- Quickstarter template
- Introduction
- Installation
- Writing your own incentive mechanism
- Writing your own subnet API
- Subnet Links
- License
This template contains all the required installation instructions, scripts, and files and functions for:
- Building Bittensor subnets.
- Creating custom incentive mechanisms and running these mechanisms on the subnets.
In order to simplify the building of subnets, this template abstracts away the complexity of the underlying blockchain and other boilerplate code. While the default behavior of the template is sufficient for a simple subnet, you should customize the template in order to meet your specific requirements.
IMPORTANT: If you are new to Bittensor subnets, read this section before proceeding to Installation section.
The Bittensor blockchain hosts multiple self-contained incentive mechanisms called subnets. Subnets are playing fields in which:
- Subnet miners who produce value, and
- Subnet validators who produce consensus
determine together the proper distribution of TAO for the purpose of incentivizing the creation of value, i.e., generating digital commodities, such as intelligence or data.
Each subnet consists of:
- Subnet miners and subnet validators.
- A protocol using which the subnet miners and subnet validators interact with one another. This protocol is part of the incentive mechanism.
- The Bittensor API using which the subnet miners and subnet validators interact with Bittensor's onchain consensus engine Yuma Consensus. The Yuma Consensus is designed to drive these actors: subnet validators and subnet miners, into agreement on who is creating value and what that value is worth.
This starter template is split into three primary files. To write your own incentive mechanism, you should edit these files. These files are:
template/protocol.py: Contains the definition of the protocol used by subnet miners and subnet validators.neurons/miner.py: Script that defines the subnet miner's behavior, i.e., how the subnet miner responds to requests from subnet validators.neurons/validator.py: This script defines the subnet validator's behavior, i.e., how the subnet validator requests information from the subnet miners and determines the scores.
The Bittensor Subnet 1 for Text Prompting is built using this template. See Bittensor Text-Prompting for how to configure the files and how to add monitoring and telemetry and support multiple miner types. Also see this Subnet 1 in action on Taostats explorer.
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 owner, or 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.
- Note that installation instructions differ based on your situation: For example, installing for local development and testing will require a few additional steps compared to installing for testnet. Similarly, installation instructions differ for a subnet owner vs a validator or a miner.
- Running locally: Follow the step-by-step instructions described in this section: Running Subnet Locally.
- Running on Bittensor testnet: Follow the step-by-step instructions described in this section: Running on the Test Network.
- Running on Bittensor mainnet: Follow the step-by-step instructions described in this section: Running on the Main Network.
As described in Quickstarter template section above, when you are ready to write your own incentive mechanism, update this template repository by editing the following files. The code in these files contains detailed documentation on how to update the template. Read the documentation in each of the files to understand how to update the template. There are multiple TODOs in each of the files identifying sections you should update. These files are:
template/protocol.py: Contains the definition of the wire-protocol used by miners and validators.neurons/miner.py: Script that defines the miner's behavior, i.e., how the miner responds to requests from validators.neurons/validator.py: This script defines the validator's behavior, i.e., how the validator requests information from the miners and determines the scores.template/forward.py: Contains the definition of the validator's forward pass.template/reward.py: Contains the definition of how validators reward miner responses.
In addition to the above files, you should also update the following files:
README.md: This file contains the documentation for your project. Update this file to reflect your project's documentation.CONTRIBUTING.md: This file contains the instructions for contributing to your project. Update this file to reflect your project's contribution guidelines.template/__init__.py: This file contains the version of your project.setup.py: This file contains the metadata about your project. Update this file to reflect your project's metadata.docs/: This directory contains the documentation for your project. Update this directory to reflect your project's documentation.
To leverage the abstract SubnetsAPI in Bittensor, you can implement a standardized interface. This interface is used to interact with the Bittensor network and can is used by a client to interact with the subnet through its exposed axons.
What does Bittensor communication entail? Typically two processes, (1) preparing data for transit (creating and filling synapses) and (2), processing the responses received from the axon(s).
This protocol uses a handler registry system to associate bespoke interfaces for subnets by implementing two simple abstract functions:
prepare_synapseprocess_responses
These can be implemented as extensions of the generic SubnetsAPI interface. E.g.:
This is abstract, generic, and takes(*args, **kwargs) for flexibility. See the extremely simple base class:
class SubnetsAPI(ABC):
def __init__(self, wallet: "bt.wallet"):
self.wallet = wallet
self.dendrite = bt.dendrite(wallet=wallet)
async def __call__(self, *args, **kwargs):
return await self.query_api(*args, **kwargs)
@abstractmethod
def prepare_synapse(self, *args, **kwargs) -> Any:
"""
Prepare the synapse-specific payload.
"""
...
@abstractmethod
def process_responses(self, responses: List[Union["bt.Synapse", Any]]) -> Any:
"""
Process the responses from the network.
