From Gaming laptops and Video editing to training Advanced Gen AI models, GPUs play an important role. But what exactly is a GPU? How does it simplify our daily lives? 🧵 A thread (1/15) #RavenProTales

Talk about $raven from @raven_protocol followed by CZ, the one and only running distributed models training network out of all the AI projects

@bamboobee5 Hi, you can read it here: https://t.co/Ao0VlTaojA Enjoy :) 🤖

Deep insights

Fantastic write-up. Read it all. Would be fun to see more AI and Blockchain related tweets other than announcements too. Keep it up. $RAVEN is a winner.

An interesting thread on AI and Blockchain

For those of you looking for AI / blockchain projects. Great thread here

AI and Blockchain are changing the world for the better. ChatGPT, OpenAI's popular chatbot, has 100 million users in 60 days. But what do these terms mean? Let’s find out: (1/24)

✨ Announcing AigenML and AigenML Server! ✨ We are excited to announce the open-source release of AigenML and AigenML Server on Github! Our libraries are designed to simplify the process of creating AINFTs for AI models and provide robust encryption algorithms to protect your AI assets. Visit our GitHub repositories now to learn more and try it out for yourself! AigenML: https://github.com/aigenprotocol/aigenml AigenML Server: https://github.com/aigenprotocol/aigenml-server Aigen: https://github.com/aigenprotocol/aigen 🌟 Don't forget to star our repositories on Github! 🌟 💻 We welcome contributions from the community to help us improve our open-source libraries. If you are interested in contributing, please visit our Github repositories and check out issues.

@worldcoin trying to collect as much training data 🥹 We need distributed ML right now @raven_protocol

@raven_protocol has been working on something similar for years now. A #SETI like project where you can earn $RAVEN crypto tokens by adding your GPU to a computational grid connected by blockchain for both training and execution of models. https://t.co/8UMBNKPqQt

What is Parameter-Efficient Fine-Tuning? Fine-tuning Large models with trillions of parameters becomes infeasible on local devices. PEFT is a new technique to fine-tune large models by training some (small) model parameters and freezing most of them

We are excited to announce the next version of Ravenverse which supports concurrent training of a Requester Graph across multiple participating Providers. https://t.co/Oz2uyXHDwX

🚀🚀 New Release Announcement: GPU Support in Ravenverse 🚀🚀 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 Retweet: https://twitter.com/raven_protocol/status/1653022713827434499 Reshare: https://www.linkedin.com/posts/ravenprotocol_new-release-announcement-gpu-support-in-activity-7058791499802505216-NuV5 Like: https://medium.com/ravenprotocol/new-release-announcement-gpu-support-in-ravenverse-e64ae8499033

RT @acknoledger: 🚨 New video alert 🚨 Explore the benefits and challenges of decentralisation in AI with our latest video! 🤖🌐 @rbkasr @Kun…

🚀Announcement🚀 We are excited to announce a new iteration of Ravenverse!! In this release our primary focus has been on Selective Participation. The previous releases of Ravenverse and it’s supporting libraries relied on the fact that providers can connect to the network any time they wish and if any ongoing requester graphs are being executed in the moment, they will instantly participate and start receiving computations. However, this approach has some caveats. For instance, we observed that if there are no available pending requester graphs in our network, there is practically no point for providers to keep their systems up and running since they won’t be receiving any computations. Also, it was not guaranteed that all providers in our network will definitely receive computations if one requester were to execute their graph. In order to fix these shortcomings of our network, we are introducing a new paradigm in this release - Selective Participation. Release Notes Provider side changes (Ravpy v0.16) : https://pypi.org/project/ravpy/ 1. Providers must now manually participate in a specific graph. This means that providers no longer need to keep their systems running and wait indefinitely to receive computations. The list of available graphs for computation can be checked by a provider by running the Provider/list_available_graphs.py in Ravenverse repository. 2. A graph starts execution when its required number of participants are met. The table will show a bunch of information about each graph including System Requirements, Minimum Stake Required, currently Active Participants and the total number of Required Participants in the graph. Before participating in a graph, Providers must ensure that their system meets the system requirements of that specific graph and they must have enough tokens in their ravenverse account to stake. 3. Along with their earnings, the staked amount will be returned back to the provider once they have successfully computed their share of subgraphs and returned back the computation results. However, A portion of the staked amount will be deducted as penalty if the Provider disconnects from the network before computing all of their share of subgraphs. 4. Once all computations of a Provider are successfully completed, they will automatically get disconnected from the network and are again free to participate in any other new graph. They can also check their earnings on their ravenverse website dashboard. 5. Quality of life improvements like Upload/Download progress bars. 6. Improvements to the benchmarking procedure by utilising a dummy torch model. Requester side changes (Ravop v0.12): https://pypi.org/project/ravop/ 7. After compilation of their graph, requesters will now be shown the cost of the graph and the maximum number of allowable participants (providers). 8. The requester must set a required number of participants in the execute function. 9. Once the graph reaches execution stage, this number of required participants and currently active participants will be broadcasted to all Providers who wish to participate. 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!

