1. Orchestrating AI Model Training Pipelines with GCP Workflows


    To orchestrate AI model training pipelines with Google Cloud Platform (GCP) Workflows, you can leverage the gcp.workflows.Workflow resource. Workflows in GCP are serverless orchestrators that connect different services together using a declarative syntax. They are commonly used to automate Google Cloud tasks and processes, such as AI model training, where several steps, such as data preprocessing, model training, model evaluation, and deployment might be involved.

    In a typical AI model training pipeline on GCP, several Google Cloud services are engaged:

    1. Google Cloud Storage (GCS): to store input data, training datasets, and the resulting model artifacts.
    2. AI Platform: provides a managed service to run your machine learning training jobs on various machine learning frameworks like TensorFlow, scikit-learn, etc.
    3. Cloud Functions: can be used to execute lightweight, serverless functions triggered by http requests or events in GCP services.
    4. Pub/Sub: a messaging service to publish and subscribe messages across your services, can be used to trigger Cloud Functions.

    In this example, I'll demonstrate how to define a simple GCP Workflow that coordinates between different cloud services for an AI model training pipeline. For simplicity, we'll assume that you have prepared data in a GCS bucket and a training script ready for use with AI Platform.

    import pulumi import pulumi_gcp as gcp # Define a GCP Workflow resource to automate model training ai_model_training_workflow = gcp.workflows.Workflow("aiModelTrainingWorkflow", region="us-central1", # specify your GCP region description="Workflow to orchestrate AI model training pipeline", source_contents=""" - initialize: call: http.get args: url: ${_URL_TO_TRIGGER_DATA_PREPROCESSING_CLOUD_FUNCTION} result: preprocessingResponse - trainModel: call: googleapis.ml.v1.projects.jobs.create args: parent: projects/${_PROJECT_ID}/jobs body: jobId: ${"train-" + string(sys.now().getEpoch())} trainingInput: args: - --data=${preprocessingResponse.body.processedDataLocation} region: us-central1 masterType: n1-standard-4 packageUris: - gs://${_GCS_BUCKET_URI_TO_TRAINING_PACKAGE} pythonModule: trainer.task runtimeVersion: '2.3' jobDir: gs://${_GCS_BUCKET_URI_FOR_JOB_DIR} scaleTier: CUSTOM trainingOutput: {} result: trainingResponse - evaluateModel: call: http.get args: url: "${_URL_TO_TRIGGER_EVALUATION_CLOUD_FUNCTION}" body: modelUri: ${trainingResponse.trainingOutput.trainedModelUri} result: evaluation """, service_account=gcp.service_account.Account("aiWorkflowServiceAccount").name ) # Export the Workflow's name and URL to access it later pulumi.export("workflow_name", ai_model_training_workflow.name) pulumi.export("workflow_url", ai_model_training_workflow.endpoint)

    In the above program, we have defined the following steps:

    • initialize: Invokes a cloud function to preprocess the data. It sends an HTTP GET request to a specified URL, which would trigger a Cloud Function that carries out data preprocessing. The URL would be that of the Cloud Function you've set up for this purpose.
    • trainModel: Submits a job to AI Platform's job submission endpoint using the googleapis.ml.v1.projects.jobs.create method. It specifies the location of your training script (packageUris), the Python module within the script to run (pythonModule), the runtime version, the target region for the training cluster, machine type, and job directory within a GCS bucket.
    • evaluateModel: This step would typically involve evaluating the trained model against a test dataset to gauge its accuracy or other metrics. For the sake of simplicity, this step again makes an HTTP GET request, symbolically triggering another Cloud Function for evaluation.

    Please note:

    • The code contains some placeholders that need to be replaced with actual values (indicated by ${_PLACEHOLDER_TEXT}) such as the URLs for the Cloud Functions for data preprocessing and model evaluation, the Cloud Storage bucket URIs, and the project ID where the resources will be deployed.
    • service_account: The Workflow is associated with a Google Cloud service account that has the appropriate permissions to execute the tasks within the Workflow. Make sure the service account has necessary roles/bindings to interact with AI Platform, GCS, and Cloud Functions.
    • The Workflow YAML (source_contents) must be correctly indented for the Workflow to parse it without errors.
    • In actual practice, you would likely have more complex logic for error handling and conditional execution.

    To apply this Pulumi program, save it as a Python file (e.g., main.py) and run pulumi up to provision the resources. Remember to replace the placeholders with your specific GCP configuration and resource identifiers.