Building Your Development Environment¶

Quickstart Guide¶

The development environment can be used in two ways:

(Recommended) All services, including Nautobot, are spun up using Docker containers and a volume mount so you can develop locally.
With a local Poetry environment if you wish to develop outside of Docker, with the caveat of using external services provided by Docker for the database (PostgreSQL by default, MySQL optionally) and Redis services.

This is a quick reference guide if you're already familiar with the development environment provided, which you can read more about later in this document.

Invoke¶

The Invoke library is used to provide some helper commands based on the environment. There are a few configuration parameters which can be passed to Invoke to override the default configuration:

nautobot_ver: the version of Nautobot to use as a base for any built docker containers (default: 2.0.0)
project_name: the default docker compose project name (default: nautobot-secrets-providers)
python_ver: the version of Python to use as a base for any built docker containers (default: 3.11)
local: a boolean flag indicating if invoke tasks should be run on the host or inside the docker containers (default: False, commands will be run in docker containers)
compose_dir: the full path to a directory containing the project compose files
compose_files: a list of compose files applied in order (see Multiple Compose files for more information)

Using Invoke these configuration options can be overridden using several methods. Perhaps the simplest is setting an environment variable INVOKE_NAUTOBOT_SECRETS_PROVIDERS_VARIABLE_NAME where VARIABLE_NAME is the variable you are trying to override. The only exception is compose_files, because it is a list it must be overridden in a YAML file. There is an example invoke.yml (invoke.example.yml) in this directory which can be used as a starting point.

Docker Development Environment¶

Tip

This is the recommended option for development.

This project is managed by Python Poetry and has a few requirements to setup your development environment:

Install Poetry, see the Poetry Documentation for your operating system.
Install Docker, see the Docker documentation for your operating system.

Once you have Poetry and Docker installed you can run the following commands to install all other development dependencies in an isolated python virtual environment:

poetry shell
poetry install
invoke start

Nautobot server can now be accessed at http://localhost:8080.

To either stop or destroy the development environment use the following options.

invoke stop - Stop the containers, but keep all underlying systems intact
invoke destroy - Stop and remove all containers, volumes, etc. (This results in data loss due to the volume being deleted)

Local Poetry Development Environment¶

Copy development/creds.example.env to development/creds.env (This file will be ignored by Git and Docker)
Uncomment the POSTGRES_HOST, REDIS_HOST, and NAUTOBOT_ROOT variables in development/creds.env
Create an invoke.yml file with the following contents at the root of the repo (you can also cp invoke.example.yml invoke.yml and edit as necessary):

---
nautobot_secrets_providers:
  local: true

Run the following commands:

poetry shell
poetry install --extras nautobot
export $(cat development/dev.env | xargs)
export $(cat development/creds.env | xargs)
invoke start && sleep 5
nautobot-server migrate

If you want to develop on the latest develop branch of Nautobot, run the following command: poetry add --optional git+https://github.com/nautobot/nautobot@develop. After the @ symbol must match either a branch or a tag.

You can now run nautobot-server commands as you would from the Nautobot documentation for example to start the development server:

nautobot-server runserver 0.0.0.0:8080 --insecure

Nautobot server can now be accessed at http://localhost:8080.

It is typically recommended to launch the Nautobot runserver command in a separate shell so you can keep developing and manage the webserver separately.

Updating the Documentation¶

Documentation dependencies are pinned to exact versions to ensure consistent results. For the development environment, they are defined in the pyproject.toml file.

If you need to update any of the documentation dependencies to a newer version, make sure you copy the exact same versions pinned in pyproject.toml to the docs/requirements.txt file as well. The latter is used in the automated build pipeline on ReadTheDocs to build the live version of the documentation.

CLI Helper Commands¶

The project features a CLI helper based on Invoke to help setup the development environment. The commands are listed below in 3 categories:

dev environment
utility
testing

Each command can be executed with invoke <command>. All commands support the arguments --nautobot-ver and --python-ver if you want to manually define the version of Python and Nautobot to use. Each command also has its own help invoke <command> --help

Local Development Environment¶

  build            Build all docker images.
  debug            Start Nautobot and its dependencies in debug mode.
  destroy          Destroy all containers and volumes.
  restart          Restart Nautobot and its dependencies in detached mode.
  start            Start Nautobot and its dependencies in detached mode.
  stop             Stop Nautobot and its dependencies.

