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Redirect Notice

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The page you requested has been relocated to model-serving/1.1/index.html.

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Knowledge Check

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Quiz: Components of Red Hat OpenShift AI

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Identify components of the Red Hat OpenStack AI on OpenShift product and how a Red Hat OpenShift AI Deploys Resources to support AI/ML Development

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    Which of the following Operator components are required to enable Red Hat OpenShift AI on OpenShift with Single Model Serving Platform capabilities ?

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    After the installation of OpenShift AI Operator via Operator Hub, which of the following actions are required to enable the OpenShift AI console or dashboard ?

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      Create a new OpenShift AI data science project

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      Modify the data science cluster YAML file to use custom TLS certificates

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      Create a new OpenShift AI data science cluster

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      A third-party vendor operator is required by Red Hat OpenShift AI.

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    What resources / services are available in a new OpenShift AI data science project ?

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Components of Red Hat OpenShift AI

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    Which of the following Operator components are either are required to enable Red Hat OpenShift AI on OpenShift with Single Model Serving Platform capabilities ?

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      Red Hat OpenShift serverless operator
      -Correct

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      Red Hat OpenShift service mesh operator
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      Red Hat OpenShift pipelines operator
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      Red Hat OpenShift node feature discovery operator

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      Red Hat OpenShift Nvidia gpu operator

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      Red Hat OpenShift gitOps operator

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      Red Hat OpenShift smart gateway operator

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    After the installation of OpenShift AI Operator via Operator Hub, which of the following actions are required to enable the OpenShift AI console or dashboard ?

    -
    -
      -
    • -

      Create a new OpenShift AI data science project

      -
      • -
    • -

      Modify the data science cluster YAML file to use custom TLS certificates

      -
      • -
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      Create a new OpenShift AI data science cluster
      -Correct

      -
      • -
    • -

      Create a new data science project workbench

      -
      • -
    • -

      A third-party vendor operator is required by Red Hat OpenShift AI.

      -
      • -
    -
    -
    3. -
  3. -

    What resources / services are available in a new OpenShift AI data science project ?

    -
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      Workbench
      -Correct

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      • -
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      Data connections
      -Correct

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      • -
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      Pipelines
      -Correct

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      • -
    • -

      Model serving
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Technical Component Intoduction

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Why this technical course?

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A Formula One Driver doesn’t need to know how to build an engine to be an F1 champion. However, she/he needs to have a mechanical sympathy, which is understanding of the car’s mechanics to drive it effectively and get the best out of it.

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The same applies to AI. We don’t need to be AI experts to harness the power of large language models, but we do need to develop a certain level of "technological awareness" about how LLM Models are trained, selected, operationalized, delivered, inferred from, fined-tuned, augmented and kept up-to-date. Not just as users, but as aficionados who understand the underlying components to effectively communicate with clients, partners, and co-workers.

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The true power lies in the platform that enables us to harness a diverse range of AI models, tools, infrastructure and operationalize our ML projects.

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That platform, OpenShift AI, is what we learn to create, configure, and utilize to Serve LLM Models in this quick course.

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Red Hat OpenShift AI

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Kubernetes & OpenShift

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OpenShift builds upon Kubernetes by providing an enhanced platform with additional capabilities. It simplifies the deployment and management of Kubernetes clusters while adding enterprise features, developer tools, and security enhancements.

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In addition, Openshift provides a Graphic User Interface for Kubernetes. Openshift AI runs on Openshift; therefore, the engine under the hood of both products is Kubernetes.

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Most workloads are deployed in kubernetes via YAML files. A Kubernetes YAML manifest file is a configuration file written in YAML (YAML Ain’t Markup Language) that defines the desired state of a Kubernetes deployment. These YAML files are used to create, update, or delete deployments in Kubernetes / OpenShift clusters.

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Don’t worry about needing to know how to write these files. That’s what OpenShift & OpenShift AI will take care of for us. In this course, we will just need to select the options we want in the UI. OpenShift and OpenShift AI will take care of creating the YAML deployment files.

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We will have to perform a few YAML file copy-and-paste operations; instructions are provided in the course.

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Just know, YAML files create resources directly in the Kubernetes platform. We primarily use the OpenShift AI UI to perform these tasks to deliver our LLM.

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Large Language Models

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Large Language Models

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Large Language Models (LLMs) can generate new stories, summarize texts, and even perform advanced tasks like reasoning and problem solving, which is not only impressive but also remarkable due to their accessibility and easy integration into applications.

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As you probably already know, training large language models is expensive, time consuming, and most importantly requires a vast amount of data fed into the model.

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The common outcome from this training is a Foundation model: an LLM designed to generate and understand natural language text across a wide range of use cases.

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The key to this powerful language processing architecture is the Transformer! A helpful definition of a Transformer is a set of neural networks that consist of an encoder and a decoder with self-attention capabilities. The Transformer was created by Google and started as a language translation algorithm. It analyzes relationships between words in text, which is crucial for LLMs to understand and generate language.

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This is how LLMs are able to predict the next words, by using the transformer neural network & attention mechanism to focus in on keywords to determine context. Then use that context and knowledge from all the data ingested to predict the next word after a sequence of words.

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Modifications to LLMs

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As mentioned above, LLMs are normally large, require GPU or accelerator chips, and costly compute resources to load the model into memory.

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However, there are techniques for compressing large LLM models, making them smaller and faster to run on devices with limited resources.

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    Quantization reduces the precision of numerical representations in large language models to make them more memory-efficient during deployment.

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    Reducing the precision of LLM parameters to save computational resources without sacrificing performance. Trimming surplus connections or parameters to make LLMs smaller and faster yet performant.

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In this course, we will be using quantized versions of the Mistral and Llama3 Large Language Models. Instead of requiring 24Gb of memory and graphics processing unit to simulate the neural network, we are going to run our model with 4 CPUs and 8GB of ram, burstable to 8 CPU with 10Gb ram max.

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The Ollama Model Framework

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There are hundreds of popular LLMs, nonetheless, their operation remains the same: users provide instructions or tasks in natural language, and the LLM generates a response based on what the model "thinks" could be the continuation of the prompt.

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Ollama is not an LLM Model - Ollama is a relatively new but powerful open-source framework designed for serving machine learning models. It’s designed to be efficient, scalable, and easy to use; making it an attractive option for developers and organizations looking to deploy their AI models into production.

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How does Ollama work?

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At its core, Ollama simplifies the process of downloading, installing, and interacting with a wide range of LLMs, empowering users to explore their capabilities without the need for extensive technical expertise or reliance on cloud-based platforms.

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In this course, we will focus on two LLMs, Mistra and Llama3 run on the Ollama Framework. However, with the understanding of the Ollama Framework, we will be able to work with a variety of large language models utilizing the exact same configuration. Vist the Ollama library for details on supported Models.

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You will be able to switch models in minutes, all running on the same platform. This will enable you to test, compare, and evaluate multiple models with the skills gained in the course.

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OpenShift AI Initialization

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Supported configurations

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OpenShift AI is supported in two configurations:

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    A managed cloud service add-on for Red Hat OpenShift Service on Amazon Web Services (ROSA, with a Customer Cloud Subscription for AWS) or Red Hat OpenShift Dedicated (GCP). -For information about OpenShift AI on a Red Hat managed environment, see Product Documentation for Red Hat OpenShift AI Cloud Service.

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    Self-managed software that you can install on-premise or on the public cloud in a self-managed environment, such as OpenShift Container Platform. -For information about OpenShift AI as self-managed software on your OpenShift cluster in a connected or a disconnected environment, see Product Documentation for Red Hat OpenShift AI Self-Managed 2.14.

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In this course we cover installation of Red Hat OpenShift AI self-managed using the OpenShift Web Console.

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Applicable Operators

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The product name has been recently changed to Red Hat OpenShift AI (RHOAI) (old name Red Hat OpenShift Data Science). In this course, most references to the product use the new name. However, references to some UI elements might still use the previous name.

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In addition to the Red Hat OpenShift AI Operator there are additional operators that you may need to install depending on which features and components of Red Hat OpenShift AI you want to utilize.

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Red Hat OpenShift Serverless Operator
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The Red Hat OpenShift Serverless operator provides a collection of APIs that enables containers, microservices and functions to run "serverless". The Red Hat OpenShift Serverless Operator is required if you want to install the Single-model serving platform component.

