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BOLTZMANN: Bittensor Incentivized Scalable Training with Reward Optimization

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/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/unconst/boltzmann/master/run.sh)"

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This repository implements an incentive system for decentralized training on the Bittensor network.

Overview

BOLTZMANN is a framework where miners collaboratively train a shared model by processing specific subsets of data, and validators ensure the quality and integrity of these contributions. The incentive landscape is designed to reward miners for effectively training on their designated data subsets, promoting efficient and collaborative model improvement in a trustless environment.

How It Works

Miners

• Model Synchronization: Miners start by downloading the latest model state, which is a subset of model parameters (called slices) aggregated from other miners.
• Training: They receive a designated subset of data (pages) from the dataset for each window (a fixed number of blocks). They train the model on this subset, performing a single gradient update.
• Uploading Deltas: After training, miners compute the delta (the change in their model parameters) and upload this delta to an S3 bucket associated with their identity.
• Window Progression: Miners proceed to the next window, repeating the process with new data subsets.

Validators

• Model Synchronization: Validators synchronize their model state by downloading the latest aggregated slices and applying miners' deltas from the previous window.
• Fetching Deltas: Validators download the deltas uploaded by miners corresponding to the last window.
• Evaluation: Validators evaluate the miners' contributions by comparing the miners' deltas to the gradients computed locally on the same data subsets.
• Scoring: The reward for each miner is calculated based on the cosine similarity between the miner's delta and the validator's locally computed gradient. This incentivizes miners to provide genuine, high-quality updates that improve the model.

Incentive Mechanism Explained

The incentive mechanism in boltzmann ensures that miners are rewarded for authentic and beneficial contributions to the model's training. By basing rewards on the cosine similarity between the miners' updates and the validators' gradients, we promote alignment of miners' efforts with the overall training objectives.

Key Points

• Alignment of Objectives: Miners are motivated to perform authentic training on their assigned data subsets because providing updates that closely match the true gradient direction maximizes their rewards.
• Positive Contributions: By submitting deltas that positively impact the model's performance on the evaluation data, miners increase their rewards.
• Discouraging Malicious Behavior: Contributions that deviate significantly from the true gradient (e.g., random or adversarial updates) result in lower or negative rewards.
• Data Subset Specialization: Miners are evaluated based on their performance on specific data subsets, encouraging them to specialize and optimize their training for those subsets.
• Fairness: By not revealing which model slices need to be uploaded until the end of the window, all miners are on a level playing field, preventing exploitation of the system.

Mathematical Details

Notations

• $\theta$: Current model parameters.
• $\delta_i$: Delta (model update) contributed by miner $i$.
• $g_i$: Gradient of the loss with respect to the model parameters on the data subset assigned to miner $i$.
• $\hat{g}_i$: Validator's locally computed gradient on the same data subset.
• $s_i$: Cosine similarity score between $\delta_i$ and $\hat{g}_i$.
• $R_i$: Reward assigned to miner $i$.

Cosine Similarity Calculation

The cosine similarity between the miner's delta and the validator's gradient is calculated as:

$$ s_i = \frac{\delta_i \cdot \hat{g}_i}{|\delta_i| |\hat{g}_i|} $$

• $\delta_i \cdot \hat{g}_i$: Dot product of the miner's delta and the validator's gradient.
• $|\delta_i|$ and $|\hat{g}_i|$: Euclidean norms of the miner's delta and the validator's gradient, respectively.

Reward Calculation

The reward for miner $i$ is directly proportional to the cosine similarity score:

$$ R_i = \alpha \cdot s_i $$

Where $\alpha$ is a scaling factor determined by the network's economic parameters.

• A higher cosine similarity $s_i$ indicates that the miner's update is closely aligned with the true gradient, resulting in a higher reward.
• If $s_i$ is negative, it indicates that the miner's update is detrimental to the model's performance on the validation data, leading to a lower or negative reward.

Intuition Behind the Mechanism

• Positive Reinforcement: Miners are rewarded for updates that point in the same direction as the true gradient, improving the model.
• Penalty for Divergence: Miners submitting random or harmful updates receive lower rewards due to low or negative cosine similarity.
• Efficient Collaboration: This mechanism encourages miners to focus on genuine training rather than attempting to game the system.

Installation Guide

Prerequisites

• Python 3.8 or higher
• Pip for package management
• Git for cloning the repository
• AWS Account with access to S3 (Simple Storage Service)
• Compute Resources:
  • Miner: High-performance GPU (e.g., NVIDIA A100 or better) recommended for training.
  • Validator: Similar compute requirements as miners due to the need to recompute gradients.

Setup Instructions

1. Clone the Repository

   git clone https://github.com/unconst/boltzmann.git
   cd boltzmann
   

2. Set Up AWS Credentials

   Configure your AWS credentials to allow read and write access to your S3 bucket:

   export AWS_ACCESS_KEY_ID=your_access_key_id
   export AWS_SECRET_ACCESS_KEY=your_secret_access_key
   

   Ensure that your S3 bucket has the necessary permissions for read and write operations.

3. Install Dependencies

   It's recommended to use a virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   

   Install the required Python packages:

   pip install -r requirements.txt
   

4. Configure Environment Variables

   Create a .env file or export the environment variables required by the project.

5. Register on Bittensor Subnet

   The system runs on a Bittensor subnet. You need to register your miner and validator.

   # Replace <> with your actual wallet names and hotkeys.
   btcli register --wallet.name <wallet_name> --wallet.hotkey <hotkey_name> --subtensor.network test --netuid 220
   

Running the Miner and Validator

Run the Miner

python3 miner.py \
    --wallet.name <wallet_name> \
    --wallet.hotkey <hotkey_name> \
    --subtensor.network test \
    --netuid 220 \
    --bucket <your_s3_bucket_name> \
    --device cuda

Run the Validator

python3 validator.py \
    --wallet.name <wallet_name> \
    --wallet.hotkey <hotkey_name> \
    --subtensor.network test \
    --netuid 220 \
    --bucket <your_s3_bucket_name> \
    --device cuda

Hardware Requirements

Given the computational intensity of training and validating neural networks, it is highly recommended to use machines equipped with high-performance GPUs like NVIDIA A100 or better. Adequate CPU resources and memory are also necessary to handle data loading and preprocessing tasks.

Contributing

Contributions to the boltzmann project are welcome. Please open issues and submit pull requests for improvements and fixes.

License

This project is licensed under the MIT License © 2024 Chakana.tech. See the LICENSE file for details.

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Note: The mathematical formulations and mechanisms described are integral to ensuring the security and efficiency of the decentralized training process. By participating as a miner or validator, you contribute to a collaborative effort to advance decentralized machine learning.