Duration
35 hours (usually 5 days including breaks)
Requirements
- An basic understanding of container concepts
- Experience with the Linux command line
- Application development experience
Audience
Overview
Docker is an open-source platform for automating the process of building, shipping and running applications inside containers.
Kubernetes goes one step further by providing the tools needed to deploy and manage containerized applications at scale in a clustered environment.
OpenShift Container Platform (formerly OpenShift Enterprise) brings Docker and Kubernetes together into a managed platform, or PaaS (Platform as a Service), to further ease and simplify the deployment of Docker and Kubernetes. It provides predefined application environments and helps to realize key DevOps principles such as reduced time to market, infrastructure as code, continuous integration (CI), and continuous delivery (CD). OpenShift Container Platform is maintained by Red Hat and runs atop of Red Hat Enterprise Linux.
In this instructor-led, live training, participants will learn how to use OpenShift Container Platform to deploy containerized applications.
By the end of this training, participants will be able to:
- Create and configure an OpenShift setup.
- Quickly deploy applications on-premise, in public cloud or on a hosted cloud.
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice.
Course Customization Options
- This course is based on OpenShift Container Platform version 3.x.
- To customize the course or request training on a different version of OpenShift (e.g., OpenShift Container Platform 4 or OKD), please contact us to arrange.
Course Outline
Introduction
- From Docker containers, to managed clusters of containers with Kubernetes, to managed Docker and Kubernetes with OpenShift.
Docker
- Overview of Docker architecture
- Setting up Docker
- Running a web application in a container
- Managing Docker images
- Networking Docker containers
- Managing the date inside a Docker Container
Kubernetes
- Overview of Kubernetes architecture
- Managing a cluster of Docker containers with Kubernetes
OpenShift Container Platform
- Overview of OpenShift Container Platform architecture
- Creating containerized services
- Managing Docker containers with OpenShift Container Platform
- Creating and managing container images
- Deploying multi-container applications
- Setting up an OpenShift Container Platform cluster
- Deploying applications on OpenShift Container Platform using source-to-image (S2I)
Closing remarks
Duration
35 hours (usually 5 days including breaks)
Requirements
- An understanding of container concepts
- System administration or DevOps experience
- Experience with the Linux command line
Audience
- System administrators
- Architects
- Developers
Overview
Red Hat OpenShift Container Platform (formerly OpenShift Enterprise) is an on-premises platform-as-a-service used for developing and deploying containerized applications on Kubernetes. Red Hat OpenShift Container Platform runs on Red Hat Enterprise Linux.
In this instructor-led, live training, participants will learn how to manage Red Hat OpenShift Container Platform.
By the end of this training, participants will be able to:
- Create, configure, manage, and troubleshoot OpenShift clusters.
- Deploy containerized applications on-premise, in public cloud or on a hosted cloud.
- Secure OpenShift Container Platform.
- Monitor and gather metrics.
- Manage storage.
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice.
Course Customization Options
- This course is based on OpenShift Container Platform version 3.x.
- To customize the course or request training on a different version of OpenShift (e.g., OpenShift Container Platform 4 or OKD), please contact us to arrange.
Course Outline
Introduction
Overview of Docker and Kubernetes
Overview of OpenShift Container Platform Architecture
Creating Containerized Services
Managing Containers
Creating and Managing Container Images
Deploying Multi-container Applications
Setting up an OpenShift Cluster
Securing OpenShift Container Platform
Monitoring OpenShift Container Platform
Deploying Applications on OpenShift Container Platform using Source-to-Image (S2I)
Managing Storage
Closing remarks
Duration
28 hours (usually 4 days including breaks)
Requirements
- An understanding of machine learning concepts.
- Knowledge of cloud computing concepts.
- A general understanding of containers (Docker) and orchestration (Kubernetes).
- Some Python programming experience is helpful.
- Experience working with a command line.
Audience
- Data science engineers.
- DevOps engineers interesting in machine learning model deployment.
- Infrastructure engineers interesting in machine learning model deployment.
- Software engineers wishing to automate the integration and deployment of machine learning features with their application.
Overview
Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. OpenShift is a cloud application development platform that uses Docker containers, orchestrated and managed by Kubernetes, on a foundation of Red Hat Enterprise Linux.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.
- By the end of this training, participants will be able to:
- Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
- Use OpenShift to simplify the work of initializing a Kubernetes cluster.
- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
- Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction
- Kubeflow on OpenShift vs public cloud managed services
Overview of Kubeflow on OpenShift
- Code Read Containers
- Storage options
Overview of Environment Setup
- Setting up a Kubernetes cluster
Setting up Kubeflow on OpenShift
Coding the Model
- Choosing an ML algorithm
- Implementing a TensorFlow CNN model
Reading the Data
Kubeflow Pipelines on OpenShift
- Setting up an end-to-end Kubeflow pipeline
- Customizing Kubeflow Pipelines
Running an ML Training Job
Deploying the Model
- Running a trained model on OpenShift
Integrating the Model into a Web Application
- Creating a sample application
- Sending prediction requests
Administering Kubeflow
- Monitoring with Tensorboard
- Managing logs
Securing a Kubeflow Cluster
- Setting up authentication and authorization
Troubleshooting
Summary and Conclusion
Duration
28 hours (usually 4 days including breaks)
Requirements
- An understanding of machine learning concepts.
- Knowledge of cloud computing concepts.
- A general understanding of containers (Docker) and orchestration (Kubernetes).
- Some Python programming experience is helpful.
- Experience working with a command line.
Audience
- Data science engineers.
- DevOps engineers interesting in machine learning model deployment.
- Infrastructure engineers interesting in machine learning model deployment.
- Software engineers wishing to automate the integration and deployment of machine learning features with their application.
Overview
Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. OpenShift is a cloud application development platform that uses Docker containers, orchestrated and managed by Kubernetes, on a foundation of Red Hat Enterprise Linux.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.
- By the end of this training, participants will be able to:
- Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
- Use OpenShift to simplify the work of initializing a Kubernetes cluster.
- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
- Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction
- Kubeflow on OpenShift vs public cloud managed services
Overview of Kubeflow on OpenShift
- Code Read Containers
- Storage options
Overview of Environment Setup
- Setting up a Kubernetes cluster
Setting up Kubeflow on OpenShift
Coding the Model
- Choosing an ML algorithm
- Implementing a TensorFlow CNN model
Reading the Data
Kubeflow Pipelines on OpenShift
- Setting up an end-to-end Kubeflow pipeline
- Customizing Kubeflow Pipelines
Running an ML Training Job
Deploying the Model
- Running a trained model on OpenShift
Integrating the Model into a Web Application
- Creating a sample application
- Sending prediction requests
Administering Kubeflow
- Monitoring with Tensorboard
- Managing logs
Securing a Kubeflow Cluster
- Setting up authentication and authorization
Troubleshooting
Summary and Conclusion
