1. Kubernetes-based AI Pipelines Accessing 1Password Secrets.

    Python

    In order to create Kubernetes-based AI Pipelines that access secrets from 1Password, you'll need to securely retrieve those secrets in a manner that can be consumed by your Kubernetes Pods running the AI workloads. This typically involves using Kubernetes Secrets to make sensitive information like API keys or passwords available to your application inside the cluster without hard-coding them into your pod specs or Docker images.

    Here’s how you can achieve this with Pulumi:

    1. Kubernetes Secret: Define a Kubernetes Secret in your cluster that will hold the credentials obtained from 1Password.

    2. External Secret Management: Since Pulumi doesn't have a native integration with 1Password, you would need to use an intermediary service or tool that can interface with 1Password and expose the secrets to Kubernetes in an acceptable format.

    One approach is to use an external secret operator for Kubernetes that can fetch secrets from 1Password and inject them into the cluster as Kubernetes Secrets. As of now, Pulumi does not provide a direct provider for 1Password, so this step will have to be done outside Pulumi, or by using custom scripts wrapped in Dynamic Providers.

    1. Access Secrets in AI Pipelines: Modify the deployment configuration for your AI Pipelines to consume the Kubernetes Secret.

    Let's write the program for step 1, since steps 2 and 3 involve tools and processes that are currently not directly supported by Pulumi. This example assumes that you already have a way of pulling secrets from 1Password and are ready to inject them into Kubernetes as a Secret.

    import pulumi import pulumi_kubernetes as k8s # Assuming you have obtained secret data from 1Password # The data values should be base64 encoded strings. onepassword_secrets = { 'API_KEY': 'c29tZS1hcGkta2V5', # placeholder for base64 encoded API key 'ANOTHER_SECRET': 'YW5vdGhlci1zZWNyZXQ=' # placeholder for base64 encoded secret } # Create a Kubernetes Secret in the cluster that holds the 1Password secrets. ai_pipeline_secrets = k8s.core.v1.Secret('ai-pipeline-secrets', metadata=k8s.meta.v1.ObjectMetaArgs( name='ai-pipeline-secrets' ), data=onepassword_secrets, ) # Export the Kubernetes Secret name pulumi.export('secret_name', ai_pipeline_secrets.metadata['name'])

    Explanation:

    • pulumi_kubernetes.core.v1.Secret: This resource is used to define a Kubernetes Secret which stores sensitive data, such as tokens or passwords, that can be used by Kubernetes Pods.
    • metadata: Contains metadata for the secret resource like the name.
    • data: Stores the data within the Secret. The secret data must be base64 encoded as per Kubernetes Secret requirements.

    For the AI Pipeline Pods to use these secrets, you'd typically reference the secret name and the specific key within the secret in the environment variables section or mount the secrets as files in your Pod specifications. The custom integration part where you actually fetch these secrets from 1Password would have to be set up before running this Pulumi program.

    The placeholder base64 encoded strings in the onepassword_secrets dictionary should be replaced with actual base64 encoded secrets obtained from 1Password. Make sure to properly handle and secure the secret data during this process.

    Remember to install Pulumi and set up the Kubernetes provider. You’ll also need kubectl configured to communicate with your Kubernetes cluster where the AI pipeline is deployed.

    For further learning, you can explore the Pulumi documentation and the Kubernetes documentation about Secrets.


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