diff --git a/pr-preview/pr-20/_/css/site.css b/pr-preview/pr-20/_/css/site.css deleted file mode 100644 index c39da22..0000000 --- a/pr-preview/pr-20/_/css/site.css +++ /dev/null @@ -1,2608 +0,0 @@ -@font-face { - font-family: Roboto; - font-style: normal; - font-weight: 400; - src: url(../font/roboto-latin-400-normal.woff2) format("woff2"), url(../font/roboto-latin-400-normal.woff) format("woff"); - unicode-range: U+00??, U+0131, U+0152-0153, U+02bb-02bc, U+02c6, U+02da, U+02dc, U+2000-206f, U+2074, U+20ac, U+2122, U+2191, U+2193, U+2212, U+2215, U+feff, U+fffd -} - -@font-face { - font-family: Roboto; - font-style: italic; - font-weight: 400; - src: url(../font/roboto-latin-400-italic.woff2) format("woff2"), url(../font/roboto-latin-400-italic.woff) format("woff"); - unicode-range: U+00??, U+0131, U+0152-0153, U+02bb-02bc, U+02c6, U+02da, U+02dc, U+2000-206f, U+2074, U+20ac, U+2122, U+2191, U+2193, U+2212, U+2215, U+feff, U+fffd -} - -@font-face { - font-family: Roboto; - font-style: normal; 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Applications that require to make inference
Applications th...
Runtime Server
Runtime Server
With a model
With a model
Data Science team
Data Science te...
How do we deliver a model here?
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Applications that require to make inference
Applications th...
Runtime Server
Runtime Server
With a model
With a model
Data Science team
Data Science te...
Train and Deploy
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Data collection
Data collection
Data Preparation
Data Preparation
Feature Engineering
Feature Engineering
Model Training
Model Training
Select the proper algorithm to process the data
Select the proper al...
Train the model
Train the model
Model output format
Model output format
Model Registry
Model Registry
Deployable image
Deployable image
Upload to model registry
Upload to model...
Create an image that includes:
- A runtime engine for the model
- The model
Create an image that includes:...
model
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Model Serving in Red Hat OpenShift AI

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Introduction

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Generally speaking, an organization decides to train a model, because the organization wishes to empower the applications portfolio with inferences made by such model. In this way, the given known data points of a given event, can produce an inference, guess, or prediction of a topic of interest.

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In this course, you will learn how to leverage Red Hat OpenShift Data Science to serve machine learning models.

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How do we deliver a model to an inference engine, or server, so that, when the server receives a request from any of the applications in the organization’s portfolio, the inference engine can reply with a prediction made based in the model that we have previously worked hard to train?

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Machine learning models must be deployed in a production environment to process real-time data and handle the problem they were designed to solve.

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Deploying a model in a production environment means that the model that has been trained, and exported to a model file format, needs to be imported in a runtime engine, and exposed for applications to consume.

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Consume from a model, means that software applications will use a communication method, often REST/HTTP to send a prompt request to a server, such server will fire a request to the model and provide a response. It is evident that the server processing the request, and providing a response based on the model needs to have access to such model.

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Model Serving Concepts

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Model Serving

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Machine learning model serving refers to the process of deploying, managing, and exposing machine learning models for inference, allowing them to make predictions or classifications on new, unseen data. Once a machine learning model has been trained and fine-tuned on a specific task, serving involves making the model available for real-time or batch predictions in a production environment. This is a crucial step in the machine learning lifecycle, as it enables the integration of models into applications, systems, or services, allowing them to deliver value in real-world scenarios.

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Key aspects of machine learning model serving include:

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Deployment:

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After training, a machine learning model needs to be deployed to an environment where it can be accessed for inference. This can be on-premises, in the cloud, or on edge devices, depending on the application’s requirements.

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Scalability:

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Model serving systems need to be scalable to handle varying levels of demand. This involves ensuring that the infrastructure can handle increased load and that the serving architecture is designed for efficiency and resource optimization.

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Real-time Inference:

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Many applications require real-time predictions, where the model responds to input data quickly. This is common in scenarios such as fraud detection, image recognition, and natural language processing.

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Batch Inference:

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In some cases, models need to process large batches of data in a more efficient, non-real-time manner. This is common in scenarios like data preprocessing, analytics, and large-scale data transformations.

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Monitoring and Logging:

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Model serving systems often include monitoring and logging functionalities to track the performance and health of deployed models. This includes metrics such as response time, error rates, and resource utilization.