Utility¶

  cli              Launch a bash shell inside the running Nautobot container.
  create-user      Create a new user in django (default: admin), will prompt for password.
  makemigrations   Run Make Migration in Django.
  nbshell          Launch a nbshell session.

Testing¶

  bandit           Run bandit to validate basic static code security analysis.
  black            Run black to check that Python files adhere to its style standards.
  flake8           Run flake8 to check that Python files adhere to its style standards.
  ruff             Run ruff to validate docstring formatting adheres to NTC defined standards.
  pylint           Run pylint code analysis.
  tests            Run all tests for this app.
  unittest         Run Django unit tests for the app.

Developing Against Secrets Backends¶

AWS Secrets Manager¶

This assumes you are logged into the AWS Console.

Navigate to AWS Console
Navigate to AWS Secrets Manager
Click "Store a new secret"
Select “Other type of secrets”
Use Secret key/value
Enter hello=world
Use "DefaultEncryptionKey" for now
Click "Next"
Under "Secret name" fill out hello
Click "Next"
Under "Configure automatic rotation"
- Leave it as "Disable automatic rotation"
On "Store a new secret"
- Copy the sample code (see below)
Click "Store"
END

Install the AWS CLI¶

Next, install the AWS CLI.

On MacOS, this can be done using brew install awscli:

brew install awscli

On Linux, you will need to run a curl command (This assumes x86. Please see the docs for AWS CLI on Linux for ARM and other options):

curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install

Configure the AWS CLI¶

After installing the AWS CLI, you will need to configure it for authentication.

You may use an existing AWS access key or create a new one. For these instructions we cover the need to create a new access key that can be used for this.

Navigate to AWS Console
Click your username
Click "My security credentials"
Click "create access key"
Save your "Access key ID" and "Secret access key" for use when configuring the AWS CLI

Now configure the CLI:

Run aws configure
Enter your credentials from above
Choose your region
Use output format: json

Example:

$ aws configure
AWS Access Key ID [None]: AKIAIOSFODNN7EXAMPLE
AWS Secret Access Key [None]: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
Default region name [None]: us-east-2
Default output format [None]: json

Now you are ready to use the sample code to retrieve your secret from AWS Secrets Manager!

Sample Code¶

Make sure that the boto3 client is installed:

poetry install --extras aws

Next, save this as aws_secrets.py:

# Use this code snippet in your app.
# If you need more information about configurations or implementing the sample code, visit the AWS docs:
# https://aws.amazon.com/developers/getting-started/python/

import boto3
import base64
from botocore.exceptions import ClientError


def get_secret():

    secret_name = "hello"
    region_name = "us-east-2"

    # Create a Secrets Manager client
    session = boto3.session.Session()
    client = session.client(
        service_name='secretsmanager',
        region_name=region_name
    )

    # In this sample we only handle the specific exceptions for the 'GetSecretValue' API.
    # See https://docs.aws.amazon.com/secretsmanager/latest/apireference/API_GetSecretValue.html
    # We rethrow the exception by default.

    try:
        get_secret_value_response = client.get_secret_value(
            SecretId=secret_name
        )
    except ClientError as e:
        if e.response['Error']['Code'] == 'DecryptionFailureException':
            # Secrets Manager can't decrypt the protected secret text using the provided KMS key.
            # Deal with the exception here, and/or rethrow at your discretion.
            raise e
        elif e.response['Error']['Code'] == 'InternalServiceErrorException':
            # An error occurred on the server side.
            # Deal with the exception here, and/or rethrow at your discretion.
            raise e
        elif e.response['Error']['Code'] == 'InvalidParameterException':
            # You provided an invalid value for a parameter.
            # Deal with the exception here, and/or rethrow at your discretion.
            raise e
        elif e.response['Error']['Code'] == 'InvalidRequestException':
            # You provided a parameter value that is not valid for the current state of the resource.
            # Deal with the exception here, and/or rethrow at your discretion.
            raise e
        elif e.response['Error']['Code'] == 'ResourceNotFoundException':
            # We can't find the resource that you asked for.
            # Deal with the exception here, and/or rethrow at your discretion.
            raise e
    else:
        # Decrypts secret using the associated KMS CMK.
        # Depending on whether the secret is a string or binary, one of these fields will be populated.
        if 'SecretString' in get_secret_value_response:
            secret = get_secret_value_response['SecretString']
        else:
            decoded_binary_secret = base64.b64decode(get_secret_value_response['SecretBinary'])

    # Your code goes here.

# ^ Above was generated by AWS.

# This was added by us so you can run this as a script:
if __name__ == "__main__":
    secret = get_secret()
    print(f"Secret = {secret}")

Run it with python aws_secrets.py:

$ python aws_secrets.py
Secret = {"hello":"world"}.

