1. CI/CD Pipelines for Model Training Workflows.

    Python

    Creating CI/CD pipelines for model training workflows involves setting up infrastructure that automates the process of training machine learning models. In this context, you need a version control system for your code, compute resources to train models, and potentially some storage for datasets and artifacts. You might also want a system for continuous integration to run tests each time the code changes.

    Below, we'll construct a Pulumi program that sets up a simple pipeline on AWS using the following resources:

    • CodeCommit: For hosting the version-controlled source code.
    • EC2 Instance: To run the training jobs.
    • S3 Bucket: For storing datasets and model artifacts.

    This program will:

    1. Create a new AWS CodeCommit repository for your machine learning code.
    2. Provision an EC2 instance that will be used for running training jobs. We'll choose a machine appropriate for machine learning tasks.
    3. Set up an S3 bucket to store your datasets and trained models.

    Here's the Pulumi program in Python that sets up the above infrastructure:

    import pulumi import pulumi_aws as aws # Create a new AWS CodeCommit repository code_repo = aws.codecommit.Repository("model-training-repo", description="Repository for machine learning model training code") # Provision an EC2 instance to run training jobs # For this example, we'll use a general-purpose instance type, but choose one based on your needs. ec2_instance = aws.ec2.Instance("trainer-instance", instance_type="t2.medium", ami="ami-0c55b159cbfafe1f0", # Update this to the latest appropriate AMI in your region tags={"Name": "model-trainer"}) # Create an S3 bucket for storing datasets and trained models s3_bucket = aws.s3.Bucket("model-training-artifacts", acl="private", tags={"Name": "Model Training Artifacts"}) # Output the repository clone URL, EC2 instance public IP, and S3 bucket endpoint pulumi.export('code_repo_clone_url_http', code_repo.clone_url_http) pulumi.export('ec2_instance_public_ip', ec2_instance.public_ip) pulumi.export('s3_bucket_endpoint', s3_bucket.bucket_regional_domain_name)

    In this program:

    • aws.codecommit.Repository creates a new CodeCommit repository to store your machine learning code.
    • aws.ec2.Instance provisions an EC2 instance of type t2.medium, which you can use for training your models. Make sure to replace the AMI with one that is suitable for your region and needs.
    • aws.s3.Bucket sets up a new private S3 bucket where you can store your ML datasets and model outputs.

    Remember to replace the AMI in the aws.ec2.Instance resource with the correct one for your region and required specifications. You can find the appropriate AMI for your region and requirements by using the AWS Management Console, AWS CLI, or by visiting the AWS EC2 AMI Locator website.

    Additionally, to actually automate model training as part of a CI/CD pipeline, you would need to set up automation scripts that trigger training jobs on the EC2 instance when code changes in the repository. This might involve using AWS CodePipeline, Jenkins, or another CI/CD automation server, along with scripting to run the training process on EC2. However, that setup goes beyond the scope of a Pulumi program and involves more specific workflow configurations and logic.


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