• Introduction
  • How it works
  • Competition and Reward
• Installation
  • Before you proceed
  • Install
• Running
  • Running subtensor locally
  • Running miner
  • Running validator
• License

A groundbreaking healthcare subnet on the Bittensor network!

At the heart of our subnet are the miners - talented AI developers and teams dedicated to training and refining machine learning models. These models are not static; they are dynamic and continuously improved upon. Miners publish their latest models on Hugging Face, a platform renowned for its vast repository of AI models, making these advancements accessible to our community and beyond.

Validators, another crucial component of our ecosystem, play the pivotal role of assessing these models. They download the models from Hugging Face and evaluate them using their own diverse datasets. This evaluation isn't just a binary measure of success or failure; it is a nuanced process that involves ranking the models based on their loss metrics. The lower the loss, the higher the model ranks, reflecting its accuracy and efficiency in solving real-world problems.

Our subnet thrives on healthy competition. Miners are motivated to not just participate but excel, as their rewards are directly proportional to the performance of their models. The better a model performs in terms of accuracy and loss metrics, the greater the rewards for its creator. This competitive environment ensures that only the best, most efficient models rise to the top.

Validators are also rewarded for their critical role in the ecosystem. By providing fair, unbiased evaluations, they ensure that the network remains transparent and meritocratic. Their assessments are crucial in determining which models receive the highest rewards, thereby guiding the direction of AI development on our subnet.

Before you proceed with the installation of the subnet, note the following:

• Use these instructions to run your subnet locally for your development and testing, or on Bittensor testnet or on Bittensor mainnet.
• IMPORTANT: We strongly recommend that you first run your subnet locally and complete your development and testing before running the subnet on Bittensor testnet. Furthermore, make sure that you next run your subnet on Bittensor testnet before running it on the Bittensor mainnet.
• You can run your subnet either as a subnet validator or as a subnet miner.
• IMPORTANT: Make sure you are aware of the minimum compute requirements for your subnet. See the Minimum compute YAML configuration.

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/opentensor/bittensor/master/scripts/install.sh)"

git clone https://github.com/bthealthcare/healthcare-subnet.git

cd healthcare-subnet
apt install python3-pip -y
python3 -m pip install -e .

apt update && apt upgrade -y
apt install nodejs npm -y
npm i -g pm2

Please start by visiting the Hugging Face website at https://huggingface.co/ and take a moment to set up your account. After your account is successfully created, the next step involves obtaining your account's access_token, which is essential for further actions. Detailed instructions on how to secure your access_token can be found here: https://huggingface.co/docs/hub/security-tokens.

Once you have your access_token, please proceed by locating the .env.example file. After finding it, kindly rename the file to .env. The final step is to copy your access_token into this newly renamed .env file.

For validators in particular, please ensure to define the DATASET_LINK provided by demon (on Discord) in the .env file.

apt install docker.io -y
apt install docker-compose -y

git clone https://github.com/opentensor/subtensor.git
cd subtensor
docker-compose up --detach

In this innovative healthcare subnet, miners play a crucial role in contributing to disease diagnosis by predicting from medical images. Through continuous training, miners strive to improve their models, with more accurate models earning substantial rewards. Miners have the flexibility to adapt and enhance the structure of their models, datasets, and other factors influencing model accuracy. This collaborative effort aims to advance disease prediction and underscores the vital role miners play in shaping the future of medical diagnostics.

python3 healthcare/dataset/downloader.py

# To run the miner
pm2 start neurons/miner.py --name miner --interpreter python3 -- 
    --netuid # the subnet netuid, default = 
    --subtensor.network # the bittensor chain endpoint, default = finney, local, test (highly recommend running subtensor locally)
    --wallet.name # your miner wallet, default = default
    --wallet.hotkey # your validator hotkey, default = default
    --logging.debug # run in debug mode, alternatively --logging.trace for trace mode
    --num_epochs # the number of training epochs (-1 is infinite), default = -1
    --batch_size # the number of data points processed in a single iteration, default = 32
    --save_model_period # the period of batches during which the model is saved, default = 30
    --model_type # the architecture and structure of the neural network used for training, default = CNN, VGG, RES, EFFICIENT, MOBILE
    --training_mode # the training mode, whether in fast, normal, or slow mode, dictates the pace and intensity of the model training process, default = normal
    --device gpu:0,2 # the device will be used for model training, default = gpu
    --restart # if set, miners will start the training from scratch, default = False

• Example 1 (with default values):

pm2 start neurons/miner.py --name miner --interpreter python3 -- --wallet.name default --wallet.hotkey default --logging.debug

• Example 2 (with custom values):

pm2 start neurons/miner.py --name miner --interpreter python3 --
    --subtensor.network local
    --wallet.name default
    --wallet.hotkey default
    --logging.debug
    --num_epochs 10
    --batch_size 256
    --restart True
    --model_type vgg
    --training_mode fast
    --device cpu:4

Validators play a pivotal role in evaluating miner's models by periodically sending diverse images for assessment. They meticulously score miners based on their responses, contributing to the ongoing refinement of models and ensuring the highest standards of performance and accuracy in our collaborative network.

# To run the validator
pm2 start neurons/validator.py --name validator --interpreter python3 -- 
    --netuid # the subnet netuid, default = 
    --subtensor.network # the bittensor chain endpoint, default = finney, local, test (highly recommend running subtensor locally)
    --wallet.name # your miner wallet, default = default
    --wallet.hotkey # your validator hotkey, default = default
    --logging.debug # run in debug mode, alternatively --logging.trace for trace mode
    --vpermit_tao_limit # the maximum number of TAO allowed to query a validator with a vpermit, default = 4096

• Example 1 (with default values):

pm2 start neurons/validator.py --name validator --interpreter python3 -- --wallet.name default --wallet.hotkey default --logging.debug

• Example 2 (with custom values):

pm2 start neurons/validator.py --name validator --interpreter python3 --
    --subtensor.network local
    --wallet.name default
    --wallet.hotkey default
    --logging.debug
    --vpermit_tao_limit 1024

This repository is licensed under the MIT License.

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