Training Custom Models

OmniTensor offers a decentralized, scalable platform for training custom AI models using its community-powered infrastructure. This section will guide you through the process of setting up, training, and deploying your custom AI models on OmniTensor's decentralized network, ensuring efficiency, cost-effectiveness, and interoperability across blockchain and AI applications.

1. Setting Up the Training Environment

To begin training your custom model, ensure your system meets the necessary prerequisites:

• Hardware - Access to GPUs is recommended, but OmniTensor's decentralized infrastructure allows you to tap into the community's shared GPU resources (DePIN) for additional computational power.

• SDK - Install the OmniTensor SDK, which includes a comprehensive suite of tools for model training, validation, and deployment.

• Blockchain Wallet - Ensure you have a valid OmniTensor wallet with enough OMNIT tokens to cover transaction costs during training and deployment phases.

To install the OmniTensor SDK, use the following terminal command:

pip install omnitensor-sdk

Once installed, initialize the SDK with your credentials:

omnitensor-cli init --wallet-address <your-wallet-address> --token <your-api-token>

2. Model Preparation

OmniTensor supports a wide range of AI model architectures including transformers, convolutional networks, and LLMs (Large Language Models). You can either upload a pre-existing model or create a new one tailored to your dataset.

For example, to prepare a PyTorch-based custom model:

import torch
import torch.nn as nn

class CustomModel(nn.Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.layer1 = nn.Linear(128, 256)
        self.layer2 = nn.Linear(256, 10)

    def forward(self, x):
        x = torch.relu(self.layer1(x))
        x = self.layer2(x)
        return x

model = CustomModel()

Ensure that your model is saved in a format compatible with the OmniTensor platform, such as .pt for PyTorch models or .h5 for TensorFlow models.

3. Data Management

Training custom AI models on OmniTensor requires high-quality data. You can either upload your dataset or utilize datasets available from the community's Data Layer, which offers pre-validated, high-quality data sources.

To upload your dataset for model training:

omnitensor-cli dataset upload --path <your-dataset-path>

Alternatively, you can source datasets from the decentralized Data Layer:

omnitensor-cli dataset fetch --category <data-category>

4. Initiating Model Training

OmniTensor leverages its decentralized GPU network for efficient model training. You can select how much computational power to dedicate to your task, and training jobs are distributed across available AI Compute Nodes.

To begin the training process:

omnitensor-cli model train --model-path <path-to-your-model> --dataset-id <dataset-id> --epochs <number-of-epochs> --batch-size <batch-size>

The platform will automatically allocate resources, using its Proof-of-Work (PoW) mechanism to validate compute efforts and distribute rewards. Each training task is verified via OmniTensor's DualProof consensus mechanism, ensuring secure and trustless operations across the decentralized network.

5. Monitoring and Managing Training Jobs

Throughout the training process, you can monitor the performance of your model, including metrics such as loss, accuracy, and computational usage. OmniTensor provides real-time feedback via its CLI and dashboard:

omnitensor-cli job status --job-id <job-id>

You will be able to track the progress of your training tasks, visualize performance metrics, and scale resources dynamically if needed.

6. Deploying Your Trained Model

Once training is complete, the model can be deployed on OmniTensor's Decentralized Inference Network. This network ensures that your model is accessible and can perform real-time inference tasks across multiple blockchain platforms.

To deploy your trained model:

omnitensor-cli model deploy --model-path <path-to-trained-model> --inference-network

Once deployed, your model can be accessed by other users or dApps via the AI Marketplace. You can set royalties and earn OMNIT tokens based on the model's usage.

7. Fine-Tuning and Updating Models

If your model requires further refinement, OmniTensor allows for model fine-tuning on custom datasets. You can either fine-tune directly on your dataset or employ community-sourced data from the OmniTensor Data Layer.

omnitensor-cli model fine-tune --model-id <model-id> --dataset-id <dataset-id> --epochs <fine-tune-epochs>

8. Incentives and Tokenomics

As a developer contributing to the AI ecosystem, you can earn OMNIT tokens for both the computational resources used during training and for making your model available in the AI Marketplace. OmniTensor's tokenomics ensure that training, deployment, and inference are all efficiently rewarded through decentralized compute resources.

By utilizing OmniTensor's decentralized infrastructure, you gain access to a cost-efficient, scalable, and community-powered environment for AI model training and deployment, removing the reliance on centralized providers and empowering a new era of decentralized AI solutions.