Davide Moro: High quality automated docker hub push using Github, TravisCI and pyup for Python tool distributions

Let’s say you want to distribute a Python tool with docker using known good dependency versions ready to be used by end users… In this article you will see how to continuously keeping up to date a Docker Hub container with minimal managing effort (because I’m a lazy guy) using github, TravisCI and pyup.

The goal was to reduce as much as possible any manual activity for updates, check all works fine before pushing, minimize build times and keep docker container always secure and updated with a final high quality confidence.

As an example let’s see what happens under the hood behind every pytest-play Docker Hub update on the official container https://cloud.docker.com/u/davidemoro/repository/docker/davidemoro/pytest-play (by the way if you are a pytest-play user: did you know that you can use Docker for running pytest-play and that there is a docker container ready to be used on Docker Hub? See a complete and working example here https://davidemoro.blogspot.com/2019/02/api-rest-testing-pytest-play-yaml-chuck-norris.html)


The docker build/publish stuff lives on another repository, so https://github.com/davidemoro/pytest-play-docker is the repository that implements the Docker releasing workflow for https://github.com/pytest-dev/pytest-play on Docker Hub (https://hub.docker.com/r/davidemoro/pytest-play).


This is the highly automated workflow at this time of writing for the pytest-play publishing on Docker Hub:

All tests executions run against the docker build so there is a warranty that what is pushed to Docker Hub works fine (it doesn’t check only that the build was successful but it runs integration tests against the docker build), so no versions incompatibilities, no integration issues between all the integrated third party pytest-play plugins and no issues due to the operative system integration (e.g., I recently experienced an issue on alpine linux with a pip install psycopg2-binary that apparently worked fine but if you try to import psycopg2 inside your code you get an unexpected import error due to a recent issue reported here https://github.com/psycopg/psycopg2/issues/684).

So now every time you run a command like the following one (see a complete and working example here https://davidemoro.blogspot.com/2019/02/api-rest-testing-pytest-play-yaml-chuck-norris.html):

docker run –rm -v $ (pwd):/src davidemoro/pytest-play

you know what was the workflow for every automated docker push for pytest-play.


Many thanks to Andrea Ratto for the 10 minutes travis build speedup due to Docker cache, from ~11 minutes to ~1 minute is a huge improvement indeed! It was possible thanks to the docker pull davidemoro/pytest-play command, the build with the –cache-from davidemoro/pytest-play option and running the longest steps in a separate and cacheable step (e.g., the very very long cassandra-driver compilation moved to requirements_cassandra.txt will be executed only if necessary).

Relevant technical details about pytest-play-docker follows (some minor optimizations are still possible saving in terms of final size).


sudo: required
– docker
– …

  – IMAGE_NAME=davidemoro/pytest-play
  – secure: …
– …

– travis_wait docker pull python:3.7
– travis_wait docker pull “$ IMAGE_NAME:latest”
– travis_wait 25 docker build –cache-from “$ IMAGE_NAME:latest” -t “$ IMAGE_NAME” .
– docker run -i –rm -v $ (pwd)/tests:/src –network host -v /var/run/mysqld/mysqld.sock:/var/run/mysqld/mysqld.sock $ IMAGE_NAME –splinter-webdriver=remote
  –splinter-remote-url=$ REMOTE_URL
  provider: script
  script: bash docker_push
    branch: master


echo “$ DOCKER_PASSWORD” | docker login -u “$ DOCKER_USERNAME” –password-stdin
docker tag “$ IMAGE_NAME” “$ IMAGE_NAME:latest”
docker push “$ IMAGE_NAME”:”$ TRAVIS_COMMIT”
docker push “$ IMAGE_NAME”:latest


Any feedback will be always appreciated.

Do you like the Docker hub push process for pytest-play? Let me know becoming a pytest-play stargazer! Star
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Ian Ozsvald: “discover feature relationships” – new EDA tool

I’ve built a new Exploratory Data Analysis tool, I used it in a few presentations last year with the code on github and have now (finally) published it to PyPI.

The goal is to quickly check in a DataFrame using machine learning (sklearn’s Random Forests) if any column predicts any other column. I’m interested in the question “what relationships exist in my data” – particularly if I’m working in an unknown domain and on new data. I’ve used this on client projects during the discovery phase to learn more about the sort of questions I should ask a client.

The GitHub Readme includes a screenshot which will give you an idea using the Titanic classification and Boston regression examples.

This is a very light project at the moment, I think the idea has value, I’m very open to feedback.

Ian is a Chief Interim Data Scientist via his Mor Consulting. Sign-up for Data Science tutorials in London and to hear about his data science thoughts and jobs. He lives in London, is walked by his high energy Springer Spaniel and is a consumer of fine coffees.

The post “discover feature relationships” – new EDA tool appeared first on Entrepreneurial Geekiness.

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