This is the base repo for Model-Driven devops. It has the needed python requirements (requirements.txt) and Ansible Collection (requirements.yml) to run a Model-Driven Devops pipeline. It requires an ansible inventory and certain envrionment variables to be set. It also assumes you are using Cisco Modeling Labs, GitLab, and NSO. Details on running NSO and CML can be found by using the following links.
Installing in Kubernetes
Installing in CML
Installing in Docker
Installing Locally
Installing in AWS
Installing in VMware
To use this template, you can select the "Use this template" option from the GitHub UI if your MDD repo will be created in GitHub. If you intend to use GitLab, you can create a new repo from this template manually.
First clone the template locally.
git clone https://github.com/lvangink/mdd_base.git <your-repo-name>
Move to the new repo locally.
cd <your-repo-name>
Remove the git folder.
rm -rf .git
Initialize your new git repo and commit the repo contents.
git init .
git add .
git commit -m "Initialize new repo"
Create a new repo in your target env using your method of choice and then set that as the repo's remote.
git remote add origin https://<your-git>/<your-repo>.git
git push -u -f origin main
If you are using GitLab, there is an automated script in the extras directory. This script allows you to define your environment variables in the gitlab-project-create.sh file and define your GitLab credentaisl in the podvars file. When executed it will copy the mdd_base repo locally, create your GitLab project, and push a clone to GitLab.
Next, it is highly recommended that you create a virtual environment to make it easier to install the dependencies without conflict:
python3 -m venv venv-mdd
. ./venv-mdd/bin/activate
Next, install the Python requirements via pip:
pip3 install -r requirements.txt
Reactivate virtual environment to ensure your shell is using the newly installed ansible.
deactivate
. ./venv-mdd/bin/activate
The MDD tooling requires several environment variables. The first one required for base execution is:
export ANSIBLE_PYTHON_INTERPRETER=${VIRTUAL_ENV}/bin/python
export ANSIBLE_COLLECTIONS_PATH=./
ansible-galaxy collection install -r requirements.yml
Set the environment variables listed below:
The TEST_REMOTE and PROD_REMOTE passwords are used for your device authentication.
export NSO_USERNAME=
export NSO_PASSWORD=
export TEST_REMOTE_PASSWORD=
export PROD_REMOTE_PASSWORD=
export CML_HOST=cml.domain.com
export CML_USERNAME=user
export CML_PASSWORD=password
export CML_LAB=mdd
export CML_VERIFY_CERT=false
The base repository contains two ansible inventory directories. The directory labled inventory_prod will contain an nso.yml file that points to the NSO instance managing your production devices. Below is an example of how your nso.yml file should look.
nso_rest_url: http://xx.xx.xx.xx:8080
nso_username: admin
nso_auth_groups: default
default:
remote_name: admin
remote_password: "{{ lookup('env', 'PROD_REMOTE_PASSWORD') | default('admin', true) }}"
children:
nso:
vars:
ansible_user: "{{ lookup('env', 'NSO_USERNAME') | default('ubuntu', true) }}"
ansible_password: "{{ lookup('env', 'NSO_PASSWORD') | default('admin', true) }}"
ansible_python_interpreter: /usr/bin/python3
hosts:
nso1:
ansible_host: xx.xx.xx.xx:8080
Follow the same instructions for the nso.yml file in your inventory_test directory with the information for your test instance of NSO.
The network.yml file is the key element for your NetDevOps pipeline. It defines your infrastructures heirarchy and design. It also allows you to provide tags to specific devices, giving operators the ability to execute specefic tests or push modular pieces of configuration to a subset of your infrastructure. The base repository provies a sample network.yml inventory file that can be used as a template to build your network. This template is taken from the core MDD repository and maps to the excercises that an be executed against our reference topology.
When implementing MDD in a brownfield environment, we want to harvest the configurations from our poduction network, so it is recommended to start with the network.yml file in your inventory_prod folder.