Note that this blob is JSON and will also need to be decoded if you want to extract the value.

HashiCorp Vault¶

Make sure that the hvac client is installed:

poetry install --extras hashicorp

Start Services with Docker¶

invoke start

Set an alias to work with `vault`¶

This will allow you to easily run the CLI command from within the container:

alias vault="docker exec -it nautobot_secrets_providers_vault_1 vault"

Interact with the Vault vi CLI (via docker exec into the container from localhost):

$ vault status
Key             Value
---             -----
Seal Type       shamir
Initialized     true
Sealed          false
Total Shares    1
Threshold       1
Version         1.8.2
Storage Type    inmem
Cluster Name    vault-cluster-35c5d319
Cluster ID      2611f99c-a6de-a883-1fcc-bfffdc0217bc
HA Enabled      false

Using the Python `hvac` Library¶

This establishes a client, creates a basic key/value secret (hello=world) at the path hello, and then retrieves the data from the hello key at the secret path hello.

This is equivalent to the command vault kv get -field hello secret/hello.

In [1]: import hvac

In [2]: client = hvac.Client(url="http://localhost:8200", token="nautobot")

In [3]: client.secrets.kv.create_or_update_secret(path="hello", secret=dict(hello="world"))
Out[3]:
{'request_id': 'c4709868-c08f-4cb1-ab8c-605c58b82f3f',
 'lease_id': '',
 'renewable': False,
 'lease_duration': 0,
 'data': {'created_time': '2021-09-16T23:21:07.5564132Z',
  'deletion_time': '',
  'destroyed': False,
  'version': 2},
 'wrap_info': None,
 'warnings': None,
 'auth': None}

In [4]: client.secrets.kv.read_secret(path="hello")["data"]["data"]["hello"]
Out[4]: 'world'

Project Overview¶

This project provides the ability to develop and manage the Nautobot server locally (with supporting services being Dockerized) or by using only Docker containers to manage Nautobot. The main difference between the two environments is the ability to debug and use pdb when developing locally. Debugging with pdb within the Docker container is more complicated, but can still be accomplished by either entering into the container (via docker exec) or attaching your IDE to the container and running the Nautobot service manually within the container.

The upside to having the Nautobot service handled by Docker rather than locally is that you do not have to manage the Nautobot server. The Docker logs provide the majority of the information you will need to help troubleshoot, while getting started quickly and not requiring you to perform several manual steps and remembering to have the Nautobot server running in a separate terminal while you develop.

Note

The local environment still uses Docker containers for the supporting services (Postgres, Redis, and RQ Worker), but the Nautobot server is handled locally by you, the developer.

Follow the directions below for the specific development environment that you choose.

Poetry¶

Poetry is used in lieu of the "virtualenv" commands and is leveraged in both environments. The virtual environment will provide all of the Python packages required to manage the development environment such as Invoke. See the Local Development Environment section to see how to install Nautobot if you're going to be developing locally (i.e. not using the Docker container).

The pyproject.toml file outlines all of the relevant dependencies for the project:

tool.poetry.dependencies - the main list of dependencies.
tool.poetry.group.dev.dependencies - development dependencies, to facilitate linting, testing, and documentation building.

The poetry shell command is used to create and enable a virtual environment managed by Poetry, so all commands ran going forward are executed within the virtual environment. This is similar to running the source venv/bin/activate command with virtualenvs. To install project dependencies in the virtual environment, you should run poetry install - this will install both project and development dependencies.

For more details about Poetry and its commands please check out its online documentation.

Full Docker Development Environment¶

This project is set up with a number of Invoke tasks consumed as simple CLI commands to get developing fast. You'll use a few invoke commands to get your environment up and running.