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Red Hat OpenShift Service Mesh operator provides an easy way to create a network of deployed services that provides discovery, load balancing, service-to-service authentication, failure recovery, metrics, and monitoring. The Red Hat OpenShift Serverless Operator is required if you want to install the Single-model serving platform component.

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Red Hat Authorino (technical preview) Operator
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Red Hat Authorino is an open source, Kubernetes-native external authorization service to protect APIs. The Red Hat Authorino Operator is required to support enforcing authentication policies in Red Hat OpenShift AI.

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The following Operators are required to support the use of Nvidia GPUs (accelerators) with OpenShift AI:

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Node Feature Discovery Operator
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-

The Node Feature Discovery Operator is a prerequisite for the NVIDIA GPU Operator.

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NVIDIA GPU Operator
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The NVIDIA GPU Operator is required for GPU support in Red Hat OpenShift AI.

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- -
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- Red Hat -
- - - diff --git a/pr-preview/pr-19/model-serving/1.1/chapter2/rhoai_install_guide.html b/pr-preview/pr-19/model-serving/1.1/chapter2/rhoai_install_guide.html deleted file mode 100644 index f11c8c1..0000000 --- a/pr-preview/pr-19/model-serving/1.1/chapter2/rhoai_install_guide.html +++ /dev/null @@ -1,492 +0,0 @@ - - - - - - OpenShift AI Using the Web Console :: Serving an LLM using OpenShift AI - - - - - - - -
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OpenShift AI Using the Web Console

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Red Hat OpenShift AI is available as an operator via the OpenShift Operator Hub. You will install the Red Hat OpenShift AI operator and dependencies using the OpenShift web console in this section.

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-

Lab Exercise: Installation of Red Hat OpenShift AI

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This section will discuss the process for installing the dependent operators using the OpenShift Web Console. ( ~15 minutes )

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- - - - - -
- - -The installation requires a user with the cluster-admin role -
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This exercise uses the Red Hat Demo Platform; specifically the OpenShift Container Cluster Platform Resource. If you haven’t already you’ll need to launch the lab environment before continuing.

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    -
  1. -

    Login to the Red Hat OpenShift using a user who has the cluster-admin role assigned.

    -
    2. -
  2. -

    Navigate to Operators → OperatorHub and search for each of the following Operators individually. For this lab, you can skip the installation of the optional operators.

    -
    3. -
-
-
-

[*] You do not have to wait for the previous Operator to complete before installing the next. For this lab you can skip the installation of the optional operators as there is no accelerator required.

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    -
  • -

    Red Hat OpenShift Serverless

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    • -
  • -

    Red Hat OpenShift Service Mesh

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    • -
  • -

    Red Hat Authorino technical preview

    -
    • -
  • -

    GPU Support

    -
    -
      -
    • -

      Node Feature Discovery Operator (optional)

      -
      • -
    • -

      NVIDIA GPU Operator (optional)

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      • -
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    • -
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Installation of Red Hat OpenShift Serverless Operator

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The following section discusses installing the Red Hat OpenShift Serverless operator.

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-serverless operator -
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Figure 1. Animated - Operator Hub installation of Serverless Operator in OpenShift
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    -
  1. -

    Login to Red Hat OpenShift using a user who has the cluster-admin role assigned.

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    2. -
  2. -

    Navigate to Operators → OperatorHub and search for Red Hat OpenShift Serverless.

    -
    3. -
  3. -

    Click on the Red Hat OpenShift Serverless operator. In the pop-up window, select the stable channel and the most recent version of the serverless operator. Click on Install to open the operator’s installation view.

    -
    4. -
  4. -

    In the Install Operator page, select the default values for all the fields and click Install.

    -
    5. -
  5. -

    A window showing the installation progress will pop up.

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    6. -
  6. -

    When the installation finishes the operator is ready to be used by Red Hat OpenShift AI.

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    7. -
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Red Hat OpenShift Serverless is now successfully installed.

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Installation of Red Hat OpenShift Service Mesh Operator

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The following section discusses installing the Red Hat OpenShift Service Mesh operator.

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-servicemesh install -
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Figure 2. Animated - Operator Hub installation of Service Mesh Operator in OpenShift
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    -
  1. -

    Login to Red Hat OpenShift using a user who has the cluster-admin role assigned.

    -
    2. -
  2. -

    Navigate to Operators → OperatorHub and search for Red Hat OpenShift Service Mesh.

    -
    3. -
  3. -

    Click on the Red Hat OpenShift Service Mesh operator. In the pop-up window, select the stable channel and the most recent version of the server mesh operator. Click on Install to open the operator’s installation view.

    -
    4. -
  4. -

    In the Install Operator page, select the default values for all the fields and click Install.

    -
    5. -
  5. -

    A window showing the installation progress will pop up.

    -
    6. -
  6. -

    When the installation finishes the operator is ready to be used by Red Hat OpenShift AI.

    -
    7. -
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Red Hat OpenShift Service Mesh is now successfully installed.

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Installation of Red Hat Authorino Operator

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The following section discusses installing the Red Hat - Authorino operator.

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-authorino install -
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Figure 3. Animated - Operator Hub installation of Authorino (tech preview) Operator in OpenShift
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    -
  1. -

    Login to Red Hat OpenShift using a user who has the cluster-admin role assigned.

    -
    2. -
  2. -

    Navigate to Operators → OperatorHub and search for *Red Hat Authorino.

    -
    3. -
  3. -

    Click on the Red Hat Authorino *operator. In the pop-up window, select the tech-preview-v1 channel and the most recent version of the operator. Click on Install to open the operator’s installation view.

    -
    4. -
  4. -

    In the Install Operator page, select the default values for all the fields and click Install.

    -
    5. -
  5. -

    A window showing the installation progress will pop-up.

    -
    6. -
  6. -

    When the installation finishes the operator is ready to be used by Red Hat OpenShift AI.

    -
    7. -
-
-
-

Red Hat Authorino is now successfully installed.

-
-
- - - - - -
- - - Installing these Operators prior to the installation of the OpenShift AI Operator increases the speed in OpenShift AI acknowledging the availability of these components and adjusting the initial configuration to shift management of these components to OpenShift AI. -
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-
-

Installation of Red Hat OpenShift AI Operator

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-openshiftai install 214 -
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Figure 4. Animated - Operator Hub installation of OpenShift AI Operator on OpenShift
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    -
  • -

    Navigate to Operators → OperatorHub and search for OpenShift AI.

    -
    -
      -
    1. -

      Click on the Red Hat OpenShift AI operator. In the pop-up window that opens, ensure you select the latest version in the fast channel. Any version equal to or greater than 2.14 and click on Install to open the operator’s installation view.

      -
      2. -
    2. -

      In the Install Operator page, leave all of the options as default and click on the Install button to start the installation.

      -
      3. -
    3. -

      The operator Installation progress window will pop up. The installation may take a couple of minutes.

      -
      4. -
    -
    -
    • -
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-
-
-
-

Create OpenShift AI Data Science Cluster

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-
-

The next step is to create an OpenShift AI Data Science Cluster (DSC).

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-
-

A DataScienceCluster is the plan in the form of an YAML outline for Data Science Cluster API deployment. Manually editing the YAML configuration can adjust the settings of the OpenShift AI DSC.

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-
-dsc install 214 -
-
Figure 5. Animated - Create Data Science Cluster to enable OpenShift AI on OpenShift
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Return to the OpenShift Navigation Menu, Select Installed Operators, and click on the OpenShift AI Operator name to open the operator.

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    -
  1. -

    Select the option to create a Data Science Cluster.

    -
    2. -
  2. -

    Click Create to deploy the Data Science Cluster.

    -
    3. -
-
-
-
-
-

OpenShift AI install Summary

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-
-

Congratulations, you have successfully completed the installation of OpenShift AI on an OpenShift Container Cluster. OpenShift AI is now running on a new Dashboard!

