Kubeflow on OpenShift培训

课程编码

kubeflowopenshift

课程时长

28 小时 通常来说是4天,包括中间休息。

要求

  • 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.

课程概览

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 an 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.

课程大纲

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

  • Installing Kubeflow

Coding the Model

  • Choosing an ML algorithm
  • Implementing a TensorFlow CNN model

Reading the Data

  • Accessing a dataset

Kubeflow Pipelines on OpenShift

  • Setting up an end-to-end Kubeflow pipeline
  • Customizing Kubeflow Pipelines

Running an ML Training Job

  • Training a model

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

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