1. Automated ML Model Training Pipelines on OCI Kubernetes


    Creating an automated Machine Learning (ML) model training pipeline on Oracle Cloud Infrastructure (OCI) Kubernetes involves setting up a number of resources that work in conjunction to train ML models. This includes configuring OCI Kubernetes, setting up data science pipelines, and possibly utilizing job runs for the training itself. We'll use a few Pulumi resources primarily from the oci package to accomplish this.

    The key resources involved in this process would be:

    1. oci.ContainerEngine.Cluster - To create a managed Kubernetes cluster.
    2. oci.DataScience.Pipeline - To define the steps of the ML pipeline.
    3. oci.DataScience.Job - To describe the ML training job details.
    4. oci.DataScience.JobRun - To execute the ML training job.

    For the sake of this example, we will mainly focus on setting up the oci.DataScience.Pipeline, oci.DataScience.Job, and oci.DataScience.JobRun resources. We will assume that a Kubernetes cluster is already provisioned, but if it's not, remember that you can use Pulumi to create a Cluster using oci.ContainerEngine.Cluster.

    Below is the program that automates an ML model training pipeline:

    import pulumi import pulumi_oci as oci # Set up the Oracle Cloud Infrastructure (OCI) provider configuration # In this instance, Pulumi will assume that you have the OCI configuration set through # environment variables, OCI CLI configuration, or the Pulumi OCI provider configuration. # Make sure you have the appropriate access rights and permissions scoped for creating resources. # Start by defining a compartment ID and a project ID which you will use for the Data Science resources. compartment_id = "your-compartment-id" project_id = "your-project-id" # Create a new Data Science Pipeline in the specified compartment and project. pipeline = oci.datascience.Pipeline("mlPipeline", compartment_id=compartment_id, project_id=project_id, display_name="MyMLPipeline", description="Pipeline for ML model training", pipeline_parameters={ # Define any input parameters for your pipeline here. # For instance, you might want to parameterize the dataset location, model hyperparameters, etc. } # Define additional properties such as pipeline steps, configurations, etc. ) # Define the specifics of the ML training job, such as the Docker image to use, # command line arguments, environment variables, and so on. job = oci.datascience.Job("mlJob", compartment_id=compartment_id, project_id=project_id, display_name="MyTrainingJob", description="Job for training an ML model", job_configuration_details={ "job_type": "DEFAULT", # For a custom job, you might specify "CUSTOM" here. # Add additional job configuration details such as environment variables, # command line arguments, Docker image to use, etc. }, job_infrastructure_configuration_details={ "shape_name": "VM.Standard2.1", # The shape of the compute instance to run the job "subnet_id": "your-subnet-id", # Subnet ID where the job should run # Possibly add more infrastructure configuration here. } ) # In order to run the job, an OCI Data Science Job Run resource is created. # This resource is what triggers the actual execution of the job in the environment specified above. job_run = oci.datascience.JobRun("mlJobRun", compartment_id=compartment_id, project_id=project_id, job_id=job.id, # Refers to the previously defined job resource display_name="MyTrainingJobRun", description="Job run for ML model training", # You could override job configurations for specific job runs if needed here. ) # To access or manage the created resources, you might want to export certain details. pulumi.export("pipeline_id", pipeline.id) pulumi.export("job_id", job.id) pulumi.export("job_run_id", job_run.id)

    This Pulumi program provides the backbone of an automated ML training pipeline. It creates a pipeline within OCI's data science service, defines a job that outlines the specifics of the ML training task, and finally sets up a job run which executes the training job.

    Remember that the actual job definition will be more involved, typically with a Docker image that contains your ML training code and data. The environment, arguments, and compute specifications would need to be tailored to your exact training workload.

    The exported outputs at the end of the program can be used to reference the created resources within OCI's web console or through Pulumi's CLI to monitor and manage the pipeline's lifecycle further.

    Before deploying this infrastructure as code, ensure you replace placeholder ids with actual values for the compartment, project, and subnet. You may also need to specify more configuration details in the job_configuration_details and job_infrastructure_configuration_details depending on your training job's specific needs.