Overview of the Ansible OVA Deployment Role

In this second post, we will take a look at the Ansible OVA deployment role (vmware_deploy_ova), which is the core component used to deploy all of the VMware software appliances (vcenter, vro, nsxt, etc). I wanted to create a consistent yet flexible approach to importing and configuring ova appliances and allow for hardware customisation to be introduced.

The added advantage of this role is that it does not require the OVFtool to be installed and makes use of the vmware_deploy_ovf Ansible module, which uses pyVmomi to manage the ova import process.

I have also added several other features to the role, such as the ability to deploy without powering on, configure hardware settings such as CPU and Memory, add disks and configure networks.

The vmware_deploy_ova role is available from:

Ansible Galaxy: vmware_deploy_ova

Github: ansible-role-vmware_deploy_ova

You do not have to manually download this role for the purpose of this series. This role will automatically be downloaded from Ansible Galaxy as it is defined as a dependency on my appliance-based deployment roles. However, you are free to use and modify this role for any specific requirements that you may have.

This post is aimed to provide the details of what is used to deploy the appliances and how these can be configured.

Preparing an Appliance-Based Role to use the vmware_deploy_ova Role

This section provides details on how a role can utilise the vmware_deploy_ova role as a dependency and the ova properties that are required.

Role Dependency

Each appliance-based role that I create defines the vmware_deploy_ova role as a dependency in the meta/main.yml file under the dependencies section.

I also add the tag deploy, which allows me to easily skip the ova deployment process when running/re-running an appliance-based deployment playbook using the –skip-tags “deploy” parameter.

OVA Properties

All VMware appliances require that a set of OVA properties be configured, which specify details such as VM name, hostname, network details such as IP address and subnet mask, root password, etc. Each appliance is different and has a specific set of ova properties associated with it.

The vmware_deploy_ova role requires the ova_networks and ova_properties dictionary type variables to be set. The below is an example snippet of some vCenter Server Appliances ova properties:

In the above example, the values for these properties are mostly taken from other mandatory variables that need to be set for the appliance-based role.

Additional ova properties also exist that are not user-configurable. If you try and set these with ova_properties then the ova import task will fail. To overcome this issue, I have introduced an additional (optional) variable called vapp_properties. This variable includes a list of dictionary values that set the UserConfigurable attribute to True, along with the value that needs to be set for the ova property.

Note that the vapp_properties are not required for all appliances deployments (vRealize Orchestrator for example).

The ova_networks, ova_properties and vapp_properties variables are all defined in the vars/main.yml file for the appliance-based role. This file should NOT be modified and I have included mandatory and optional variables that can be set to manage these properties concerning the appliance being deployed. These will be discussed in later posts when I cover the appliance deployments.

Configuration for the vmware_deploy_ova Role

I have created several variables to influence the OVA deployments. These include mandatory variables that are needed by the role to deploy the appliance. I have also added optional variables to allow the appliance deployments to be tailored to your requirements. An example might be to deploy a vRO appliance with additional CPU and Memory.

Deployment Variables

When deploying the OVA appliances, the following deployment variables need to be set. These can be placed in the host_vars for the appliance hostname or an application group if you want to apply these to multiple appliances.

You can also configure these optional deployment variables (only supported if deploying to a vCenter Server)

OVA Source

These variables allow you to set the location of where the OVA file can be found. These variables should ideally be defined in group_vars.

Make sure not to include a leading ‘/‘ at the end of the ova_path as this will be added automatically.

There is also the option to download the ova file from HTTP by setting ova_source to “http” (default is “local”) and then setting the ova_url variable: The file will be downloaded to the path defined in the ova_path variable.

Virtual Hardware Configuration

The following variables allow the virtual hardware to be customized for a deployment. These variables should be defined in either a host_vars file or group_vars.

Enable CPU/Memory Hot Add support:

If these are not set then hot add support will be set to disable.

Set the Number of CPUs

Set the ova_hardware_num_cpus variable to specify the number of CPU sockets that should be added to the appliance.