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Versioning:

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It’s important to manage and version different iterations of a model. This allows for easy rollback in case of issues and facilitates A/B testing when deploying new models.

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Security:

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Ensuring the security of the deployed models is crucial. This involves controlling access to the models, encrypting communications, and protecting against potential attacks or adversarial inputs.

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Integration:

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Model serving systems need to integrate seamlessly with other components of the application or system architecture. This often involves using APIs (Application Programming Interfaces) to facilitate communication between the model and the rest of the application. -Popular tools and frameworks for model serving include TensorFlow Serving, Flask, FastAPI, Amazon SageMaker, and TensorFlow Extended (TFX), among others. These tools provide solutions for deploying, managing, and scaling machine learning models in production environments.

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Model Server Runtime

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The terms "model server runtime" and "inference engine" are closely related in the context of deploying and serving machine learning models, but they refer to different components of the overall system.

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A model server runtime is the execution environment or platform where a trained machine learning model runs to make predictions or inferences. It is responsible for loading the model into memory, handling requests from clients, performing the inference, and returning the results. The runtime provides the necessary infrastructure for hosting the model, managing resources, and ensuring that the model’s predictions can be efficiently served in a production environment.

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The model server runtime can be a part of a larger deployment framework or service that includes features such as scalability, versioning, monitoring, and security. Examples of model server runtimes include TensorFlow Serving, TorchServe, and ONNX Runtime. These runtimes support the deployment of models trained using popular machine learning frameworks and provide a standardized way to serve predictions over APIs (Application Programming Interfaces).

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Inference Engine

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An inference engine is a component responsible for executing the forward pass of a machine learning model to generate predictions based on input data. It is a crucial part of the model server runtime and is specifically designed for performing inference tasks efficiently. The inference engine takes care of optimizations, such as hardware acceleration and parallelization, to ensure that predictions are made quickly and with minimal resource utilization.

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The inference engine may be integrated into the model server runtime or work alongside it, depending on the specific architecture. For example, TensorFlow Serving incorporates TensorFlow’s inference engine, and ONNX Runtime serves as both a runtime and an inference engine for models in the Open Neural Network Exchange (ONNX) format.

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Relationship: -In summary, the model server runtime provides the overall environment for hosting and managing machine learning models in production, while the inference engine is responsible for the actual computation of predictions during inference. The two work together to deliver a scalable, efficient, and reliable solution for serving machine learning models in real-world applications. The choice of model server runtime and inference engine depends on factors such as the machine learning framework used, deployment requirements, and the specific optimizations needed for the target hardware.

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Unravel The Runtime

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When deploying machine learning models, we need to deploy a container that serves a Runtime and uses a Model to perform predictions, consider the following example:

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Train a Model

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Using a RHOAI instance, let us train and deploy an example.

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    In a data science project, create a Standard Data Science workbench. -Then, open the workbench to go to the JupyterLab interface.

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There are different formats and libraries to export the model, in this case we are using pickle. Other common formats are:

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The use of either of those formats depends on the target server runtime, some of them are proven to be more efficient than others for certain type of training algorithms and model sizes.

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Use the Model in Another Notebook

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The model can be deserialized in another notebook, and used to generate a prediction:

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    Run the use-purchase-amount notebook and notice the result:

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      You can get the same result without training the model again.

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      You are not training the model in the user-purchase-amount notebook, you are re-using the output from the training notebook, and using the generated model to generate an inference.

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At this moment the model can be exported and imported in other projects for its use. Normally there will be an S3 bucket or a model registry to store models and versions of such models, and instead of manually exporting the model, there would be pipelines making the model available.

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Use the Model in a Container

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For this section, you need Podman to create an image, and a registry to upload the resulting image.

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Web application that uses the model

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The pickle model that we previously exported can be used in a Flask application. In this section we present an example Flask application that uses the model.

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Although we are actually serving a model with Flask in the exercise, Flask is not considered part of the Model Serving feature. This example represents one way in which some customers decide to embed their models in containers, although RHOAI provides for mechanisms that can make this process of serving a model a simpler process, when provided with the proper model formats.

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    In your computer, create a new directory to save the source code of the web application. -Navigate to that directory.