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-
-
    -
  • -

    We installed the required OpenShift AI Operators

    -
    -
      -
    • -

      Red Hat OpenShift Serverless

      -
      • -
    • -

      Red Hat OpenShift ServiceMesh

      -
      • -
    • -

      Red Hat Authorino (technical preview)

      -
      • -
    • -

      OpenShift AI Operator

      -
      • -
    -
    -
    • -
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-
-

Create a Data Science Project

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Navigate to the menu selector, located at the top right of the OCP dashboard. Select the grid of squares, then select OpenShift AI. At the login screen, use the OCP admin credentials to login to OpenShift AI.

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-data science project -
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Figure 6. Animated - Create data science project from OpenShift AI dashboard
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Explore the dashboard navigation menus to familiarize yourself with the options.

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Navigate to & select the Data Science Projects section.

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    -
  1. -

    Select the Create Data Science Project button.

    -
    2. -
  2. -

    Enter a name for your project, such as ollama-model. (differs from animated example)

    -
    3. -
  3. -

    The resource name should be populated automatically.

    -
    4. -
  4. -

    Optionally add a description to the data science project.

    -
    5. -
  5. -

    Select Create.

    -
    6. -
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-

Once complete, you should be on the landing page of the "ollama-model" Data Science Project section of the OpenShift AI Console / Dashboard.

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- -
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- Red Hat -
- - - diff --git a/pr-preview/pr-19/model-serving/1.1/chapter2/section1old.html b/pr-preview/pr-19/model-serving/1.1/chapter2/section1old.html deleted file mode 100644 index e9741dd..0000000 --- a/pr-preview/pr-19/model-serving/1.1/chapter2/section1old.html +++ /dev/null @@ -1,498 +0,0 @@ - - - - - - Installing Red Hat OpenShift AI Using the Web Console :: Serving an LLM using OpenShift AI - - - - - -
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- -
- -
-

Installing Red Hat OpenShift AI Using the Web Console

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-
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-

Red Hat OpenShift AI is available as an operator via the OpenShift Operator Hub. You will install the Red Hat OpenShift AI operator and dependencies using the OpenShift web console in this section.

-
-
-
-
-

Lab: Installation of Red Hat OpenShift AI

-
-
- - - - - -
- - -The installation requires a user with the cluster-admin role -
-
-
-

This exercise uses the Red Hat Demo Platform; specifically the OpenShift Container Cluster Platform Resource. If you haven’t already you’ll need to launch the lab environment before continuing.

-
-
-
    -
  1. -

    Login to the Red Hat OpenShift using a user which has the cluster-admin role assigned.

    -
    2. -
  2. -

    It’s sufficient to install all prerequisite operators with default settings, no additional configuration is necessary.

    -
    3. -
  3. -

    Navigate to Operators → OperatorHub and search for each of the following Operators individually. Click on the button or tile for each. In the pop up window that opens, ensure you select the latest version in the "specified within instructions" channel and click on Install to open the operator’s installation view. For this lab you can skip the installation of the optional operators.

    -
    4. -
-
-
-

[*] You do not have to wait for the previous Operator to complete before installing the next. For this lab you can skip the installation of the optional operators as there is no accelerator required.

-
-
-
    -
  • -

    Red Hat OpenShift Serverless

    -
    • -
  • -

    Red Hat OpenShift Service Mesh

    -
    • -
  • -

    Red Hat Authorino technical preview

    -
    • -
  • -

    GPU Support

    -
    -
      -
    • -

      Node Feature Discovery Operator (optional)

      -
      • -
    • -

      NVIDIA GPU Operator (optional)

      -
      • -
    -
    -
    • -
-
-
-

This section will discuss the process for installing the dependent operators using the OpenShift Web Console.

-
-
-

Installation of Red Hat OpenShift Serverless Operator

-
-

The following section discusses installing the Red Hat OpenShift Serverless operator.

-
-
-
-serverless operator -
-
Figure 1. Animated - Operator Hub installation of Serverless Operator in OpenShift
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-
-
    -
  1. -

    Login to Red Hat OpenShift using a user who has the cluster-admin role assigned.

    -
    2. -
  2. -

    Navigate to Operators → OperatorHub and search for Red Hat OpenShift Serverless.

    -
    3. -
  3. -

    Click on the Red Hat OpenShift Serverless operator. In the pop-up window, select the stable channel and the most recent version of the serverless operator. Click on Install to open the operator’s installation view.

    -
    4. -
  4. -

    In the Install Operator page, select the default values for all the fields and click Install.

    -
    5. -
  5. -

    A window showing the installation progress will pop up.

    -
    6. -
  6. -

    When the installation finishes the operator is ready to be used by Red Hat OpenShift AI.

    -
    7. -
-
-
-

Red Hat OpenShift Serverless is now successfully installed.

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-
-
-

Installation of Red Hat OpenShift Service Mesh Operator

-
-

The following section discusses installing the Red Hat OpenShift Service Mesh operator.

-
-
-
-servicemesh install -
-
Figure 2. Animated - Operator Hub installation of Service Mesh Operator in OpenShift
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-
-
    -
  1. -

    Login to Red Hat OpenShift using a user who has the cluster-admin role assigned.

    -
    2. -
  2. -

    Navigate to Operators → OperatorHub and search for Red Hat OpenShift Service Mesh.

    -
    3. -
  3. -

    Click on the Red Hat OpenShift Service Mesh operator. In the pop-up window, select the stable channel and the most recent version of the server mesh operator. Click on Install to open the operator’s installation view.

    -
    4. -
  4. -

    In the Install Operator page, select the default values for all the fields and click Install.

    -
    5. -
  5. -

    A window showing the installation progress will pop up.

    -
    6. -
  6. -

    When the installation finishes the operator is ready to be used by Red Hat OpenShift AI.

    -
    7. -
-
-
-

Red Hat OpenShift Service Mesh is now successfully installed.

-
-
-
-

Installation of Red Hat Authorino Operator

-
-

The following section discusses installing the Red Hat - Authorino operator.

-
-
-
-authorino install -
-
Figure 3. Animated - Operator Hub installation of Authorino (tech preview) Operator in OpenShift
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-
-
    -
  1. -

    Login to Red Hat OpenShift using a user who has the cluster-admin role assigned.

    -
    2. -
  2. -

    Navigate to Operators → OperatorHub and search for *Red Hat Authorino.

    -
    3. -
  3. -

    Click on the Red Hat Authorino operator. In the pop-up window, select the tech-preview-v1 channel and the most recent version of the serverless operator. Click on Install to open the operator’s installation view.

    -
    4. -
  4. -

    In the Install Operator page, select the default values for all the fields and click Install.

    -
    5. -
  5. -

    A window showing the installation progress will pop-up.

    -
    6. -
  6. -

    When the installation finishes the operator is ready to be used by Red Hat OpenShift AI.

    -
    7. -
-
-
-

Red Hat Authorino is now successfully installed.

-
-
- - - - - -
- - - Installing these Operators prior to the installation of the OpenShift AI Operator increases the speed in OpenShift AI acknowledging the availability of these components and adjusting the initial configuration to shift management of these components to OpenShift AI. -
-
-
-
-
-
-

Installation of Red Hat OpenShift AI Operator

-
-
-
-openshiftai install 214 -
-
Figure 4. Animated - Operator Hub installation of OpenShift AI Operator on OpenShift
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-
-
    -
  • -

    Navigate to Operators → OperatorHub and search for OpenShift AI.

    -
    -
      -
    1. -

      Click on the Red Hat OpenShift AI operator. In the pop-up window that opens, ensure you select the latest version in the fast channel. Any version equal to or greater than 2.14 and click on Install to open the operator’s installation view.

      -
      2. -
    2. -

      In the Install Operator page, leave all of the options as default and click on the Install button to start the installation.

      -
      3. -
    3. -

      The operator Installation progress window will pop up. The installation may take a couple of minutes.

      -
      4. -
    -
    -
    • -
-
-
-
-
-

Create OpenShift AI Data Science Cluster

-
-
-

The next step is to create an OpenShift AI Data Science Cluster (DSC).

-
-
-

A DataScienceCluster is the plan in the form of an YAML outline for Data Science Cluster API deployment. Manually editing the YAML configuration can adjust the settings of the OpenShift AI DSC.

-
-
-
-dsc install 214 -
-
Figure 5. Animated - Create Data Science Cluster to enable OpenShift AI on OpenShift
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-
-

Return to the OpenShift Navigation Menu, Select Installed Operators, and click on the OpenShift AI Operator name to open the operator.

