zynapse 0.1.1

ZkAGI SDK

ZkAGI SDK

Description: ZkAGI SDK is a Python library that provides a comprehensive framework for building scalable, secure, and privacy-preserving applications. It integrates GPU clustering, contribution tracking, ML/LLM models, and privacy-preserved infrastructure to enable developers to build robust and efficient systems.

Features:

• GPU Clustering: Efficiently distribute tasks across a GPU cluster using Ray, allowing for scalable and high-performance computing.
• Contribution Tracking: Track user contributions and usage metrics, and reward users with tokens based on their contributions. This feature enables developers to incentivize user engagement and participation.
• ML/LLM Models: Run ML and LLM models using popular libraries like scikit-learn, TensorFlow, and Hugging Face Transformers. This feature enables developers to build and deploy machine learning models at scale.
• Privacy-Preserved Infrastructure: Generate Zero-Knowledge Proofs (ZkProofs) internally to ensure privacy preservation and data confidentiality. This feature enables developers to build privacy-preserving applications that protect user data.

Getting Started:

Installation

To install the ZkAGI SDK, simply run the following command:

pip install zynapse

Importing the SDK

To use the ZkAGI SDK, import it into your Python script or application:

import zynapse

Creating a Frame Instance

Create a Frame instance to access the SDK's features:

frame = zynapse.Frame()

Connecting to a GPU Cluster

Connect to a GPU cluster using the gpu_cluster attribute:

frame.gpu_cluster.connect()

Running an ML Model

Run an ML model using the model attribute:

result = frame.model.run(data)

Tracking a User's Contribution

Track a user's contribution using the contribution attribute:

frame.contribution.track(user_id, metrics)

Generating a Zero-Knowledge Proof

Generate a Zero-Knowledge Proof using the privacy attribute:

zkproof = frame.privacy.generate_zkproof(data)