Decentralized ML Use Case

This page details our Decentralized ML Use Case on Cardano.

See project scoping discussion and implementation with external stakeholders and full description as a Cardano Catalyst Fund8 proposal on the Catalyst platform. Read our blog on NuNet Public Alpha Testnet for more information on the Decentralized ML use case. Please read NuNets Disclaimer before installing any software on your devices.

Public Alpha is released with the ML use case, which allows users to run simple open source machine learning training on NuNet on-boarded computers and pay for the compute in NTX. We can use widely-used machine learning libraries, such as TensorFlow, PyTorch, and scikit-learn, ensuring that users can effortlessly integrate their preferred tools and frameworks. Moreover, the platform provides the flexibility to run jobs on either CPUs or GPUs, catering to various computational needs and budget constraints.

Designed with a user-centric approach, the Service Provider Dashboard has a simple interface that allows users to easily submit their ML models, define resource usage based on their job requirements, and keep track of their job's progress in real-time. This level of transparency and control empowers users to manage their machine learning jobs effectively and efficiently, ultimately facilitating and accelerating the development & deployment of innovative AI solutions.

For Public Alpha we implemented a smart contract on the PreProd Cardano Network to lock service provider NTX funds and reward compute provider users for the use of their resources.

You need to choose one role to play on this use case: you can be a service provider that will requires to run a ML job on NuNet’s decentralized community hardware or you can be a compute provider who has on-boarded their devices onto the NuNet platform and will be compensated in NTX (NuNet’s Utility Token) for running the ML job requested by some service provider.

During this testing you can contribute in NuNet's development by reporting bugs and suggesting improvements. Please, refer to this documentation about the contribution guidelines: https://gitlab.com/nunet/documentation/-/wikis/Contribution-Guidelines

You can also connect with us on Discord at: https://discord.gg/pg5BnFM89n