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-model_name = "Time to purchase amount predictor"
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    click==8.0.3
-cycler==0.11.0
-Flask==2.0.2
-fonttools==4.28.5
-gunicorn==20.1.0
-itsdangerous==2.0.1
-Jinja2==3.0.3
-kiwisolver==1.3.2
-MarkupSafe==2.0.1
-matplotlib==3.5.1
-numpy==1.22.0
-packaging==21.3
-pandas==1.3.5
-Pillow==9.0.0
-pyparsing==3.0.6
-python-dateutil==2.8.2
-pytz==2021.3
-scikit-learn==1.0.2
-scipy==1.7.3
-six==1.16.0
-sklearn==0.0
-threadpoolctl==3.0.0
-Werkzeug==2.0.2
    -
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    5. -
  5. -

    Create a Containerfile to build an image with the Flask application:

    -
    -
    -
    # Base image
-FROM python:3.9
-
-# Set working directory
-WORKDIR /app
-
-# Copy files
-COPY app.py /app (1)
-COPY requirements.txt /app (2)
-COPY mymodel.pkl /app (3)
-
-# Install dependencies
-RUN pip install -r requirements.txt
-
-# Run the application
-EXPOSE 8000
-ENTRYPOINT ["gunicorn", "-b", "0.0.0.0:8000", "--access-logfile", "-", "--error-logfile", "-", "--timeout", "120"]
-CMD ["app:app"]
    -
    -
    -
    - - - - - - - - - - - - - -
    1The python application source code
    2The list of packages to install
    3The model
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    6. -
  6. -

    Build and push the image to an image registry

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    $ podman login quay.io
-$ podman build -t purchase-predictor:1.0 .
-$ podman tag purchase-predictor:1.0 quay.io/user_name/purchase-predictor:1.0
-$ podman push quay.io/user_name/purchase-predictor:1.0
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    After you push the image, open quay.io in your browser and make the image public.

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    7. -
  7. -

    Deploy the model image to OpenShift

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    $ oc login api.cluster.example.com:6443
-$ oc new-project model-deploy
-$ oc new-app --name purchase-predictor quay.io/user_name/purchase-predictor:1.0
-$ oc expose service purchase-predictor
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    8. -
-
-
-

Now we can use the Flask application with some commands such as:

-
-
-
-
$ curl http://purchase-predictor-model-deploy.apps.cluster.example.com/health
-ok
-$ curl http://purchase-predictor-model-deploy.apps.cluster.example.com/info
-{"name":"Time to purchase amount predictor","version":"v1.0.0"}
-$ curl -d '{"time":4}' -H "Content-Type: application/json" \
-> -X POST \
-> http://purchase-predictor-model-deploy.apps.cluster.example.com/predict
-{"prediction":34,"status":200}
-
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-
- - - - - -
- - -
-

In this section we have manually:

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  1. -

    Developed an application that uses the model

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    2. -
  2. -

    Built an image with such application

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  3. -

    Push the image to a registry

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    4. -
  4. -

    Deployed the containerized application in OpenShift

    -
    5. -
  5. -

    Exposed the application’s endpoint in OpenShift by creating a route

    -
    6. -
  6. -

    Consumed the model through the application’s REST API to request a prediction

    -
    7. -
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There are automated and faster ways to perform these steps. In the following sections, we will learn about runtimes that only require you to provide a model, and they automatically provision an inference service for you.

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RHOAI Model Serving Runtimes

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In the previous example, we manually created a Model Server by sending the model to an image that can interpret the model and expose it for consumption. In our example we used Flask.

-
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However, in Red Hat OpenShift AI, you do not need to manually create serving runtimes. -By default, Red Hat OpenShift AI includes a pre-configured model serving runtime, OpenVINO, which can load, execute, and expose models trained with TensorFlow and PyTorch. -OpenVINO supports various model formats, such as the following ones:

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-
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ONNX
-
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An open standard for machine learning interoperability.

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OpenVino IR
-
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The proprietary model format of OpenVINO, the model serving runtime used in OpenShift AI.

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In order to leverage the benefits of OpenVINO, you must:

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  1. -

    Export the model in a format compatible with one of the available RHOAI runtimes.

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    Upload the model to an S3

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    3. -
  3. -

    Create a Data Connection to the S3 containing the model

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    4. -
  4. -

    Create or use one of the available serving runtimes in a Model Server configuration that specifies the size and resources to use while setting up an inference engine.

    -
    5. -
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    Start a model server instance to publish your model for consumption

    -
    6. -
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While publishing this model server instance, the configurations will allow you to define how applications securely connect to your model server to request for predictions, and the resources that it can provide.

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Model Serving Resources

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When you use model serving, RHOAI uses the ServingRuntime and InferenceService custom resources.

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ServingRuntime
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Defines a model server.

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InferenceService
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Defines a model deployed in a model server.

