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RedHat Open Tour 2023

Red Hat is bringing a special event in collaboration with industry experts. Come join us in a lively conversation on DevOps methodologies, the exciting world of Kubernetes, Openshift, and more

September 13, 2023

8:30 - 17:00

Stockholm, Sweden

multi-tenant environments in OpenShift1.png

Meet Our Team

R asheed Amir

Rasheed is the CEO of Stakater AB, a Kubernetes enablement company. Rasheed has been a Kubernetes and OpenShift aficionado in Stockholm since the early days of these technologies!

Rasheed.png

Quang is a co-founder and head of partnerships at Stakater. Quang is passionate about constantly improving cloud technology and has deep expertise in developing scalable web systems

quang.png

Karl Johan Grahn

Karl is the Engineering Manager at Stakater and leads teams across multiple products. He is passionate about delivering cutting-edge DevSecOps products and services with a focus on quality and automation.

karl.png

Kent Löndahl

Kent is the head of consulting at Stakater. He is an experienced technology leader and has been an active player in the Sweden technology eco-system for a long time.

Kent-Londhal.png

Lunch & Learn Session

Curious about making openshift work for multiple tenants or teams.

                         for an insightful conversation on setting up multi-tenant environments with OpenShift. We'll take you through our real-world experiences managing services on OpenShift.

OpenShift provides a solid foundation, but building multi-tenant setups comes with its share of challenges. We'll break down these challenges in simple terms and suggest practical solutions that have proven effective in our journey.

Learn how multi-tenancy can actually make your life easier.

At this session, Rasheed will talk about how it helps optimize resources and makes managing things smoother. We've got some interesting stories to share about how businesses have benefited from multi-tenancy.

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Create a Red Hat OpenShift AI environment

Featured image for AI/ML content on Red Hat Developer.

Red Hat OpenShift AI combines the scalability and flexibility of containerization with the capabilities of machine learning and data analytics. With Red Hat OpenShift AI, data scientists and developers can efficiently collaborate, deploy, and manage their models and applications in a secure and streamlined environment. Snorkel is an open source Python library for programmatically building training datasets without manual labeling. It was created in 2017 to support reproducibility of early research papers on programmatic labeling and weak supervision.  

In this tutorial, you will learn how to create an OpenShift AI environment and walk through 2 Snorkel tutorials provided by the Snorkel open source library, one for data labeling and for information extraction. This tutorial uses the Snorkel open source Python library. This is different from—though related to—Snorkel AI. The team that developed the Snorkel open source project founded Snorkel AI to continue building on their core ideas. They have since built the Snorkel Flow AI data development platform, which is neither free nor open source. For information on the Snorkel project, visit snorkel.org . For information on Snorkel AI the company, visit snorkel.ai .

Prerequisites

For this tutorial you will need Red Hat OpenShift 4.14 with OpenShift AI and access to the open source  Snorkel library .

Install OpenShift AI

OpenShift AI can be installed from the OpenShift web console. Navigate to the Operators tab and select OperatorHub . In the text box, type red hat openshift data science , select the Red Hat OpenShift Data Science operator, and click  Install  (Figure 1).

Searching for the Red Hat OpenShift Data Science operator in OperatorHub.

To start the installation, click the blue Install button again. You can check to see if our operator successfully deployed under the Operators tab and select Installed Operators  (Figure 2).

Locate the Red Hat OpenShift Data Science operator in the Installed Operators tab.

Now that our operator has successfully deployed, click it and create a new Data Science Cluster instance (Figure 3). 

Create a new data science cluster instance.

We don't need to make any changes to the default settings, so we'll just click Create . If you would like to use an accelerator, it’s recommended to prepare that before accessing the OpenShift AI dashboard (Figure 4).  

Click Create to deploy the cluster.

Once this has successfully deployed, we'll now access our OpenShift AI dashboard. On the administrator menu, head to Networking and click  Routes . In Routes, we're going to change the project to redhat-ods-application , which is where we can access the address to our OpenShift AI application (Figure 5).

Under Routes, change the project to redhat-ods-application.