If this variable is not set then the number of CPUs will not be changed.

Set Memory Size

Set the ova_hardware_mem_gb variable to specify the amount of memory (in GB) to allocate to the appliance.

If this variable is not set then the amount of memory will not be changed. Read More

Automate the VMware SDDC using a Multi-Stage Environment Using Ansible

Welcome to the first post in my new series: Ansible for VMware SDDC where I will provide a series of posts to help you deploy the VMware SDDC stack in a multi-stage environment using Ansible. Automating VMware environments using DevOps tooling is no easy task, as most administrative tasks are achieved using PowerShell, and often lack a consumable API to perform certain tasks. I have decided to take on this daunting task and provide a complete end-end automated solution.

Managing your VMware infrastructure in this way will allow your SDDC configuration to be treated as Infrastructure as Code (IaC) and stored in a source control repository like Git. This further allows for immutable infrastructure where appliances can be re-deployed and configuration applied in several minutes, should the need arise. This can provide an alternative approach to restoring services due to a major issue, such as corruption or loss of an appliance by simply redeploying it from code.

I started this piece of work as a side project for deploying my own environments but have been fortunate enough to extend the work to help my clients automate their VMware infrastructure. I, therefore, decided to collate the work that I have done into a series to help others.

The Multi-Stage environment idea was based on a great article I read called How to Manage Multistage Environments with Ansible by Justin Ellingwood, which I recommend you read.

This post will demonstrate how to create the Ansible working environment from scratch and utilise a number of roles that I have created for various VMware deployments. I will provide follow-up posts in this series which will cover the deployment of a specific VMware technology, i.e. vCenter Server and to end the series I will demonstrate how you can deploy a full SDDC stack, including nested variants that can be quickly spun up for development and testing purposes.

Prepare the Environment

The roles that I have created have some dependencies that first need to be installed. I recommend that you use Python virtual environments when setting up your Ansible environment. I have created a guide here on how to install Python 3 and how to create and activate a virtual environment, where the dependencies can be installed. Note that this is not a requirement.

I have prepared an ‘ansible_vmware_sddc_requirements.txt‘ file that includes all dependencies that are required to run the VMware SDDC roles, including Ansible and the vSphere Python SDK (pyVmomi). These include specific versions of the core dependencies that I have tested against and other transient dependencies used by these modules.

You can find the ‘ansible_vmware_sddc_requirements.txt‘ requirements file on my Github Gist account here.

Or simply install the dependencies using pip:

Your environment will now be ready to run Ansible and my VMware SDDC roles.

Create Ansible Environment

This section will detail the creation of a multi-stage Ansible environment that includes an inventory for Development, Testing, Staging and Production. Each of these inventories will include its own hosts’ inventory file, group_vars and host_vars. The Development inventory will be set as the default if none has been specified when running a playbook on the command line. This helps to ensure that other environments are not accidentally targeted.

The Ansible environment is typically implemented specifically for the project/company/user so it is difficult to create an all-encompassing solution that suites all uses-cases. However, I have created a recommended setup for creating these deployments.

This is a base configuration that you will likely need or want to tailor to your own specific requirements. This typically boils down to variable placement within the groups, however, as long as the required variables have been defined, then it is up to you where to place them.

This post will only cover setting up the Development Environment.

Start by creating a new empty folder where Ansible should be set up:

I have created a script that I use to bootstrap my own environments when testing. You can download the ansible_bootstrap_vmware_sddc.sh script via my Github Gist account using wget.

Read More

Run Multiple Ansible Versions Side by Side Using Python 3 Virtual Environments

I often start many of my blog posts describing how to set up a Python virtual environment and install the required modules, when working with Ansible. I have therefore decided to create this post to cover the topic in a bit more detail and for better consistency.

When I am developing Ansible playbooks and depending on the project, I often require a different set of Python modules. I could install all of the modules that I use on the base system, but then it starts to get contaminated and it becomes difficult to manage all of the dependencies.

