New Release Announcement: GPU Support in Ravenverse

Photo by Nana Dua on Unsplash

🚀🚀 Release Notes 🚀🚀

We are excited to introduce GPU support to Ravenverse! Requesters can now have complex Graphs computed by Providers that can contribute GPU resources.

Requester Side Changes:

• Requesters can request for GPU resources by specifying the gpu_required=“yes” parameter in the R.Graph function during Graph definition.
• Based on the complexity of the Graph, Ravsock generates minimum system and GPU requirements and only allows Providers meeting those requirements to participate.
• Requesters can now directly load PyTorch models into RavDL by using the R.model op. This op takes the path to the TorchScript file of the PyTorch model as a parameter and allows distributed training and inference via the Ravenverse.
• Requesters can now persist their entire R.model and retrieve from the Ravenverse post training for further use
• Significant speed up in compilation, distribution and computation observed.

RavDL: https://github.com/ravenprotocol/ravdl

Provider Side Changes:

• Improvements in connectivity checks
• list_graphs functions can be used to check for GPU requirements along with system requirements
• Providers can only participate in graphs for which they meet the minimum requirements
• Computation speed and efficiency increase observed
• Memory Usage optimised

Ravpy: https://github.com/ravenprotocol/ravpy

We have included helper codes in our Ravenverse GitHub Repository for Requesters and Providers.

Ravenverse GitHub: https://github.com/ravenprotocol/ravenverse

You can find the documentation for each library in the respective repo Readme files. Please try them out and let us know if you run into any problems.

Raven Protocol GitHub: https://github.com/ravenprotocol

Enjoy the new release! ❤️

— The Raven Protocol Team

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