"""
...Here is a toy example:
from bittensor.subnets import SubnetsAPI
from MySubnet import MySynapse
class MySynapseAPI(SubnetsAPI):
def __init__(self, wallet: "bt.wallet"):
super().__init__(wallet)
self.netuid = 99
def prepare_synapse(self, prompt: str) -> MySynapse:
# Do any preparatory work to fill the synapse
data = do_prompt_injection(prompt)
# Fill the synapse for transit
synapse = StoreUser(
messages=[data],
)
# Send it along
return synapse
def process_responses(self, responses: List[Union["bt.Synapse", Any]]) -> str:
# Look through the responses for information required by your application
for response in responses:
if response.dendrite.status_code != 200:
continue
# potentially apply post processing
result_data = postprocess_data_from_response(response)
# return data to the client
return result_dataYou can use a subnet API to the registry by doing the following:
- Download and install the specific repo you want
- Import the appropriate API handler from bespoke subnets
- Make the query given the subnet specific API
See a simplified example for subnet 21 (FileTao storage) below. See examples/subnet21.py file for a full implementation example to follow:
# Subnet 21 Interface Example
class StoreUserAPI(SubnetsAPI):
def __init__(self, wallet: "bt.wallet"):
super().__init__(wallet)
self.netuid = 21
def prepare_synapse(
self,
data: bytes,
encrypt=False,
ttl=60 * 60 * 24 * 30,
encoding="utf-8",
) -> StoreUser:
data = bytes(data, encoding) if isinstance(data, str) else data
encrypted_data, encryption_payload = (
encrypt_data(data, self.wallet) if encrypt else (data, "{}")
)
expected_cid = generate_cid_string(encrypted_data)
encoded_data = base64.b64encode(encrypted_data)
synapse = StoreUser(
encrypted_data=encoded_data,
encryption_payload=encryption_payload,
ttl=ttl,
)
return synapse
def process_responses(
self, responses: List[Union["bt.Synapse", Any]]
) -> str:
for response in responses:
if response.dendrite.status_code != 200:
continue
stored_cid = (
response.data_hash.decode("utf-8")
if isinstance(response.data_hash, bytes)
else response.data_hash
)
bt.logging.debug("received data CID: {}".format(stored_cid))
break
return stored_cid
class RetrieveUserAPI(SubnetsAPI):
def __init__(self, wallet: "bt.wallet"):
super().__init__(wallet)
self.netuid = 21
def prepare_synapse(self, cid: str) -> RetrieveUser:
synapse = RetrieveUser(data_hash=cid)
return synapse
def process_responses(self, responses: List[Union["bt.Synapse", Any]]) -> bytes:
success = False
decrypted_data = b""
for response in responses:
if response.dendrite.status_code != 200:
continue
decrypted_data = decrypt_data_with_private_key(
encrypted_data,
response.encryption_payload,
bytes(self.wallet.coldkey.private_key.hex(), "utf-8"),
)
return dataExample usage of the FileTao interface, which can serve as an example for other subnets.
# import the bespoke subnet API
from storage import StoreUserAPI, RetrieveUserAPI
wallet = bt.wallet(wallet="default", hotkey="default") # the wallet used for querying
metagraph = bt.metagraph(netuid=21) # metagraph of the subnet desired
query_axons = metagraph.axons... # define custom logic to retrieve desired axons (e.g. validator set, specific miners, etc)
# Store the data on subnet 21
bt.logging.info(f"Initiating store_handler: {store_handler}")
cid = await StoreUserAPI(
axons=query_axons, # the axons you wish to query
# Below: Parameters passed to `prepare_synapse` for this API subclass
data=b"Hello Bittensor!",
encrypt=False,
ttl=60 * 60 * 24 * 30,
encoding="utf-8",
uid=None,
)
# The Content Identifier that corresponds to the stored data
print(cid)
> "bafkreifv6hp4o6bllj2nkdtzbq6uh7iia6bgqgd3aallvfhagym2s757v4
# Now retrieve data from SN21 (storage)
data = await RetrieveUserAPI(
axons=query_axons, # axons desired to query
cid=cid, # the content identifier to fetch the data
)
print(data)
> b"Hello Bittensor!"In order to see real-world examples of subnets in-action, see the subnet_links.py document or access them from inside the template package by:
import template
template.SUBNET_LINKS
[{'name': 'sn0', 'url': ''},
{'name': 'sn1', 'url': 'https://github.com/opentensor/prompting/'},
{'name': 'sn2', 'url': 'https://github.com/bittranslateio/bittranslate/'},
{'name': 'sn3', 'url': 'https://github.com/gitphantomman/scraping_subnet/'},
{'name': 'sn4', 'url': 'https://github.com/manifold-inc/targon/'},
...
]This repository is licensed under the MIT License.
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