Another requirement is that I want to be able to test my Ansible playbooks against different versions (maybe environments are using different versions or to test a new release). Fortunately, both of these problems are easy to solve using Python’s venv (Virtual Environments) module.

You don’t need to be using containers to run multiple versions of Ansible for this purpose like I have witnessed many people do.

The benefits of using a virtual environment include:

  • Each project can have its own isolated environment and modules;
  • The base system is not affected;
  • Does not require root access as virtual environments can be created in your home directory;

In the following sections, I will provide details on how to set up virtual environments and some examples of these with Ansible.

Install Python 3

The first thing you need to do is install Python3. You can create virtual environments with Python 2, but as that is going end of life in January 2020, you really should make every effort to move away from this version. We will need to install Python 3.6 or later.

CentOS 7

Python 3 can be installed from one of the following repositories, depending on your preference (but only choose one). Note that this does not change the default ‘python‘ interpreter on the system.

Extra Packages for Enterprise Linux (EPEL)

Install this repository if not already installed:

Install Python 3.

Software Collections (SCL)

Install this repository if not already installed:

Install Python 3.

Inline with Upstream Stable (IUS)

Install this repository if not already installed:

Install Python 3.


Check out this post: https://www.tecmint.com/install-python-in-ubuntu

Verify that Python is installed and working:

Create Virtual Environments

First, we will need to create a folder that we’ll use to store the virtual environments. I recommend that you do not create these environments inside your project folders. I have a folder called ‘python-venv‘ in my home directory, which I use for all my virtual environments.

We create a virtual environment using the Python ‘venv‘ module (note that this is built into Python 3):

In this example, I am going to create two environments that will provide different versions of Ansible for me to use.

This will create the directories ‘ansible2.7.0‘ and ‘ansible2.8.0‘ under ‘~/python-env‘, that contains the binaries and base libraries for the environment.

Next, we need to activate an environment by sourcing an environment file in the bin directory. Let’s activate the ‘ansible2.7.0‘ environment.

You will notice that the shell will now display the virtual environment that you are using:

Currently, this environment has no modules installed. The first thing we will want to do is upgrade ‘pip‘ and ‘setuptools‘.

Next, let’s install ansible 2.7.0. PIP will default to install the latest version, but we can override this using == and force a specific version to be installed.

Once the installation process completes, we can confirm that the version of Ansible has been installed:

Read More

IaC for vRealize: Updating Archetypes and Adding Boilerplate For Your vRO Actions

Welcome to the 5th post in the series. This is going to be a relatively small post where we will take a look at archetypes and how to update these and also discuss a standard layout for creating vRO Actions. At this stage, the focus is on ensuring that we have everything required when creating new projects, instead of having to add new files to the project or modify existing ones.


By now you have come across the term ‘archetype‘ when working with Maven. If you’re not a Java developer or ever worked with Maven before, then you likely have no idea what this means in the context of creating projects.

Here is a dictionary definition for archetype:

So if we look at the first definition, we can think of a Maven archetype as a model for our projects. In essence, think of an archetype as a ‘template’ for Maven projects.

Notice what happens when we create a project for the first time, we would execute a command similar to this:

We specify ‘archetype:generate‘, which tells Maven that the project should be generated from an existing archetype. We also specify ‘archetypeGroupId‘, ‘archetypeArtifactId‘, and ‘archetypeVersion‘, which tells Maven how to find the archetype.

Once we have executed the above command, Maven will download several artefacts that it requires for the project. Once this task completes, you will have a directory structure that looks something like the following (depending on what type of project you are creating):

The ‘src‘ directory also includes a ‘main‘ and ‘test‘ subdirectory that includes a sample Action and unit test.

This directory structure and the files have been predefined inside of an archetype. When a new project is created, Maven downloads this archetype and unpacks the contents to form the project directory. This means that every project that we create will look the same.

But what if we want to change this, or perhaps add additional files or update existing ones? Well, it’s quite easy, we just have to locate the artefact for the archetype and update it.

