#37 Paris Women in Machine Learning & Data Science: scikit-learn sprint
On March 12th, on a Saturday morning, we joined for our first scikit-learn sprint at CybelAngel! It was our first event during a week-end, and the first event to last a whole day, we are very proud that everything went well!
What is a scikit-learn sprint you may ask? The scikit-learn sprint is a hands-on “hackathon” where we work on issues in the scikit-learn GitHub repo and learn to contribute to open source. Did you know that, on a rough estimation, only 6% of open source contributors were women (study not made on scikit-learn)?! This is awfully low. The scikit-learn team really cares about diversity, gender being one of their focus, this why we decided to partner with them to help.
Under the guidance of no less than five people from the scikit-learn team, we set up our environments, learned how to use tools ranging from conda to git, black or pytest, and finally made our first pull requests!
If you don’t feel like solving issues and submitting pull request, another way to contribute to scikit-learn as a user is simply to open issues when you run into one!
For a full replay of the event, you can check Chloé Azencott’s twitter page and the #pariswimlds hashtag: she documented every step, from the tee-shirt distribution in the morning to the successful pull requests, without forgetting learning how to use VS Code and of course… the 🍕.
A couple of numbers from this sprint:
- Almost 30 pull requests
- 19 of them (when these lines are written) have been accepted already
- 19 participants
- 100% happiness and pride!
If you want to do this at home, here are a couple of links to be guided and starting to contribute to scikit learn:
- All the setup and guidelines were explained in specific github repository. It was crystal clear, and guided step to step: you can rely on it if you want to start.
- During this sprint, a couple of issues, and more exactly meta-issues (which are issues listing a problem in plenty of different places, to be fixed individually), were listed in a specific board. Although we made some progress, they are still open if you want to have a look!
- The mainteners and core contributers of scikit learn label some issues as “good first issue”. It’s a label to encourage people to step in with these easier issues. You can find them here.
- Scikit-learn has a webpage explaining how to contribute.
Some participants were interviewed during the sprint…
… And you can find it here already!
We would like to thank our mentors Olivier Grisel, Adrin Jalali, Béa Hernandez, and Gaël Varoquaux. Thank you for coming, sometimes from quite far, for being pedagogical, and reviewing and accepting our pull requests so quickly!
The organization of this sprint was made possible by scikit-learn community manager François Goupil, who was instrumental, making the connection between the mentors and us, the WiMLDS team.
Finally, thank you to CybelAngel for hosting, Giulia Bianchi for presenting CybelAngel and Marie Sacksick for the logistic.
Our next meetup is planned! It will take place on April 5th, and we are proud to have as speakers: Olesia Khrapunova, data scientist at L’Oréal, Irène Vignon Clémentel, research director at l’INRIA, and Anastasiia Tryputen data scientist and AI researcher. It will be at L’Oréal, in their new offices Beauty Tech (21 Place des Nations Unies, 92110 Clichy).
💌 You can subscribe on our meetup page!
Final good news: our BBL meetups are starting again! The next one will be on March 23rd. We will discuss “Modeling strict age-targeted mitigation strategies for COVID-19” written by Maria Chikina and Wesley Pegden, presented by the author Maria herself! 🎆
💌 See you on this event to register.
You can also:
📑 check our Google spreadsheet if you want to speak 📣, host 💙, help 🌠
📍join our Slack channel for more discussions about machine learning, data science, and diversity in tech!
📩 send an email to the Paris WiMLDS team to keep in touch >email@example.com
🔥 Share your company or lab’s job positions for free on WiMLDS’ website.