1. Automated Deployment of LLMs with Cloud Build Triggers.


    When you want to automate the deployment of Large Language Models (LLMs) using Cloud Build on Google Cloud Platform (GCP), you'll utilize Cloud Build Triggers. A Cloud Build Trigger automatically starts a build whenever changes are pushed to a source code repository.

    The main steps to set up automated deployment of LLMs using Cloud Build Triggers are:

    1. Source Code Repository: Your LLMs code should be stored in a source code repository that Cloud Build can access. You might be using a repository in GitHub, Cloud Source Repositories, or any supported source.

    2. Trigger Configuration: You'll define a Cloud Build Trigger, specifying when the build should be triggered, such as on any push to a specific branch or tag.

    3. Build Configuration: The build configuration file (cloudbuild.yaml or cloudbuild.json) will contain instructions for Cloud Build on how to build and deploy your LLMs. This could include pulling dependencies, training, testing, and ultimately deploying the model.

    4. Deployment: The deployment instruction will typically use a tool like gcloud or kubectl within the build steps to deploy your LLMs to services like Compute Engine, Kubernetes Engine, or Cloud Run.

    Below is a Python program using Pulumi, which sets up a Cloud Build Trigger to automatically deploy LLMs when changes are pushed to the master branch on a GitHub repository. Note that you will need the GCP SDK installed and configured with appropriate credentials to run this program.

    In this example, we're only setting up the Cloud Build Trigger. The actual content of the build steps in cloudbuild.yaml, which is responsible for deploying the LLMs, is not covered here. You will need to tailor this to your specific build and deployment process.

    import pulumi import pulumi_gcp as gcp # Replace these variables with the details of your GitHub repository. project_id = 'your-gcp-project-id' repo_name = 'your-repo-name' repo_owner = 'your-github-username' branch_name = 'master' # The branch name to build from. # Create a Cloud Build Trigger cloudbuild_trigger = gcp.cloudbuild.Trigger('llm-deploy-trigger', project=project_id, description='Trigger for deploying LLMs on changes to master branch', github={ 'owner': repo_owner, 'name': repo_name, 'push': { 'branch': branch_name, }, }, # The following is a placeholder for the build steps you'll define. filename='cloudbuild.yaml' ) # Export the trigger's name pulumi.export('trigger_name', cloudbuild_trigger.name)

    You will replace the 'your-gcp-project-id', 'your-repo-name', 'your-github-username', and 'master' with the actual values that correspond to your GCP project ID, GitHub repository name, GitHub username, and the branch from which you want to trigger the builds.

    In this program, pulumi_gcp.cloudbuild.Trigger creates a new build trigger in your GCP project. The github block is configured to create a trigger that responds to push events on the specified branch of the GitHub repository.

    Lastly, the pulumi.export line is used to output the name of the created trigger which you can use to verify that the trigger has been set up correctly.

    Remember, you'll need to have cloudbuild.yaml or cloudbuild.json in your repository with the necessary build steps. Cloud Build will use this file to understand how to build and deploy your LLMs.

    Please ensure that you have the appropriate permissions set up in your GitHub repository to allow Google Cloud Build to access it. You would typically set up a service account with permissions to trigger builds and deploy resources in your GCP project.