Activity of opentensor/bittensor repository


Contribution activity is growing

Activity badge for opentensor/bittensor repository

Why opentensor/bittensor is evolving?

The result is based on ratio of number of commits and code additions from initial and final time ranges.

Initial time range – from 5 Jul, 2023 to 5 Oct, 2023

Final time range – from 5 Apr, 2024 to 5 Jul, 2024

From 1 to 25 commits per week
From 3443 to 2327 additions per week
From 3775 to 1231 deletions per week
Data calculated on 5 Jul, 2024

Summary of opentensor/bittensor

Opentensor/bittensor is a GitHub repository used for developing an open-source peer-to-peer machine learning network known as Bittensor.

Bittensor, in essence, enables an incentivized, distributed network for machine learning. This AI development is set to foster an environment where developers can earn cryptocurrency tokens by participating in the building and maintenance of a global, decentralized neural network.

This repository provides the tools, examples, and documentation necessary for developers to engage with Bittensor network including creating applications that interact with the network, contributing to the network's training, or learning from the global model.

The network protocol used by Bittensor utilizes blockchain incentives in the form of its native cryptocurrency to stimulate the production of more intelligent federated learning models.

The main features include:

  • Wallet: A utility for generating cold-stake Bittensor wallets, with capabilities to check balances and send transactions.
  • Neuron: A base class for all Bittensor applications.
  • Metagraph: A peer-to-peer datastore containing information about the Bittensor network, including peers and stake weights.
  • Dendrite: An object for interfacing with the Bittensor network.
  • Axon: A server object for serving machine learning models to the network.
  • Subtensor: An interface to the subtensor chain for neuron roles such as staking, or producing blocks.

In this vibrant community, there are plentiful opportunities for contributions; from improving documentation, developing models, optimizing the P2P system, to designing user interfaces. It's a brilliant initiative to enhance and reimagine the potentials of machine learning.

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