ZkAGI SDK

Work in Progress 👷

Description: ZkAGI SDK provides a comprehensive toolkit 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 TensorFlow, and custom model classes. 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:

from zynapse import Zynapse

Creating a Zynapse Instance

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

instance = Zynapse("cluster_url", "auth_token", "user_id")

Connecting to a GPU Cluster

Connect to a GPU cluster:

instance.connect()

Running an ML Model

Run an ML model on input data:

result, token_amount = instance.run(input)

Tracking a User's Contribution

Track a user's contribution:

instance.contributions.track("compute_time", execution_time)