Load-balanced TensorFlow Serving Clusters for High Availability

Question

Pulumi · Accepted Answer

When creating a load-balanced TensorFlow Serving cluster for high availability (HA), we need to consider a few architectural components:
1. **TensorFlow Serving Instances**: You would run multiple TensorFlow Serving instances for scalability and redundancy.
2. **Load Balancer**: A load balancer is used to distribute traffic among the TensorFlow Serving instances, improving availability and robustness of the system.
3. **Kubernetes Cluster**: Often, a container orchestration system like Kubernetes is used to manage and scale the TensorFlow Serving instances.
4. **Pod Disruption Budget**: In Kubernetes, Pod Disruption Budgets (PDB) can be used to ensure that a certain percentage or number of pods are always running, even during maintenance operations, to maintain service availability.
5. **Replica Set**: A Kubernetes ReplicaSet ensures that a specified number of pod replicas are running at any given time, further contributing to high availability.

Below is a Pulumi program written in Python that sets up such an architecture. The program assumes you have a Docker image for your TensorFlow Serving application ready to be deployed.

```python
import pulumi
import pulumi_kubernetes as k8s

# Configuring the Kubernetes provider
kubeconfig = pulumi.Config('kubernetes').get('kubeconfig')
k8s_provider = k8s.Provider('k8s', kubeconfig=kubeconfig)

# Configuring the TensorFlow Serving application
app_name = 'tensorflow-serving'
replica_count = 3 # You can adjust the number of replicas based on your requirements

# Define a Kubernetes Deployment for TensorFlow Serving
app_labels = {'app': app_name}
tf_deployment = k8s.apps.v1.Deployment(
    f'{app_name}-deployment',
    metadata={'name': app_name},
    spec=k8s.apps.v1.DeploymentSpecArgs(
        replicas=replica_count,
        selector={'matchLabels': app_labels},
        template=k8s.core.v1.PodTemplateSpecArgs(
            metadata={'labels': app_labels},
            spec=k8s.core.v1.PodSpecArgs(
                containers=[k8s.core.v1.ContainerArgs(
                    name=app_name,
                    image='your-docker-image', # Replace with your TensorFlow Serving Docker image
                    ports=[k8s.core.v1.ContainerPortArgs(container_port=8501)] # Default TensorFlow Serving port
                )],
            ),
        ),
    ),
    __opts__=pulumi.ResourceOptions(provider=k8s_provider)
)

# Define a Kubernetes Service to load balance traffic to the TensorFlow Serving instances
tf_service = k8s.core.v1.Service(
    f'{app_name}-service',
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name=f'{app_name}-service',
    ),
    spec=k8s.core.v1.ServiceSpecArgs(
        selector=app_labels,
        ports=[k8s.core.v1.ServicePortArgs(
            port=80,
            target_port=8501,
        )],
        type='LoadBalancer', # Using a load balancer type to manage external access to the service
    ),
    __opts__=pulumi.ResourceOptions(provider=k8s_provider)
)

# Define a PodDisruptionBudget to ensure high availability during maintenance
tf_pdb = k8s.policy.v1beta1.PodDisruptionBudget(
    f'{app_name}-pdb',
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name=f'{app_name}-pdb',
    ),
    spec=k8s.policy.v1beta1.PodDisruptionBudgetSpecArgs(
        min_available=1, # At least one replica should always be available
        selector={'matchLabels': app_labels},
    ),
    __opts__=pulumi.ResourceOptions(provider=k8s_provider)
)

# Exports
pulumi.export('tf_serving_service_ip', tf_service.status.apply(lambda status: status.load_balancer.ingress[0].ip))
```

This program performs the following actions:
- Sets up a Kubernetes provider using your existing `kubeconfig`.
- Defines a Kubernetes `Deployment` that specifies how to run the TensorFlow Serving Docker application, including the number of replicas.
- Creates a Kubernetes `Service` of type `LoadBalancer` that will distribute incoming traffic across the available TensorFlow Serving pods.
- Implements a `PodDisruptionBudget` that ensures at least one instance of TensorFlow Serving is always running, even during maintenance events which may cause pod evictions.

To use the above code:
1. Replace `'your-docker-image'` with the actual image path of your TensorFlow Serving Docker image.
2. Make sure the `kubeconfig` entry in the Pulumi configuration file is correctly set up to point to the Kubernetes cluster where you want to deploy your application.
3. Run this Pulumi program using your Pulumi CLI to deploy your highly available TensorFlow Serving application to the configured Kubernetes cluster.

This setup will ensure you have a highly available TensorFlow Serving application, backed by a load balancer, running in a Kubernetes environment with the safeguards of a PDB.