Portfolio

Overview

ASGI RESTful API by iyazerski/cookiecutter-api

Requirements

Make sure that all requirements related for your platform will be installed before main package installation.

Linux | Windows | macOS

Docker

Normally, to build project image you will need to install following packages:

• Python

• Docker and docker-compose

Installation

Linux | Windows | macOS

Following instructions are guidelines, you can install it differently, however, executing the following commands will ensure that portfolio is installed correctly:

Clone repository and create virtual environment:

foo@bar:~$ cd portfolio
foo@bar:~/portfolio$ python -m venv venv && source venv/bin/activate
foo@bar:~/portfolio$ python -m pip install --upgrade pip wheel setuptools

Install requirements:

foo@bar:~/portfolio$ pip install -r requirements/dev.txt

Docker

Make sure that you have execution permissions for docker, docker-compose, python and pydeployhelp.

Use pydeployhelp package to build project image:

foo@bar:~/portfolio$ pydeployhelp
Enter deploy tasks from following: all | build up down: build
        ✓ processing deploy tasks: build
Enter deploy targets from following: all | portfolio-backend-dev: all
        ✓ processing deploy targets: portfolio-backend-dev
Do you agree to start processing (y/N)?: y

Environment

Create .env file in project root with environment variables:

ENV=dev
PROJECT_SLUG=portfolio
HOST=localhost
PORT=8080
ENABLE_CORS=True
VOLUMES_ROOT=$HOME/projects/portfolio

What does each variable mean:

• ENV: environment type (dev, prod), could contain any value, but for production please set ENV=prod

• PROJECT_SLUG: this name will be used for docker containers creation

• HOST: backend host (e.g. localhost, project.dns.com)

• PORT: backend port, will be ignored when ENV=prod

• ENABLE_CORS: if True, all origins and methods will be allowed

• VOLUMES_ROOT: path to directory, where docker volumes will be stored; it's highly recommended to set this value to the path of the project root (on development machine) - this will allow you to use the same data when debugging from IDE and when deploying via docker (pydeployhelp)

Usage

Linux | Windows | macOS

You can manually start project:

foo@bar:~/portfolio$ python run.py

Docker

Use pydeployhelp package to run project image:

foo@bar:~/portfolio$ pydeployhelp
Enter deploy tasks from following: all | build up down: up
        ✓ processing deploy tasks: up
Enter deploy targets from following: all | portfolio-backend-dev: all
        ✓ processing deploy targets: portfolio-backend-dev
Do you agree to start processing (y/N)?: y

Documentation

portfolio is equipped with automatic documentation (via sphinx), which can be generated from docs directory as follows:

foo@bar:~/portfolio$ cd docs
foo@bar:~/portfolio/docs$ make html

After executing this command you will find autogenerated documentation entrypoint at docs/build/index.html