Training Node Guide

This guide provides step-by-step instructions for the entire Training Node workflow. By the end you will have successfully staked as a Training Node, completed a training task, and claimed your staking rewards.

0. Navigate to train.flock.io and connect you wallet.

Follow the steps here to connect your wallet.

1. Stake gmFLOCK as Training Node from train.flock.io

1. Once you have gmFLOCK tokens, Navigate to the Training Node tab on the Stake page

2. Select a training task

3. Stake gmFLOCK

Once you’ve confirmed and approved the transaction in your web3 wallet, you will see a box on the Training Node tab with your stake details.

2. Accept delegator

Note that you have the option to accept delegator. To do so, go to "Accept Delegator" tab. Select your profit-sharing ratio, then click "Create Delegation Contract":

Once the transaction is completed. You have the option to add a profile and a profile picture, as well as to modify your profit-sharing ratio here:

3. Get API key

Your API key is required for all remaining Validator steps. You can get it from the web app.

1. Select dropdown in upper right corner of web app

2. Select API

3. On the API page, copy your API key

Once you have your API key, you can proceed to the next step.

Video Tutorial

4. Set up your environment

Windows

For Windows users, we suggest installing WSL. Follow the guidance: WSL installation

Mac/Linux

You can install Anaconda via HERE

5. Clone the QuickStart repo

The quickstart repo contains everything you need to run our full_automation.py script. To clone it run:

git clone https://github.com/FLock-io/testnet-training-node-quickstart.git

To set up all packages within the project directory

conda create -n training-node python==3.10
conda activate training-node
pip install -r requirements.txt

6. Get information required to run automation script

The script described in the following step automates the following:

• Gets a task

• Downloads the training data

• Finetunes Gemma-2B on training data

• Merges weights

• Uploads to your Hugging Face model repo

• Submits the task to fed-ledger

To run it, you will need:

• TASK_ID - the id of the task you are staking on as Training Node

• FLOCK_API_KEY - available at https://train.flock.io/flock_api

• HF_TOKEN - your Hugging Face Access Token (Hugging Face > Profile > Settings > Access Tokens)

• HG_USERNAME - your Hugging Face user name

7. Run full automation script

Once you have all the information listed in the previous step, paste the script below into your terminal, update the repsective values, and run the command.

TASK_ID=<task-id> FLOCK_API_KEY="<your-flock-api-key-stakes-as-node-for-the-task>" HF_TOKEN="<your-hf-token>" CUDA_VISIBLE_DEVICES=0 HF_USERNAME="your-hf-user-name" python full_automation.py

set TASK_ID=<task-id> && 
set FLOCK_API_KEY="<your-flock-api-key-stakes-as-node-for-the-task>" && 
set HF_TOKEN="<your-hf-token>" && 
set CUDA_VISIBLE_DEVICES=0 && 
set HF_USERNAME="your-hf-user-name" && 
python full_automation.py

$env:TASK_ID = "<task-id>"
$env:FLOCK_API_KEY = "<your-flock-api-key-stakes-as-node-for-the-task>"
$env:HF_TOKEN = "<your-hf-token>"
$env:CUDA_VISIBLE_DEVICES = "0"
$env:HF_USERNAME = "your-hf-user-name"
python full_automation.py

8. Claim rewards

Reward distribution is triggered every 24 hours at midnight UTC. The final round of reward distribution is triggered at the once the task training period is complete.

You can claim your rewards via train.flock.io.