Deploy a Django REST API to Heroku in 5 minutes

Sometimes you’re super eager to get started with a new project! Seems easy – you set up a git repo, use django-admin startproject to generate an empty project, start adding dependencies… You want to use Django REST framework for the API, so you install that. Then you need to connect Postgres for the database… Oh, but you want to deploy to Heroku, so you need to configure that DATABASE_URL environment variable and hook that up in settings.py – there was that one project where we had that working already, we can copy/paste it for sure… Celery for async tasks… Redis… 😴… Wait, what were we building again?

If the scenario described above sounds familiar, then like me you’ve run into the problem of boring, repetitive and uncreative work necessary to set up a modern web app project. A lot of boilerplate is needed to get a basic project working and best practices keep changing. Sure, there are solutions like cookiecutter-django which is quite nice, but with ~40 requirements spread across 3 files and ~500 lines of settings spread across 4 files it might be overkill when you’re just getting started. More importantly, while cookiecutter is great for initial project generation, it doesn’t allow you to easily update a project afterwards. Regenerating the project, even when exactly the same prompt answers are selected results in “Error: “my_awesome_project” directory already exists”. As already stated, best practices change over time and it would be useful to have an easy way to update your project boilerplate occasionally.

To address these issues, I’ve developed generator-django-rest – a Yeoman generator for quickly bootstrapping a (relatively simple) Django REST API project that’s deployable to Heroku in 5 minutes. In this post we’ll explain how it works (and why use a JavaScript framework for generating Pythong code 😱), but first let’s see it in action in this screencast:

Continue reading Deploy a Django REST API to Heroku in 5 minutes

Compiling TensorFlow with GPU support on a MacBook Pro

OK, so TensorFlow is the popular new computational framework from Google everyone is raving about (check out this year’s TensorFlow Dev Summit video presentations explaining its cool features). Of course, a fun way to learn TensorFlow is to play with it on your own laptop, so that you can iterate quickly and work offline (perhapse build a hot dog recognition app). In these cases a GPU is very useful for training models more quickly. There used to be a tensorflow-gpu package that you could install in a snap on MacBook Pros with NVIDIA GPUs, but unfortunately it’s no longer supported these days due to some driver issues. Luckily, it’s still possible to manually compile TensorFlow with NVIDIA GPU support. I’ve hunted through a lot of different tutorials (1, 2, 3, 4 – this last one helped me the most) to bring you this hopefully complete description of how to set everything up correctly and get deep into learning (and I know, in 2 months probably become just another one in that list of outdated tutorials, but that’s life 🙂 ).

For the sake of verbosity, I’m using a MacBook Pro 10,1 with an NVIDIA GT 650M and OS X 10.12. Hopefully, though, it will work on a couple of other configurations as well. In any case, let’s start…

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Simple way to set up Django and a SPA frontend on Heroku

This post was adapted from my Stack Overflow answer to a question about using single page applications (SPAs) with Django on Heroku.

Update: check out my django-spa package for a ready-to-use solution for serving SPAs from Django.

Here’s how to set up Django to serve your static files and index.html on / while still having the possibility to use Django views for the admin dashboard, registration etc.:

from django.conf.urls import include, url
from django.contrib import admin
from django.contrib.staticfiles.views import serve
from django.views.generic import RedirectView

admin.autodiscover()

urlpatterns = [

    # / routes to index.html
    url(r'^$', serve,
        kwargs={'path': 'index.html'}),

    # static files (*.css, *.js, *.jpg etc.) served on /
    # (assuming Django uses /static/ and /media/ for static/media urls)
    url(r'^(?!/?static/)(?!/?media/)(?P<path>.*\..*)$',
        RedirectView.as_view(url='/static/%(path)s', permanent=False)),

    # other views still work too
    url(r'^admin/', include(admin.site.urls)),
]

Continue reading Simple way to set up Django and a SPA frontend on Heroku

Home automation using Python, Flask & Celery

We are all a bit lazy in this post-holiday period, so what better project to work on during these relaxed evenings at home, but on a home automation system. Having Docker containers on a physical device that has access to all other IoT devices in our network with exposed APIs like TVs, speakers or maybe even droids and being able to iteratively upgrade these containers gives us ample opportunity to play.

I love the elegance of resin.io’s Docker container deployment & upgrade method, so I use it a lot for hobby projects & freelance work. In this tutorial, I’ll show you how to create a Python Flask app with periodic Celery tasks for controlling your TV via the Chromecast API. All of the source code can be found in this repo. So, go get a hot cup of tea, clone the repo and let’s get started…

Source: Home automation using Python, Flask & Celery

A talking Raspberry Pi using Resin.io

Just trying out resin.io which is a nice new service that enables you to deploy apps to Raspberry Pi devices in a Heroku-like workflow:

git push resin master

In this post I’ll explain how I deployed a simple app that recites “N green bottles” written in Node.js to my Raspberry Pi via resin.io – without having to ssh to the Pi even once. Continue reading A talking Raspberry Pi using Resin.io