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For example, if you create a model server called foo, then RHOAI creates the following resources:

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    modelmesh-serving Service

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    foo ServingRuntime

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      modelmesh-serving-foo Deployment

      -
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        modelmesh-serving-foo-…​ ReplicaSet

        -
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          modelmesh-serving-foo-…​-…​ Pod

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The ServingRuntime defines your model server and owns a Deployment that runs the server workload. -The name of this deployment is prefixed with the modelmesh-serving- prefix. -Initially, when no models are deployed, the deployment is scaled to zero, so no pod replicas are running.

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When creating the first model server in a data science project, RHOAI also creates a Service called modelmesh-serving to map HTTP, HTTPs and gRPC traffic into the model servers.

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- - -
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The modelmesh-serving service maps traffic for all model servers. -No additional services are created when you create more than one model server.

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After you create a model server, you are ready to deploy models. -When you deploy a model in a model server, RHOAI creates an InferenceService custom resource, which defines the deployed model properties, such as the name and location of the model file. -For example, If you deploy a model called my-model, then RHOAI creates the following resources.

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    my-model InferenceService

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      my-model Route, which points to the modelmesh-serving Service.

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The route is only created if you have selected the Make deployed models available through an external route checkbox when creating the server. -The InferenceService owns the route.

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At the same time, to be able to serve the model, RHOAI starts the model server by scaling the model-serving- deployment up to one pod replica. -This model serving pod runs the model serving containers:

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    -
  • -

    mm: the ModelMesh model serving framework.

    -
    • -
  • -

    The model serving runtime container, such as ovms for OpenVINO.

    -
    • -
  • -

    The ModelMesh runtime adapter for your specifc serving runtime. -For example, if you are using OpenVINO, then the container is ovms-adapter.

    -
    • -
  • -

    rest-proxy: For HTTP traffic.

    -
    • -
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    oauth-proxy: For authenticating HTTP requests.

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- - - - - -
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The modelmesh-serving pod runs the model server, which handles one or more deployed models. -No additional pods are created when you deploy multiple models.

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- Red Hat -
- - - diff --git a/pr-preview/pr-20/rhods-deploy/1.33/chapter1/section2.html b/pr-preview/pr-20/rhods-deploy/1.33/chapter1/section2.html deleted file mode 100644 index a9eccd5..0000000 --- a/pr-preview/pr-20/rhods-deploy/1.33/chapter1/section2.html +++ /dev/null @@ -1,728 +0,0 @@ - - - - - - OpenVINO Model Serving :: Deploying Machine Learning Models with Red Hat OpenShift AI - - - - - - - -
- -
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OpenVINO Model Serving

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In this section we will work in an exercise to deploy a model to an OpenVINO Serving Runtime.

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Prepare MinIO

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-
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MinIO is a high-performance, S3 compatible object store. It is built for large scale AI/ML, data lake and database workloads. It is software-defined and runs on any cloud or on-premises infrastructure.

-
-
-

We will need an S3 solution to share the model from training to deploy, in this exercise we will prepare MinIO to be such S3 solution.

-
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-
    -
  1. -

    In OpenShift, create a new namespace with the name object-datastore.

    -
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    -
    $ oc new-project object-datastore
    -
    -
    -
    2. -
  2. -

    Run the following yaml to install MinIO:

    -
    -
    -
    $ curl https://raw.githubusercontent.com/RedHatQuickCourses/rhods-qc-apps/main/4.rhods-deploy/chapter2/minio.yml
-$ oc apply -f ./minio.yml -n object-datastore
    -
    -
    -
    3. -
  3. -

    Get the route to the MinIO dashboard.

    -
    -
    -
    $ oc get routes -n object-datastore | grep minio-ui | awk '{print $2}'
    -
    -
    -
    -
    -
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    Use this route to navigate to the S3 dashboard using a browser. With the browser, you will be able to create buckets, upload files, and navigate the S3 contents.

    -
    -
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    -
    4. -
  4. -

    Get the route to the MinIO API.

    -
    -
    -
    $ oc get routes -n object-datastore | grep minio-api | awk '{print $2}'
    -
    -
    -
    -
    -
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    Use this route as the S3 API endpoint. Basically, this is the URL that we will use when creating a data connection to the S3 in RHOAI.

    -
    -
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    5. -
-
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Training The Model

-
-
-

We will use the iris dataset model for this excercise.

-
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    -
  1. -

    Using a JupyterLab workbench at RHOAI, import the repository: https://github.com/RedHatQuickCourses/rhods-qc-apps.git

    -
    - - - - - -
    - - -
    -

    It is recommended to use a workbench that was created with the Standard Data Science Notebook image.