-
-
-
    -
  1. -

    Select the option to create a Data Science Cluster.

    -
    2. -
  2. -

    Click Create to deploy the Data Science Cluster.

    -
    3. -
-
-
-

This takes ~5 minutes to complete, once the DSC becomes Ready, use the navigation menu at the top right to drop down the menu and select Red Hat OpenShift AI. Use the same credentials as used log into OpenShift.

-
-
-
-
-

OpenShift AI install Summary

-
-
-

Congratulations, you have successfully completed the installation of OpenShift AI on an OpenShift Container Cluster. OpenShift AI is now running on a new Dashboard!

-
-
-
    -
  • -

    We installed the required OpenShift AI Operators

    -
    -
      -
    • -

      Red Hat OpenShift Serverless

      -
      • -
    • -

      Red Hat OpenShift ServiceMesh

      -
      • -
    • -

      Red Hat Authorino (technical preview)

      -
      • -
    • -

      OpenShift AI Operator

      -
      • -
    -
    -
    • -
-
-
-
-
-

Create a Data Science Project

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-
-

Navigate to the menu selector, located at the top right of the OCP dashboard. Select the grid of squares, then select OpenShift AI. At the login screen, use the OCP admin credentials to login to OpenShift AI.

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-
-
-data science project -
-
Figure 6. Animated - Create data science project from OpenShift AI dashboard
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-
-

Explore the dashboard navigation menus to familiarize yourself with the options.

-
-
-

Navigate to & select the Data Science Projects section.

-
-
-
    -
  1. -

    Select the Create Data Science Project button.

    -
    2. -
  2. -

    Enter a name for your project, such as ollama-model.

    -
    -
      -
    1. -

      (the animated example shows fraud-detection entered for the data science project, for this course use the ollama-model to follow along with rest of the labs)

      -
      2. -
    -
    -
    3. -
  3. -

    The resource name should be populated automatically.

    -
    4. -
  4. -

    Optionally add a description to the data science project.

    -
    5. -
  5. -

    Select Create.

    -
    6. -
-
-
-

Once complete, you should be on the landing page of the "ollama-model" Data Science Project section of the OpenShift AI Console / Dashboard.

-
-
-
-
-
-
-
-
- Red Hat -
- - - diff --git a/pr-preview/pr-19/model-serving/1.1/chapter2/section2old.html b/pr-preview/pr-19/model-serving/1.1/chapter2/section2old.html deleted file mode 100644 index 058f313..0000000 --- a/pr-preview/pr-19/model-serving/1.1/chapter2/section2old.html +++ /dev/null @@ -1,306 +0,0 @@ - - - - - - Modifying the OpenShift AI TLS Certificate :: Serving an LLM using OpenShift AI - - - - - -
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Modifying the OpenShift AI TLS Certificate

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- - - - - -
- - -An SSL/TLS certificate is a digital object that allows systems to verify the identity & subsequently establish an encrypted network connection to another system using the Secure Sockets Layer/Transport Layer Security (SSL/TLS) protocol. -
-
-
-

By default, the Single Model Serving Platform in Openshift AI v2.10 uses a self-signed certificate generated during installation for the endpoints that are created when deploying a Model server.

-
-
-

This can be counterintuitive because the OCP Cluster already has certificates configured which will be used by default for endpoints like Routes.

-
-
-

This following procedure explains how to use the same certificate from the OpenShift Container cluster for OpenShift AI.

-
-
-
-
-

Use OpenShift Certificates for Ingress Routes

-
-
- - - - - -
- - -Most customers will not use the self-signed certificates, opting instead to use certificates generated by their own authority. Therefore, this step of adding secrets to OpenShift & OpenShift AI is a common process during installation. -
-
-
-

Navigate to the OpenShift Container Cluster Dashboard

-
- - - - - -
- - -The content of the Secret (data) should contain two items, tls.cert and tls.key. They are the certificate and key that are used for all the OpenShift Routes. The actual name will include the term cert, but could be different from the example. -
-
-
-
-

Collect the Secret YAML Text:

-
-
    -
  1. -

    In the Navigation pane on the left, click on the Workloads section, then Secrets under Workloads.

    -
    2. -
  2. -

    From the Project dropdown, toggle the show default projects radial button to on.

    -
    3. -
  3. -

    Select the openshift-ingress project from the list.

    -
    4. -
-
-
-
-openshiftingress project -
-
-
-
    -
  1. -

    Locate the file named cert-manager-ingress-cert, type should be kubernetes.io/tls

    -
    2. -
  2. -

    Click on the filename to open the secret, select the YAML Tab

    -
    3. -
  3. -

    Copy all the text from the window, and ensure that you scroll down. (CTL-A should work).

    -
    4. -
-
-
-
-ocp ingress cert yaml -
-
-
-
-

Clean & Deploy the Secret YAML Text:

-
-
    -
  • -

    Click on the Project dropdown again, select the Istio-system project

    -
    • -
  • -

    Select the Create button on the right, then Select the from YAML option.

    -
    • -
  • -

    Delete the text from the Window, and paste the copied Secret text

    -
    • -
  • -

    Cleanup the YAML Text to just keep the relevant content. It should look like the YAML file below (the name of the secret will be different, it’s normally tied to the date the RDHP Cluster was deployed).

    -
    • -
-
-
-
-
kind: Secret
-apiVersion: v1
-metadata:
-name: cert-manager-ingress-cert
-data:
-  tls.crt: >-
-    LS0tLS1CRUd...
-  tls.key: >-
-    LS0tLS1CRUd...
-type: kubernetes.io/tls
-
-
-
-
-ocp istio cert -
-
-
-
    -
  • -

    Copy the name of the secret from line 4, just the name (optional, but helpful)

    -
    • -
  • -

    Click create to apply this YAML into the istio-system project (namespace).

    -
    • -
-
-
-

We have copied the Secret used by OCP & made it available to be used by OAI.

-
-
-
-
-
-
-
-
-
- Red Hat -
- - - diff --git a/pr-preview/pr-19/model-serving/1.1/chapter2/section3old.html b/pr-preview/pr-19/model-serving/1.1/chapter2/section3old.html deleted file mode 100644 index a786a12..0000000 --- a/pr-preview/pr-19/model-serving/1.1/chapter2/section3old.html +++ /dev/null @@ -1,263 +0,0 @@ - - - - - - Configure the OpenShift AI Data Science Cluster :: Serving an LLM using OpenShift AI - - - - - -
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- -
- -
-

Configure the OpenShift AI Data Science Cluster

-
-
-
-
- -
-
-
-
-
-

Create OpenShift AI Data Science Cluster

-
-
-

With our secrets in place, the next step is to create an OpenShift AI Data Science Cluster.

-
-
-

A DataScienceCluster is the plan in the form of an YAML outline for Data Science Cluster API deployment.

-
-
-

Return to the OpenShift Navigation Menu, Select Installed Operators, and Click on the OpenShift AI Operator name to open the operator.

-
-
-
    -
  1. -

    Select the Option to create a Data Science Cluster.

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    Find the section below in the YAML file, in the Kserve Section find the Serving/Certificate area; add the line: secretName: followed by the name of the secret name that we deployed in the istio-system project. In addition, change the type from SelfSigned to Provided. See below for the example.

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Once you have made those changes to the YAML file, Click Create to Deploy the Data Science Cluster.

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Single Model Serve Platform will now be deployed to expose ingress connections with the same certificate as OpenShift Routes. Endpoints will be accessible using TLS without having to ignore error messages or create special configurations.

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OpenShift AI install summary

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Congratulations, you have successfully completed the installation of OpenShift AI on an OpenShift Container Cluster. OpenShift AI is now running on a new Dashboard!

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Additionally, we took this installation a step further by sharing TLS certificates from the OpenShift Cluster with OpenShift AI.

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We will pick up working with the OpenShift AI UI in the next Chapter.

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OpenShift AI Configuration

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This chapter begins with running and configured OpenShift AI environment. If you don’t already have your environment running, head over to Chapter 2.

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There’s a lot to cover in this section, we add the Ollama custom Runtime, setup MinIO storage, create a workbench, and finally serve the Ollama Framework, utilizing the Single Model Serving Platform to deliver our model to our Notebook Application.

