Ollama - Part4 - Vision assistant using LLaVA

In this exercise we will interact with LLaVA which is an end-to-end trained large multimodal model and vision assistant. We will use the Ollama REST API to prompt the model using Python.

Full project in my GitHub

https://github.com/vineethac/Ollama/tree/main/ollama_vision_assistant

LLaVA, being a large multimodal model and vision assistant, can be utilized for various tasks. Here are a couple of use cases:

• Image Description Generation

Input: Provide LLaVA with an image.

Use Case: LLaVA can generate descriptive text or captions for the content of the image. This is particularly useful for automating image cataloging or enhancing accessibility for visually impaired users.

• Question-Answering on Text and Image

Input: Ask LLaVA a question related to a given text or show it an image.

Use Case: LLaVA can comprehend the context and provide relevant answers. For instance, you could ask about details in a picture or seek information from a paragraph, and LLaVA will attempt to answer accordingly.

These are just a few examples, and the versatility of LLaVA allows for exploration across a wide range of multimodal tasks and applications.

Sample interaction with LLaVA model

Image

Image credits: shutterstock

Prompt

python3 query_image.py --path=images/img1.jpg --prompt="describe the picture 
and what are the essentials that one need to carry generally while going these 
kind of places?"

Response

{
    "model": "llava",
    "created_at": "2024-01-23T17:41:27.771729767Z",
    "response": " The image shows a man riding his bicycle on a country road, surrounded by 
    beautiful scenery and mountains. He appears to be enjoying the ride as he navigates 
    through the countryside. \n\nWhile cycling in such environments, an essential item one 
    would need to carry is a water bottle or hydration pack, to ensure they stay well-hydrated 
    during the journey. In addition, it's important to have a map or GPS device to navigate 
    through potentially less familiar routes and avoid getting lost. Other useful items for 
    cyclists may include a multi-tool, first aid kit, bike lock, snacks, spare clothes, 
    and a small portable camping stove if planning an overnight stay in the wilderness.",

Hope it was useful. Cheers!

Ollama - Part3 - Web UI for Ollama to interact with LLMs

In the previous blog posts, we covered the deployment of Ollama on Kubernetes cluster and demonstrated how to prompt the Language Models (LLMs) using LangChain and Python. Now we will delve into deploying a web user interface (UI) for Ollama on a Kubernetes cluster. This will provide a ChatGPT like experience when engaging with the LLMs.

Full project in my GitHub

https://github.com/vineethac/Ollama/tree/main/ollama_webui

The above referenced GitHub repository details all the necessary steps required to deploy the Ollama web UI. The Following diagram outlines the various components and services that interact with each other as part of this entire system:

For detailed information on deploying Prometheus, Grafana, and Loki on a Kubernetes cluster, please refer this blog post.

A sample interaction with the mistral model using the web UI is given below.

Hope it was useful. Cheers!

GitOps using Argo CD - Part2 - Mini project

In the previous blog post, we discussed deploying Argo CD on a Kubernetes cluster and explored the fundamentals of application management. This time, we'll leverage Argo CD to deploy the applications from our Kubernetes mini project.

Full project in my GitHub

https://github.com/vineethac/Kubernetes/tree/main/gitops-argocd

Following are the different components of the project that will get deployed on to a Kubernetes cluster using the Argo CD application resource:

1. Ingress controller
2. Prometheus stack
3. FastAPI web app
4. FastAPI service monitor
5. Loki stack

Deploy each of these components by applying the corresponding YAML manifest, following the outlined steps in the GitHub repository mentioned above. After the successful deployment of all components, you can observe them in the Argo CD web UI, as illustrated below.

Hope it was useful. Cheers!

Kubernetes mini project

In this mini project, we are going to learn the following:

• Deploy a simple Python based web application on a Kubernetes cluster.
• We will use Helm to deploy this app.
• This web app uses FastAPI and exposes some metrics using the Prometheus Python client.
• To store and visualize these metrics we will deploy Prometheus and Grafana in the K8s cluster.
• We will also deploy and use an ingress controller for exposing the web app, Prometheus, and Grafana to external users.
• For logging we will deploy and use Grafana Loki stack.
  
  

Full project in my GitHub

https://github.com/vineethac/Kubernetes/tree/main/mini-project
 

High-level steps to complete this project

Step1: Write the Python app.

Step2: Create the Dockerfile for the app.

Step3: Create the container image.

Step4: Push the container image to an image registry like Docker Hub.

Step5: Get access to a K8s cluster.

Step6: Deploy an ingress controller.

Step7: Create the Helm chart for your app and deploy it to the K8s cluster.

Step8: Deploy Prometheus stack on the K8s cluster using Helm.

Step9: Create a servicemonitor resource which defines the target to be monitored by Prometheus.

Step10: Verify targets and service discovery in Prometheus.

Step11: Configure Grafana dashboard and verify.

Step12. Deploy Grafana Loki stack using Helm.

