bittensor#

Subpackages#

Submodules#

Package Contents#

Classes#

AxonInfo

BTStreamingResponseModel

BTStreamingResponseModel() is a Pydantic model that encapsulates the token streamer callable for Pydantic validation.

ChainDataType

Create a collection of name/value pairs.

DefaultConfig

A Config with a set of default values.

DelegateInfo

Dataclass for delegate info.

IPInfo

Dataclass for associated IP Info.

Mockkeyfile

The Mockkeyfile is a mock object representing a keyfile that does not exist on the device.

NeuronInfo

Dataclass for neuron metadata.

NeuronInfoLite

Dataclass for neuron metadata, but without the weights and bonds.

PriorityThreadPoolExecutor

Base threadpool executor with a priority queue

PrometheusInfo

Dataclass for prometheus info.

ProposalVoteData

dict() -> new empty dictionary

StakeInfo

Dataclass for stake info.

StreamingSynapse

The StreamingSynapse() class is designed to be subclassed for handling streaming responses in the Bittensor network.

SubnetHyperparameters

Dataclass for subnet hyperparameters.

SubnetInfo

Dataclass for subnet info.

Synapse

Represents a Synapse in the Bittensor network, serving as a communication schema between neurons (nodes).

Tensor

Represents a Tensor object.

TerminalInfo

TerminalInfo encapsulates detailed information about a network synapse (node) involved in a communication process.

axon

The axon class in Bittensor is a fundamental component that serves as the server-side interface for a neuron within the Bittensor network.

cli

config

Implementation of the config class, which manages the configuration of different Bittensor modules.

dendrite

keyfile

logging

Standardized logging for Bittensor.

metagraph

subtensor

tensor

wallet

Implementation of the wallet class, which manages balances with staking and transfer. Also manages hotkey and coldkey.

Functions#

ask_password_to_encrypt()

Prompts the user to enter a password for key encryption.

cast_dtype(raw)

Casts the raw value to a string representing the torch data type.

cast_float(raw)

Converts a string to a float, if the string is not None.

cast_int(raw)

Converts a string to an integer, if the string is not None.

cast_shape(raw)

Casts the raw value to a string representing the tensor shape.

debug([on])

decrypt_keyfile_data(keyfile_data[, password, ...])

Decrypts the passed keyfile data using ansible vault.

deserialize_keypair_from_keyfile_data(keyfile_data)

Deserializes Keypair object from passed keyfile data.

display_mnemonic_msg(keypair, key_type)

Display the mnemonic and a warning message to keep the mnemonic safe.

encrypt_keyfile_data(keyfile_data[, password])

Encrypts the passed keyfile data using ansible vault.

from_scale_encoding(input, type_name[, is_vec, is_option])

from_scale_encoding_using_type_string(input, type_string)

get_coldkey_password_from_environment(coldkey_name)

Retrieves the cold key password from the environment variables.

get_size(obj[, seen])

Recursively finds size of objects.

keyfile_data_encryption_method(keyfile_data)

Returns true if the keyfile data is encrypted.

keyfile_data_is_encrypted(keyfile_data)

Returns true if the keyfile data is encrypted.

keyfile_data_is_encrypted_ansible(keyfile_data)

Returns true if the keyfile data is ansible encrypted.

keyfile_data_is_encrypted_legacy(keyfile_data)

Returns true if the keyfile data is legacy encrypted.

keyfile_data_is_encrypted_nacl(keyfile_data)

Returns true if the keyfile data is NaCl encrypted.

legacy_encrypt_keyfile_data(keyfile_data[, password])

serialized_keypair_to_keyfile_data(keypair)

Serializes keypair object into keyfile data.

trace([on])

turn_console_off()

turn_console_on()

validate_password(password)

Validates the password against a password policy.

Attributes#

ALL_COMMANDS

NACL_SALT

ProposalCallData

RAOPERTAO

T

TORCH_DTYPES

U16_MAX

U64_MAX

__archive_entrypoint__

__bellagene_entrypoint__

__blocktime__

__console__

__delegates_details_url__

__finney_entrypoint__

__finney_test_entrypoint__

__local_entrypoint__

__network_explorer_map__

__networks__

__new_signature_version__

__pipaddress__

__rao_symbol__

__ss58_address_length__

__ss58_format__

__tao_symbol__

__type_registry__

__use_console__

__version__

__version_as_int__

configs

custom_rpc_type_registry

defaults

version_split

bittensor.ALL_COMMANDS#
class bittensor.AxonInfo#
property is_serving: bool#

True if the endpoint is serving.