Update Existing Archetype

If you think back to the very first post, we downloaded the vRealize Build Tools package from the VMware flings website. From within this package, there was an ‘iac-maven-repository.zip‘ file, which we copied to a CI server (I used the Artifactory server in my first post). This file includes all the archetypes that are used for our projects.

Depending on which project type you create, the following archetypes are available:

  • iac-maven-repository/com/vmware/pscoe/o11n/archetypes/package-actions-archetype
  • iac-maven-repository/com/vmware/pscoe/o11n/archetypes/package-mixed-archetype
  • iac-maven-repository/com/vmware/pscoe/o11n/archetypes/package-vrealize-archetype
  • iac-maven-repository/com/vmware/pscoe/o11n/archetypes/package-xml-archetype
  • iac-maven-repository/com/vmware/pscoe/vra/archetypes/package-vra-archetype

Inside each of these directories, there is a ‘<version>/<archetype>-<version>.jar‘ file and an associated pom file. For a JS based project, you will see:

The jar file is just a zip file and can be opened with any zip software. When you open this file (I’m using 7Zip on WIndows) then you will see two folders, ‘META-INF‘ and ‘archetype-resources‘.

If you navigate into ‘archetype-resources‘, you will see the same folder structure that you get when creating a new project. Read More

IaC for vRealize: Documenting Code with JSDoc and Syntax Checking and Code Style Management with ESLint

Welcome to the 4th post in the series where I will take a look at how we can add comments to our code using JSDoc and perform syntax checking and style management with ESLint. I felt this was a good point to intersect the series on these topics, where you will establish fundamental development practices and make your vRO development life easier for both yourself and your peers.

Once you have implemented these techniques, you will begin on a journey of well documented and standardised code and take a step into the doorway of Test Driven Development (TDD).


Several packages need to be installed, which will be used in this post. I created a previous article on Using Visual Studio Code for your vRealize Orchestrator Development which covers the installation of NodeJS (needed for NPM), ESLint and some other plugins.

Please have a look at this post and ensure you have everything installed. Don’t pay too much attention to the JSDoc examples in that post as they are very specific to documenting Actions (with no function declaration). I’ll be demonstrating a different approach here with Maven.

Also, you will need to install the following packages using NPM (I’m installing globally for this tutorial, but production environments should make use of local dependencies, something a bit too long for this post, which I will cover in a later one):

I will detail how and where these are used throughout this post.

You can also download my ‘.eslintrc.json‘ configuration here:

Documenting Code with JSDoc

Developers are often expected to add comments to their code as this can help both yourself and others when trying to figure out what your code is trying to achieve. However, this practice has evolved using tools that are much more integrated and can automate the creation of documentation.

Many programming languages offer some form of documentation library, for example,  java has Javadoc, and Python has the native docstring and In JavaScript, we have JSDoc.

JSDoc makes use of annotations and tags that are added alongside the code and are used to describe constructs such as functions, classes, namespaces, and variables. Some tags allow metadata such as author information, versioning and licensing to be added to the documentation.

JSDoc can also be used to describe variable types and with some advanced settings in VScode, and a few extra lines of code, it can enforce static type checking. It’s one of the major reasons why developers turn to TypeScript for their JavaScript development. However, my attempts to get this to work have been unsuccessful and I leave this open to anyone willing to give it a try. The issue was dealing with vRO object types, such as ‘REST:RESTHost’, where VScode/TypeScript interprets the colon as an error.

Adding JSDoc Annotations To Your Code

Adding JSDoc annotations to your code is a hard requirement. This is due to the way the vRealize Build Tools has been implemented. The ‘vRealize:pull‘ goal has been designed to depend on the JSDoc description and tags (specifically @param and @returns) when creating the Action in vRO. If these properties are missing or specify the wrong data types, then these will be carried through to vRO.

The best way to begin to explain JSDoc and how to use it would be to provide an example of this being used to describe one of my vRO functions.

