Metadata-Version: 2.3
Name: zynapse
Version: 0.1.2
Summary: ZkAGI SDK
Author-email: Lulzx <wiserishabh@gmail.com>
Requires-Python: >=3.8
Requires-Dist: ezkl>=10.4.2
Requires-Dist: fabric>=3.2.2
Description-Content-Type: text/markdown

**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)`