Redhat Openshift for Data Science Project

 

Photo by Tim Mossholder

Red Hat OpenShift Data Science is a powerful platform designed for data scientists and developers working on artificial intelligence (AI) applications. Let’s dive into the details:

1. What is Red Hat OpenShift Data Science?

   • Red Hat OpenShift Data Science provides a fully supported environment for developing, training, testing, and deploying machine learning models.
   • It allows you to work with AI applications both on-premises and in the public cloud.
   • You can use it as a managed cloud service add-on to Red Hat’s OpenShift cloud services or as self-managed software that you can install on-premise or in the public cloud.

2. Key Features and Benefits:

   • Rapid Development: OpenShift Data Science streamlines the development process, allowing you to focus on building and refining your models.
   • Model Training: Train your machine learning models efficiently within the platform.
   • Testing and Validation: Easily validate your models before deployment.
   • Deployment Flexibility: Choose between on-premises or cloud deployment options.
   • Collaboration: Work collaboratively with other data scientists and developers.

3. Creating a Data Science Project:

   • From the Red Hat OpenShift Data Science dashboard, you can create and configure your data science project.
   • Follow these steps:
     • Navigate to the dashboard and select the Data Science Projects menu item.
     • If you have existing projects, they will be displayed.
     • To create a new project, click the Create data science project button.
     • In the pop-up window, enter a name for your project. The resource name will be automatically generated based on the project name.
     • You can then configure various options for your project.

4. Data Science Pipelines:

   • Enhance your data science projects by building portable machine learning workflows using data science pipelines.
   • These pipelines use Docker containers to standardize and automate machine learning workflows.
   • With pipelines, you can develop and deploy your data science models more efficiently¹².

In summary, Red Hat OpenShift Data Science provides a robust platform for data scientists to create, train, and deploy machine learning models, whether you’re working on-premises or in the cloud. It’s a valuable tool for data science projects, offering flexibility, collaboration, and streamlined development processes.

Let’s explore how you can leverage Red Hat OpenShift Data Science in conjunction with a Kubernetes cluster for your data science project. I’ll provide a step-by-step guide along with an example.

Using OpenShift Data Science with Kubernetes for Data Science Projects

1. Set Up Your Kubernetes Cluster:

   • First, ensure you have a functional Kubernetes cluster. You can use a managed Kubernetes service (such as Azure Kubernetes Service (AKS), Google Kubernetes Engine (GKE), or Amazon Elastic Kubernetes Service (EKS)) or set up your own cluster using tools like kubeadm or Minikube.
   • Make sure your cluster is properly configured and accessible.

2. Install Red Hat OpenShift Data Science:

   • Deploy OpenShift Data Science on your Kubernetes cluster. You can do this by installing the necessary components, such as the OpenShift Operator, which manages the data science resources.
   • Follow the official documentation for installation instructions specific to your environment.

3. Create a Data Science Project:

   • Once OpenShift Data Science is up and running, create a new data science project within it.
   • Use the OpenShift dashboard or command-line tools to create the project. For example:

     oc new-project my-data-science-project
     

4. Develop Your Data Science Code:

   • Write your data science code (Python, R, etc.) and organize it into a Git repository.
   • Include any necessary dependencies and libraries.

5. Create a Data Science Pipeline:

   • Data science pipelines in OpenShift allow you to define a sequence of steps for your project.
   • Create a Kubernetes Custom Resource (CR) that describes your pipeline. This CR specifies the steps, input data, and output locations.
   • Example pipeline CR:

     apiVersion: datascience.openshift.io/v1alpha1
     kind: DataSciencePipeline
     metadata:
       name: my-data-pipeline
     spec:
       steps:
         - name: preprocess-data
           image: my-preprocessing-image
           inputs:
             - dataset: my-dataset.csv
           outputs:
             - artifact: preprocessed-data.csv
         # Add more steps as needed
     

6. Build and Deploy Your Pipeline:

   • Build a Docker image for each step in your pipeline. These images will be used during execution.
   • Deploy your pipeline using the OpenShift Operator. It will create the necessary Kubernetes resources (Pods, Services, etc.).
   • Example:

     oc apply -f my-data-pipeline.yaml
     

7. Monitor and Debug:

   • Monitor the progress of your pipeline using OpenShift’s monitoring tools.
   • Debug any issues that arise during execution.