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Let’s get started!

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Creating OpenShift AI Resources - 1

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Model Serving Runtimes

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A model-serving runtime provides integration with a specified model server and the model frameworks that it supports. By default, Red Hat OpenShift AI includes the following model serving runtimes:

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      OpenVINO Model Server

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      OpenVINO Model Server

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      Caikit Standalone ServingRuntime for KServe

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      Caikit ServingRuntime TGIS for KServe

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      TGIS Standalone ServingRuntime for KServe

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      vLLMServingRuntime For KServe

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However, if these runtimes do not meet your needs (if they don’t support a particular model framework, for example), you might want to add your own custom runtimes.

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As an administrator, you can use the OpenShift AI interface to add and enable custom model-serving runtimes. You can then choose from your enabled runtimes when you create a new model server.

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This exercise will guide you through the steps necessary to deploy a custom Serving Runtime in order to serve a model using the Ollama Model Serving Framework.

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While RHOAI supports the ability to add your own runtime, it is up to you to configure, adjust, and maintain your custom runtimes.

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Add The Ollama Custom Runtime

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Figure 1. Animated - Add Ollama serving runtime
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    Log in to RHOAI with a user who is part of the RHOAI admin group, for this lab we will be using the admin account.

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    In the RHOAI Console, Navigate to the Settings menu, then select Serving Runtimes

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    Select: the Add Serving Runtime button

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    For the model serving platform runtime, select: Single-Model Serving Platform

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    For API protocol this runtime supports, select: REST

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    Click on: Start from scratch in the window that opens up, paste the following YAML

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    apiVersion: serving.kserve.io/v1alpha1
-kind: ServingRuntime
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-  opendatahub.io/dashboard: "true"
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-    openshift.io/display-name: Ollama
-  name: ollama
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-  builtInAdapter:
-    modelLoadingTimeoutMillis: 90000
-  containers:
-    - image: quay.io/rh-aiservices-bu/ollama-ubi9:0.1.45
-      env:
-        - name: OLLAMA_MODELS
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-      name: kserve-container
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    After clicking the Create button at the bottom of the input area, you will see the new Ollama Runtime in the list. We can re-order the list as needed (the order chosen here is the order in which the users will see these choices).

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The next step is to create a Data Connection in our Data Science Project. Before we can create our Data Connection, we will setup MinIO as our S3 compatible storage for this Lab.

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Continue to the next section to deploy and configure Minio.

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Installing Red Hat OpenShift AI Using the Web Console

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Red Hat OpenShift AI is available as an operator via the OpenShift Operator Hub. You will install the Red Hat OpenShift AI operator and dependencies using the OpenShift web console in this section.

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Lab: Installation of Red Hat OpenShift AI

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This exercise uses the Red Hat Demo Platform; specifically the OpenShift Container Cluster Platform Resource. If you haven’t already you’ll need to launch the lab environment before continuing.

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    Login to the Red Hat OpenShift using a user which has the cluster-admin role assigned.

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    It’s sufficient to install all prerequisite operators with default settings, no additional configuration is necessary.

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    Navigate to Operators → OperatorHub and search for each of the following Operators individually. Click on the button or tile for each. In the pop up window that opens, ensure you select the latest version in the "specified within instructions" channel and click on Install to open the operator’s installation view. For this lab you can skip the installation of the optional operators.

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[*] You do not have to wait for the previous Operator to complete before installing the next. For this lab you can skip the installation of the optional operators as there is no accelerator required.

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    Red Hat OpenShift Serverless

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    GPU Support

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This section will discuss the process for installing the dependent operators using the OpenShift Web Console.

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Installation of Red Hat OpenShift Serverless Operator

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The following section discusses installing the Red Hat OpenShift Serverless operator.

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Figure 1. Animated - Operator Hub installation of Serverless Operator in OpenShift
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    Login to Red Hat OpenShift using a user who has the cluster-admin role assigned.

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    Navigate to Operators → OperatorHub and search for Red Hat OpenShift Serverless.

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    Click on the Red Hat OpenShift Serverless operator. In the pop-up window, select the stable channel and the most recent version of the serverless operator. Click on Install to open the operator’s installation view.

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    A window showing the installation progress will pop up.

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    When the installation finishes the operator is ready to be used by Red Hat OpenShift AI.

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Red Hat OpenShift Serverless is now successfully installed.

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Installation of Red Hat OpenShift Service Mesh Operator

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The following section discusses installing the Red Hat OpenShift Service Mesh operator.

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Figure 2. Animated - Operator Hub installation of Service Mesh Operator in OpenShift
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    Login to Red Hat OpenShift using a user who has the cluster-admin role assigned.

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    Navigate to Operators → OperatorHub and search for Red Hat OpenShift Service Mesh.

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    Click on the Red Hat OpenShift Service Mesh operator. In the pop-up window, select the stable channel and the most recent version of the server mesh operator. Click on Install to open the operator’s installation view.

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Red Hat OpenShift Service Mesh is now successfully installed.

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Installation of Red Hat Authorino Operator

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The following section discusses installing the Red Hat - Authorino operator.

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Figure 3. Animated - Operator Hub installation of Authorino (tech preview) Operator in OpenShift
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    Login to Red Hat OpenShift using a user who has the cluster-admin role assigned.

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    Navigate to Operators → OperatorHub and search for *Red Hat Authorino.

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    Click on the Red Hat Authorino operator. In the pop-up window, select the tech-preview-v1 channel and the most recent version of the serverless operator. Click on Install to open the operator’s installation view.

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    A window showing the installation progress will pop-up.

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    When the installation finishes the operator is ready to be used by Red Hat OpenShift AI.

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Red Hat Authorino is now successfully installed.

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Installation of Red Hat OpenShift AI Operator

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Figure 4. Animated - Operator Hub installation of OpenShift AI Operator on OpenShift
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    Navigate to Operators → OperatorHub and search for OpenShift AI.

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      Click on the Red Hat OpenShift AI operator. In the pop-up window that opens, ensure you select the latest version in the fast channel. Any version equal to or greater than 2.14 and click on Install to open the operator’s installation view.

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      The operator Installation progress window will pop up. The installation may take a couple of minutes.

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Create OpenShift AI Data Science Cluster

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The next step is to create an OpenShift AI Data Science Cluster (DSC).

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A DataScienceCluster is the plan in the form of an YAML outline for Data Science Cluster API deployment. Manually editing the YAML configuration can adjust the settings of the OpenShift AI DSC.

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Figure 5. Animated - Create Data Science Cluster to enable OpenShift AI on OpenShift
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Return to the OpenShift Navigation Menu, Select Installed Operators, and click on the OpenShift AI Operator name to open the operator.

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    Select the option to create a Data Science Cluster.

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    Click Create to deploy the Data Science Cluster.

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This takes ~5 minutes to complete, once the DSC becomes Ready, use the navigation menu at the top right to drop down the menu and select Red Hat OpenShift AI. Use the same credentials as used log into OpenShift.

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OpenShift AI install Summary

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Congratulations, you have successfully completed the installation of OpenShift AI on an OpenShift Container Cluster. OpenShift AI is now running on a new Dashboard!

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    We installed the required OpenShift AI Operators

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      Red Hat OpenShift Serverless

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      Red Hat OpenShift ServiceMesh

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      Red Hat Authorino (technical preview)

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      OpenShift AI Operator

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Create a Data Science Project

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Navigate to the menu selector, located at the top right of the OCP dashboard. Select the grid of squares, then select OpenShift AI. At the login screen, use the OCP admin credentials to login to OpenShift AI.

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Figure 6. Animated - Create data science project from OpenShift AI dashboard
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Explore the dashboard navigation menus to familiarize yourself with the options.

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Navigate to & select the Data Science Projects section.

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    Select the Create Data Science Project button.

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    Enter a name for your project, such as ollama-model.

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      (the animated example shows fraud-detection entered for the data science project, for this course use the ollama-model to follow along with rest of the labs)

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    The resource name should be populated automatically.

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    Optionally add a description to the data science project.

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  5. -

    Select Create.

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Once complete, you should be on the landing page of the "ollama-model" Data Science Project section of the OpenShift AI Console / Dashboard.

