Tinkerbell: Provisioning Bare Metal Servers Treningskurs

Last updated

Kurskode

tinkerbell

Varighet

7 timer (vanligvis 1 dag inkuldert pauser)

Krav

  • An understanding of cloud infrastructure provisioning
  • Experience with the Linux command line

Audience

  • Developers
  • System administrators
  • Cloud architects

Oversikt

Tinkerbell er en arbeidsflytmotor for å levere bare metallservere. Denne arbeidsflyten motor, sammen med fem ytterligere støtte komponenter (mikroservices) utgjør Tinkerbell's forsyning stack.

Denne instruktørledede, live-trening (online eller on-site) er rettet mot ingeniører som ønsker å levere og administrere bare metall servere i skyen og på premise.

Ved slutten av denne treningen vil deltakerne være i stand til å:

    Installasjon og konfigurasjon av Tinkerbell forutsigbar stack. Konsistent implementering av bare metallinfrastruktur ved hjelp av en CI/CD-lignende, deklarativ tilnærming. Levering og livssyklus heterogen fysisk infrastruktur i skyen eller på premise. Automatisere bare metaller på maskinvarelaget, uavhengig av prosessorarkitektur, lagringskonfigurasjon, nettverksmiljø eller operativsystem.

Format av kurset

    Interaktiv forelesning og diskusjon. Mange øvelser og praksis. Hands-on implementering i et live-lab miljø.

Kurs tilpassingsalternativer

    For å be om en tilpasset opplæring for dette kurset, vennligst kontakt oss for å arrangere.

Machine Translated

Kursplan

Introduction

  • What is bare metal cloud?

Overview of Tinkerbell Technologies, Components and Approach

  • Technologies: iPXE, DHCP, TFTP, gRPC, Docker, etc.
  • Microservices: Boots, Hegel, OSIE, Tink, and PBnJ
  • YAML based definitions
  • Control plane for managing servers

Case Study: Global Bare Metal Provisioning at Packet

  • Provisioning Ubuntu Servers at Scale

The Provisioning Workflow

  • Assigning an IP address and image through the Boots DHCP Server
  • Booting into OSIE (In-memory Operating System Installation Environment)
  • Running Docker containers
  • Tracking workflow execution
  • Logging error messages

Defining the Target Machine Hardware

  • CPU, pxe mode, ip address, hard disk partitions, hostname, etc.
  • Uploading to Tinkerbell

Creating a Workflow Template

  • Setting disk-wipe, disk-partition, bootloader, OS, etc.
  • Assigning the workflow template to target machine

Storing and Retrieving Metadata

  • Running the Metadata Service (Hegel) over gRPC and HTTP.
  • Interfacing with the AWS EC2 metadata format.

Running the Workflow

  • Operating the workflow engine using the CLI

Setting up VMs and Servers

  • Running Power and Boot service (PBnJ)

Troubleshooting

Summary and Conclusion

Testimonials

★★★★★
★★★★★

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