8. Deploy Your Model:

   • Once your pipeline completes successfully, deploy your trained machine learning model as a Kubernetes Deployment.
   • Expose the model using a Kubernetes Service (LoadBalancer, NodePort, or Ingress).

9. Access Your Model:

   • Your model is now accessible via the exposed service endpoint.
   • You can integrate it into your applications or use it for predictions.

Example Scenario: Sentiment Analysis Model

Let’s say you’re building a sentiment analysis model. Here’s how you might structure your project:

1. Data Collection and Preprocessing:

   • Collect tweets or reviews (your dataset).
   • Preprocess the text data (remove stopwords, tokenize, etc.).

2. Model Training:

   • Train a sentiment analysis model (e.g., using scikit-learn or TensorFlow).
   • Save the trained model as an artifact.

3. Pipeline Definition:

   • Define a pipeline that includes steps for data preprocessing and model training.
   • Specify input and output artifacts.

4. Pipeline Execution:

   • Deploy the pipeline.
   • Execute it to preprocess data and train the model.

5. Model Deployment:

   • Deploy the trained model as a Kubernetes service.
   • Expose the service for predictions.

Remember that this is a simplified example. In practice, your data science project may involve more complex steps and additional components. OpenShift Data Science provides the infrastructure to manage these processes efficiently within your Kubernetes cluster.

https://developers.redhat.com/articles/2023/01/11/developers-guide-using-openshift-kubernetes

Kubernetes Ingress

Kubernetes Ingress is an API object that provides HTTP and HTTPS routing to services based on rules. It acts as an entry point for external traffic into the cluster, managing external access to services. Ingress allows you to define how external HTTP/S traffic should be processed and routed to different services within the cluster.

If you want to start with the beginning then you can click here

Key components and concepts of Kubernetes Ingress include:

1. Ingress Resource:

   - An Ingress resource is created to define the rules for how external HTTP/S traffic should be handled.

2. Rules:

   - Rules define how requests should be routed based on the host and path specified in the incoming request.

3. Backend Services:

   - Ingress directs traffic to backend services based on the defined rules.

4. TLS Termination:

   - Ingress can handle TLS termination, allowing you to configure HTTPS for your services.

5. Annotations:

   - Annotations provide additional configuration options, allowing you to customize Ingress behavior.

Example Ingress YAML:

```yaml

apiVersion: networking.k8s.io/v1

kind: Ingress

metadata:

  name: my-ingress

spec:

  rules:

    - host: example.com

      http:

        paths:

          - path: /app

            pathType: Prefix

            backend:

              service:

                name: app-service

                port:

                  number: 80

  tls:

    - hosts:

        - example.com

      secretName: tls-secret

```

In this example:

- Requests to `example.com/app` are directed to the `app-service`.

- TLS termination is configured using the secret `tls-secret` for HTTPS.

Key Benefits:

- Simplifies external access management.

- Allows centralized control of routing rules.

- Supports TLS termination for secure communication.

Kubernetes Ingress controllers (like NGINX Ingress Controller, Traefik, etc.) are responsible for implementing Ingress rules and managing traffic accordingly. The choice of Ingress controller may depend on specific requirements and features needed for your environment.

Kubernetes Ingress is similar to how a reverse proxy like NGINX works, but it operates at the Kubernetes cluster level. 

Ingress in Kubernetes is an API object that defines how external HTTP/S traffic should be processed and routed to different services within the Kubernetes cluster. The Ingress resource itself is just an abstraction. To make it effective, you need an Ingress controller, and NGINX is one of the popular choices for that.