    -
    -
    -
    -
    2. -
  2. -

    Make sure that the workbench environment serves the required python packages for the notebook to run, for this to happen, open a terminal and run the following command to verify that the packages are already installed:

    -
    -
    -
    $ pip install -r /opt/app-root/src/rhods-qc-apps/4.rhods-deploy/chapter2/requirements.txt
    -
    -
    -
    3. -
-
-
- - - - - -
- - -
-

You might also want to execute the preceding command in the notebook kernel by using the %pip syntax in the notebook. -Alternatively, you can create a custom notebook image that includes the skl2onnx package.

-
-
-
-
-
    -
  1. -

    Open and run the notebook iris_to_onnx from rhods-qc-apps/4.rhods-deploy/chapter2 directory

    -
    -
    -iris training to onnx format -
    -
    -
    - - - - - -
    - - -
    -

    Converting a model to ONNX format depends on the library that you use to create the model. -In this case, the model is created with Scikit-Learn, so you must use the sklearn-onnx library to perform the conversion.

    -
    -
    -

    To convert from PyTorch, see Introduction to ONNX in the PyTorch docs.

    -
    -
    -

    To convert from TensorFlow, use the tf2onnx library.

    -
    -
    -
    -
    2. -
  2. -

    Observe that a file has been created: rf_iris.onnx, download this file to your computer, so that we can upload it to S3.

    -
    -
    -iris model download -
    -
    -
    3. -
  3. -

    Upload the file rf_iris.onnx to a bucket named models, with a path iris in your S3. The username is minio and the password is minio123.

    -
    -
    -iris model s3 upload -
    -
    -
    - - - - - -
    - - -
    -

    Make sure to create a new path in your bucket, and upload to such path, not to root. Later, when requesting to deploy a model to the Model Server, you will be required to provide a path inside your bucket.

    -
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Create A Data Connection

-
-
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    -
  1. -

    In the RHOAI dashboard, create a project named iris-project.

    -
    2. -
  2. -

    In the Data Connections section, create a Data Connection to your S3.

    -
    -
    -Add iris data connection from minio -
    -
    -
    - - - - - -
    - - -
    -
      -
    • -

      The credentials (Access Key/Secret Key) are minio/minio123.

      -
      • -
    • -

      Make sure to use the API route, not the UI route (oc get routes -n object-datastore | grep minio-api | awk '{print $2}').

      -
      • -
    • -

      The region is not important when using MinIO, this is a property that has effects when using AWS S3. -However, you must enter a non-empty value to prevent problems with model serving.

      -
      • -
    • -

      Mind typos for the bucket name.

      -
      • -
    • -

      You don’t have to select a workbench to attach this data connection to.

      -
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Using boto3

-
-
-

Although the previous section indicates that you should manually download the rf_iris.onnx file to your computer and upload it to S3, you can also upload your model directly from your notebook or Python file, by using the boto3 library. -To use this approach, you must:

-
-
-
    -
  • -

    Have the boto3 library installed in your workbench (most of the RHOAI notebook images include this library).

    -
    • -
  • -

    Attach your data connection to the workbench.

    -
    • -
-
-
-

After training the model, you can upload the file as the following example demostrates:

-
-
-
-
import os
-import boto3
-
-source_path = "model.onnx"
-s3_destination_path = "models/model.onnx"
-
-key_id = os.getenv("AWS_ACCESS_KEY_ID")
-secret_key = os.getenv("AWS_SECRET_ACCESS_KEY")
-endpoint = os.getenv("AWS_S3_ENDPOINT")
-bucket_name = os.getenv("AWS_S3_BUCKET")
-
-s3 = boto3.client(
-   "s3",
-   aws_access_key_id=key_id,
-   aws_secret_access_key=secret_key,
-   endpoint_url=endpoint,
-   use_ssl=True)
-
-s3.upload_file(source_path, bucket_name, Key=s3_destination_path)
-
-
-
- - - - - -
- - -
-

You can also use the boto3 library to download data. -This can be helpful in the data collection stage, for example for gathering data files from S3.

-
-
-
-
s3_data_path = "dataset.csv"
-s3.download_file(bucket_name, s3_data_path, "my/local/path/dataset.csv")
-
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Create a Model Server

-
-
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    -
  1. -

    In the Models and model servers section, add a server.

    -
    -
    -add server -
    -
    -
    2. -
  2. -

    Fill the form with the following values:

    -
    -
    -
    -
      -
    • -

      Server name: iris-model-server.

      -
      • -
    • -

      Serving runtime: OpenVINO Model Server.