Below is a JSDoc annotation used to define my ‘attachSecurityTagToVcVm’ function.

The JSDoc annotations are always placed inside a comment block (/**   */). Each line that is defined within the comment block starts with an asterisk (*). Pay close attention to the indentation of the commented lines, these are single-spaced. The comment block itself should be placed on the immediate line before the item you are documenting (i.e. the function declaration).

The first line should always be the description. It should clearly describe what the function does or is used for. The description is mandatory and is added to the description field for the vRO Action.

@author allows me to add author details such as my name and email address. This tag is optional

@function allows the function name to be specified. I use this because function declarations for vRO Actions are anonymous. Therefore, when generating documentation, JSDoc doesn’t know what the function is called. Using this tag can help JSDoc. This tag is optional.

@param describes the parameters that the function expects, which includes the data type, name and description. Note that the data type is specified exactly as it would appear in an Action in vRO for example, ‘VC:VirtualMachine‘ and not ‘vcVirtualMachine‘. The ‘vRealize:push‘ goal will apply these data types exactly as-is to the Actions inputs.

The parameters can also be specified as optional by enclosing square brackets around the parameter name, i.e. in my example, if ‘tagName‘ was optional, it would be specified as ‘[tagName]‘. This does not affect how parameters are presented to vRO and are used for documentation purposes only. You will have to rely on adding code that handles mandatory and optional parameters, including setting defaults.

@returns describes any variable that is returned by the function. Normally, it would be fine to remove this tag if the function does not return a value. However, vRO Actions must specify ‘void‘ if no value is returned. It’s also good to be explicit. Note that when the vRealize Build Tools pulls down any of your Actions for the first time, it will set an @return tag, but it is recommended to use @returns.

Check out https://devdocs.io/jsdoc/ to learn more about JSDoc and all the available tags.

One thing to note is that any additional tags you use, such as @author, @function or anything else apart from @param and @returns, will be appended to the description field of the vRO Action. Read More

Automate vSphere Virtual Machine and OVA Appliance Deployments Using Ansible

If you have read any of my posts, you will quickly discover that I use Ansible a lot, for deploying virtual machines and VMware OVA appliances, on vSphere.

Ansible support for VMware is constantly growing and in the latest versions, it has become an essential tool that I use as part of my development process for standing up required infrastructure that is easy and quick to deploy or tear down. The key part of using Ansible for my deployments is that the process is repeatable and consistent.

In this post, I am going to cover some of the core Ansible modules that I use to perform these deployments and provide various use case examples. Once you understand these modules and lay down the groundwork, you’ll be deploying virtual machines or appliances in mere minutes, with the simple editing of some configuration files.

If you are not too familiar with what Ansible is, or what it’s used for, then I recommend that you check out the official documentation. You can also get a brief overview of what Ansible is at cloudacademy.com, and there is a wealth of online training and other material available to get you up to speed.

All examples used in this post, including a fully working Ansible solution, can be found on my Ansible-VMware Github.


You will need to have the following packages installed (through PIP) on your Ansible control machine:

  • ansible>=2.8.0
  • pyvmomi>=

I have also provided a requirements.txt file that you can install using PIP

Deploying Virtual Machines

The most basic task that you are ever likely to perform on any vSphere environment, is the deployment of a virtual machine. In this section, I am going to show you how Ansible can make the task of spinning up dozens of virtual machines a breeze.

Ansible provides the core module vmware_guest that can be used to create a new virtual machine or clone from an existing virtual machine template.

In these examples, I am going to demonstrate how you can create new virtual machines or clone an existing virtual machine from a template for both Windows and Linux, perform customization and configure hardware and advanced settings.

Create a New Virtual Machine (no template)

This is an example play that will create a virtual machine with no OS installed (not from a template). When the virtual machine is powered on, it will automatically try and PXE boot from the network, which can be useful in deployment pipelines where VMs are bootstrapped in this way.

