1. Machine Learning Model Versioning with AWS ECR Public Repositories

    Python

    To achieve machine learning model versioning with AWS ECR Public Repositories, we need to create a repository in the Amazon Elastic Container Registry where we will store different versions of our Docker images that contain the machine learning models.

    Here are the steps using Pulumi with AWS in Python:

    1. Create an ECR Public Repository: Set up a new repository to store our model images.
    2. Tagging Docker Images: Use version tags to differentiate between the different model images you push to the repository.
    3. Pushing Images: Push your Docker images containing the models to ECR using the appropriate version tags.

    Below is a Pulumi program in Python that sets up an AWS ECR Public repository for storing machine learning models:

    import pulumi import pulumi_aws as aws # Step 1: Create a new ECR Public Repository for storing machine learning model images ml_model_repo = aws.ecrpublic.Repository("mlModelRepo", repository_name="ml-model-repo", # Provide a unique name for your repository # Additional properties can be set if needed. For example, setting the tag immutability option to `IMMUTABLE` ensures that images cannot be overwritten once they are pushed with a specific tag. image_tag_mutability="IMMUTABLE", ) # Output the repository URI, which will be used to tag and push images pulumi.export('repository_uri', ml_model_repo.repository_uri)

    Explanation

    • We use aws.ecrpublic.Repository to create a public repository in AWS Elastic Container Registry (ECR). We set the repository name to ml-model-repo, and we make sure that once a Docker image is tagged and pushed to the repository, the tag is immutable. This means no other image can be pushed using the same tag, which helps maintain version integrity.
    • repository_uri is an output that gives you the URI of the repository created. This URI is used when you need to push or pull images from the repository.

    Next Steps

    After the repository is created, you'll want to tag and push your machine learning model images accordingly. For example, if you have a model version 1.0, you can tag your Docker image as ml-model-repo:1.0 and push it to ECR. When you have an updated model version, say 1.1, you tag it as ml-model-repo:1.1 and push the new image. This way, you can maintain a history of model versions within your repository.

    Note: Pushing Docker images to the ECR repository and managing versions is typically done through a CI/CD pipeline or manually through the command line using the AWS CLI or Docker CLI after you build your Docker image with the model included.

    Remember that to work with AWS resources through Pulumi, you need to configure your AWS credentials for Pulumi to access your AWS account. This usually involves setting up the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables or configuring an AWS profile with aws configure.

    Refer to the Pulumi AWS Documentation for more details on the resources used and other configurations you can apply to your ECR Public repository.


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