Here's the breakdown:

1. Ingress Resource:

   - Defines the rules for routing external HTTP/S traffic to different services within the Kubernetes cluster.

2. Ingress Controller:

   - Actively watches for changes to Ingress resources.

   - Implements the rules defined in Ingress resources.

   - Manages the actual routing and traffic processing.

3. NGINX Ingress Controller:

   - One of the many available Ingress controllers for Kubernetes.

   - Implements Ingress rules using NGINX as a reverse proxy.

   - Handles the actual HTTP/S traffic based on the defined rules.

So, in a sense, you can think of Kubernetes Ingress working in conjunction with an Ingress controller like NGINX to manage external access and routing within your Kubernetes cluster.

Kubernets by Docker Desktop

 

I am using Mac OS with M1 [Apple chip]. You can read my other article for beginning with Kubernetes here

To see the Kubernetes dashboard on Docker Desktop for Mac OS, follow these steps:

1. Open Docker Desktop.
2. Click on the Kubernetes tab.
3. Under Dashboard, click on Open in browser.

This will open the Kubernetes dashboard in your web browser.

To control the Kubernetes cluster and its pods, you can use the kubectl command-line tool. kubectl is a command-line interface for running commands against Kubernetes clusters.

To get started with kubectl, you will need to create a Kubernetes configuration file. This file will tell kubectl how to connect to your Kubernetes cluster.

To create a Kubernetes configuration file, follow these steps:

1. Open a terminal window.
2. Run the following command:

kubectl config view --minify > config

This will create a Kubernetes configuration file called config in your current working directory.

3. Move the config file to the following directory:

~/.kube/config

Once you have created the Kubernetes configuration file, you can start using kubectl to control your Kubernetes cluster.

Here are some examples of kubectl commands:

# List all pods in the default namespace
kubectl get pods

# List all deployments in the default namespace
kubectl get deployments

# Create a deployment
kubectl create deployment my-deployment --image nginx

# Scale a deployment to 3 replicas
kubectl scale deployment my-deployment --replicas=3

# Delete a deployment
kubectl delete deployment my-deployment

You can find more information about kubectl commands in the Kubernetes documentation: https://kubernetes.io/docs/home/.

Here are some additional tips for using Kubernetes:

• Use labels and selectors to organize your Kubernetes resources. This will make it easier to manage and find your resources.
• Use namespaces to isolate your Kubernetes resources from other users and applications.
• Use Kubernetes resources such as deployments, services, and pods to manage your applications.
• Use Kubernetes features such as autoscaling and self-healing to make your applications more reliable.

However, you find that `kubectl` error the first time as it installed by Docker Desktop and might not installed in default path when you install directly into your system.

The error message kubectl get pods error: unable to load root certificates: unable to parse bytes as PEM block indicates that Kubernetes is unable to load the root certificates for the Kubernetes cluster. This can happen for a number of reasons, such as:

The root certificates are corrupted or missing. 

The root certificates are in an invalid format.

The root certificates are not trusted by Kubernetes.

The error message zsh: command not found: kubectl indicates that the kubectl command is not found in your current shell environment. This can happen for a number of reasons, such as:

• kubectl is not installed on your system.
• kubectl is not in your PATH environment variable.
• You are using a different shell environment than the one where kubectl is installed.

To resolve this error, you can try the following:

1. Make sure that kubectl is installed on your system. You can install kubectl using your system's package manager or by downloading the kubectl binary from the Kubernetes website.
2. Make sure that kubectl is in your PATH environment variable. You can check your PATH environment variable by running the following command:

echo $PATH

If kubectl is not in your PATH environment variable, you can add it by editing your .zshrc file. To do this, open your .zshrc file in a text editor and add the following line to the end of the file:

export PATH=$PATH:/path/to/kubectl

Replace /path/to/kubectl with the path to the kubectl binary.

