1. AI Model Training Workflows on Harness


    Harness is a platform that facilitates continuous delivery, and while it does provide ways to manage your infrastructure and workflows, it doesn't specialize specifically in AI model training. If you are looking to create AI training workflows, you might consider using a combination of cloud services that specialize in machine learning, such as Amazon SageMaker, Google AI Platform, or Azure Machine Learning, along with managed Kubernetes services for scalable workflow management.

    If you're intent on using Harness, using it to manage the CI/CD aspect of your AI model training workflows is plausible. You would typically containerize your training application, use Harness for orchestration, and then deploy it to a Kubernetes cluster where the actual computation takes places.

    Since your goal is to create AI Model Training Workflows, and you mentioned Harness, let's sketch out a high-level program that creates a simple workflow using Harness' platform components.

    Please note that the details would vary greatly depending on your specific CI/CD requirements, cloud provider specifics, and the structure of your AI model training application. The following is a conceptual Pulumi program, illustrating how you might begin to structure your Harness resources:

    import pulumi import pulumi_harness as harness # Create a new Harness project to organize all the related resources. # This encapsulates all the different parts of our AI workflows. project = harness.Project("aiModelTraining", name="AI Model Training Workflows", color="blue", # You can choose a customizable color to identify the project. description="Project to manage AI Model Training Workflows", ) # Create a new application within our project. # This represents our AI model training workflow application. application = harness.Application("aiModelTrainingApp", name="AI Model Training Application", description="Application to manage AI model training workflows", tags=["ai", "ml", "training"], isManualTriggerAuthorized=True, ) # Define a pipeline within the application for the training workflow. # A pipeline in Harness could represent a sequence of stages, including building a Docker container with your model training code, # pushing it to a registry, and deploying it to the Kubernetes cluster. pipeline = harness.Pipeline("trainingPipeline", name="Model Training Pipeline", orgId=project.orgId, projectId=project.id, identifier="modelTraining", yaml=""" # Here you would define your workflow in YAML format. # For the sake of brevity, I'm omitting the full detail. version: 1 kind: Pipeline metadata: name: Model Training projectIdentifier: AI Model Training Workflows orgIdentifier: default spec: stages: - stage: type: Build # More staging details go here... """, ) # You might also have environments that represent different stages of deployment # such as development, staging, and production. environment = harness.Environment("devEnvironment", name="Development", type="PreProduction", # This could be PreProduction or Production. appId=application.id, description="Development environment for model training", # Environment variables and other configurations can be defined here. ) # Lastly, we can define services that represent our deployable components. # In this case, our service could be our AI model training application. service = harness.Service("mlTrainingService", name="ML Training Service", orgId=project.orgId, projectId=project.id, yaml=""" # Here you would define your service in YAML format. # This defines the deployable components of your application. version: 1 type: Kubernetes """, tags=["ml", "training-service"], ) # Exporting some outputs for accessing the created resources pulumi.export("project_identifier", project.identifier) pulumi.export("application_name", application.name) pulumi.export("pipeline_identifier", pipeline.identifier) pulumi.export("development_environment", environment.name) pulumi.export("service_identifier", service.identifier)

    This program only scratches the surface and provides a framework for you to start with. You would need to fill in the details for the resources, particularly the YAML definitions of the pipeline and service, which outline the actual steps and configuration needed to train your AI models.

    Remember, to manage this Pulumi stack, you will need to install Pulumi CLI and the Harness provider. The actual payloads (YAML for pipeline and service definition, etc.) would need to be crafted according to your workflow's specifications and could involve multiple stages, triggers, and potentially other resources like secrets or variable groups.

    Furthermore, this program does not include cloud resources where the model training would take place. Incorporating these would require additional Pulumi resources from the corresponding cloud provider's Pulumi package.

    Lastly, always make sure that you check the official Pulumi Harness documentation to understand how to correctly configure these resources according to your requirements.