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README.rst

Team and repository tags

image

Training labs

About

Training-labs provides an automated way to deploy Vanilla OpenStack, closely following the OpenStack Install Guide.

Training-labs offers an easy way to set up an OpenStack cluster which is a good starting point for beginners to learn OpenStack, and for advanced users to test out new features, and check out different capabilities of OpenStack.

On top of that training-labs is also a good way to test the installation instructions on a regular basis.

Training-labs is a project under OpenStack Documentation. For more information see the OpenStack wiki.

Pre-requisite

VirtualBox is the default hypervisor used by training-labs. Alternatively, you can use KVM (just set PROVIDER=kvm in labs/config/localrc).

Getting the Code for an OpenStack Release

The current release is master which usually deploys the current stable OpenStack release. Unless you have a reason to go with an older release, we recommend using master.

For non-development purposes (training, etc.), the easiest way to get the code is through downloading the desired archive from OpenStack Training Labs <https://docs.openstack.org/training_labs/>_. Unpack the archive and you are good to go.

How to run the scripts for GNU/Linux and macOS

Change directory:

$ cd training-labs/labs/

By default, the cluster is built on Virtualbox VMs.

Run the script by:

$ ./st.py -b cluster

How to run the scripts for Windows

The easiest and recommended way to get everything you need besides VirtualBox is to download a zip file for Windows from the Training Labs page.

The zip files include pre-generated Windows batch files.

Creates the host-only networks used by the node VMs to communicate:

> create_hostnet.bat

Creates the base disk:

> create_base.bat

Creates the node VMs based on the base disk:

> create_ubuntu_cluster_node.bat

What the script installs

Running this will automatically spin up 2 virtual machines in VirtualBox/KVM:

  • Controller node
  • Compute node

Now you have a multi-node deployment of OpenStack running with the following services installed.

  • Keystone
  • Nova
  • Neutron
  • Glance
  • Cinder
  • Horizon

How to access the services

There are two ways to access the services:

  • OpenStack Dashboard (horizon)

You can access the dashboard at: http://10.0.0.11/horizon

Admin Login:

  • Username: admin
  • Password: admin_pass

Demo User Login:

  • Username: demo
  • Password: demo_pass

You can ssh to each of the nodes by:

# Controller node
$ ssh osbash@10.0.0.11

# Compute node
$ ssh osbash@10.0.0.31

Credentials for all nodes:

  • Username: osbash
  • Password: osbash

After you have ssh access, you need to source the OpenStack credentials in order to access the services.

Two credential files are present on each of the nodes:

  • demo-openstackrc.sh
  • admin-openstackrc.sh

Source the following credential files

For Admin user privileges:

$ source admin-openstackrc.sh

For Demo user privileges:

$ source demo-openstackrc.sh

Now you can access the OpenStack services via CLI.

Specs

To review specifications, see http://specs.openstack.org/openstack/docs-specs/specs/liberty/traininglabs.html

Mailing lists, IRC

To contribute, join the IRC channel, #openstack-doc, on IRC freenode or write an e-mail to the OpenStack Documentation Mailing List openstack-docs@lists.openstack.org. Please use [training-labs] tag in the subject of the email message.

You may have to subscribe to the OpenStack Documentation Mailing List to have your mail accepted by the mailing list software.

Sub-team leads

Feel free to ping Roger, Julen, or Pranav via email or on the IRC channel #openstack-doc regarding any queries about training-labs.

  • Roger Luethi
    • Email: rl@patchworkscience.org
    • IRC: rluethi
  • Pranav Salunke
    • Email: dguitarbite@gmail.com
    • IRC: dguitarbite
  • Julen Larrucea
    • Email: julen@larrucea.eu
    • IRC: julen, julenl

Meetings

Training-labs uses the Doc Team Meeting: https://wiki.openstack.org/wiki/Meetings/DocTeamMeeting

Wiki

Follow various links on training-labs here: https://wiki.openstack.org/wiki/Documentation/training-labs