3. Make sure that you are using the correct shell environment. If you have kubectl installed in a different shell environment, you can switch to that shell environment by running the following command:

zsh

Once you have made sure that kubectl is installed, in your PATH environment variable, and you are using the correct shell environment, you should be able to run the kubectl command without any errors.

If you are still having trouble resolving this error, please provide more information about your environment, such as:

• What operating system are you using?
• What version of zsh are you using?
• How did you install kubectl?

If the output of the ls -la command indicates that the kubectl binary is installed in the /Applications/Docker.app/Contents/Resources/bin directory. This is the default location for the kubectl binary when you install Docker Desktop for Mac.

To resolve the error message zsh: command not found: kubectl, you can add the following line to the end of your .zshrc file:

export PATH=$PATH:/Applications/Docker.app/Contents/Resources/bin

This will add the /Applications/Docker.app/Contents/Resources/bin directory to your PATH environment variable. Once you have added this line to your .zshrc file, you can source the file to make the changes take effect:

source ~/.zshrc

Once you have sourced your .zshrc file, you should be able to run the kubectl command without any errors.

To verify that the kubectl command is working, you can run the following command:

kubectl version

This command should print the version of the kubectl binary.

If you are still having trouble running the kubectl command, you can try the following:

• Make sure that you have the correct version of kubectl installed. You can check the version of kubectl by running the kubectl version command.
• Make sure that the kubectl binary is executable. You can check the permissions of the kubectl binary by running the following command:

ls -l /Applications/Docker.app/Contents/Resources/bin/kubectl

If the permissions of the kubectl binary are not set to -rwxr-xr-x, you can change them by running the following command:

chmod +x /Applications/Docker.app/Contents/Resources/bin/kubectl

To get the Kubernetes localhost desktop to see all, you need to make sure that the following are true:

• Kubernetes is running and accessible on localhost:6443.
• You have the kubeconfig file configured to connect to your Kubernetes cluster.
• You are using the correct kubectl context.

To verify that Kubernetes is running and accessible on localhost:6443, you can run the following command:

kubectl cluster-info

This command should print the information about your Kubernetes cluster, including the API server address. If the API server address is not localhost:6443, you need to update your kubeconfig file to point to the correct API server address.

To verify that you have the kubeconfig file configured to connect to your Kubernetes cluster, you can run the following command:

kubectl config view

This command should print the contents of your kubeconfig file. If the kubeconfig file does not contain the information for your Kubernetes cluster, you need to update it.

To verify that you are using the correct kubectl context, you can run the following command:

kubectl config current-context

This command should print the name of the current kubectl context. If the current kubectl context is not pointing to your Kubernetes cluster, you need to switch to the correct context.

Once you have verified that Kubernetes is running and accessible, your kubeconfig file is configured correctly, and you are using the correct kubectl context, you should be able to see all of the resources in your Kubernetes cluster.

To see all of the resources in your Kubernetes cluster without SSL localhost type, you can use the following command:

kubectl get all -o wide

This command will list all of the resources in all of the namespaces in your Kubernetes cluster, including the internal IP addresses of the services.

For example, to see all of the resources in the default namespace without SSL localhost type, you would run the following command:

kubectl get all -o wide -n default

This will output a table of all of the resources in the default namespace, including the internal IP addresses of the services.

You can then access the services using their internal IP addresses. For example, to access the Kubernetes dashboard, you would open the following URL in your web browser:

http://10.96.0.1:443

You can also use the kubectl proxy command to start a proxy server on your local machine that will forward requests to the Kubernetes cluster. This can be useful if you want to access the Kubernetes cluster without having to remember the internal IP addresses of the services.

To start the kubectl proxy server, run the following command:

kubectl proxy

Once the kubectl proxy the server is running, you can access the services in the Kubernetes cluster using the following URL:

http://localhost:8001/<resource-name>

For example, to access the Kubernetes dashboard, you would open the following URL in your web browser:

http://localhost:8001/api/v1/namespaces/kube-system/services/https:kubernetes-dashboard:/proxy/