Tracing Distributed AI Inference Workloads with Elastic APM on Kubernetes

Question

Pulumi · Accepted Answer

To set up tracing for distributed AI inference workloads with Elastic APM on Kubernetes, you need to have an application instrumented to send data to Elastic APM. Kubernetes can then run this application within its pods. We'll leverage the Pulumi infrastructure as code tool to orchestrate and manage these resources.

Here's what you will be doing:
1. **Deploy an Elastic APM Server**: This acts as the central point that collects tracing data from your application instances. It processes the data and sends it to Elasticsearch.
2. **Deploy Elasticsearch**: Elasticsearch stores and indexes the tracing data. You can then visualize and analyze this data through Kibana.
3. **Instrument Your AI Inference Application**: Modify your application code to use the Elastic APM agent. This agent sends the trace data to your APM server.
4. **Create Kubernetes Resources**: Deploy the Elastic APM Server and your application within a Kubernetes cluster using Pulumi.

Elastic provides an [Elastic Cloud on Kubernetes](https://www.elastic.co/guide/en/cloud-on-k8s/current/index.html) (ECK) Operator which makes it simpler to deploy APM server, Elasticsearch, and Kibana on Kubernetes clusters.

Let's consider that you already have a Kubernetes cluster up and running and have access to it via `kubectl`. In the Pulumi program below, we'll create the necessary Kubernetes resources for Elastic APM, Elasticsearch, and deploy a sample AI inference service that sends tracing to the APM server.

```python
import pulumi
import pulumi_kubernetes as k8s

# Example assumes you have configured the Pulumi Kubernetes provider
# to connect to your existing Kubernetes cluster where you want to deploy Elastic APM

# Define the Elastic APM server deployment
elastic_apm_deployment = k8s.apps.v1.Deployment(
    "elastic-apm-server",
    spec={
        "selector": {"matchLabels": {"app": "apm-server"}},
        "replicas": 1,
        "template": {
            "metadata": {"labels": {"app": "apm-server"}},
            "spec": {
                "containers": [{
                    "name": "apm-server",
                    "image": "docker.elastic.co/apm/apm-server:7.6.0",  # Use the appropriate version
                    "ports": [{"containerPort": 8200}],
                    # Remember to set your environment variables for the APM server such as secret tokens or configuration settings
                    "env": [
                        # Configure the APM server to point to Elasticsearch
                        {"name": "ELASTICSEARCH_URL", "value": "http://elasticsearch-cluster:9200"},
                        {"name": "ELASTICSEARCH_USERNAME", "value": "elastic"},
                        {"name": "ELASTICSEARCH_PASSWORD", "value": "password"}  # Use Kubernetes secrets in production
                    ],
                }],
            },
        },
    })

# Define the Elasticsearch deployment using the ECK operator
# This is a simplified example and in production, you might need to set resource limits, storage volumes and more
elasticsearch_deployment = k8s.apiextensions.CustomResource(
    "elasticsearch",
    api_version="elasticsearch.k8s.elastic.co/v1",
    kind="Elasticsearch",
    metadata={"name": "elasticsearch-cluster"},
    spec={
        "version": "7.6.0",  # Use the appropriate version
        "nodeSets": [{
            "name": "default",
            "count": 1,
            "config": {
                "node.master": True,
                "node.data": True
            }
        }]
    })

# Instrumented AI Inference service deployment example
ai_service_deployment = k8s.apps.v1.Deployment(
    "ai-inference-service",
    spec={
        "selector": {"matchLabels": {"app": "ai-inference-service"}},
        "replicas": 1,  # You can scale this up based on your needs
        "template": {
            "metadata": {"labels": {"app": "ai-inference-service"}},
            "spec": {
                "containers": [{
                    "name": "ai-service",
                    "image": "your-ai-service-image",  # Your docker image with the app and Elastic APM agent
                    "ports": [{"containerPort": 8080}],
                    # Environment configuration for the Elastic APM agent
                    "env": [
                        {"name": "ELASTIC_APM_SERVICE_NAME", "value": "ai-service"},
                        {"name": "ELASTIC_APM_SERVER_URL", "value": "http://apm-server:8200"},
                        {"name": "ELASTIC_APM_SECRET_TOKEN", "value": "token"}  # Ideally, use Kubernetes secrets
                    ],
                }],
            },
        },
    })

# Stack exports to retrieve the DNS names/IPs after deploying
pulumi.export("elastic_apm_server_ip", elastic_apm_deployment.status.apply(lambda s: s.load_balancer.ingress[0].ip))
pulumi.export("elasticsearch_cluster_service_ip", elasticsearch_deployment.status.apply(lambda s: s.service.name))
```

In this program:
- We define the Elastic APM server as a Kubernetes Deployment. You can access it using port 8200, and it connects to the Elasticsearch cluster.
- We also define an Elasticsearch cluster using the ECK Operator, which runs Elasticsearch within the Kubernetes cluster.
- For the AI Inference service, you deploy it as another Kubernetes Deployment. Note that you need to integrate your AI service with the Elastic APM agent, [which you can check out in the official documentation](https://www.elastic.co/guide/en/apm/agent/index.html).
- Lastly, we export some information that could be useful for you after deployment, such as IPs and service names.

Remember, in a real-world scenario, secure your Elasticsearch and APM server deployments with proper secrets management, use persistent volumes for Elasticsearch data, configure resource requests and limits, setup network policies among other security practices.

It's important to instrument your AI application correctly with the Elastic APM agent. Depending on the language and framework your application is written in, you will need to add the appropriate Elastic APM agent and initialize it within your application code. Refer to the [Elastic APM documentation](https://www.elastic.co/guide/en/apm/agent/index.html) for specific guidance on how to do this.

Once your application is sending data to APM Server, and Elasticsearch is storing this data, you can use Kibana to visualize and analyze the performance of your distributed AI inference workloads. You might also want to deploy Kibana using Kubernetes and Pulumi, although it's not covered in this program.