neurion-sanctum 0.4.0

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Neurion Sanctum

Overview

Neurion Sanctum provides a secure, decentralized infrastructure for AI model training and dataset usage requests. It allows users to securely train AI models while ensuring data privacy using Trusted Execution Environments (TEE).

This library enables users to create and manage tasks, handling dataset usage requests seamlessly while abstracting the complexities of enclave execution and secure computing.

Features

• Task Management: Users can start AI model training tasks with ease.
• Secure Processing: The processor securely handles dataset usage requests using TEE-enabled AWS Nitro Enclaves.
• Automatic Model Upload: Trained models are uploaded to storage solutions like Hugging Face.
• Seamless Blockchain Integration: Automates blockchain-based dataset access requests.

Installation

Ensure you have Python 3.8+ installed. Then, install the required dependencies:

pip install -r requirements.txt

Usage

Running a Training Task

Users can start a training task with the following:

from neurion_sanctum.task.task import Task

def train_model(key: str):
    """
    Function to train a model using a secure dataset.
    """
    pass  # Implement your training logic here

def upload(data: dict):
    """
    Function to handle model uploads.
    """
    pass  # Implement your upload logic here

if __name__ == "__main__":
    Task.create_training_task(train_model, upload).start()

Running the Processor

The processor handles dataset usage requests and manages enclave execution:

from neurion_sanctum.processor.processor import Processor

if __name__ == "__main__":
    Processor.new().start()

The processor continuously checks for pending dataset usage requests and securely executes them within an enclave.

Environment Variables

Ensure the following environment variables are set before running:

NEURION_PRIVATE_KEY=<private_key>
NEURION_MNEMONIC=<mnemonic>
NEURION_NETWORK=alphanet
AWS_ACCESS_KEY=<your_aws_access_key>
AWS_SECRET_KEY=<your_aws_secret_key>
AWS_REGION=us-east-1
AWS_INSTANCE_TYPE=c6a.2xlarge
AWS_AMI=<your_aws_ami>
AWS_SECURITY_GROUP_NAME=<your_security_group>
AWS_INSTANCE_EBS_SIZE_IN_GB=100
AWS_ENCLAVE_IMAGE_SIZE_IN_MB=10000
AWS_ENCLAVE_ALLOWED_EGRESS=huggingface.co,cdn-lfs-us-1.hf.co,hf-hub-lfs-us-east-1.*.amazonaws.com
DOCKER_USERNAME=<your_docker_username>
DOCKER_TOKEN=<your_docker_token>

License

This project is licensed under the MIT License. See the LICENSE file for details.

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For further information, refer to the documentation or open an issue in the repository.