1. OCI Artifacts Registry for Machine Learning Models


    Creating an OCI Artifacts Registry to store machine learning models involves several steps and considerations. An OCI Artifacts Registry is a managed repository provided by Oracle Cloud Infrastructure for storing artifacts like container images, Helm charts, and other types of content. However, when it comes to storing Machine Learning models specifically, we are looking at an OCI supported service that provides model storage and versioning capabilities.

    In the context of Pulumi, if you want to create resources on Oracle Cloud Infrastructure (OCI) to store machine learning models, you would typically need to use the OCI Pulumi provider (pulumi_oci). However, as of my knowledge cutoff in 2023, there is no direct implementation of an "OCI Artifacts Registry" resource within the Pulumi Registry for managing machine learning models.

    A common approach for building a registry-like system for machine learning models would be to create a storage solution like an Object Storage Bucket where you can store the model files, or to use a service specifically designed for machine learning, such as Oracle's Data Science service which offers managed infrastructure to build, train, deploy and manage machine learning models but does not directly offer a registry for machine learning models. The integration and setup of these services would be a bit more involved and would require some additional development and configuration in terms of data structuring, model versioning, and access controls.

    In the absence of a direct Pulumi implementation for an OCI Artifacts Registry for ML models and given the user’s novice status, it would be beneficial to familiarize oneself with the services that OCI provides for machine learning. I’ll guide you through creating a simple OCI Object Storage Bucket which could serve as a starting point for storing machine learning models.

    Here's a Pulumi Python program that creates an OCI Object Storage Bucket:

    import pulumi import pulumi_oci as oci # Create a new OCI Object Storage Bucket to store machine learning models # The Bucket will be used as a simple registry-like store. # When creating a bucket, several options can be configured like versioning, # access tier, and others to manage how your Machine Learning models are stored. # First, create or select a compartment where the bucket will be created. compartment_id = "your-compartment-ocid" # Replace with your compartment OCID # Next, define the storage bucket with its configurations. machine_learning_models_bucket = oci.objectstorage.Bucket("machineLearningModelsBucket", compartment_id=compartment_id, name="machine-learning-models-bucket", storage_tier="Standard", public_access_type="NoPublicAccess", # Enable versioning if you want to keep track of multiple versions of your models versioning="Enabled", # You can also set auto-tiering to archive or delete old versions after a set period auto_tiering="ObjectLifecyclePolicyDriven" ) # Optionally, you can also create Object Lifecycles to automatically manage objects within the bucket lifecycle_rule = oci.objectstorage.Bucket("machineLearningModelsLifecycleRule", # Specify the bucket to which the lifecycle rule will apply bucket=machine_learning_models_bucket.name, # Specify the actions and conditions of the lifecycle rule object_lifecycle_policy={ "rules": [ { "name": "ArchiveOldModels", "action": { "type": "ARCHIVE", "time_amount": "30", "time_unit": "DAYS", }, "target": { "prefix": "", "tags": {"stage": "old"}, }, }, { "name": "DeleteExpiredModels", "action": { "type": "DELETE", "time_amount": "365", "time_unit": "DAYS", }, "target": { "prefix": "", "tags": {"stage": "expired"}, }, } ] } ) # Export the bucket's name and OCID so it can be easily referenced pulumi.export("bucket_name", machine_learning_models_bucket.name) pulumi.export("bucket_id", machine_learning_models_bucket.id)

    In this program, we define a new OCI Bucket specifically for storing machine learning models. We enable versioning on the bucket to keep different versions of each stored model, allowing for better management and traceability of model updates. We also create an Object Lifecycle rule that will automatically archive models (if not accessed or modified in the last 30 days) and eventually delete them after a year.

    After the bucket is created, you can start uploading your machine learning models to it. Each model may be represented as a separate object in the bucket, and you can use tags or specific naming conventions to organize them further.

    To actually upload and manage the models in OCI, you'd need to use the OCI SDK (in Python, Node.js, Go, etc.), the OCI CLI, or the OCI Management Console. Pulumi is used to set up the infrastructure but not directly for file management within that infrastructure.

    Remember, the above code is a starting point for an artifacts registry. A full-fledged solution for machine learning model management would involve additional features like security controls, metadata management, access logging, and possibly integration with other OCI services or custom tooling to manage the lifecycle and versioning of the machine learning models more effectively.