Getting Started

Getting Started with ZkSurfer AI

Welcome to ZkTerminal, your solution using advanced AI techniques. With ZkSurfer AI, you can automate setting up your node, sending email and telegram messages on your browser effortlessly. This guide will walk you through the installation and usage of ZkSurfer AI.

Table of Contents

• Installing and Running

• How it Works - The Action Cycle

• Tech Stack

• Supported Use Cases

• Resources

Installing and Running

Installing the Extension

ZkSurfer AI is currently available exclusively through our GitHub repository. Follow these steps to build and install the extension locally on your machine:

1. Ensure you have Node.js installed, preferably version 16 or later.

2. Clone the ZkSurfer AI repository from GitHub.

3. Navigate to the cloned repository directory.

4. Install the dependencies using Yarn:

   yarn install

5. Build the package:

   yarn build

6. Load the extension in Chrome:

   • Navigate to chrome://extensions/ in your Chrome browser.

   • Enable Developer mode.

   • Click on "Load unpacked extension" and select the build folder generated by yarn build.

Running in Your Browser

Once the extension is installed, you can access it in two forms:

• Popup: Press Cmd+Shift+Y (Mac) or Ctrl+Shift+Y (Windows/Linux), or click on the extension logo in your browser.

• Devtools Panel: Open the browser's developer tools and navigate to the ZkSurfer AI panel.

Next, you'll need to obtain an API key from Zynapse and paste it into the provided box within the extension. This key will be securely stored in your browser and will not be uploaded to any third-party servers.

Finally, navigate to the webpage you want ZkSurfer AI to automate actions on (e.g., the OpenAI playground) and start experimenting!

How it Works - The Action Cycle

ZkSurfer AI utilizes our custom launched transformer model and Zynapse API to control your browser and perform predefined or ad-hoc instructions. The action cycle involves capturing user instructions, processing them using our transformer model through Zynapse API, and executing the actions on the browser.

For more details on how to use ZkSurfer AI and its advanced features, refer to our GitHub repository and documentation.

Tech Stack

ZkSurfer AI is built using the following technologies:

• Node.js

• Chrome Extension API

• Custom Transformer Model

• Zynapse API

Certainly! Here's the updated list with a more concise format:

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Supported Use Cases

Node Setup Automation:

• Automated setup process for nodes, catering to both technical and non-technical users.

• Streamlined resource allocation for optimal node performance.

• Compatibility with various node configurations and networks.

Marketing Automation:

• Telegram scraping for data collection.

• Automated email outreach with personalized messaging.

• Bulk distribution capabilities for efficient marketing campaigns.

• Integration with popular messaging platforms like Telegram for direct messaging automation.

Leo-Code Generation:

• Code generation functionality for the Aleo network.

• Generation of secure and efficient code based on user input.

• Integration with Aleo development tools for seamless workflow.

Privacy-Preserving Image and Video Generation:

• Integration with Zynapse API for privacy-preserving image and video generation.

• Secure handling of user data and content.

• Support for various image and video formats and resolutions

Resources

• GitHub Repository: ZkSurfer AI Repository

• Documentation: ZkSurfer AI Documentation

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Getting Started with Decentralized GPU Clustering

Welcome to the world of decentralized GPU clustering, where you can contribute your GPU resources to a network for running heavy ML models in a privacy-preserving manner. This guide will walk you through the steps to set up your environment and start contributing to the network.

Prerequisites

Before you begin, ensure you have the following prerequisites installed:

• Docker

• Nvidia GPU with CUDA support

• Python 3.x

Setup

1. Installation

First, clone the decentralized GPU clustering repository:

Then, install the required Python packages:

pip install -r requirements.txt

2. Configuration

Navigate to the config directory and edit the config.yaml file to configure your settings. You can specify your Ethereum wallet address for receiving rewards.

3. Running the Dashboard

To access the dashboard and monitor GPU utilization, run the following command:

python dashboard.py

This will start the dashboard server. You can access the dashboard by opening your web browser and navigating to http://localhost:8080.

4. Contributing GPU Resources

To contribute your GPU resources to the network, run the following command:

python contribute.py

This will start your GPU node and connect it to the decentralized clustering network. Your GPU will now be available for running ML models.

Privacy-Preserved Computing

Our decentralized GPU clustering system utilizes zero-knowledge proofs (zkproofs) to ensure that computations are performed in a privacy-preserving manner. This means that while your GPU is contributing to the network and running ML models, your data and computations remain private and secure.

Fractional Computing

In addition to running full ML models, our system also supports fractional computing, allowing you to contribute fractional GPU resources to the network. This enables efficient utilization of GPU resources and maximizes the network's computational power.

Integration and Capablities

• Scalable libraries for common machine learning tasks such as data preprocessing, distributed training, hyperparameter tuning, reinforcement learning, and model serving.

• Pythonic distributed computing primitives for parallelizing and scaling Python applications.

• Integrations and utilities for integrating and deploying a cluster with existing tools and infrastructure such as Kubernetes, AWS, GCP, and Azure.

For data scientists and machine learning practitioners, it lets you scale jobs without needing infrastructure expertise:

• Easily parallelize and distribute ML workloads across multiple nodes and GPUs.

• Leverage the ML ecosystem with native and extensible integrations.

For ML platform builders and ML engineers:

• Provides compute abstractions for creating a scalable and robust ML platform.

• Provides a unified ML API that simplifies onboarding and integration with the broader ML ecosystem.

• Reduces friction between development and production by enabling the same Python code to scale seamlessly from a laptop to a large cluster.

For distributed systems engineers, it automatically handles key processes:

• Orchestration–Managing the various components of a distributed system.

• Scheduling–Coordinating when and where tasks are executed.

• Fault tolerance–Ensuring tasks complete regardless of inevitable points of failure.

• Auto-scaling–Adjusting the number of resources allocated to dynamic demand.

Conclusion

Congratulations! You have successfully set up your environment and started contributing to the decentralized GPU clustering network. You can now monitor GPU utilization, run heavy ML models, and contribute to the network's computational power in a privacy-preserving manner.

For more information and advanced usage, refer to the documentation or reach out to our community for support.

Happy computing!

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