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- - - diff --git a/pr-preview/pr-19/model-serving/1.1/chapter3/section2.html b/pr-preview/pr-19/model-serving/1.1/chapter3/section2.html deleted file mode 100644 index 036dc02..0000000 --- a/pr-preview/pr-19/model-serving/1.1/chapter3/section2.html +++ /dev/null @@ -1,523 +0,0 @@ - - - - - - MinIO S3 Compatible Storage Setup :: Serving an LLM using OpenShift AI - - - - - - - -
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MinIO S3 Compatible Storage Setup

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Deploy MinIO as S3 compatible storage

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MinIO overview

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MinIO is a high-performance, S3-compatible object store. It can be deployed on a wide variety of platforms, and it comes in multiple flavors.

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This segment describes a very quick way of deploying the community version of MinIO in order to quickly setup a fully standalone Object Store, in an OpenShift Cluster. This can then be used for various prototyping tasks that require Object Storage.

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MinIO Deployment

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To Deploy MinIO, we will utilize the OpenShift Dashboard.

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Figure 1. Animated - Minio Deployment steps for OpenShift
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    Then Select the + (plus) icon from the top right of the dashboard.

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    In the new window, we will paste the following YAML file. In the YAML below its recommended to change the default user name & password.

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    Click on the Project Selection list dropdown and select the ollama-model project or the data science project you created in the previous step.

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    Select the create button to deploy the Minio software.

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Minio Deployment Code

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kind: PersistentVolumeClaim
-apiVersion: v1
-metadata:
-  name: minio-pvc
-spec:
-  accessModes:
-    - ReadWriteOnce
-  resources:
-    requests:
-      storage: 40Gi
-  volumeMode: Filesystem
----
-kind: Secret
-apiVersion: v1
-metadata:
-  name: minio-secret
-stringData:
-  # change the username and password to your own values.
-  # ensure that the user is at least 3 characters long and the password at least 8
-  minio_root_user: minio
-  minio_root_password: minio321!
----
-kind: Deployment
-apiVersion: apps/v1
-metadata:
-  name: minio
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app: minio
-  template:
-    metadata:
-      creationTimestamp: null
-      labels:
-        app: minio
-    spec:
-      volumes:
-        - name: data
-          persistentVolumeClaim:
-            claimName: minio-pvc
-      containers:
-        - resources:
-            limits:
-              cpu: 250m
-              memory: 1Gi
-            requests:
-              cpu: 20m
-              memory: 100Mi
-          readinessProbe:
-            tcpSocket:
-              port: 9000
-            initialDelaySeconds: 5
-            timeoutSeconds: 1
-            periodSeconds: 5
-            successThreshold: 1
-            failureThreshold: 3
-          terminationMessagePath: /dev/termination-log
-          name: minio
-          livenessProbe:
-            tcpSocket:
-              port: 9000
-            initialDelaySeconds: 30
-            timeoutSeconds: 1
-            periodSeconds: 5
-            successThreshold: 1
-            failureThreshold: 3
-          env:
-            - name: MINIO_ROOT_USER
-              valueFrom:
-                secretKeyRef:
-                  name: minio-secret
-                  key: minio_root_user
-            - name: MINIO_ROOT_PASSWORD
-              valueFrom:
-                secretKeyRef:
-                  name: minio-secret
-                  key: minio_root_password
-          ports:
-            - containerPort: 9000
-              protocol: TCP
-            - containerPort: 9090
-              protocol: TCP
-          imagePullPolicy: IfNotPresent
-          volumeMounts:
-            - name: data
-              mountPath: /data
-              subPath: minio
-          terminationMessagePolicy: File
-          image: >-
-            quay.io/minio/minio:RELEASE.2023-06-19T19-52-50Z
-          args:
-            - server
-            - /data
-            - --console-address
-            - :9090
-      restartPolicy: Always
-      terminationGracePeriodSeconds: 30
-      dnsPolicy: ClusterFirst
-      securityContext: {}
-      schedulerName: default-scheduler
-  strategy:
-    type: Recreate
-  revisionHistoryLimit: 10
-  progressDeadlineSeconds: 600
----
-kind: Service
-apiVersion: v1
-metadata:
-  name: minio-service
-spec:
-  ipFamilies:
-    - IPv4
-  ports:
-    - name: api
-      protocol: TCP
-      port: 9000
-      targetPort: 9000
-    - name: ui
-      protocol: TCP
-      port: 9090
-      targetPort: 9090
-  internalTrafficPolicy: Cluster
-  type: ClusterIP
-  ipFamilyPolicy: SingleStack
-  sessionAffinity: None
-  selector:
-    app: minio
----
-kind: Route
-apiVersion: route.openshift.io/v1
-metadata:
-  name: minio-api
-spec:
-  to:
-    kind: Service
-    name: minio-service
-    weight: 100
-  port:
-    targetPort: api
-  wildcardPolicy: None
-  tls:
-    termination: edge
-    insecureEdgeTerminationPolicy: Redirect
----
-kind: Route
-apiVersion: route.openshift.io/v1
-metadata:
-  name: minio-ui
-spec:
-  to:
-    kind: Service
-    name: minio-service
-    weight: 100
-  port:
-    targetPort: ui
-  wildcardPolicy: None
-  tls:
-    termination: edge
-    insecureEdgeTerminationPolicy: Redirect
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This should finish in a few seconds. Now it’s time to deploy our storage buckets.

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MinIO Storage Bucket Creation

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From the OCP Dashboard:

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Figure 2. Animated - Accessing Minio dashboard and Bucket creation
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    Select Networking / Routes from the navigation menu.

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    This will display two routes, one for the UI & another for the API. (if the routes are not visible, make sure you have the project selected that matches your data sicence project created earlier)

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    For the first step, select the UI route and paste it or open in a new browser tab or window.

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    If you see a landing page with the message application not available, refresh the page a few times as the service is still loading.

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    The displayed page is the MinIO Dashboard. Log in with the username/password combination you set, or the defaults listed below.

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      username = minio

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      password = minio321!

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Once logged into the MinIO Console:

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    Click Create Bucket to get started.

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    Create two Buckets:

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      models

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      storage (optional)

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    Via the Navigation menu, select object browser, then click on the Model Bucket.

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    From the models bucket page, click add path, and type ollama as the name of the sub-folder or path.

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- - -In most cases, to serve a model, the trained model would be uploaded into this sub-directory. However, Ollama is a special case, as it can download and manage Several LLM models as part of the runtime. -
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    We still need a file available in this folder for the model deployment workflow to succeed.

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    So we will copy an emptyfile.txt file to the ollama subdirectory.

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You can download the file from this location(use right click, save link as). Alternatively, you can create your own file called emptyfile.txt and upload it.

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    Once you have this file ready, upload it into the Ollama path in the model bucket by clicking the upload button and selecting the file from your local desktop.

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Figure 3. Animated - Upload emptyfile.txt to models/ollama
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With our S3 Compatible ready to go and our "model files" uploaded. Let’s head back to the OpenShift AI Web UI and continue configuring resources.

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- - - diff --git a/pr-preview/pr-19/model-serving/1.1/chapter3/section2old.html b/pr-preview/pr-19/model-serving/1.1/chapter3/section2old.html deleted file mode 100644 index dacd958..0000000 --- a/pr-preview/pr-19/model-serving/1.1/chapter3/section2old.html +++ /dev/null @@ -1,306 +0,0 @@ - - - - - - Modifying the OpenShift AI TLS Certificate :: Serving an LLM using OpenShift AI - - - - - -
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Modifying the OpenShift AI TLS Certificate

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By default, the Single Model Serving Platform in Openshift AI v2.10 uses a self-signed certificate generated during installation for the endpoints that are created when deploying a Model server.

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This can be counterintuitive because the OCP Cluster already has certificates configured which will be used by default for endpoints like Routes.

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This following procedure explains how to use the same certificate from the OpenShift Container cluster for OpenShift AI.

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Use OpenShift Certificates for Ingress Routes

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Navigate to the OpenShift Container Cluster Dashboard

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- - - - - -
- - -The content of the Secret (data) should contain two items, tls.cert and tls.key. They are the certificate and key that are used for all the OpenShift Routes. The actual name will include the term cert, but could be different from the example. -
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Collect the Secret YAML Text:

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    In the Navigation pane on the left, click on the Workloads section, then Secrets under Workloads.

