Open-Source

If you’re interested in supporting mybinder.org with cloud resources, financial resources, or human resources, please see the Support Binder page for how you can help. tl;dr: The 2i2c team is joining the mybinder.

When sharing a storage disk between users, as is usually the case in a JupyterHub deployment, it is important to put in guardrails so that one user cannot eat up the whole storage capacity from the rest of the users.

A key challenge in the open source space is that projects are often independent and autonomous, with relatively few formal ways to collaborate and coordinate efforts. While this usually isn’t a big deal, it means that there is a missed opportunity to grow the impact of an ecosystem because it requires coordinated development among multiple stakeholders within it.

In this post, we’re sharing our Commitment to Open Technology. It is focused on software licenses for reasons we’ll describe below. We hope that it clarifies what kind of licenses we’ll use, and assures our communities that we will not change our stance towards open source technology in the future.

2i2c is pleased to announce the frx-challenges project, a new open source tool to help communities host data challenges on shared infrastructure: 2i2c-org/frx-challenges This project aims to make it easier for administrators to provide a service that enables users to submit code and data that are evaluated on secure infrastructure with access to private data and resources.

On most research oriented JupyterHub installations, users would like to customize their server (the environment, resources available, etc) after logging in. In Kubernetes based JupyterHub environments, a profile list provides this functionality.

Cross-posted from the Jupyter Book blog. Note that some MyST functionality is not supported on the 2i2c website – please see the original post for previews. Over the last ten months, the Jupyter Book team have been hard at work; Jupyter Book has become a Jupyter subproject, and the team1 announced a plan to release Jupyter Book 2.

The DeepLabCut Team # Animal pose estimation using deep neural networks. Courtesy of the DeepLabCut Jupyter Book The DeepLabCut team is a group of researchers and developers who are working on open source tools for analyzing animal pose estimation by training deep neural networks on videos.

Thank you to Sajjad Anwar and Sanjay Bhangar for contributing to this post. The VEDA dashboard The 2i2c team are proud to continue our strong working collaboration with Development Seed, following our previous work on launching the US GHG center (also see the Development Seed blog post).