I have a simple play called ‘vmware_create_virtual_machine.yml‘, which includes the tasks to create a virtual machine in VMware vSphere.

Many of the properties should be self-explanatory, but we’re creating a virtual machine called Linux_VM, with 1 CPU, 2GB of Memory, a 20GB think hard disk, etc.

Because we are creating a new virtual machine, the guest_id needs to be provided, which sets the Guest Operating system profile on the VM. You can get the full list of supported guest_ids from the VMware developer support page.

To run the playbook, I invoke the ansible-playbook command.

You can see that the execution was successful (ok=1) and that a change was made (changed=1), which means the virtual machine was created. If we take a look at vCenter we can see the virtual machine now exists, with the specified configuration:

Update Virtual Machine

The great thing about Ansible is that if you were to run this play again, it would not try and create another VM. Instead, it will simply exit with a status of OK, if it discovers that the specified virtual machine already exists and is powered on.

But what if we made some changes to the configuration that we want to apply to the virtual machine? Well, Ansible will only make these changes to the virtual machine if the ‘state‘ parameter has been set to ‘present‘ in the play. Also, if you are making configuration changes to hardware, then the virtual machine may also need to be powered off first (Ansible will display an error if this is required).

So let’s assume that the virtual machine is powered off and we want to enable CPU and memory hot add support. We simply add these configurations under the hardware section:

Now if we run the play again:

And we can see that a reconfigure task is performed on the VM in vCenter server.

Make sure to check the documentation for all the parameters that can be configured. Read More

IaC for vRealize: Define Dependencies, Manage Versions, Prepare & Release Packages & Deploy Artifacts

Welcome to the third part in the series working with the vRealize Build Tools. At this stage, you should have a fully working CI infrastructure and have all of your vRO code exported using packages and stored in Git repositories. In this post, I will show you how to manage dependencies across your packages and how you can use the Maven plugins to automatically update packages so that they are using the latest dependency versions.

I will also show you how to manage versioning for development (Snapshots) and production (release) code. Once we have our versioning in place, I will detail how to prepare the releases and finally push the artefacts to the Artifactory repositories, which can be picked up by a release pipeline (something I will cover in much more detail in a later post).

I also want to point out that I had to update my previous post to include the git scm connections in your pom.xml files, as this is required for this post, so make sure to go back and check that out.

Understanding Snapshot vs Release Versions

The first thing to understand is the ‘-SNAPSHOT‘ string that is suffixed to package versions. You will have noticed that all of the package versions you created in my previous post, were version ‘1.0.0-SNAPSHOT’. This suffix tells Maven that the code inside this package is in a development stage and not suitable for production release. The Maven release plugins understand this and will prevent the dependency handler from including these as valid dependencies for your packages, as this could result in unstable code (though, they can be defined manually).

If you are following these posts, then currently, all your packages will be at version 1.0.0-SNAPSHOT. Don’t worry, we will change this later, but for now, accept this as-is.

Define Dependencies

In my previous post, I provided an example of 3 packages that I have created. I also created a table that detailed the ‘groupId‘ and ‘artifactId‘ for these packages. I am going to extend this table to list their dependencies on other packages. I have kept this simple, in that no dependencies exist outside of these 3 packages.

A dependency exists when one package is calling code from another package (typically with a System.getModule()). When dependencies are mapped, their artefacts and required versions are released as part of the release process.

groupId artifactId Dependency
com.simplygeek.library logger
com.simplygeek.library rest logger
com.simplygeek.library nsx logger

I have added the ‘Dependency‘ column, which lists the projects/artefacts that this package depends on. All of my packages depend on ‘logger‘, whereas ‘nsx‘ also depends on ‘rest‘.

We now need to add some XML to the projects ‘pom.xml‘ file, that is located at the root of the project folder. These are ‘<dependencies>‘ tags that are used to define a set of ‘<dependency>‘ tags for the project. You should insert these tags immediately after the ‘<packaging>‘ tags but before the ‘<scm>‘ tags.