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    From the Project dropdown, toggle the show default projects radial button to on.

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    Select the openshift-ingress project from the list.

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    Locate the file named cert-manager-ingress-cert, type should be kubernetes.io/tls

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    Click on the filename to open the secret, select the YAML Tab

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    Copy all the text from the window, and ensure that you scroll down. (CTL-A should work).

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Clean & Deploy the Secret YAML Text:

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    Click on the Project dropdown again, select the Istio-system project

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    Select the Create button on the right, then Select the from YAML option.

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    Delete the text from the Window, and paste the copied Secret text

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    Cleanup the YAML Text to just keep the relevant content. It should look like the YAML file below (the name of the secret will be different, it’s normally tied to the date the RDHP Cluster was deployed).

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kind: Secret
-apiVersion: v1
-metadata:
-name: cert-manager-ingress-cert
-data:
-  tls.crt: >-
-    LS0tLS1CRUd...
-  tls.key: >-
-    LS0tLS1CRUd...
-type: kubernetes.io/tls
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    Copy the name of the secret from line 4, just the name (optional, but helpful)

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We have copied the Secret used by OCP & made it available to be used by OAI.

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OpenShift AI Resources - 2

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Create Data Connection

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Navigate to the Data Science Project section of the OpenShift AI Console /Dashboard. Select the Ollama-model project.

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    Select the Data Connection menu, followed by create data connection

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      Name: models

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      Access Key: use the minio_root_user value from previous section’s YAML file

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      Secret Key: use the minio_root_password value from previous section’s YAML File

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      Endpoint: use the MinIO API URL from the Routes page in Openshift Dashboard

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      Region: This is required for AWS storage & cannot be blank, set value to "no-region-minio"

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      Bucket: use the Minio Storage bucket name: models

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Repeat the same process for the Storage data connection, using storage for the "Name" & "Bucket".

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Creating a WorkBench

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Navigate to the Data Science Project section of the OpenShift AI Console /Dashboard. Select the Ollama-model project.

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    Select the WorkBench button, then click create workbench

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      Name: ollama-model

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      Notebook Image: Minimal Python

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      Leave the remaining options default.

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      Optionally, scroll to the bottom, check the Use data connection box.

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      Select storage from the dropdown to attach the storage bucket to the workbench.

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    Select the Create Workbench option.

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Creating The Model Server

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From the ollama-model WorkBench Dashboard in the ollama-model project, navigate to the Models section, and select Deploy Model from the Single Model Serving Platform Button.

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-ollama model deploy -
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Figure 1. Animated - Single model deployment walkthrough
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Create the model server with the following values:

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    Model name: ollama-mistral (differs from animated deployment, use this name)

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    Serving Runtime: Ollama

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    Model framework: Any

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    Model Server Size: Medium

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    Model Route: Check the box to make models available via an external route.

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    Token Authentication: Uncheck the box that requires token authentication.

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    Model location data connection: models

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    Model location path: /ollama

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After clicking the Deploy button at the bottom of the form, the model is added to our Models & Model Server list. When the model is available, the inference endpoint will populate & the status will indicate a green checkmark.

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We are now ready to interact with our newly deployed LLM Model. Join me in the next section to explore Mistral running on OpenShift AI using Jupyter Notebooks.

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- - - diff --git a/pr-preview/pr-19/model-serving/1.1/chapter3/section3old.html b/pr-preview/pr-19/model-serving/1.1/chapter3/section3old.html deleted file mode 100644 index 1e4e6e1..0000000 --- a/pr-preview/pr-19/model-serving/1.1/chapter3/section3old.html +++ /dev/null @@ -1,263 +0,0 @@ - - - - - - Configure the OpenShift AI Data Science Cluster :: Serving an LLM using OpenShift AI - - - - - -
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Configure the OpenShift AI Data Science Cluster

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Create OpenShift AI Data Science Cluster

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With our secrets in place, the next step is to create an OpenShift AI Data Science Cluster.

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A DataScienceCluster is the plan in the form of an YAML outline for Data Science Cluster API deployment.

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Return to the OpenShift Navigation Menu, Select Installed Operators, and Click on the OpenShift AI Operator name to open the operator.

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    Select the Option to create a Data Science Cluster.

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    Select the radial button to switch to the YAML view.

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    Find the section below in the YAML file, in the Kserve Section find the Serving/Certificate area; add the line: secretName: followed by the name of the secret name that we deployed in the istio-system project. In addition, change the type from SelfSigned to Provided. See below for the example.

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kserve:
-  managementState: Managed
-  serving:
-    ingressGateway:
-      certificate:
-        secretName: cert-manager-ingress-cert
-        type: Provided
-    managementState: Managed
-    name: knative-serving
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Once you have made those changes to the YAML file, Click Create to Deploy the Data Science Cluster.

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-dsc deploy complete -
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Single Model Serve Platform will now be deployed to expose ingress connections with the same certificate as OpenShift Routes. Endpoints will be accessible using TLS without having to ignore error messages or create special configurations.

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OpenShift AI install summary

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Congratulations, you have successfully completed the installation of OpenShift AI on an OpenShift Container Cluster. OpenShift AI is now running on a new Dashboard!

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    We installed the required OpenShift AI Operators

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      Red Hat OpenShift Serverless

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      Red Hat OpenShift ServiceMesh

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      Red Hat Authorino (technical preview)

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      OpenShift AI Operator

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Additionally, we took this installation a step further by sharing TLS certificates from the OpenShift Cluster with OpenShift AI.

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We will pick up working with the OpenShift AI UI in the next Chapter.

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Jupyter Notebooks & LLMs

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This chapter begins with running and configured OpenShift AI environment. If you don’t already have your environment running, head over to OpenShift AI Initialization.

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In section, we will explore using the Jupyter Notebook from our workbench to infer data from the Mistral and Llama3 Large Language Models. While less technical than previous sections of this hands-on course, there are some steps to download the Model when updating our notebook with an inference endpoints that requires code modifications.

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Let’s get started!

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Jupyter Notebooks

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Open JupyterLab

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JupyterLab enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner. For a demonstration of JupyterLab and its features, you can view this video.

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Return to the ollama-model workbench dashboard in the OpenShift AI console.

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    Select the Open link to the right of the status section.

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    When the new window opens, use the OpenShift admin user & password to login to JupyterLab.

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    Click the Allow selected permissions button to complete login to the notebook.

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Inside JupyterLab

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This takes us to the JupyterLab screen where we can select multiple options / tools / to begin our data science experimentation.

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Our first action is to clone a git repository that contains a collection of LLM projects including the notebook we are going to use to interact with the LLM.

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Clone the github repository to interact with the Ollama Framework from this location: -https://github.com/rh-aiservices-bu/llm-on-openshift.git

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    Copy the URL link above

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    Paste the link into the clone a repo pop up, make sure the included submodules are checked, then click the clone.

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    Navigate to the llm-on-openshift/examples/notebooks/langchain folder:

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    Explore the notebook, and then continue.

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Configure the Ollama Framework with a Large Language Model

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    From the Notebook page, add a new cell above the inference url

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The Ollama Model Runtime we deployed using the Single Model Serving Platform in OpenShift AI is a Framework that can host various large language models. It is currently running, but is waiting for the command to instruct the framework on which model to download and serve.

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    To load the mistral model, we are going to use the following python code to instruct the runtime to download and serve a quantized 4 bit version of the mistral large language model.

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    Copy the code below and paste this code in the new cell added to the notebook in the previous step.

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We’ll need to modify the url in the bottom line beginning with response = in the next step.

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Update the Inference Endpoints

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Head back to the RHOAI ollama-model workbench dashboard, from the models tab, copy the inference endpoint for the ollama-mistral model.

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Return the Jupyter notebook

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We will be updating two cells with the inference endpoint.

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    Replace "https://your-endopint" from python code we copied into the new cell. Ensure you leave the /api/pull portion appended to the url.

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    We can now start executing the code in the cells, beginning with the new cell added to the top. Click on the cell to activate the blue indicator to the left of the cell. Next click on the play icon to run the selected cells and advance to the next. While the cell is executing, an * (asterisk icon) will appear in the brackets, when complete this will change to a number.