If you are using this base repository with CML and you do not have a physical environment, you do not need to provide any additional information for each device. The ansible collection takes advantage of the CML API to automatically grab the IP addresses for each device.
children:
network:
children:
switches:
hosts:
hq-sw1:
hq-sw2:
site1-sw1:
site2-sw1:
If you are harvesting your data from a physical environment, you will need to provide IP addresses for each device as depicted in the example below:
children:
network:
children:
switches:
hosts:
hq-sw1:
ansible_host: 10.0.0.1
hq-sw2:
ansible_host: 10.0.0.2
site1-sw1:
ansible_host: 10.0.0.3
site2-sw1:
ansible_host: 10.0.0.4
Now that our inventories have been created, we need to update our production instance of NSO with our inventory. Start out by adding the default auth group to NSO so it can connect to your devices.
ansible-playbook ciscops.mdd.nso_init -i=inventory_prod
Now that our auth group has been created, we can have NSO connect to the devices by running the following playbook:
ansible-playbook ciscops.mdd.nso_update_devices -i=inventory_prod
Once the devices from the network.yml file in your inventory_prod directory have been added, we can execute a sync action for NSO to ingest the configurations into its database.
ansible-playbook ciscops.mdd.nso_sync_from -i=inventory_prod
Next, we will harvest the configurations as structured data in the OpenConfig format. If you would like to harvest the native data, refer to the additional commands section of this guide.
ansible-playbook ciscops.mdd.harvest -i=inventory_prod
You should start to see the configurations being populated under the mdd-data directory matching the heirarchy defined in your network.yml file.
You will see files parsed as both oc-feature.yml and config-remaining.yml. We are regularly adding supported OpenConfig features to the NSO service. While we continue to add support, you will have your data represented as both OpenConfig and Native.
If you do not have an existing simulation environment for pipeline testing, our ansible collection includes a playbook that will use CDP and LLDP to map your network and build a simulation that can be used to import into CML. This playbook also builds the interfce mapping table necessary to ensure your simulation matches your production interface naming convention.
Before you run the simulation creation playbook, navigate to your group_vars directory under your inventory_prod directory and you will see a cml.yml file. This file enables the use of the virtual catalyst 9000 switch for features specefic to layer 3 switches. If you require L3 switching, uncomment the following:
l3switch:
node_definition: Cat9000v
image_definition: Cat9000v-24p
ram: 18432
cpus: 4
tags:
- l3switch
type: l3switch
If you do not require L3 switching, you can leave the Cat9000v commented out and make sure the following is uncommented:
l3switch:
node_definition: iosvl2
ram: 768
tags:
- l3switch
type: l3switch
Now we can execute the playbook with the proper Cat9kv flag set:
ansible-playbook ciscops.mdd.cml_update_lab -i inventory_prod -e start_from=1 -e inventory_dir=inventory_test -e use_cat9kv=yes
If you are not using the Cat9kv, you can set use_cat9kv=no
Once the playbook executes, you will have a cml.yml file created under the files directory and a cml_intf_map.yml file created under your inventory_test directory. You can import the cml.yml file directly into CML.
Note: Your test instance of NSO does not differentiate between simulated or physical devices. This means the devices in your topology must have IP reachability. This playbook uses the first port of each device as a management port and adds an external connector to pull DHCP addresses.
Once your topology is up and running in CML and your devices have IP reachability, we can add them to our test instance of NSO using the same commands in previous steps, but targeting our test inventory.
If you haven't done so already, copy your production network.yml file into your inventory_test directory. Since we are using CML the host names of the devices will remain the same, but you can remove the IP addresses. If you are not using CML, make sure you update the IP addresses for each device before adding them to your test NSO instance.
Once your inventory is completed, we are going to follow the same steps to add the devices to our test NSO but target the inventory_test directory when running our playbooks.
ansible-playbook ciscops.mdd.nso_init -i=inventory_test
ansible-playbook ciscops.mdd.nso_update_devices -i=inventory_test
ansible-playbook ciscops.mdd.nso_sync_from -i=inventory_test
Now our simulated devices are added and synced with our test NSO. The next step is to push the configurations we harvested from our production network to the test network.
ansible-playbook ciscops.mdd.update -i=inventory_test
Note: By default, the update playbook executes a dry run, which means it just sends the data to NSO and verifies whether the data can be pushed to the devices. To have NSO push the configs, you need to set the dry-run flag to "no".
ansible-playbook ciscops.mdd.update -i=inventory_test -e dry_run=no
Once this playbook executes, your digital twin will be configured to match your production network. Now we are ready to create our pipeline.
Before pushing our newely created source-of-truth to Git, it is important to note that this template has a built in gitlab.ci file. This file is commented out, allowing you to prevent the pipeline from running once changes to the repository are pushed. Below is a visual depiction of the pipeline phases. We recommend understanding the phases before pushing your changes.
Now we are ready to commit our changes.
git add .
git commit -m "initial source of truth push"
git push
Our initial push is to the main branch. Once the data is pushed, login to your repository and set the main branch as a protected branch. Only changes that get merged into main will get deployed into the production network. Protecting main will prevent any changes being implemented without validation, checks, and approval.
In GitLab, this option can be found under settings, repository.