      -
      • -
    • -

      Select the checkboxes to expose the models through an external route, and to enable token authentication. -Enter iris-serviceaccount as the service account name.

      -
      • -
    -
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    -
    -
    -
    -Add Server Form -
    -
    -
    - - - - - -
    - - -
    -

    The model server you are creating works as a template for deploying models. As you can see, we have not specified the model that we will deploy, or the data connection from where that model will be retrieved, in this form we are specifying the resources, constraints, and engine that will define the engine where the model will be deployed later. -It is important to pay special attention to the following characteristics:

    -
    -
    -
      -
    • -

      Serving Runtime: By default we have OpenVINO and OpenVINO with GPU. The important aspects when defining these runtimes are: The framework that is capable of reading models in a given format, and weather such platform supports using GPUs. The use of GPUs allow for complex and lengthy computations to be delivered faster, as there are huge models that require a good amount of power to calculate, based on the given parameters a prediction.

      -
      • -
    • -

      Number of replicas to deploy: Planning for expected performance and number of expected requests is essential for this part of the form. Here we select if we will load balance a given request between multiple container replicas.

      -
      • -
    • -

      Model Server Size: In this part of the form we define the resources assigned to each model server container. You can create and select a pre-defined size from the dropdown, or you can select custom, in which case, new fields will be displayed to request the processing and memory power to be assigned to your containers.

      -
      -
      -model server size -
      -
      -
      • -
    • -

      Model Route: There are models that can be consumed only from other containers inside the same OpenShift cluster, here we have the ability to not make this server available to entities outside our cluster, or to instruct the model server configuration to assign an external route. When we don’t expose the model externally through a route, click on the Internal Service link in the Inference endpoint section:

      -
      -
      -Inference endpoint -
      -
      -
      -

      A popup will display the address for the gRPC and the REST URLs:

      -
      -
      -
      -Endpoint URLs -
      -
      -
      • -
    • -

      Token authorization: In this part of the form we have a helper checkmark to add authorization to a service account that will be created with access to our model server. Only API requests that present a token that has access to the given service account will be able to run the inference service.

      -
      • -
    -
    -
    -
    -
    3. -
  3. -

    After clicking the Add button at the bottom of the form, you will be able to see a new Model Server configuration in your project, you can click the Tokens column, which will make visible the tokens that you can share with the applications that will consume the inference API.

    -
    -
    -Model Server with token -
    -
    -
    4. -
-
-
-
-
-

Deploy The Model

-
-
-
    -
  1. -

    At the right side of the Model Server, we can find the Deploy Model button, let’s click the Deploy Model button, to start filling the Deploy Model form:

    -
    -
    -Deploy Model button -
    -
    -
    2. -
  2. -

    Fill the Deploy Model form.

    -
    -
    -
    -
      -
    • -

      Model name: iris-model

      -
      • -
    • -

      Model framework: onnx - 1

      -
      • -
    • -

      Model location data connection: iris-data-connection

      -
      • -
    • -

      Model location path: iris

      -
      • -
    -
    -
    -
    -
    -
    -Deploy Model form -
    -
    -
    3. -
  3. -

    After clicking the Add button at the bottom of the form, you will be able to see a new entry at the Deployed models column for your Model Server, clicking in the column will eventually show a check mark under the Status column:

    -
    -
    -Deploy model success -
    -
    -
    4. -
  4. -

    Observe and monitor the assets created in your OpenShift iris-project namespace.

    -
    -
    -
    $ oc get routes -n iris-project
-$ oc get secrets -n iris-project | grep iris-model
-$ oc get events -n iris-project
    -
    -
    -
    -
    -Iris project events -
    -
    -
    - - - - - -
    - - -
    -

    Deploying a Model Server triggers a ReplicaSet with ModelMesh, which attach your model to the inference runtime, and exposes it through a route. Also, notice the creation of a secret with your token.

    -
    -
    -
    -
    5. -
-
-
-
-
-

Test The Model

-
-
-

Now that the model is ready to use, we can make an inference using the REST API

-
-
-
    -
  1. -

    Assign the route to an environment variable in your local machine, so that we can use it in our curl commands.

    -
    -
    -
    $ export IRIS_ROUTE=https://$(oc get routes -n iris-project | grep iris-model | awk '{print $2}')
    -
    -
    -
    2. -
  2. -

    Assign an authentication token to an environment variable in your local machine.

    -
    -
    -
    $ export TOKEN=$(oc whoami -t)
    -
    -
    -
    3. -
  3. -

    Request an inference with the REST API.