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      You will receive a message about an Unverified HTTPs request. This is because we didn’t use authentication for this application. You can ignore this for this lab experience, but in production we would enable authentication using Authorino secured connections.

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      The mistral model files are now being downloaded to the Ollama Framework. This may take a few minutes, the * will change to a number when complete.

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    Continue executing through the cells.

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    This "Create the LLM instance" cell sets the variables that determine how we are going to interact with our model and how it should respond - sets that into an array using the llm variable.

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    Next run the "Create the prompt" cell. Here we are setting the template variable with the details of how the model operates, including constraints and boundaries when generating the response. We often do not experience the system message when interacting with an LLM, but this is a standard field that is included along with the user prompt.

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    Continue executing the cells, "memory for the conversation" keeps the previous context / conversation history so the full history of the chat conversation is sent as part of the prompt.

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    The create the chain cell, combines each of previous variables: llm, prompt, memory, and adds a verbose boolean to create the conversation variable, which will be sent to Models inference endpoint running in OpenShift AI. The verbose option set to true displays the entire conversation sent to the Model in the notebook before the Models (AI’s) response.

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In the next section, we’ll send our first input to the running Mistral Large Language Model.

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Mistral LLM Model Inference

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Let’s Talk with the LLM

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The blue indicator on the left should now be located at the first input cell. If you have not executed or run all the cells above, please do so now.

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First Input

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The first input cell sent via the notebook to the Mistral model askes it to describe: "Paris in 100 words or less". Go ahead and run this cell.

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Immediately, green text is shown in the notebook, this is the entire prompt that is sent along with the single sentence question. There are three parts to the prompt:

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    The System message which describes to the model how to consider and respond to the question

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    The current conversation section, which describes the prompt or question to consider

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    The response from Mistral Model, which is currently processing and will appear in black text

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It takes a few seconds for the OpenShift AI model to respond with the first words of the reply. The response answered the question in a well-considered informative paragraph that meets the requirement of less than 100 words in length.

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Second Input

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The Second input - "Is there a River" - does not specify where the location is. Due to the conversation history being passed with the second input, there is no need to specify any additional information. Shown in the current conversation section below, notice the first input and the response was included with the second input.

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Second Example

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Before we continue with the London example, we execute a cell to change the conversation mode to non-verbose. This eliminates the context of the prompt displayed in the notebook to instead just show the model’s reply.

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We also execute a cell to clear memory, or the conversation history regarding Paris.

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We did not disable the memory, or the verbosity of the conversation; we simply hid that section from being visible in the notebook.

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Experiment with Mistral

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There are multiple different types of large language models, while we can read about them, using them first hand is the best way to experience how they perform.

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So now it’s time to experiment on your own, or optionally continue to follow along with this guide.

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Add a few new cells to the bottom of the Notebook.

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Then I asked one of my standard questions across models to determine it’s knowledge of history:

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Was George Washington Married?

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Why I ask this question is because several models say GW was married twice. I believed the first one, and this had me thinking several of the next models were wrong. It’s critical that we evaluate models to determine their viability for business use cases. The Mistral model was wrong in this instance.

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Try clearing the memory again and asking more of your own questions to continue to experiment with the Mistral model.

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When you’re ready, move to the next section, where we evaluate a different large language model.

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Llama3 LLM Model Inference

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Lab: Switch to the Llama3 large language model

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Meta Llama 3, a family of models developed by Meta Inc. are new state-of-the-art , available in both 8B and 70B parameter sizes (pre-trained or instruction-tuned).

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Llama 3 instruction-tuned models are fine-tuned and optimized for dialogue/chat use cases and outperform many of the available open-source chat models on common benchmarks.

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In this Lab, we experiment with a quantized version of the base Llama3 model

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Delete existing deployed model

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Return to the OpenShift AI Dashboard and ollama-model workbench

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    Head to the Model section of the workbench

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    No need to wait, continue onto the next section.

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Creating The Model Server

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From the ollama-model WorkBench Dashboard in the ollama-model project, navigate to the Models section, and select Deploy Model.

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Create the model server with the following values:

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    Model name: ollama-llama3

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    Model framework: Any

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    Model Server Size: Medium

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    Model location data connection: models

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    Model location path: /ollama

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After clicking the Deploy button at the bottom of the form, the model is added to our Models & Model Server list. When the model is available, the inference endpoint will populate & the status will indicate a green checkmark.

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Copy the Inference endpoint for the newly deployed model, we need to replace the original inference endpoints used in our notebook’s top two cells.

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Update the inference endpoints & change the model name

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    Return to the jupyter notebook

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Execute the cells again

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      You will again receive the message about an unverified HTTPs request. This is because we didn’t use authentication for this application.

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      The llama3 model files are now being downloaded to the Ollama Framework.

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    Continue executing the cells in the notebook.

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When you reach the bottom of the notebook, feel free to experiment with the Llama3 model by creating your own more complex prompts.

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Experiment with Llama3

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Instead of editing the questions try experimenting with system prompt:

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Once you complete your learning, don’t forget to return to Red Hat Demo Hub to stop the environment if you plan to return. Delete the environment when you’re finished.

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Thanks for completing this course!

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- - - diff --git a/pr-preview/pr-19/model-serving/1.1/index.html b/pr-preview/pr-19/model-serving/1.1/index.html deleted file mode 100644 index ca9d0b8..0000000 --- a/pr-preview/pr-19/model-serving/1.1/index.html +++ /dev/null @@ -1,333 +0,0 @@ - - - - - - Serving an LLM using OpenShift AI :: Serving an LLM using OpenShift AI - - - - - - -
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Serving an LLM using OpenShift AI

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Welcome to this quick course on Serving an LLM using OpenShift AI.

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This program was designed to guide you through the process of installing an OpenShift AI Platform using the OpenShift Container Platform Web Console UI. We get hands-on experience in each component needed to enable a RHOAI Platform using an Openshift Container Platform Cluster.

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Once we have an operational OpenShift AI Platform, we will login and begin the configuration of: model runtimes, data science projects, data connections, & finally use a jupyter notebook to infer the answers to easy questions.

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There will be some challenges along the way, all designed to teach us about a component, or give us the knowledge needed to utilize OpenShift AI and host a Large Language Model.

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If you’re ready, let’s get started!

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- - -The hands-on labs in this course were updated to and tested with RHOAI v2.14. Labs should work without any changes in minor dot release upgrades of the product. Please open issues in this repository if you face any problem or feel free to send me a note on slack. -
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Authors

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The PTL team acknowledges the valuable contributions of the following Red Hat associates:

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    Christopher Nuland

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    Vijay Chebolu

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    Hunter Gerlach

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    Karlos Knox

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Classroom Environment

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We will use the Red Hat OpenShift Container Platform Cluster (AWS) catalog item in the Red Hat Demo Platform (RHDP) to run the hands-on exercises in this course.

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Figure 1. Animated - Walkthrough of Demo Hub order selections.
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When ordering this catalog item in RHDP:

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    Select Red Hat OpenShift Container Platform Cluster (AWS) catalog item.

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    Select order from the pop up lab menu page.

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    Select Practice/Enablement for the Activity field.

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    Click order.

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For Red Hat partners who do not have access to RHDP, provision an environment using the Red Hat Hybrid Cloud Console. Unfortunately, the labs will NOT work on the trial sandbox environment. You need to provision an OpenShift AI cluster on-premises, or in the supported cloud environments by following the Product Documentation for installing Red Hat OpenShift AI 2.14.

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Prerequisites

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    Basic experience with Red Hat OpenShift Container Platform is recommended but is not mandatory.

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    We will encounter & modify code segments, deploy resources using YAML files, & modify launch configurations; but we will not have to write code.

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Objectives

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The overall objectives of this course include:

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    Utilize Red Hat OpenShift AI to serve & interact with an LLM.

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    Install Red Hat OpenShift AI operators & dependencies.

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    Add a custom model serving runtime.

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    Create a data science project, workbench & data connections.

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    Load an LLM model into the Ollama runtime framework.

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    Import (from git repositories), interact with LLM model via Jupyter Notebooks.

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    Experiment with the Mistral LLM and Llama3 large language models.

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