OmniTensor
  • Welcome
  • Introduction
    • What is OmniTensor?
    • Vision & Mission
    • Key Features
    • Why OmniTensor?
      • Current Challenges in AI
      • How OmniTensor Addresses These Challenges
  • Core Concepts
    • AI Grid as a Service (AI-GaaS)
      • Overview of AI-GaaS
      • Benefits of AI-GaaS
      • Use Cases of AI-GaaS
    • Decentralized Physical Infrastructure Network (DePIN)
      • GPU Sharing Model
      • Incentive Mechanisms
    • AI OmniChain
      • Layer 1 and Layer 2 Integration
      • AI Model Marketplace and Interoperability
    • DualProof Consensus Mechanism
      • Proof-of-Work (PoW) for AI Compute
      • Proof-of-Stake (PoS) for Validation
    • OMNIT Token
      • Overview
      • Utility
      • Governance
  • Tokenomics
    • Token Allocations
    • Token Locks
    • ERC20 Token
    • Audit
  • OmniTensor Infrastructure
    • L1 EVM Chain
      • Overview & Benefits
      • Development Tools & API
    • AI OmniChain
      • Interoperability
      • Scalability
      • Decentralized Data & Model Management
    • Nodes & Network Management
      • AI Consensus Validator Nodes
      • AI Compute Nodes (GPUs)
  • Roadmap & Updates
    • Roadmap
    • Future Features
  • PRODUCTS
    • AI Model Marketplace
    • dApp Store
    • Data Layer
    • Customizable Solutions
    • AI Inference Network
  • For the Community
    • Contributing to OmniTensor
      • Sharing Your GPU
      • Data Collection & Validation
    • Earning OMNIT Tokens
      • Computation Rewards
      • Data Processing & Validation Rewards
    • Community Incentives & Gamification
      • Participation Rewards
      • Leaderboards & Competitions
  • For Developers
    • Building on OmniTensor
      • dApp Development Overview
      • Using Pre-trained AI Models
    • SDK & Tools
      • OmniTensor SDK Overview
      • API Documentation
    • AI Model Training & Deployment
      • Training Custom Models
      • Deploying Models on OmniTensor
    • Decentralized Inference Network
      • Running AI Inference
      • Managing and Scaling Inference Tasks
    • Advanced Topics
      • Cross-Chain Interoperability
      • Custom AI Model Fine-Tuning
  • For Businesses
    • AI Solutions for Businesses
      • Ready-Made AI dApps
      • Custom AI Solution Development
    • Integrating OmniTensor with Existing Systems
      • Web2 & Web3 Integration
      • API Usage & Examples
    • Privacy & Security
      • Data Encryption & Privacy Measures
      • Secure AI Model Hosting
  • Getting Started
    • Setting Up Your Account
    • Installing SDK & CLI Tools
  • Tutorials & Examples
    • Building AI dApps Step by Step
    • Integrating AI Models with OmniTensor
    • Case Studies
      • AI dApp Implementations
      • Real-World Applications
  • FAQ
    • Common Questions & Issues
    • Troubleshooting
  • Glossary
    • Definitions of Key Terms & Concepts
  • Community and Support
    • Official Links
    • Community Channels
  • Legal
    • Terms of Service
    • Privacy Policy
    • Licensing Information
Powered by GitBook
On this page
  • Step 1: Prerequisites
  • Step 2: Setup Environment
  • Step 3: Deploying a Pre-Trained AI Model
  • Step 4: Training a Custom AI Model
  • Step 5: Deploying Custom Models
  • Step 6: Running Inference on Deployed Models
  • Step 7: Managing AI Models
  1. Tutorials & Examples

Building AI dApps Step by Step

This tutorial will walk you through the process of integrating AI models with the OmniTensor decentralized platform. Leveraging OmniTensor's infrastructure, you can deploy pre-trained or custom AI models into a decentralized AI-powered dApp. OmniTensor provides tools for easy deployment, model management, and decentralized inference via its AI OmniChain. This guide assumes familiarity with blockchain concepts and AI model development, with a focus on seamless integration using OmniTensor's SDK and CLI tools.

Step 1: Prerequisites

Before we start, ensure you have the following:

  • OmniTensor SDK: Install the latest version from the OmniTensor GitHub repository or via CLI.

