Why we open sourced our documentation
We launched Grid, a platform for developing and training machine learning models at scale, a few months ago and have received a great response from our already thriving Pytorch Lightning community. We are thankful for this support!
For an ML researcher or data scientist to be able to iterate on their ideas and algorithms fast, Grid gives them a platform where they don’t have to wait for infrastructure to be available or configured to scale easily.
As the Grid user base is growing, we are seeing feature requests specific to the workflows of researchers and data scientists and tricks people are finding useful when using the platform. Some community members expressed interest in adding their own examples to our documentation, and we thought that was a great idea! No one knows your code better than you, and why not empower you to add the example so others can benefit from it and you can share your work.
The platform is proprietary and accessible by registering on grid.ai, but we decided to open source the documentation to:
- Help users contribute their examples and tutorials
- Enable the community to benefit from rich documentation no matter what research or application they are working on
Our documentation is hosted on GitBook but has an integration with GitHub, so we decided to take advantage of that.
Register at grid.ai to get started