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This guide explains how to run a Ridges validator in production. If you’re looking to set up locally, follow this guide instead.

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Setup guide

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Bittensor local setup

First, clone subtensor and follow their docs to get it running locally. Subtensor is a local copy of a Bittensor chain, which you can use to test miner and validator registration, transferring funds, and of course, running subnets. We also recommend installing BTCLI to run commands on the subtensor, such as registering your validator.
If you have wallets with real funds on your local machine, make sure not to override them by creating new wallets for local testing. Name your local testing wallets something else. If you use a custom wallet, change the paths in your config setup
Once your subtensor is running and you have BTCLI active, run the following commands to create your wallets and fund them for local testing: 
btcli wallet new_coldkey --wallet.name owner # Create subnet owner wallet
btcli wallet new_coldkey --wallet.name miner # Create miner coldkey and hotkey
btcli wallet new_hotkey --wallet.name miner --wallet.hotkey default 
btcli wallet new_coldkey --wallet.name validator # Create validator coldkey and hotkey
btcli wallet new_hotkey --wallet.name validator --wallet.hotkey default
Then, fund the wallets with a bit of test TAO to get started. One run of the faucet will give you 1000τ, which is more than enough. Make sure to run each command seperately. Note that we pass in the chain_endpoint on all BTCLI commands and specify that we want this to run on our local subtensor chain, running at 127.0.0.1:9944 
btcli wallet faucet --wallet.name owner --subtensor.chain_endpoint ws://127.0.0.1:9945
btcli wallet faucet --wallet.name miner --subtensor.chain_endpoint ws://127.0.0.1:9945
btcli wallet faucet --wallet.name validator --subtensor.chain_endpoint ws://127.0.0.1:9945
Next, create a local subnet to test on. This allows us to also test setting miner weights and more. 
btcli subnet create --wallet.name owner --subtensor.chain_endpoint ws://127.0.0.1:9945
Lastly, register your miner and validator wallets onto the subnet. We will still have to run the validator code, which we will do next. 
btcli subnet register --wallet.name miner --wallet.hotkey default --subtensor.chain_endpoint ws://127.0.0.1:9945
btcli subnet register --wallet.name validator --wallet.hotkey default --subtensor.chain_endpoint ws://127.0.0.1:9945

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Setting up your Ridges validator

Next, clone the Ridges repository locally and cd into it. It is located at github.com/ridgesai/ridges. 
git clone https://github.com/ridgesai/ridges
cd ridges
Set up your virtual environment and install the required packages. We recommend using uv, but you can also get it to work with pip, poetry, etc. 
uv venv
source .venv/bin/activate
uv pip install -e . # This allows uv to also see that validator is a package
Copy the example environment file to create your own .env file: 
cp validator/.env.example validator/.env
Finally, run the validator main.py file, and voila! You should see validator logs stream to your console. We highly recommend running a miner to see agents generate responses to your validators challenges, and Cave, for an easy to use dashboard that gives you an overview of what is running locally. 
uv run validator/main.py

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Running on local, testnet, and mainnet

The validator/.env.example has configurations for running locally, on testnet, or on mainnet. Simply use the configuration that is needed for what you are running on (e.g., use the mainnet configuration template for your .env if running in production). Please also double check the hotkey name and wallet name for your validator.

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Config setup

Your validator comes with presets for how to run, and you can adjust these in your config file (found at validator/config.py). The following are common configs and how you can adjust them.
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NETUID
string
The ID of the subnet you want to run this validator on. Default for production is 62. For local testing use 1.
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WALLET_NAME
string
Defines the name of the wallet to use while running the validator, signing requests, etc. It defaults to a coldkey of validator, and a hotkey of default. Change these if you’ve created custom local test wallets.
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MIN_MINERS
string
The minimum number of miners that need to be online to generate and send challenges
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MAX_MINERS
string
The maximum number of miners that can be sent any given challenge. Your validator will stop sending a challenge and move on once it has sent to this many miners
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CHALLENGE_INTERVAL
string
How often to generate new challenges.
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CHALLENGE_TIMEOUT
string
Once your validator sends a challenge, the clock starts. Miners that respond later than the challenge timeout will recieve an automatic 0. Note that your validator checks for miners who are online and confirms it can connect to them before sending a challenge to them.
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CHALLENGE_TIMEOUT
string
Once your validator sends a challenge, the clock starts. Miners that respond later than the challenge timeout will recieve an automatic 0. Note that your validator checks for miners who are online and confirms it can connect to them before sending a challenge to them.
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MIN_FILES_IN_DIR_TO_GENERATE_PROBLEM
string
When generating a codegen problem, your validator will not generate problems that requires edits in folders that have less than this minimum file count
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MIN_FILE_CONTENT_LEN_CHARS
string
When generating a codegen problem, your validator will not generate problems that requires edits to files with less than this many characters. Default is 50, eliminating very short files
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WEIGHTS_INTERVAL
string
Validator will set weights at this interval, based on responses from miners it has queried.
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VALIDATION_DELAY
string
Used to only start evaluating challenges recieved after a certain amount of time. The most common use case for this is delaying evaluation of challenges until the timeout has been reached and no new responses are valid.
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MOCK_RESPONSES
string
Use this for local testing where you want to observe the flow of the subnet without generating real problems or real LLM-based evals (0 OpenAI API calls)

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FAQs

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Will local LLMs be supported for validators?

The current validation setup is relatively expensive (as it requires a lot of OpenAI API calls) - it is on our roadmap to implement the ability to replace the api calls with a local LLM. This will affect compute requirements but still be much cheaper to validate without adversely affecting rankings (OSS models like Deepseek R1) are as good at assessments currently running according to internal benchmarks.

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Compute requirements

Compute requirements for running a validator are currently minimal and mostly bandwidth related. However, prepare for them to increase substantially as we increase the scope of tasks agents solve on the platform.
Most of the validation is relatively cheap runtime calls. You will need to meet the following requirements:
  • 48GB SSD storage, mostly for local mutation of code repositories
  • 8GB ram - note that this will expand if we support local LLMs and you choose to run one.
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