Kubernetes Secrets for Sensitive Data in Machine Learning Pipelines

Question

Pulumi · Accepted Answer

When working with machine learning pipelines on Kubernetes, you often need to handle sensitive data such as API keys, credentials, or other secrets. Kubernetes provides a resource called `Secret` to securely store and manage sensitive information.

In Pulumi, to create a Kubernetes `Secret`, you can use the `pulumi_kubernetes.core.v1.Secret` resource. The `Secret` resource allows you to store sensitive data as key-value pairs. You can use the data stored in the `Secret` in your Kubernetes pods by referencing it in your pod definition.

Here's a program that demonstrates how to create a Kubernetes `Secret` using Pulumi. We will use a fictitious example where we have `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` as our secrets to be used in a machine learning pipeline.

First, I'll explain how to set up a `Secret` resource, and then I'll provide the actual Pulumi program in Python:

1. **Import the Pulumi Kubernetes package**: This gives you access to the Kubernetes resource APIs that Pulumi provides.

2. **Create a `Secret` resource**: Pass the secrets as key-value pairs under the `data` or `stringData` parameter. With `stringData`, you can directly pass the secrets as strings without encoding them. Pulumi will handle the base64 encoding for you.

3. **Reference the `Secret` in a Kubernetes Pod**: While this example will not create a pod, I'll show where you would reference the `Secret`.

Let's see how this works in code:

```python
import pulumi
import pulumi_kubernetes as k8s

# This example assumes you have a Kubernetes cluster configured and set as the current context in your kubeconfig.

# Creating a Kubernetes Secret with sensitive data for a machine learning pipeline.
ml_secret = k8s.core.v1.Secret("ml-secret",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name="ml-secret",  # Name of the secret
    ),
    # Data within stringData is managed as plaintext and does not require manual base64 encoding.
    # The Pulumi program will automatically encode it as it deploys to the Kubernetes cluster.
    string_data={
        "AWS_ACCESS_KEY_ID": "youraccesskeyid",        # Replace with your AWS Access Key ID
        "AWS_SECRET_ACCESS_KEY": "yoursecretaccesskey" # Replace with your AWS Secret Access Key
    }
)

# Exports the secret name. This is useful for referencing it in other resources such as pods.
pulumi.export('secret_name', ml_secret.metadata['name'])
```

### Note:
- In the `string_data`, replace `"youraccesskeyid"` and `"yoursecretaccesskey"` with your actual AWS credentials or any other secrets you want to manage.

- Remember to never hard-code credentials in your Pulumi program. Instead, use environment variables or Pulumi configuration to pass sensitive data.

- When referencing this secret in a Kubernetes pod, you would use the secret's name in the pod's `env` or `envFrom` configuration to expose the secret's keys as environment variables inside the containers.

This program will create a Kubernetes secret named `ml-secret` with two keys for AWS credentials. You can apply the same pattern to store other kinds of secrets you may need in your machine learning pipelines.