattern="^main$" \
    --build-config .cloud-build/run_pipelines_cloudbuild.yaml \
    --substitutions=_GCP_BUCKET_NAME="gs://MY_BUCKET_NAME/pipeline_staging"

Also see this for more options: https://cloud.google.com/sdk/gcloud/reference/beta/builds/triggers/create/github For more details, see: https://cloud.google.com/build/docs/automating-builds/build-repos-from-github

2. Create a trigger to run pipelines every week on Monday

There doesn't seem to be a way to do this without using the Google Cloud web console UI, so please see instructions here: https://cloud.google.com/build/docs/automating-builds/create-scheduled-triggers