Integrating AI Models with OmniTensor

This tutorial demonstrates how to integrate AI models into the OmniTensor decentralized infrastructure. You will deploy a pre-trained AI model, customize its parameters, and utilize OmniTensor's decentralized inference network for seamless AI task execution. By leveraging OmniTensor's SDK and blockchain-powered network, you gain scalable, cost-efficient, and decentralized AI services.

Step 1: Setting up OmniTensor SDK

First, ensure that the OmniTensor SDK is installed on your local environment. The SDK provides essential tools for interacting with the OmniTensor platform, including APIs for AI model integration and deployment.

1. Install SDK

   Open your terminal and run the following command to install the OmniTensor SDK:

   pip install omnitensor-sdk

2. Initialize the SDK

   After installation, initialize the SDK in your project directory:

   omnitensor-sdk init

   This command sets up the project structure and necessary configuration files. It also links your local environment to the OmniTensor decentralized AI network.

Step 2: Choose and Download a Pre-trained AI Model

OmniTensor provides a wide range of pre-trained AI models available in the decentralized AI marketplace, such as language models, computer vision models, and text-to-speech (TTS) models.

1. Explore AI Models in the Marketplace

   Use the following API call to list available pre-trained models on the OmniTensor network:

   from omnitensor_sdk import AIModelMarketplace
   
   marketplace = AIModelMarketplace()
   available_models = marketplace.list_models()
   print(available_models)

2. Download a Model

   Once you have selected an AI model (e.g., a language model based on GPT), you can download it as follows:

   model_id = "gpt3-textgen"
   model = marketplace.get_model(model_id)
   model.download()

Step 3: Deploy the AI Model on the OmniTensor Network

Now that you have your AI model, it’s time to deploy it on the OmniTensor decentralized infrastructure. You will need to specify the inference parameters and assign compute nodes from the decentralized inference network.

1. Set Model Inference Parameters

   Customize the inference parameters such as context, temperature, and maximum token limit. This allows for flexibility in the AI model’s behavior:

   inference_params = {
       "context": "The current state of decentralized AI is...",
       "temperature": 0.7,
       "max_tokens": 150
   }

2. Deploy to the Decentralized Inference Network

   Once the parameters are set, deploy the model to the OmniTensor decentralized GPU network for inference:

   from omnitensor_sdk import InferenceNetwork
   
   inference_network = InferenceNetwork()
   result = inference_network.run_inference(model_id, inference_params)
   print(result)

Step 4: Monitoring Inference Tasks

OmniTensor offers real-time monitoring of AI inference tasks to ensure efficient execution across distributed GPU nodes.

1. Track the Inference Status

   You can monitor the task status, track token usage, and view the processing nodes:

   task_id = result["task_id"]
   task_status = inference_network.get_status(task_id)
   print(task_status)

2. Retrieve Inference Results

   Once the task is complete, retrieve the output from the network:

   output = inference_network.get_result(task_id)
   print("AI Model Output:", output)

Step 5: Scaling with OmniTensor

For production environments, OmniTensor’s decentralized infrastructure automatically scales to meet demand. The platform ensures optimal load balancing across its GPU nodes, which can be expanded based on usage.

1. Set Up Auto-Scaling

   You can configure automatic scaling for inference tasks. This ensures that as the demand for your AI model grows, more compute resources are allocated:

   inference_network.enable_auto_scaling(max_gpus=10)

2. Monitor Resource Utilization

   Track resource usage, such as GPU power and latency, in real time using OmniTensor’s dashboard or via API calls:

   resource_stats = inference_network.get_resource_usage()
   print(resource_stats)

Step 6: Secure Data Handling

For sensitive AI tasks, OmniTensor offers privacy-enhanced deployments using private cloud setups. This ensures that your data and model computations are not processed on the public network but in a secured environment.

1. Request Private Cloud Deployment

   If you require private handling of AI model data, request a private cloud setup:

   omnitensor-cli request-private-cloud

   You will be contacted by OmniTensor’s support team to configure your private environment.

Step 7: Earn OMNIT Tokens by Contributing AI Models

You can also contribute your custom AI models to the OmniTensor marketplace. By doing so, you earn OMNIT tokens whenever your model is used by others on the network.

1. Contribute an AI Model

   Submit your custom AI model to the decentralized marketplace:

   my_model = "/path/to/my_custom_model"
   marketplace.submit_model(my_model, metadata={
       "description": "Custom GPT-based model for text generation",
       "category": "language_model"
   })

   Upon approval, your model will be listed in the OmniTensor marketplace for others to use.