Announcement. We are excited to announce a new release of Ravenverse.
10 Feb 2023, 14:03
🚀🚀 Announcement! 🚀🚀
We are excited to announce a new release of Ravenverse!
Introducing the revamped versions of Ravpy, RavDL, Ravop libraries.
1. Ravpy (v0.15):
2. RavDL (v0.10):
3. Ravop (v0.11):
We have included helper codes in our Ravenverse GitHub Repository for Requesters and Providers.
Ravenverse GitHub:
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:
Enjoy the new release!
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10 Feb 2023, 14:36
🚀🚀New Release Announcement🚀🚀
Introducing the revamped versions of Ravpy, RavDL, Ravop libraries
New Release Announcement. Introducing the revamped versions of Ravpy, RavDL, Ravop libraries.
🚀🚀New Release Announcement🚀🚀
Introducing the revamped versions of Ravpy, RavDL, Ravop libraries
https://t.co/GScd5cF71S