New Release Announcement: Introducing the revamped versions of Ravpy, RavDL, Ravop libraries
🚀🚀 Announcement! 🚀🚀
We are excited to announce a new release of Ravenverse!
Introducing the revamped versions of Ravpy, RavDL, Ravop libraries.
1. Ravpy (v0.15): https://pypi.org/project/ravpy/
2. RavDL (v0.10): https://pypi.org/project/ravdl/
3. Ravop (v0.11): https://pypi.org/project/ravop/
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.
Release Notes:
Ravdl (v0.10)
- Massive performance improvement in Graph computations and model training, thanks to a revamped approach to the backpropagation algorithm.
- Added a collection of new layers to the library along with provision for defining Custom layers.
- Apart from Sequential Models, Requesters can now create Functional Custom Models. This will allow them to deploy state of the art complex models (like GPT variants) with ease.
- Many new Activation functions added to the library.
- Deprecated the use of the old ravdl.v1 API. The new ravdl.v2 API is much more intuitive and powerful to use.
- Refactoring changes to the ravdl codebase.
- Updated RavDL readme with a detailed tutorial on how to use the new ravdl.v2 API.
Ravpy (v0.15)
- Added support for new deep learning, machine learning and math ops.
- Optimized model training computations.
- The mathematical backend for ravpy has been shifted to Pytorch. This will allow us to leverage the power of GPU acceleration and other features in future releases.
- FTP broken-pipe connection issues resolved.
- Execution of Backpropagation Algorithm has been optimized for better performance.
- Refactoring changes to the ravpy codebase.
- Minimized the probability of subgraph failure. Improved the robustness of the system with better reassignment strategies in case of mid-computation internet connectivity issues.
Ravop (v0.11)
- Added support for new deep learning, machine learning and math ops.
- Requesters will now receive the exact error messages in case their graph fails.
- The post-execution results of computed ops can now be fetched as torch.Tensor objects.
Ravsock & Scheduler-Service
- Major improvements to the scheduler-service. The scheduler-service now uses a more efficient algorithm to generate and assign subgraphs to workers. This will result in faster execution of subgraphs.
- Improved handling of failed and redundant subgraphs. Graphs will fail only after 5 re-attempts at execution.
- Optimized payload formation due to which the size of the payload has been significantly reduced.
- Error serving features for Requester.
- Dynamic cleanup for inessential data.
- Dynamic split size for graph splitting based on complexity.
- Revamped the communication channel between the scheduler-service and the ravsock server running on multiple worker threads.
- Added support for new deep learning, machine learning and math ops.
Raven Protocol GitHub: https://github.com/ravenprotocol
Enjoy the new release! ❤️
— The Raven Protocol Team
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