8/27/2024

Deploying Ollama on Kubernetes: A Step-by-Step Guide

The world of AI has been ever-evolving and deploying Large Language Models (LLMs) for various applications is becoming a norm. One of the exciting players in this area is Ollama, a tool that provides an efficient way to run LLMs locally using Kubernetes to orchestrate their deployment. In this guide, we will walk you through how to deploy Ollama on Kubernetes, using some good practices along the way.

Why Deploy Ollama on Kubernetes?

Before we dive into the nitty-gritty of the setup, let’s talk about the REASONS why deploying Ollama on Kubernetes is a smart choice:

Scalability: Kubernetes allows you to scale your application seamlessly, making it easy to handle fluctuating loads.
High Availability: By deploying on Kubernetes, you ensure your application is resilient and can recover quickly from failures.
Easier Management: Kubernetes simplifies the management of multiple instances of your application.
Microservices Architecture: With Kubernetes, you can implement microservices, which is beneficial for applications needing independent scaling.

Prerequisites

Before you get started with the deployment, make sure you have the following:

A running Kubernetes cluster.
Installed
1kubectl
, the command line tool to interact with the cluster.
Helm, the package manager for Kubernetes.
A basic understanding of how to use Docker, Kubernetes, & Helm charts.
Your Kubernetes cluster has access to sufficient resources (CPU & RAM).

Step 1: Set Up Your Kubernetes Cluster

If you haven't set up your Kubernetes cluster yet, you can use tools like MicroK8s or Minikube for local development. The installation process can differ depending on your choice of system, but generally, you can follow the installation process detailed in the respective documentation.

Step 2: Install Ollama Helm Chart

Helm charts make it easier to deploy applications. The Ollama Helm Chart simplifies the deployment of Ollama on Kubernetes. Follow these steps:

Add the Ollama Helm repository:
1 2 3bash helm repo add ollama-helm https://otwld.github.io/ollama-helm/ helm repo update
Install Ollama: To install Ollama, run the following command:
1 2bash helm install ollama ollama-helm/ollama --namespace ollama
Here, we're creating a new namespace called
1ollama
.

Step 3: Check the Installation

Once installed, you can check if everything is running smoothly by listing the pods in the

ollama

namespace:

1
2

bash
kubectl get pods --namespace ollama

If everything is working as expected, you should see the Ollama pods listed.

Step 4: Exposing Ollama

Now that you have Ollama running, you might want to expose it to access the REST API. You can achieve this by creating a service:

Create a service type LoadBalancer to expose Ollama:
1 2 3 4 5 6 7 8 9 10 11 12 13yaml apiVersion: v1 kind: Service metadata: name: ollama-service namespace: ollama spec: type: LoadBalancer ports: - port: 11434 targetPort: 11434 selector: app: ollama
Save this YAML as
1ollama-service.yaml
and run:
1 2bash kubectl apply -f ollama-service.yaml
Accessing the Ollama API: Once the service is running, you should be able to access Ollama using:
1 2bash curl http://<service-external-ip>:11434/api/generate
Replace
1<service-external-ip>
with the actual IP assigned to your service. You can find it with:
1 2bash kubectl get service ollama-service --namespace ollama

Step 5: Interacting with Ollama

With everything set up, now comes the exciting part: interacting with Ollama!

Ollama REST API: You can use various client libraries to interact with Ollama:
- Ollama Python Client
- Ollama JS Client

Simply import these libraries into your project and start making calls to the Ollama API.

Best Practices

Use the latest version of Kubernetes: Make sure your Kubernetes cluster is updated to the latest version for the best features and security fixes. Control which version of Ollama you are using; keep track of version compatibility with the official release notes.
Monitor the usage: Utilize tools like Prometheus and Grafana to monitor your Ollama deployment, keeping an eye on performance metrics.
Back up configurations: Regularly back up your deploy configurations. This may include saving your Helm values in a Git repository.
Test Locally First: Use a local Kubernetes setup (like MicroK8s) to validate your deployments before anything goes live.

Conclusion

Deploying Ollama on Kubernetes is not just about leveraging powerful AI but also about creating an infrastructure capable of scaling and managing your AI services without hassle. In just a few steps, you can have a fully operational Ollama environment!

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