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
Install OmniTensor SDK:
To begin with, install the OmniTensor SDK by running the following command in your terminal:
Once installed, verify the installation using:
Set Up Your CLI Credentials:
You'll need to authenticate your OmniTensor account via the CLI. Run:
This will link your account to the OmniTensor network, enabling you to manage AI models and dApps.
Connect to the OmniTensor Node:
If you are contributing your own GPU, connect your hardware by running:
If you are using a decentralized GPU, you can list available nodes by running:
Select a node and connect to it using the node ID.
Step 3: Deploying a Pre-Trained AI Model
Browse AI Model Marketplace:
OmniTensor offers a wide selection of pre-trained models. To view available models:
You’ll see models like Mistral, Vicuna, and LLaMA. Select one by noting its model ID.
Deploy the Model:
Deploy the model to your dApp by executing:
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.
Monitor Deployment Status:
To check the deployment status, run:
This ensures your model is live and ready to handle AI inference tasks.
Step 4: Training a Custom AI Model
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:
Train the Model:
Once the dataset is uploaded, initiate the training process:
This initiates the training across the decentralized GPU network, utilizing available resources.
Monitor Training Progress:
Check the training progress in real-time:
Upon completion, the model will be automatically available for deployment.
Step 5: Deploying Custom Models
Package and Deploy the Model:
After training, package your custom model for deployment:
Deploy the packaged model to a decentralized GPU node:
Again, confirm the OMNIT transaction to deploy your model.
Step 6: Running Inference on Deployed Models
Invoke Inference:
After deployment, you can run inference requests against the deployed AI model:
This sends a request to the decentralized node and returns the AI model’s output.
Optimize Parameters:
You can fine-tune inference by adjusting parameters like
temperatureandcontextin the request:
Step 7: Managing AI Models
Update or Retrain Models:
You can update or retrain models as needed. To retrain:
Scale Model Inference:
To scale inference across multiple GPU nodes:
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