Real-Time AI Model Metrics Aggregation with Humio on Kubernetes

Question

Pulumi · Accepted Answer

The goal of your project is to aggregate metrics from an AI model in real-time and send this data to Humio for analysis and visualization. Humio is a log management system that gives you the ability to gain live insights into system metrics, logs, and traces. To perform real-time AI model metrics aggregation with Humio on Kubernetes, you will need to:

1. Have a Kubernetes cluster where your AI models are running, probably within Pods.
2. Instrument your AI model to output metrics, which could be done using standard logging mechanisms or specialized observability tools compatible with Kubernetes.
3. Deploy a log shipper or an agent inside your Kubernetes cluster to collect logs and metrics. Fluentd or Promtail can often be used for this purpose, as they can collect logs and metrics and forward them to Humio.
4. Ensure that Humio is set up to receive data from your Kubernetes cluster and that it is configured to visualize and analyze the metrics as required.

We are going to write a Pulumi program that sets up the infrastructure on a Kubernetes cluster which can be used to accomplish steps 3 and 4 above. We will assume that you have already instrumented your AI models to output metrics and that you have an existing Humio instance set up to receive data.

The Pulumi program will use the `pulumi_kubernetes` library to deploy the necessary resources to the cluster. We'll set up a ConfigMap to hold configuration for the log shipping, a Deployment for the log shipper agent, and necessary RBAC (Role-based access control) to allow the agent to access logs in the cluster.

Here's the program:

```python
import pulumi
import pulumi_kubernetes as k8s

# Your Kubernetes cluster's context name that you want to deploy to
k8s_context_name = 'your-k8s-context-name'

# Creating a provider to deploy resources to our selected cluster
k8s_provider = k8s.Provider('k8s-provider', context=k8s_context_name)

# Configuration for your log shipper, adjust as per log shipper and your AI model output format
log_shipper_config = {
    "log": {
        "format": "json",  # Assuming your AI model outputs logs in JSON format
        "path": "/var/log/my-ai-model/*.log",  # Log path based on your AI model's logging configuration
        "humio_repository": "ai-metrics",  # Humio repository name to send data to
        "tags": {"ai-model": "my-ai-model"},
    }
}

# ConfigMap to store configuration for log shipper
config_map = k8s.core.v1.ConfigMap(
    'log-shipper-config',
    metadata=k8s.meta.v1.ObjectMetaArgs(name="log-shipper-config"),
    data=log_shipper_config,
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# Define the role and role binding for the log shipper to access the logs
log_shipper_role = k8s.rbac.v1.Role(
    'log-shipper-read-logs',
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name="log-shipper-read-logs"
    ),
    rules=[k8s.rbac.v1.PolicyRuleArgs(
        api_groups=[""],
        resources=["pods/logs"],
        verbs=["get", "list"],
    )],
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

log_shipper_role_binding = k8s.rbac.v1.RoleBinding(
    'log-shipper-read-logs-binding',
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name="log-shipper-read-logs-binding"
    ),
    role_ref=k8s.rbac.v1.RoleRefArgs(
        api_group="rbac.authorization.k8s.io",
        kind="Role",
        name=log_shipper_role.metadata.name,
    ),
    subjects=[k8s.rbac.v1.SubjectArgs(
        kind="ServiceAccount",
        name="default",  # Assumes you are using the default service account
        namespace="default",
    )],
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# A Kubernetes Deployment for the log shipper/agent
log_shipper_deployment = k8s.apps.v1.Deployment(
    'log-shipper-deployment',
    spec=k8s.apps.v1.DeploymentSpecArgs(
        selector=k8s.meta.v1.LabelSelectorArgs(
            match_labels={"app": "log-shipper"},
        ),
        template=k8s.core.v1.PodTemplateSpecArgs(
            metadata=k8s.meta.v1.ObjectMetaArgs(labels={"app": "log-shipper"}),
            spec=k8s.core.v1.PodSpecArgs(
                containers=[k8s.core.v1.ContainerArgs(
                    name='log-shipper',
                    # The image for your log shipper, for example, a Fluentd image configured with Humio plugin
                    image='your-log-shipper-image',
                    volume_mounts=[k8s.core.v1.VolumeMountArgs(
                        name='config',
                        mount_path='/etc/log-shipper',
                    )],
                )],
                volumes=[k8s.core.v1.VolumeArgs(
                    name='config',
                    config_map=k8s.core.v1.ConfigMapVolumeSourceArgs(
                        name=config_map.metadata.name,
                    ),
                )],
            ),
        ),
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# Export the name of the deployment
pulumi.export('log_shipper_deployment_name', log_shipper_deployment.metadata.name)
```

In this program, we create:

- A `ConfigMap` resource to hold configuration details for your log shipper, which allows customizing how logs are collected and sent to Humio.
- A service account, role, and role binding that permits the log shipper to access logs from other pods in the cluster.
- A Deployment for the log shipper agent. This Deployment specifies the container image for the logging agent that you will be using (e.g., a preconfigured Fluentd image).

While fluentd, as mentioned, is a common choice for log shipping, you may need to replace `'your-log-shipper-image'` with the actual image name for your chosen log shipper that comes preconfigured to send logs to Humio.

You need to replace `k8s_context_name` with the actual name of your Kubernetes cluster's context if you are using a kubeconfig file with multiple contexts.

Also, we assume that your Kubernetes user or service account has sufficient permissions to deploy these resources. If you encounter permission issues, make sure to review your cluster's RBAC settings and consult your security policy to provision the correct permissions.

To use the above code, you will require Pulumi and the `pulumi_kubernetes` Python package installed, a Pulumi account, and a Kubernetes cluster. After setting up your Pulumi stack and configuring the Pulumi CLI with the desired state backend, you can save the aforementioned Python program to a file (e.g., `main.py`) and deploy it using the `pulumi up` command.

Remember, the actual visualization and analysis of metrics in Humio will require further configuration on the Humio side to set up dashboards, alerts, or conduct live queries on the data that this log shipper will forward to your Humio instance.