RT @acknoledger: 🌟 Buckle up, folks! We're taking on the biggest challenge in AI - decentralisation - with @raven_protocol on Twitter Space…

RT @Ahkailash1: Day 2 ⁦@ETHDubaiConf⁩ From my talk on decentralization of artificial intelligence #deai #artificialintelligence https://…

RT @Ahkailash1: @raven_protocol @ETHDubaiConf Wanna use Raven's decentralized computing network to train your large AI models. Reach out to…

We here @ETHDubaiConf! 🇦🇪 #ETHDubai https://t.co/4SaaKSd9sQ

RT @Kadesh_bull: @RunemirQi @raven_protocol is one to be checked. Decentralized AI Training

RT @Ahkailash1: @SoumyaShah_ @shobhitic Decentralized computing is going to further democratize AI and will reduce the cost of model traini…

RT @Zblaba: @TheEliteCrypto @binance Indeed, @raven_protocol is one of the few projects that have been building in #AI and #ML on #BSC for…

RT @patrick__nehme: @NEST_Protocol @CryptoCity53 @iPolloverse @AlveyChain @4metas @raven_protocol @WhaleCoinTalk @BFEtoken @raven_protocol…

RT @SHERM8N: I get to speak with @Ahkailash1 about @raven_protocol every day. Tomorrow, you will too!

https://twitter.com/Ahkailash1/status/1628344574883270657?s=20

RT @Ahkailash1: Looking forward to discussing AI tools and blockchain in the "NESTRoundtable 26: #AI tools & Blockchain" Twitter space host…

RT @NEST_Protocol: 🔥NESTRoundtable 26: #AI tools & Blockchain ⏰ Feb 23, 2PM UTC Host: @NEST_Protocol x @CryptoCity53 x @iPolloverse 👏We'…

RT @erlingjohnsenDK: @Saboo_Shubham_ And the decentralized @raven_protocol that utilizes a distributed ledger without single point of entry…

RT @The_Mainlander: @sama Aye. #raven @raven_protocol which is a fully working decentralized #AI #ML BE and ready to go. 💪🤓💯

Hello everyone, We need a solidity expert to work on our contracts v2.0. Please connect us if you know someone who would be a good fit for this position.

RT @cryptoval_: Announcing the RAVEN/SPARTA Liquidity Pool! by @raven_protocol https://t.co/i6XzA4bMJ6

RT @Ahkailash1: @gauravmunjal We are building Raven Protocol, a decentralised computing protocol for artificial intelligence, creating a de…

RT @patrick__nehme: @AltcoinDailyio Raven Protocol is a future-oriented computing protocol that specializes in Decentralized Computing Para…

RT @Ahkailash1: Raven, it's time to fly 🚀 #ravenverse #ravenprotocol #deai

🚀🚀 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!

RT @gazDeAi: @milesdeutscher @raven_protocol can help fix this!! $raven https://t.co/rZaBPcXQwy

RT @LuxdeCentral: @IncomeSharks This!!! That’s why a bond @raven_protocol. Been building for 3/4 years!!

RT @gazDeAi: It's official, Ai season is here!! Open the floodgates to $sparta $raven pool @SpartanProtocol @raven_protocol https://t.co/B…

RT @hillsidechase: @raven_protocol 👀. One of my longest plays. Initially bought it back in 2021 because I’m a huge crow fan. Was stoked w…

RT @creativejace: @JaroCrypto @raven_protocol Click this picture to expand their description. 👀 https://t.co/w0cSaz38Ee

RT @SpartanProtocol: Recently, the $RAVEN : $SPARTA pool has seen an increase in swap volume, resulting in a high #RealYield APY which has…

RT @KingLeonidas_SP: Time to #OpenTheGates for the $RAVEN : $SPARTA pool @SpartanProtocol @raven_protocol https://t.co/34BQMklvwH

RT @The_Mainlander: @cryptocevo @raven_protocol is time. #DeAI fully function product suite for #AI platforms as their back end. 👀💯😎👍

RT @The_Mainlander: @doug_scott @tuga_ray @DreadBong0 @SeikatsuIO @bittensor_ @raven_protocol Committed and OGs, my man! #raven will fly! 😎…

RT @doug_scott: @tuga_ray @DreadBong0 @SeikatsuIO @bittensor_ @raven_protocol Super smart nice team who have just been heads down working…

RT @tuga_ray: @DreadBong0 @SeikatsuIO @bittensor_ What do you think about @raven_protocol ? This was the first project from 2019 I heard wa…

RT @Ahkailash1: @raven_protocol @ Taipei Blockchain Week 2022 🚀🔥 #TBW2022 #ravenprotocol #decentralizedcomputing #dai https://t.co/sV2r6gO…

RT @bamboobee5: @devloper_xyz @raven_protocol it is easy, run the computation on Raven protocol