1. Scalable Workflow Management for LLM Training on GCP


    To manage a scalable workflow for Large Language Model (LLM) training on Google Cloud Platform (GCP), we can leverage Google Cloud's Workflows service, which allows us to orchestrate and automate cloud resources. We'll also use other services that are common in the training of machine learning models such as Cloud Machine Learning Engine and Dataflow for data processing tasks.

    Here's how you can build such an infrastructure:

    1. Google Cloud Workflows: We will define a workflow in which various tasks like data preprocessing, model training, and model evaluation are orchestrated in a specific order. Workflows allow conditional logic, variables, and parameters making it a versatile tool for such tasks.

    2. Google Machine Learning (ML) Engine: We will use Google ML Engine to run the LLM training job. This service provides managed resources to facilitate machine learning model training with support for popular ML frameworks.

    3. Google Dataflow: For data preprocessing, we can use Dataflow which is a fully managed service for stream and batch data processing. Dataflow can be used to prepare the training data which could involve tasks like data cleaning, augmentation, and transformation.

    Below is a Pulumi program that defines a basic setup for such a workflow using the GCP provider. This program assumes that you've already prepared your model and data preprocessing scripts and that you've set up your GCP credentials and configured Pulumi for use with GCP.

    import pulumi import pulumi_gcp as gcp # Define a ML Engine model # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/ml/enginemodel/ engine_model = gcp.ml.EngineModel("llm_engine_model", description="A model for LLM training", ) # Define a Dataflow job for preprocessing # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/dataflow/flextemplatejob/ dataflow_preprocess_job = gcp.dataflow.FlexTemplateJob("llm_preprocess_job", container_spec_gcs_path="gs://path/to/preprocessing/template", parameters={ "inputData": "gs://path/to/raw/data", "outputData": "gs://path/to/processed/data", }, on_delete="drain", # Ensures that the job is drained instead of canceled immediately on deletion region="us-central1", ) # Define a Workflow to orchestrate the tasks # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/workflows/workflow/ workflow = gcp.workflows.Workflow("llm_training_workflow", description="A workflow to manage LLM training on GCP", region="us-central1", source_contents=""" - init: assign: projectId: "${sys.get_env('GOOGLE_CLOUD_PROJECT_ID')}" region: "us-central1" engineModelName: "${model.name}" preprocessingJobName: "${preprocessing_job.name}" - preprocessData: call: googleapis.dataflow.projects.locations.templates.launch args: projectId: ${projectId} gcsPath: "gs://path/to/preprocessing/template" location: ${region} launchParameters: jobName: ${preprocessingJobName} parameters: inputData: "gs://path/to/raw/data" outputData: "gs://path/to/processed/data" result: preprocessResult - trainModel: call: googleapis.ml.projects.models.create args: parent: projects/${projectId} model: name: ${engineModelName} onlinePredictionConsoleLogging: true result: trainingResult """, service_account="service-account-for-workflows@your-project.iam.gserviceaccount.com", ) # Exports # These URLs and names can be used to track the resources in GCP console or through `gcloud` tool pulumi.export("engine_model_name", engine_model.name) pulumi.export("dataflow_preprocess_job_name", dataflow_preprocess_job.name) pulumi.export("workflow_name", workflow.name)

    This program does the following:

    • Declares a machine learning model resource that will hold the configuration for your LLM training job.
    • Defines a Dataflow job to preprocess data. You have to provide the path to your container spec in a GCS bucket and any other required parameters for your job.
    • Uses a Workflow to orchestrate the tasks. The written workflow:
      • Initializes variables with project and region information, along with names for the engine model and Dataflow job.
      • Defines a step to launch a Dataflow job to preprocess your training data.
      • Defines another step to create a new model in ML Engine for the LLM training.

    This is a foundational workflow that defines the orchestration of steps. You'll need to modify the source_contents to fit with your specific tasks and requirements for data preprocessing and LLM training. Additionally, you would need to set up appropriate IAM roles for the service accounts to access the necessary resources.

    By using Pulumi to define infrastructure as code, you can easily adjust your infrastructure as your workflow evolves, replicating or destroying environments programmatically and maintaining version controlled configurations.