  • OmniTensor CLI Tools: Provides commands for managing deployments, models, and nodes.

  • Access to a Decentralized GPU Node: You can contribute your own GPU or use available decentralized nodes on OmniTensor.

  • OMNIT tokens: Required for deployment transactions and model execution.

  • Pre-trained or custom AI model: Choose a model from OmniTensor's AI marketplace or use your own.

Step 2: Setup Environment

  1. Install OmniTensor SDK:

    To begin with, install the OmniTensor SDK by running the following command in your terminal:

    pip install omnitensor-sdk

    Once installed, verify the installation using:

    omnitensor-sdk --version
  2. Set Up Your CLI Credentials:

    You'll need to authenticate your OmniTensor account via the CLI. Run:

    omnitensor-sdk auth --key <your-private-key>

    This will link your account to the OmniTensor network, enabling you to manage AI models and dApps.

  3. Connect to the OmniTensor Node:

    If you are contributing your own GPU, connect your hardware by running:

    omnitensor-sdk node connect --gpu

    If you are using a decentralized GPU, you can list available nodes by running:

    omnitensor-sdk node list --available

    Select a node and connect to it using the node ID.

    omnitensor-sdk node connect --node-id <node-id>

Step 3: Deploying a Pre-Trained AI Model

  1. Browse AI Model Marketplace:

    OmniTensor offers a wide selection of pre-trained models. To view available models:

    omnitensor-sdk models list

    You’ll see models like Mistral, Vicuna, and LLaMA. Select one by noting its model ID.

  2. Deploy the Model:

    Deploy the model to your dApp by executing:

    omnitensor-sdk models deploy --model-id <model-id> --gpu-node <node-id>

    This command registers the model with the chosen GPU node. You’ll be prompted to confirm the deployment cost in OMNIT tokens. Once confirmed, the deployment will begin, and you’ll receive a deployment ID.

  3. Monitor Deployment Status:

    To check the deployment status, run:

    omnitensor-sdk models status --deployment-id <deployment-id>

    This ensures your model is live and ready to handle AI inference tasks.

Step 4: Training a Custom AI Model

  1. Prepare Your Training Dataset:

    If you are deploying a custom AI model, prepare your dataset. Ensure it is validated and pre-processed. OmniTensor supports community-validated datasets, or you can upload your own.

    Upload your dataset to OmniTensor’s decentralized storage:

    omnitensor-sdk dataset upload --path ./your-dataset.csv
  2. Train the Model:

    Once the dataset is uploaded, initiate the training process:

    omnitensor-sdk models train --dataset-id <dataset-id> --gpu-node <node-id>

    This initiates the training across the decentralized GPU network, utilizing available resources.

  3. Monitor Training Progress:

    Check the training progress in real-time:

    omnitensor-sdk models training-status --training-id <training-id>

    Upon completion, the model will be automatically available for deployment.

Step 5: Deploying Custom Models

  1. Package and Deploy the Model:

    After training, package your custom model for deployment:

    omnitensor-sdk models package --model-id <training-id>

    Deploy the packaged model to a decentralized GPU node:

    omnitensor-sdk models deploy --model-id <model-id> --gpu-node <node-id>

    Again, confirm the OMNIT transaction to deploy your model.

Step 6: Running Inference on Deployed Models

  1. Invoke Inference:

    After deployment, you can run inference requests against the deployed AI model:

    omnitensor-sdk inference run --model-id <model-id> --input-data <input-file.json>

    This sends a request to the decentralized node and returns the AI model’s output.

  2. Optimize Parameters:

    You can fine-tune inference by adjusting parameters like temperature and context in the request:

    omnitensor-sdk inference run --model-id <model-id> --input-data <input-file.json> --temperature 0.7 --context "custom-context"

Step 7: Managing AI Models

  1. Update or Retrain Models:

    You can update or retrain models as needed. To retrain:

    omnitensor-sdk models retrain --model-id <model-id> --dataset-id <dataset-id>
  2. Scale Model Inference:

    To scale inference across multiple GPU nodes:

    omnitensor-sdk models scale --model-id <model-id> --nodes <node-list
PreviousInstalling SDK & CLI ToolsNextIntegrating AI Models with OmniTensor

Last updated 8 months ago