    -
    -
    -
    $ curl -H "Authorization: Bearer $TOKEN" $IRIS_ROUTE/v2/models/iris-model/infer \
- -X POST \
- --data '{"inputs" : [{"name" : "X","shape" : [ 1, 4 ],"datatype" : "FP32","data" : [ 3, 4, 3, 2 ]}],"outputs" : [{"name" : "output0"}]}'
    -
    -
    -
    4. -
-
-
-

The result of using the inference service looks like the following output:

-
-
-
-
{"model_name":"iris-model__isvc-590b5324f9","model_version":"1","outputs":[{"name":"label","datatype":"INT64","shape":[1],"data":[1]},{"name":"scores","datatype":"FP32","shape":[1,3],"data":[4.851966,3.1275764,3.4580243]}]}
-
-
-
-

Model Serving Request Body

-
-

As you tested with the preceding curl command, to make HTTP requests to a deployed model you must use a specific request body format. -The basic format of the input data is as follows:

-
-
-
-
{
-  "inputs": [{
-    "name" : "input", (1)
-    "shape" : [2,3], (2)
-    "datatype"  : "INT64", (3)
-    "data" : [[34, 54, 65], [4, 12, 21]] (4)
-  }]
-}
-
-
-
- - - - - - - - - - - - - - - - - -
1The name of the input tensor. -The data scientist that creates the model must provide you with this value.
2The shape of the input tensor.
3The data type of the input tensor.
4The tensor contents provided as a JSON array.
-
-
-

The API supports additional parameters. -For a complete list, refer to the Kserve Predict Protocol docs.

-
-
-

To make a request in Python, you can use the requests library, as the following example shows:

-
-
-
-
import requests
-
-input_data = [-0.15384616, -0.9909186]
-
-# You must adjust this path or read it from an environment variable
-INFERENCE_ENDPOINT = "https://my-model.apps.my-cluster.example.com/v2/models/my-model/infer"
-
-# Build the request body
-payload = {
-    "inputs": [
-        {
-            "name": "dense_input",
-            "shape": [1, 2],
-            "datatype": "FP32",
-            "data": input_data
-        }
-    ]
-}
-
-# Send the POST request
-response = requests.post(INFERENCE_ENDPOINT, json=payload)
-
-# Parse the JSON response
-result = response.json()
-
-# Print predicted values
-print(result['outputs'][0]['data'])
-
-
-
-
-
- -
-
-
-
-
- Red Hat -
- - - diff --git a/pr-preview/pr-20/rhods-deploy/1.33/chapter1/section3.html b/pr-preview/pr-20/rhods-deploy/1.33/chapter1/section3.html deleted file mode 100644 index 60783f2..0000000 --- a/pr-preview/pr-20/rhods-deploy/1.33/chapter1/section3.html +++ /dev/null @@ -1,462 +0,0 @@ - - - - - - Creating a Custom Model Serving Runtime :: Deploying Machine Learning Models with Red Hat OpenShift AI - - - - - - -
- -
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Creating a Custom Model Serving Runtime

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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 OpenVINO Model Server runtime. However, if this runtime doesn’t meet your needs (it doesn’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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Prerequisite

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In order to run this exercise, be sure to have handy the model we created in the previous section, that is:

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    An s3 bucket with a model in format onnx

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This exercise will guide you through the broad steps necessary to deploy a custom Serving Runtime in order to serve a model using the Triton Runtime (NVIDIA Triton Inference Server).

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While RHOAI supports the ability to add your own runtime, it does not support the runtimes themselves. Therefore, it is up to you to configure, adjust and maintain your custom runtimes.

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Adding The Custom Runtime

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    Log in to RHOAI with a user who is part of the RHOAI admin group

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    Navigate to the Settings menu, then Serving Runtimes

