Secrets in Workloads¶

Kubernetes Secrets¶

Sometimes you want to use sensitive information within your code. For example passwords, OAuth tokens, or ssh keys. The best practice for saving such information in Kubernetes is via Kubernetes Secrets. Kubernetes Secrets let you store and manage sensitive information. Access to secrets is limited via configuration.

A Kubernetes secret may hold multiple key - value pairs.

Using Secrets in Run:ai Workloads¶

Our goal is to provide Run:ai Workloads with secrets as input in a secure way. Using the Run:ai command line, you will be able to pass a reference to a secret that already exists in Kubernetes.

Creating a secret¶

For details on how to create a Kubernetes secret see: https://kubernetes.io/docs/concepts/configuration/secret/. Here is an example:

apiVersion: v1
kind: Secret
metadata:
  name: my-secret
  namespace: runai-<project-name>
data:
  username: am9obgo=
  password: bXktcGFzc3dvcmQK

Then run:

kubectl apply -f <file-name>

Attaching a secret to a Workload on Submit¶

When you submit a new Workload, you will want to connect the secret to the new Workload. To do that, run:

runai submit -e <ENV-VARIABLE>=SECRET:<secret-name>,<secret-key> ....

For example:

runai submit -i ubuntu -e MYUSERNAME=SECRET:my-secret,username

Secrets and Projects¶

As per the note above, secrets are namespace-specific. If your secret relates to all Run:ai Projects, do the following to propagate the secret to all Projects:

• Create a secret within the runai namespace.
• Run the following once to allow Run:ai to propagate the secret to all Run:ai Projects:

runai-adm set secret <secret name> --cluster-wide

To delete a secret from all Run:ai Projects, run:

runai-adm remove secret <secret name> --cluster-wide

Secrets and Policies¶

A Secret can be set at the policy level. For additional information see policies guide.