Docker is a powerful tool for spinning up isolated, reproducible application environment containers. This piece looks at just that—how to containerize a Flask app for local development along with delivering the application to a cloud hosting provider via Docker Compose and Docker Machine.
- 11/16/2015: Updated to the latest versions of Docker - Docker client (v1.9.0), Docker compose (v1.5.0), and Docker Machine (v0.5.0)
- 04/25/2015: Fixed small typo, and updated the docker-compose.yml file to properly copy static files.
- 04/19/2015: Added a shell script for copying static files.
Interested in creating a similar environment for Django? Check out this blog post.
Along with Docker (v1.9.0) we’ll be using -
- Docker Compose (v1.5.0) - previously known as fig - for orchestrating a multi-container application into a single app, and
- Docker Machine (v0.5.0) for creating Docker hosts both locally and in the cloud.
Running either Mac OS X or Windows, then your best bet is to install Docker Toolbox.
Test out the installs:
$ docker-machine --version docker-machine version 0.5.0 (04cfa58) $ docker-compose --version docker-compose version: 1.5.0
Next clone the project from the repository or create your own project based on the project structure found on the repo:
├── docker-compose.yml ├── nginx │ ├── Dockerfile │ └── sites-enabled │ └── flask_project └── web ├── Dockerfile ├── app.py ├── config.py ├── create_db.py ├── models.py ├── requirements.txt ├── static │ ├── css │ │ ├── bootstrap.min.css │ │ └── main.css │ ├── img │ └── js │ ├── bootstrap.min.js │ └── main.js └── templates ├── _base.html └── index.html
We’re now ready to get the containers up and running. Enter Docker Machine.
To start Docker Machine, first make sure you’re in the project root and then simply run:
$ docker-machine create -d virtualbox dev; Running pre-create checks... Creating machine... Waiting for machine to be running, this may take a few minutes... Machine is running, waiting for SSH to be available... Detecting operating system of created instance... Provisioning created instance... Copying certs to the local machine directory... Copying certs to the remote machine... Setting Docker configuration on the remote daemon... To see how to connect Docker to this machine, run: docker-machine env dev
create command setup a “machine” (called dev) for Docker development. In essence, it downloaded boot2docker and started a VM with Docker running. Now just point the Docker client at the dev machine via:
$ eval "$(docker-machine env dev)"
Run the following command to view the currently running Machines:
$ docker-machine ls NAME ACTIVE DRIVER STATE URL SWARM dev * virtualbox Running tcp://192.168.99.100:2376
Next, let’s fire up the containers with Docker Compose and get the Flask app and Postgres database up and running.
Take a look at the docker-compose.yml file:
web: restart: always build: ./web expose: - "8000" links: - postgres:postgres volumes: - /usr/src/app/static env_file: .env command: /usr/local/bin/gunicorn -w 2 -b :8000 app:app nginx: restart: always build: ./nginx/ ports: - "80:80" volumes: - /www/static volumes_from: - web links: - web:web data: restart: no image: postgres:latest volumes: - /var/lib/postgresql command: true postgres: restart: always image: postgres:latest volumes_from: - data ports: - "5432:5432"
Here, we’re defining four services - web, nginx, postgres, and data.
- First, the web service is built via the instructions in the Dockerfile within the “web” directory - where the Python environment is setup, requirements are installed, and the Flask app is fired up on port 8000. That port is then forwarded to port 80 on the host environment - e.g., the Docker Machine. This service also adds environment variables to the container that are defined in the .env file.
- The nginx service is used for reverse proxy to forward requests either to the Flask app or the static files.
- Next, the postgres service is built from the the official PostgreSQL image from Docker Hub, which install Postgres and runs the server on the default port 5432.
- Finally, notice how there is a separate volume container that’s used to store the database data, data. This helps ensure that the data persists even if the Postgres container is completely destroyed.
Now, to get the containers running, build the images and then start the services:
$ docker-compose build $ docker-compose up -d
Grab a cup of coffee. Or two. Check out the Real Python courses. This will take a while the first time you run it.
We also need to create the database table:
$ docker-compose run web /usr/local/bin/python create_db.py
Open your browser and navigate to the IP address associated with Docker Machine (
To see which environment variables are available to the web service, run:
$ docker-compose run web env
To view the logs:
$ docker-compose logs
You can also enter the Postgres Shell - since we forward the port to the host environment in the docker-compose.yml file - to add users/roles as well as databases via:
$ psql -h 192.168.99.100 -p 5432 -U postgres --password
Once done, stop the processes via
So, with our app running locally, we can now push this exact same environment to a cloud hosting provider with Docker Machine. Let’s deploy to a Digital Ocean droplet.
$ docker-machine create \ -d digitalocean \ --digitalocean-access-token=ADD_YOUR_TOKEN_HERE \ production
This will take a few minutes to provision the droplet and setup a new Docker Machine called production:
Running pre-create checks... Creating machine... Waiting for machine to be running, this may take a few minutes... Machine is running, waiting for SSH to be available... Detecting operating system of created instance... Provisioning created instance... Copying certs to the local machine directory... Copying certs to the remote machine... Setting Docker configuration on the remote daemon... To see how to connect Docker to this machine, run: docker-machine env production
Now we have two Machines running, one locally and one on Digital Ocean:
$ docker-machine ls NAME ACTIVE DRIVER STATE URL SWARM dev * virtualbox Running tcp://192.168.99.100:2376 production - digitalocean Running tcp://220.127.116.11:2376
Then set production as the active machine and load the Docker environment into the shell:
$ eval "$(docker-machine env production)"
Finally, let’s build the Flask app again in the cloud:
$ docker-compose build $ docker-compose up -d $ docker-compose run web /usr/local/bin/python create_db.py
Grab the IP address associated with that Digital Ocean account from the control panel and view it in the browser. If all went well, you should see your app running.
- Grab the code from the Github repo (star it too!).
- Comment below with questions.
- Next time we’ll extend this workflow to include two more Docker containers running the Flask app and incorporate load balancing into the mix. Stay tuned!