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

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    apiVersion: serving.kserve.io/v1alpha1
-kind: ServingRuntime
-metadata:
-  name: triton-23.05-20230804
-  labels:
-    name: triton-23.05-20230804
-  annotations:
-    maxLoadingConcurrency: "2"
-    openshift.io/display-name: "Triton runtime 23.05"
-spec:
-  supportedModelFormats:
-    - name: keras
-      version: "2"
-      autoSelect: true
-    - name: onnx
-      version: "1"
-      autoSelect: true
-    - name: pytorch
-      version: "1"
-      autoSelect: true
-    - name: tensorflow
-      version: "1"
-      autoSelect: true
-    - name: tensorflow
-      version: "2"
-      autoSelect: true
-    - name: tensorrt
-      version: "7"
-      autoSelect: true
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-  protocolVersions:
-    - grpc-v2
-  multiModel: true
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-  grpcEndpoint: "port:8085"
-  grpcDataEndpoint: "port:8001"
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-  volumes:
-    - name: shm
-      emptyDir:
-        medium: Memory
-        sizeLimit: 2Gi
-  containers:
-    - name: triton
-      image: nvcr.io/nvidia/tritonserver:23.05-py3
-      command: [/bin/sh]
-      args:
-        - -c
-        - 'mkdir -p /models/_triton_models;
-          chmod 777 /models/_triton_models;
-          exec tritonserver
-          "--model-repository=/models/_triton_models"
-          "--model-control-mode=explicit"
-          "--strict-model-config=false"
-          "--strict-readiness=false"
-          "--allow-http=true"
-          "--allow-sagemaker=false"
-          '
-      volumeMounts:
-        - name: shm
-          mountPath: /dev/shm
-      resources:
-        requests:
-          cpu: 500m
-          memory: 1Gi
-        limits:
-          cpu: "5"
-          memory: 1Gi
-      livenessProbe:
-        # the server is listening only on 127.0.0.1, so an httpGet probe sent
-        # from the kublet running on the node cannot connect to the server
-        # (not even with the Host header or host field)
-        # exec a curl call to have the request originate from localhost in the
-        # container
-        exec:
-          command:
-            - curl
-            - --fail
-            - --silent
-            - --show-error
-            - --max-time
-            - "9"
-            - http://localhost:8000/v2/health/live
-        initialDelaySeconds: 5
-        periodSeconds: 30
-        timeoutSeconds: 10
-  builtInAdapter:
-    serverType: triton
-    runtimeManagementPort: 8001
-    memBufferBytes: 134217728
-    modelLoadingTimeoutMillis: 90000
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    After clicking the Add button at the bottom of the input area, we are able to see the new 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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Creating The Model Server

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    Using the iris-project created in the previous section, scroll to the Models and model servers section, and select the Add server button

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    After clicking the Add button at the bottom of the form, we are able to see our iris-custom-server model server, created with the Triton runtime 23.05 serving runtime.

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      Model name: iris-custom-model

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      Model location data connection: iris-data-connection

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      Model location path: iris

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Test The Model With CURL

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Now that the model is ready to use, we can make an inference using the REST API

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- $IRIS_ROUTE/v2/models/iris-custom-model/infer -X POST \
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    The result received from the inference service looks like the following:

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Deploying Machine Learning Models with Red Hat OpenShift AI

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Welcome to this quick course on the Deploying Machine Learning Models with Red Hat OpenShift AI. -This course is the fourth in a series of five courses about Red Hat OpenShift AI:

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    Introduction to Red Hat OpenShift AI

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    Red Hat OpenShift AI Administration

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    Creating Machine Learning Models with Red Hat OpenShift Data Science

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    Deploying Machine Learning Models with Red Hat OpenShift AI (This course)

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    Automation using Data Science Pipelines

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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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    Diego Alejandro Torres

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    Jaime Ramirez Castillo

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    Max Murakami

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    Ravi Srinivasan

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Classroom Environment for Red Hat Associates

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Continue using the Red Hat OpenShift Container Platform 4.13 Workshop classroom that you provisioned in the Red Hat OpenShift AI Administration course.

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You are expected to complete the Red Hat OpenShift AI Administration course, where you install and configure a basic RHOAI instance, and then continue with this course.

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Classroom Environment for Red Hat Partners

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Red Hat partners should continue to use the Red Hat OpenShift AI cluster provisioned in the Red Hat OpenShift AI Administration course, using the Red Hat Hybrid Cloud Console at https://console.redhat.com/openshift/overview.

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You are expected to complete the Red Hat OpenShift AI Administration course, where you install and configure a basic RHOAI instance, and then continue with this course.

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Prerequisites

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    Take the Introduction to Python Programming (AD141) training course or demonstrate equivalent knowledge of Python programming

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    Basic knowledge of machine learning foundations is recommended.

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    Basics of creating and consuming REST APIs programmatically

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    Basic knowledge of the Python libraries for machine learning and deep learning

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    Experience with OpenShift is recommended, but not mandatory (in case students have to do troubleshooting)

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Objectives

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The overall objectives of this course include:

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    Deploy and serve machine learning models on OpenShift

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    Model deployment with RHOAI model serving

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    Model serving authentication and authorization

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    Troubleshoot deployed models

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    Send inference requests to deployed models via REST API calls

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