Copy the credentials file for Nautobot¶

First, you need to create the development/creds.env file - it stores a bunch of private information such as passwords and tokens for your local Nautobot install. You can make a copy of the development/creds.example.env and modify it to suit you.

cp development/creds.example.env development/creds.env

Invoke - Building the Docker Image¶

The first thing you need to do is build the necessary Docker image for Nautobot that installs the specific nautobot_ver. The image is used for Nautobot and the Celery worker service used by Docker Compose.

➜ invoke build
... <omitted for brevity>
#14 exporting to image
#14 sha256:e8c613e07b0b7ff33893b694f7759a10d42e180f2b4dc349fb57dc6b71dcab00
#14 exporting layers
#14 exporting layers 1.2s done
#14 writing image sha256:2d524bc1665327faa0d34001b0a9d2ccf450612bf8feeb969312e96a2d3e3503 done
#14 naming to docker.io/nautobot-secrets-providers/nautobot:2.0.0-py3.11 done

Invoke - Starting the Development Environment¶

Next, you need to start up your Docker containers.

➜ invoke start
Starting Nautobot in detached mode...
Running docker-compose command "up --detach"
Creating network "nautobot_secrets_providers_default" with the default driver
Creating volume "nautobot_secrets_providers_postgres_data" with default driver
Creating nautobot_secrets_providers_redis_1 ...
Creating nautobot_secrets_providers_docs_1  ...
Creating nautobot_secrets_providers_postgres_1 ...
Creating nautobot_secrets_providers_postgres_1 ... done
Creating nautobot_secrets_providers_redis_1    ... done
Creating nautobot_secrets_providers_nautobot_1 ...
Creating nautobot_secrets_providers_docs_1     ... done
Creating nautobot_secrets_providers_nautobot_1 ... done
Creating nautobot_secrets_providers_worker_1   ...
Creating nautobot_secrets_providers_worker_1   ... done
Docker Compose is now in the Docker CLI, try `docker compose up`

This will start all of the Docker containers used for hosting Nautobot. You should see the following containers running after invoke start is finished.

➜ docker ps
****CONTAINER ID   IMAGE                            COMMAND                  CREATED          STATUS          PORTS                                       NAMES
ee90fbfabd77   nautobot-secrets-providers/nautobot:2.0.0-py3.11  "nautobot-server rqw…"   16 seconds ago   Up 13 seconds                                               nautobot_secrets_providers_worker_1
b8adb781d013   nautobot-secrets-providers/nautobot:2.0.0-py3.11  "/docker-entrypoint.…"   20 seconds ago   Up 15 seconds   0.0.0.0:8080->8080/tcp, :::8080->8080/tcp   nautobot_secrets_providers_nautobot_1
d64ebd60675d   nautobot-secrets-providers/nautobot:2.0.0-py3.11  "mkdocs serve -v -a …"   25 seconds ago   Up 18 seconds   0.0.0.0:8001->8080/tcp, :::8001->8080/tcp   nautobot_secrets_providers_docs_1
e72d63129b36   postgres:13-alpine               "docker-entrypoint.s…"   25 seconds ago   Up 19 seconds   0.0.0.0:5432->5432/tcp, :::5432->5432/tcp   nautobot_secrets_providers_postgres_1
96c6ff66997c   redis:6-alpine                   "docker-entrypoint.s…"   25 seconds ago   Up 21 seconds   0.0.0.0:6379->6379/tcp, :::6379->6379/tcp   nautobot_secrets_providers_redis_1

Once the containers are fully up, you should be able to open up a web browser, and view:

The Nautobot homepage at http://localhost:8080
A live version of the documentation at http://localhost:8001

Note

Sometimes the containers take a minute to fully spin up. If the page doesn't load right away, wait a minute and try again.

Invoke - Creating a Superuser¶

The Nautobot development image will automatically provision a super user when specifying the following variables within creds.env which is the default when copying creds.example.env to creds.env.

NAUTOBOT_CREATE_SUPERUSER=true
NAUTOBOT_SUPERUSER_API_TOKEN=0123456789abcdef0123456789abcdef01234567
NAUTOBOT_SUPERUSER_PASSWORD=admin

Note

The default username is admin, but can be overridden by specifying NAUTOBOT_SUPERUSER_USERNAME.

If you need to create additional superusers, run the follow commands.