Hope it was useful. Cheers!

vSphere with Tanzu using NSX-T - Part9 - Monitoring

In the previous posts we discussed the following:

Part1 - Prerequisites

Part2 - Configure NSX

Part3 - Edge Cluster

Part4 - Tier-0 Gateway and BGP peering

Part5 - Tier-1 Gateway and Segments

Part6 - Create tags, storage policy, and content library

Part7 - Enable workload management

Part8 - Create namespace and deploy Tanzu Kubernetes Cluster

In this article, I will explain some of the popular tools used for monitoring Kubernetes clusters that provides insight into different objects in K8s, status, metrics, logs, and so on.

• Lens
• Octant
• Prometheus and Grafana
• vROps and Kubernetes Management Pack
• Kubebox

-Lens-

Download the Lens binary file from: https://k8slens.dev/

I am installing it on a Windows server. Once the installation is complete, the first thing you have to do is to provide the Kube config file details so that Lens can connect to the Kubernetes cluster and start monitoring it.

Add Cluster

Click File - Add Cluster

You can either browse and select the Kube config file or you can paste the content of your Kube config file as text. I am just pasting it as text.

 

Once you have pasted your Kube config file contents, make sure to select the context, and then click Add cluster.

Deploy Prometheus stack

If you aren't seeing CPU and memory metrics, you will need to install the Prometheus stack on your K8s cluster. And Lens has a feature that deploys the Prometheus stack on your K8s cluster with the click of a button!

Select the cluster icon and click Settings.

Scroll all the way to the end, and under Features, you will find an Install button. In my case, I've already installed it, that's why it's showing the Uninstall button.

Once you click the Install button, Lens will go ahead and install the Prometheus stack on the selected K8s cluster. After few minutes, you should be able to see all the metrics.

You can see a namespace called "lens-metrics" and under that, the Prometheus stack components are deployed.

Following are the service objects that are created as part of the Prometheus stack deployment.

And, here is the PVC that is attached to the Prometheus pod.

Terminal access

Click on Terminal to get access directly to the K8s cluster.

Pod metrics, SSH to the pod, and container logs

Scaling

 

Note: In a production environment, it is always a best practice to apply configuration changes to your K8s cluster objects through a version control system.

You can also see the Service Accounts, Roles, Role Bindings, and PSPs under the Access Control tab. For more details see https://docs.k8slens.dev/main/.

-Octant-

https://vineethac.blogspot.com/2020/08/visualize-your-kubernetes-clusters-and.html

-Prometheus and Grafana-

https://vineethac.blogspot.com/2020/09/monitoring-tanzu-kubernetes-cluster.html

-vROps and Kubernetes Management Pack-

https://blogs.vmware.com/management/2020/12/announcing-the-vrealize-operations-management-pack-for-kubernetes-1-5-1.html

https://rudimartinsen.com/2021/03/07/vrops-kubernetes-mgmt-pack/

https://www.brockpeterson.com/post/vrops-management-pack-for-kubernetes

-Kubebox-

curl -Lo kubebox https://github.com/astefanutti/kubebox/releases/download/v0.9.0/kubebox-linux && chmod +x kubebox

Select namespace

Select Pod

This will show the selected pod metrics and logs.

Note: Kubebox relies on cAdvisor to retrieve the resource usage metrics. It’s recommended to use the provided cadvisor.yaml file, that’s tested to work with Kubebox. 

kubectl apply -f https://raw.github.com/astefanutti/kubebox/master/cadvisor.yaml

Kubebox: https://github.com/astefanutti/kubebox

Hope it was useful. Cheers!

Monitoring Tanzu Kubernetes cluster using Prometheus and Grafana

Updated: June 26, 2021

In this post, we will see how to deploy Prometheus and Grafana using Helm and Prometheus Operator to monitor Tanzu Kubernetes clusters. 

Following is my Tanzu K8s cluster setup:

Install Helm 3

curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get-helm-3 > get_helm.sh

chmod 700 get_helm.sh

./get_helm.sh

Install Prometheus Operator using Helm

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts

helm repo update

helm install pro-mon -n pro-mon prometheus-community/kube-prometheus-stack

Verify all

Port forward Grafana to 3000 to access the dashboards

Login to Grafana using a web browser at localhost:3000/login .

The default username is "admin" and the password is "prom-operator".

Go to Dashboards - Manage to view/ access the list of out-of-the-box K8s dashboards. The following are some of the sample dashboards.

Kubernetes | Compute Resources | Namespace (Pods)

Kubernetes | Networking | Namespace (Pods)

Kubernetes | API server

This is not limited to just Tanzu K8s clusters. You can also monitor OpenShift and Upstream K8s clusters following this method. Hope it was useful. Cheers!

References

https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack
[www.bogotobogo.com] Docker_Kubernetes_Prometheus_Deploy_using_Helm_and_Prometheus_Operator