How to bootstrap an open source project – talk at KubeCon

This week I’m attending the KubeCon conference in Seattle. The conference’s full name is KubeCon + CloudNativeCon North America 2018. I’m taking notes from some of the sessions I attend. Any mistakes in these notes are my own, and all credit goes to the presenters.

Peter MacKinnon talked about bootstrapping Kubeflow, an open source project that aims to provide stability, composability, and portability for machine learning workflows. His talk was entitled “Eco-Friendly ML: How the Kubeflow Ecosystem Bootstrapped Itself“. Peter discussed how an open source project can rapidly achieve its potential by working with other projects, and how those inter-project collaborations enrich the entire Kubernetes community.

About Kubeflow

Peter gave a quick overview of Kubeflow, and how it helps people develop a machine learning (ML) pipeline. The goal of the Kubeflow project is to enable machine learning for everyone. This is a difficult problem to solve.

The Kubeflow team started with a mission statement: The ML pipeline should be portable (from bare metal to cloud), scalable, and composable (a micro-service architecture).

Then the team decided on a baseline platform – Kubernetes fits the bill. From this decision came the goal:

Anywhere you can run Kubernetes, you can run Kubeflow.

Open is key

To bootstrap an open source project, get in touch with other communities and see what they’re working on and whether their goals align with yours. Look at blogs, forums, conferences. Talk to people, in person, at conferences, on Slack. Get involved. Go deeper on GitHub – raise issues, contribute to your chosen projects by raising pull requests.

Observe the open source etiquette. Get your git technique right, help other people with those techniques, and respect the community of the projects you’ve chosen.

The Kubeflow tech

Peter talked us through some of the technical projects that Kubeflow has integrated with. The examples he gave were ksonnet, Ambassador, Argo, JupyterHub, Kaggle, RAPIDS AI at NVIDIA, TensorFlow, Pachyderm, Arrikto, and SeldonIO. You can see some of the details in the Kubeflow docs and on GitHub.

The Kubeflow team holds open conversations with these related projects, so that they can work together to develop solutions that are advantageous to all.

What makes a good community?

Kubeflow has a strong code of conduct, based on the Cloud Native Computing Foundation (CNCF) code, with the goal of ensuring a harassment-free experience for everyone. They have documented community standards and conflict resolution protocols, and they work with other communities in their ecosystem to support the same standards.

The power of open source

Peter says Kubeflow is a great community. It’s only a year old, and he has a lot of fun there. Everyone wins when communities collaborate, and Peter encourage contributions and ideas from other communities. Open source communities, when they work well, give contributors a sense of empowerment and achievement beyond what they do in their day job.

Thanks

Thanks Peter for an engaging look at a new open source community.

About Sarah Maddox

Technical writer, author and blogger in Sydney

Posted on 13 December 2018, in open source and tagged , . Bookmark the permalink. 1 Comment.

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