There should only be one route available, so go ahead and click the address to take you to the OpenShift AI dashboard (Figure 6).

Click the route address on the Routes page.

Create a new data science project

Now we're going to set up our Jupyter environment so we can run the Snorkel tutorials by creating a new data science project. In the left side menu, navigate to the Data Science Projects tab (Figure 7). 

Click Data Science Projects in the left side menu.

Next, click Create data science project . Type your preferred project name (in our case, snorkel ) and click  Create . This is the namespace where all the resources tailored to this demo will be deployed (Figure 8). 

alt text

Create workbench

Inside our project, select Create workbench . This will guide us to the workbench configuration page. Complete the fields to match the following:

  • Name : snorkel (or insert your preferred name)
  • Image selection : PyTorch
  • Version selection : 2023.1
  • Container size : Small
  • Check the Create new persistent storage box.
  • Name : snorkel (or insert your preferred name)
  • Persistent storage size : 20 GiB    

Once you've completed the form, click  Create workbench . You should be taken to the project home page. Once the workbench has finished provisioning, click Open to access the workbench. Log in with your OpenShift credentials and allow the selected permissions (Figure 9).

alt text

Snorkel open source tutorials

Now that we're in our Jupyter environment, let's clone the Snorkel repository. On the left, select the Git logo and click Clone a repository and paste the repository URL:

See Figure 10 for the Git logo icon and Figure 11 for the field to input the repository URL.

alt text

Now that we've cloned the repository, we're going to try out the spam  and spouse  tutorials (which you can find in the directories with the corresponding name). Both notebooks are incredibly detailed, as they walk you through and explain at length each step. These tutorials can serve as a reference point when using Snorkel API  in the future. 

The spam  tutorial is a data labeling exercise that shows you how to build a training set for classifying YouTube comments as spam or not spam. To access it, enter the spam directory and begin with 01_spam_tutorial.ipynb . This is the first of 3 notebook files to walk through data labeling, augmentation, and slicing. It's an introductory tutorial and it is one avenue you could perform data labeling within OpenShift AI utilizing Snorkel. 

The spouse tutorial is a little more complex, it walks through how to utilize Snorkel for information extraction by using keywords and distant supervision. In this tutorial, you will learn how to identify and classify someone's spouse utilizing labeling functions and models to do so. To begin this tutorial, enter the spouse directory and open spouse_demo.ipynb . This exercise is to demonstrate how simple it is to integrate Snorkel's libraries and tools with your OpenShift AI environment. 

Now that you’ve walked through the tutorial, the Snorkel open source library is now available on your OpenShift AI environment. For more information on Snorkel, you can visit their websites to learn more about the Snorkel project and their AI services .  

If you would like to try out OpenShift, you can with our Developer Sandbox using a no-cost trial. Learn more about OpenShift and our AI initiatives .

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8.4.6. Using OpenSCAP to Remediate the System

  • OpenSCAP performs a regular XCCDF evaluation.
  • An assessment of the results is performed by evaluating the OVAL definitions. Each rule that has failed is marked as a candidate for remediation.
  • OpenSCAP searches for an appropriate fix element, resolves it, prepares the environment, and executes the fix script.
  • Any output of the fix script is captured by OpenSCAP and stored within the rule-result element. The return value of the fix script is stored as well.
  • Whenever OpenSCAP executes a fix script, it immediatelly evaluates the OVAL definition again (to verify that the fix script has been applied correctly). During this second run, if the OVAL evaluation returns success, the result of the rule is fixed , otherwise it is an error .
  • Detailed results of the remediation are stored in an output XCCDF file. It contains two TestResult elements. The first TestResult element represents the scan prior to the remediation. The second TestResult is derived from the first one and contains remediation results.

8.4.6.1. OpenSCAP Online Remediation

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COMMENTS

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  17. Create a Red Hat OpenShift AI environment

    Navigate to the Operators tab and select OperatorHub. In the text box, type red hat openshift data science, select the Red Hat OpenShift Data Science operator, and click Install (Figure 1). Figure 1: Searching for the Red Hat OpenShift Data Science operator in OperatorHub. To start the installation, click the blue Install button again.

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