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Guardrail Resource Management

Within DynamoGuard, each policy is associated with a specific model that runs on a GPU. To efficiently manage compute resources for input content policies, you have the ability to scale up (deploy) or scale down (deactivate) policies. When a policy is spun down, it cannot be used via the /analyze/ or /chat/ endpoints. This can be helpful when a policy is being created and testing but is not yet integrated into an application.

Scaling Up and Down Policies via UI​

To manage the deployment state of policies, you can toggle the policy from the UI. For content policies where traning is complete, selecting More Options will show a button to either Scale Up Policy or Scale Down Policy based on the policy's current state.

Scaling Up and Down Policies via API​

To manage the deployment state of policies, you send a PUT request to the Policy API Endpoint using the isEnabled parameter in the request body. To scale up a policy, set isEnabled to true, and to scale down a policy, set isEnabled to false. Below is an example of scaling down a policy:

    url = "{YOUR_DYNAMOAI-URL}/moderation/policy/{POLICY_ID}"
    headers = {
        "Authorization": "Bearer {YOUR_DYNAMOAI-TOKEN}",
        "Content-Type": "application/json"
    }

    data = {
        "isEnabled": False 
    }

    response = requests.put(url, headers=headers, json=data)

Scaling Up and Down - Policy Statuses​

After scaling policies up or down, you will observe the following status changes.These states help you track the readiness of your model-backed policies for inference tasks.:

  • Scaling Up: Policies will transition from Not Deployed to Scaling Up to Deployed
  • Scaling Down: Policies will transition from Deployed to Scaling Down to Not Deployed