**ZkAGI SDK**
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**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:**
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### 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)`