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Development and Deployment of Cookiecutter-Django via Docker

Let’s look at how to bootstrap a Django Project pre-loaded with the basic requirements needed in order to quickly get a project up and running.Further, beyond the project structure, most bootstrapped projects also take care of setting up the development and production environment settings, without troubling the user much – so we’ll look at that as well.

Development and Deployment of Cookiecutter-Django via Docker

We’ll be using the popular cookiecutter-django as the bootstrapper for our Django Project along with Docker to manage our application environment.

Let’s begin!

Local Set up

Start by installing cookiecutter globally:

$ pip install cookiecutter==1.3.0 

Now execute the following command to generate a bootstrapped django project:

$ cookiecutter https://github.com/pydanny/cookiecutter-django.git 

This command runs cookiecutter with the cookiecutter-django repo, allowing us to enter project-specific details:

Cloning into 'cookiecutter-django'... remote: Counting objects: 4810, done. remote: Compressing objects: 100% (7/7), done. remote: Total 4810 (delta 0), reused 0 (delta 0), pack-reused 4803 Receiving objects: 100% (4810/4810), 844.79 KiB | 582.00 KiB/s, done. Resolving deltas: 100% (3039/3039), done. Checking connectivity... done. project_name [project_name]: django_cookiecutter_docker repo_name [django_cookiecutter_docker]: django_cookiecutter_docker author_name [Your Name]: Michael Herman email [Your email]: michael@realpython.com description [A short description of the project.]: Tutorial on bootstrapping django projects domain_name [example.com]: example.com version [0.1.0]: 0.1.0 timezone [UTC]: UTC now [2015/11/04]: 2015/11/04 year [2015]: 2015 use_whitenoise [y]: y use_celery [n]: n use_mailhog [n]: n use_sentry [n]: n use_newrelic [n]: n use_opbeat [n]: n windows [n]: n use_python2 [n]: n 

Project Structure

Take a quick look at the generated project structure, taking specific note of the following directories:

  1. “config” includes all the settings for our local and production environments.
  2. “requirements” contains all the requirement files – base.txt , local.txt , production.txt , test.txt – which you can make changes to and then install via pip install -r file_name .
  3. “django_cookiecutter_docker” is the main project directory which consists of the “static”, “contrib” and “templates” directories along with the users app containing the models and boilerplate code associated with user authentication.

Docker Set up

Start by downloading and then installing the Docker Toolbox (v 1.9.1f ) to obtain virtualbox and the required Docker components:

  • docker 1.9.1
  • docker-machine 0.5.4
  • docker-compose 1.5.2

Docker Machine

Once installed, create a new Docker host within the root of the newly created Django Project:

$ docker-machine create -d virtualbox dev $ eval "$(docker-machine env dev)" 

NOTE: dev can be named anything you want. For example, if you have more than one development environment, you could name them djangodev1 , djangodev2 , and so forth.

To view all Machines, run:

$ docker-machine ls 

You can also view the IP of the dev Machine by running:

$ docker-machine ip dev 

Finally, let’s create a “/data” partition within the VM itself so that changes are persistent:

$ docker-machine ssh dev $ sudo su $ echo 'ln -sfn /mnt/sda1/data /data' >> /var/lib/boot2docker/bootlocal.sh 

Docker Compose

Now we can fire everything up – e.g., Django and Postgres – via Docker Compose:

$ docker-compose -f dev.yml build $ docker-compose -f dev.yml up -d 

The first build will take a while. Due to caching , subsequent builds will run much faster.

Sanity Check

Now we can test our Django Project by applying the migrations and then running the server:

$ docker-compose -f dev.yml run django python manage.py makemigrations $ docker-compose -f dev.yml run django python manage.py migrate $ docker-compose -f dev.yml run django python manage.py createsuperuser 

Navigate to the dev IP (port 8000) in your browser to view the Project quick start page with debugging mode on and many more development environment oriented features installed and running.

Kill the server, initialize a new git repo, commit, and PUSH to GitHub.

Deployment Setup

So, we have successfully set up our Django Project locally using cookiecutter-django and served it up using the traditional manage.py command line utility via Docker.

In this section, we move on to the deployment part, where the role of a web server comes into play. We will be setting up a Docker Machine on a Digital Ocean droplet, with Postgres as our database and Nginx as our web server.

Along with this, we will be making use of gunicorn instead of Django’s single-threaded development server to run the server process.

Why Nginx?

Apart from being a high-performance HTTP server, which almost every good web server out in the market is, Nginx has some really good features that make it stand out from the rest – namely that it:

  • Can couple as a reverse proxy server ,
  • Can host more than one site,
  • Has an asynchronous way of handling web requests, which means that since it doesn’t rely on threads to handle web requests, it has a higher performance while handling multiple requests.

Why Gunicorn?

Gunicorn is a Python WSGI HTTP server that can be easily customized and provides better performance in terms of reliability than Django’s single-threaded development server within production environments.

Digital Ocean Setup

We will be using a Digital Ocean server for this tutorial. After you sign up (if necessary), generate a Personal Access Token, and then run the following command:

$ docker-machine create / -d digitalocean / --digitalocean-access-token=ADD_YOUR_TOKEN_HERE / prod 

This should only take few minutes to provision the Digital Ocean droplet and set up a new Docker Machine called prod . While you wait, navigate to the Digital Ocean Control Panel ; you should see a new droplet being created, again, called prod .

Once done, there should now be two machines running, one locally ( dev ) and one on Digital Ocean ( prod ). Run docker-machine ls to confirm:

$ cookiecutter https://github.com/pydanny/cookiecutter-django.git 

0

Set prod as the active machine and then load the Docker environment into the shell:

$ cookiecutter https://github.com/pydanny/cookiecutter-django.git 

1

Docker Compose (take 2)

Start by renaming env.example to .env . Update the DJANGO_ALLOWED_HOSTS variable to match the Digital Ocean IP address – i.e., DJANGO_ALLOWED_HOSTS=159.203.77.132 . Keep the remaining defaults for now.

Now we can create the build and then fire up the services in the cloud:

$ cookiecutter https://github.com/pydanny/cookiecutter-django.git 

2

Sanity Check (take 2)

Apply all the migrations:

$ cookiecutter https://github.com/pydanny/cookiecutter-django.git 

3

That’s it!

Now just visit your server’s IP address, associated with the Digital Ocean droplet, and view it in the browser.

You should be good to go.

For further reference just grab the code from the repository . Thanks a lot for reading! Looking forward to your questions.

原文  https://realpython.com/blog/python/development-and-deployment-of-cookiecutter-django-via-docker/

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