We also want to omit ‘-SNAPSHOT‘ from the version, since we’ll be pushing actual releases.

Below is an example of what the dependency XML insert looks like for the ‘rest‘ package:

Once you have completed defining your dependencies, save the ‘pom.xml‘ file and make sure to commit the changes to git.

Repeat this process for all of your projects.

Prepare & Release Packages

Maven provides several lifecycle phases and goals that can be used to prepare and perform the releases. We will also need to push the release artefacts to the Artifactory repositories, as this will be queried for dependencies when releasing a package that has any defined.

You should first be focusing on all the core projects that make up most of your dependencies. Also, start with the projects that have dependencies in the lowest order, i.e. the ‘logger‘ project has no dependencies, so that will be released first, ‘rest‘ depends on ‘logger‘, so that will be released next, etc.

For all the release tasks, we’re going to specify the ‘release‘ plugin followed by the goal. We will use the following goals:

  • Clean
  • Prepare
  • Perform

Read More

IaC for vRealize: Manage Existing vRO Code With vRealize Build Tools & Set up Git Repositories

In my previous post on Deploying vRealize Build Tools To Allow Infrastructure As Code for vRA and vRO, I covered how to set up the CI infrastructure and your developer workstation, in preparation for managing your vRO code as projects with Visual Studio Code and Maven. In this post, I will explain how you can work with your existing code base and manage it using the build tools. A major part of this will be creating and managing new projects that will map to our existing code in vRO.

Once projects have been created, I will detail how Git repositories can be used to store and manage your vRO code, and then we can map your project dependencies and allow development teams to work collaboratively, without risk of overwriting the work of others (a major problem when developing using the vRO client). Git is going to bring some very useful processes and methodologies to the table such as branching, tagging and merge conflict resolution.

I also want to point out that I am currently only focusing on Actions, as I believe this is where all your code should exist. I will have followup posts that will cover strategies for managing Workflows and other items.

Setting Up GitLab

One thing that I didn’t cover in my previous post, was setting up Git. I purposely reserved this topic for this post, as it was more relevant. To start, you will need to have a GitLab server deployed that can be used to create the repositories for storing your vRO projects. You can use GitHub if you so wish, it doesn’t matter too much, but using GitLab doesn’t require that your environments have access to the Internet. GitLab also allows you to create groups to organise multiple repositories, which is going to be useful.

If you need to install GitLab, then there is a good guide on VULTR, that details how to install GitLab and enable HTTPS.

Create User

You should be using your user account when working with Git and not the default root account. So log into the admin area and create a new local account for your personal use. Alternatively, you can also configure GitLab to allow users from Active Directory to log in, you can use the guide provided by Git here.

Create a Personal Access Token

Once you have a Git user account set up, you will need to create a Personal Access Token. An Access Token can be used instead of a password when authenticating with Git over HTTPS. This will provide safer storage of user credentials in the Git configuration files.

Click on the profile icon on the top right of the page and select Settings:

On the Settings page select Access Tokens.

On the ‘Add a personal access token‘ page, give the access token a name and the ‘write_repository‘ permissions. Set an expiry date if you wish, or leave blank to never expire.

Once you click ‘Create personal access token‘, the token will be displayed. You will need to copy and save this token somewhere safe as you will not be able to view it again. If you lose this token, you will have to create a new one to replace it.

Create a Group

A Group allows multiple projects/repositories to be created under a single namespace. This is useful when a project spans many repositories and you need to keep these together so that they are easy to locate and manage. Our vRO projects will be using multiple repositories, therefore we’ll great a Group for these. A Group also simplifies granting access to projects, as collaborators can be granted access to the group and inherently, the projects it contains.

To create a new group, select ‘Groups‘ from the main menu at the top and then select ‘Your groups‘.

Click the ‘New group‘ button and give your group a name and settings that you require (I simply called my group ‘vRO’).

We will create all vRO projects under this new group.

Install Git Client

You will also need to install the Git client for your workstation. You can download the client for your OS here. Read More