7 hours (usually 1 day including breaks)
- An understanding of cloud infrastructure provisioning
- Experience with the Linux command line
- System administrators
- Cloud architects
Tinkerbell is a workflow engine for provisioning bare metal servers. This workflow engine, together with five additional supporting components (microservices) make up Tinkerbell's provisioning stack.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to provision and manage bare metal servers in the cloud and on premise.
By the end of this training, participants will be able to:
- Install and configure the Tinkerbell provisioning stack.
- Consistently deploy bare metal infrastructure using a CI/CD-like, declarative approach.
- Provision and lifecycle heterogeneous physical infrastructure in the cloud or on premise.
- Automate bare metal deployments at the hardware layer, regardless of processor architecture, storage configuration, networking environment, or operating system.
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.
- 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)
Summary and Conclusion
Trainer is trying to answer to the additional requests, even if it involves to change his plans.
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