➜ invoke createsuperuser
Running docker-compose command "ps --services --filter status=running"
Running docker-compose command "exec nautobot nautobot-server createsuperuser --username admin"
Error: That username is already taken.
Username: ntc
Email address: ntc@networktocode.com
Password:
Password (again):
Superuser created successfully.

Invoke - Stopping the Development Environment¶

The last command to know for now is invoke stop.

➜ invoke stop
Stopping Nautobot...
Running docker-compose command "down"
Stopping nautobot_secrets_providers_worker_1   ...
Stopping nautobot_secrets_providers_nautobot_1 ...
Stopping nautobot_secrets_providers_docs_1     ...
Stopping nautobot_secrets_providers_redis_1    ...
Stopping nautobot_secrets_providers_postgres_1 ...
Stopping nautobot_secrets_providers_worker_1   ... done
Stopping nautobot_secrets_providers_nautobot_1 ... done
Stopping nautobot_secrets_providers_postgres_1 ... done
Stopping nautobot_secrets_providers_redis_1    ... done
Stopping nautobot_secrets_providers_docs_1     ... done
Removing nautobot_secrets_providers_worker_1   ...
Removing nautobot_secrets_providers_nautobot_1 ...
Removing nautobot_secrets_providers_docs_1     ...
Removing nautobot_secrets_providers_redis_1    ...
Removing nautobot_secrets_providers_postgres_1 ...
Removing nautobot_secrets_providers_postgres_1 ... done
Removing nautobot_secrets_providers_docs_1     ... done
Removing nautobot_secrets_providers_worker_1   ... done
Removing nautobot_secrets_providers_redis_1    ... done
Removing nautobot_secrets_providers_nautobot_1 ... done
Removing network nautobot_secrets_providers_default

This will safely shut down all of your running Docker containers for this project. When you are ready to spin containers back up, it is as simple as running invoke start again as seen previously.

Warning

If you're wanting to reset the database and configuration settings, you can use the invoke destroy command, but you will lose any data stored in those containers, so make sure that is what you want to do.

Real-Time Updates? How Cool!¶

Your environment should now be fully setup, all necessary Docker containers are created and running, and you're logged into Nautobot in your web browser. Now what?

Now you can start developing your app in the project folder!

The magic here is the root directory is mounted inside your Docker containers when built and ran, so any changes made to the files in here are directly updated to the Nautobot app code running in Docker. This means that as you modify the code in your app folder, the changes will be instantly updated in Nautobot.

Warning

There are a few exceptions to this, as outlined in the section To Rebuild or Not To Rebuild.

The back-end Django process is setup to automatically reload itself (it only takes a couple of seconds) every time a file is updated (saved). So for example, if you were to update one of the files like tables.py, then save it, the changes will be visible right away in the web browser!

Note

You may get connection refused while Django reloads, but it should be refreshed fairly quickly.

Docker Logs¶

When trying to debug an issue, one helpful thing you can look at are the logs within the Docker containers.

➜ docker logs <name of container> -f

Note

The -f tag will keep the logs open, and output them in realtime as they are generated.

Info

Want to limit the log output even further? Use the --tail <#> command line argument in conjunction with -f.

So for example, our app is named nautobot-secrets-providers, the command would most likely be docker logs nautobot_secrets_providers_nautobot_1 -f. You can find the name of all running containers via docker ps.

If you want to view the logs specific to the worker container, simply use the name of that container instead.

To Rebuild or Not to Rebuild¶

Most of the time, you will not need to rebuild your images. Simply running invoke start and invoke stop is enough to keep your environment going.

However there are a couple of instances when you will want to.

Updating Environment Variables¶

To add environment variables to your containers, thus allowing Nautobot to use them, you will update/add them in the development/development.env file. However, doing so is considered updating the underlying container shell, instead of Django (which auto restarts itself on changes).

To get new environment variables to take effect, you will need stop any running images, rebuild the images, then restart them. This can easily be done with 3 commands:

➜ invoke stop
➜ invoke build
➜ invoke start

Once completed, the new/updated environment variables should now be live.

Installing Additional Python Packages¶

If you want your app to leverage another available Nautobot app or another Python package, you can easily add them into your Docker environment.

➜ poetry shell
➜ poetry add <package_name>

Once the dependencies are resolved, stop the existing containers, rebuild the Docker image, and then start all containers again.

➜ invoke stop
➜ invoke build
➜ invoke start

Installing Additional Nautobot Apps¶

Let's say for example you want the new app you're creating to integrate into Slack. To do this, you will want to integrate into the existing Nautobot ChatOps App.

➜ poetry shell
➜ poetry add nautobot-chatops

Once you activate the virtual environment via Poetry, you then tell Poetry to install the new app.

Before you continue, you'll need to update the file development/nautobot_config.py accordingly with the name of the new app under PLUGINS and any relevant settings as necessary for the app under PLUGINS_CONFIG. Since you're modifying the underlying OS (not just Django files), you need to rebuild the image. This is a similar process to updating environment variables, which was explained earlier.

➜ invoke stop
➜ invoke build
➜ invoke start

Once the containers are up and running, you should now see the new app installed in your Nautobot instance.

Note

You can even launch an ngrok service locally on your laptop, pointing to port 8080 (such as for chatops development), and it will point traffic directly to your Docker images.

Updating Python Version¶

To update the Python version, you can update it within tasks.py.

namespace = Collection("nautobot_secrets_providers")
namespace.configure(
    {
        "nautobot_secrets_providers": {
            ...
            "python_ver": "3.11",
        ...
        }
    }
)

Or set the INVOKE_NAUTOBOT_SECRETS_PROVIDERS_PYTHON_VER variable.

Updating Nautobot Version¶

To update the Nautobot version, you can update it within tasks.py.

namespace = Collection("nautobot_secrets_providers")
namespace.configure(
    {
        "nautobot_secrets_providers": {
            ...
            "nautobot_ver": "2.0.0",
        ...
        }
    }
)

Or set the INVOKE_NAUTOBOT_SECRETS_PROVIDERS_NAUTOBOT_VER variable.

Other Miscellaneous Commands To Know¶

Python Shell¶

To drop into a Django shell for Nautobot (in the Docker container) run:

➜ invoke nbshell

This is the same as running:

➜ invoke cli
➜ nautobot-server nbshell

iPython Shell Plus¶

Django also has a more advanced shell that uses iPython and that will automatically import all the models:

➜ invoke shell-plus

This is the same as running:

➜ invoke cli
➜ nautobot-server shell_plus

Tests¶

To run tests against your code, you can run all of the tests that TravisCI runs against any new PR with:

➜ invoke tests

To run an individual test, you can run any or all of the following:

➜ invoke unittest
➜ invoke bandit
➜ invoke black
➜ invoke flake8
➜ invoke ruff
➜ invoke pylint

App Configuration Schema¶

In the package source, there is the nautobot_secrets_providers/app-config-schema.json file, conforming to the JSON Schema format. This file is used to validate the configuration of the app in CI pipelines.

If you make changes to PLUGINS_CONFIG or the configuration schema, you can run the following command to validate the schema:

invoke validate-app-config

To generate the app-config-schema.json file based on the current PLUGINS_CONFIG configuration, run the following command:

invoke generate-app-config-schema

This command can only guess the schema, so it's up to the developer to manually update the schema as needed.

Building Your Development Environment¶

Quickstart Guide¶

Invoke¶

Docker Development Environment¶

Local Poetry Development Environment¶

Updating the Documentation¶

CLI Helper Commands¶

Local Development Environment¶

Utility¶

Testing¶

Developing Against Secrets Backends¶

AWS Secrets Manager¶

Install the AWS CLI¶

Configure the AWS CLI¶

Sample Code¶

HashiCorp Vault¶

Start Services with Docker¶

Set an alias to work with vault¶

Using the Python hvac Library¶

Project Overview¶

Poetry¶

Full Docker Development Environment¶

Copy the credentials file for Nautobot¶

Invoke - Building the Docker Image¶

Invoke - Starting the Development Environment¶

Invoke - Creating a Superuser¶

Invoke - Stopping the Development Environment¶

Real-Time Updates? How Cool!¶

Docker Logs¶

To Rebuild or Not to Rebuild¶

Updating Environment Variables¶

Installing Additional Python Packages¶

Installing Additional Nautobot Apps¶

Updating Python Version¶

Updating Nautobot Version¶

Other Miscellaneous Commands To Know¶

Python Shell¶

iPython Shell Plus¶

Tests¶

App Configuration Schema¶

Set an alias to work with `vault`¶

Using the Python `hvac` Library¶