Return type:

bool

coldkey: str#
hotkey: str#
ip: str#
ip_type: int#
placeholder1: int = 0#
placeholder2: int = 0#
port: int#
protocol: int = 4#
version: int#
__eq__(other)#

Return self==value.

Parameters:

other (AxonInfo) –

__repr__()#

Return repr(self).

__str__()#

Return str(self).

classmethod from_neuron_info(neuron_info)#

Converts a dictionary to an axon_info object.

Parameters:

neuron_info (dict) –

Return type:

AxonInfo

classmethod from_parameter_dict(parameter_dict)#

Returns an axon_info object from a torch parameter_dict.

Parameters:

parameter_dict (torch.nn.ParameterDict) –

Return type:

axon_info

classmethod from_string(s)#

Creates an AxonInfo object from its string representation using JSON.

Parameters:

s (str) –

Return type:

AxonInfo

ip_str()#

Return the whole IP as string

Return type:

str

to_parameter_dict()#

Returns a torch tensor of the subnet info.

Return type:

torch.nn.ParameterDict

to_string()#

Converts the AxonInfo object to a string representation using JSON.

Return type:

str

class bittensor.BTStreamingResponseModel(**data)#

Bases: pydantic.BaseModel

BTStreamingResponseModel() is a Pydantic model that encapsulates the token streamer callable for Pydantic validation. It is used within the StreamingSynapse() class to create a BTStreamingResponse() object, which is responsible for handling the streaming of tokens.

The token streamer is a callable that takes a send function and returns an awaitable. It is responsible for generating the content of the streaming response, typically by processing tokens and sending them to the client.

This model ensures that the token streamer conforms to the expected signature and provides a clear interface for passing the token streamer to the BTStreamingResponse class.

Parameters:

data (Any) –

token_streamer#

Callable[[Send], Awaitable[None]] The token streamer callable, which takes a send function (provided by the ASGI server) and returns an awaitable. It is responsible for generating the content of the streaming response.

token_streamer: Callable[[starlette.types.Send], Awaitable[None]]#
exception bittensor.BlacklistedException#

Bases: Exception

This exception is raised when the request is blacklisted.

exception bittensor.ChainConnectionError#

Bases: ChainError

Error for any chain connection related errors.

class bittensor.ChainDataType(*args, **kwds)#

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

  • attribute access:

    >>> Color.RED
    <Color.RED: 1>
    
  • value lookup:

    >>> Color(1)
    <Color.RED: 1>
    
  • name lookup:

    >>> Color['RED']
    <Color.RED: 1>
    

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3
>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

DelegateInfo = 3#
DelegatedInfo = 5#
IPInfo = 7#
NeuronInfo = 1#
NeuronInfoLite = 4#
StakeInfo = 6#
SubnetHyperparameters = 8#
SubnetInfo = 2#
exception bittensor.ChainError#

Bases: BaseException

Base error for any chain related errors.

exception bittensor.ChainQueryError#

Bases: ChainError

Error for any chain query related errors.

exception bittensor.ChainTransactionError#

Bases: ChainError

Error for any chain transaction related errors.

class bittensor.DefaultConfig(parser=None, args=None, strict=False, default=None)#

Bases: config

A Config with a set of default values.

Parameters:
abstract classmethod default()#

Get default config.

Return type:

T

class bittensor.DelegateInfo#

Dataclass for delegate info.

hotkey_ss58: str#
nominators: List[Tuple[str, bittensor.utils.balance.Balance]]#
owner_ss58: str#
registrations: List[int]#
return_per_1000: bittensor.utils.balance.Balance#
take: float#
total_daily_return: bittensor.utils.balance.Balance#
total_stake: bittensor.utils.balance.Balance#
validator_permits: List[int]#
classmethod delegated_list_from_vec_u8(vec_u8)#

Returns a list of Tuples of DelegateInfo objects, and Balance, from a vec_u8.

This is the list of delegates that the user has delegated to, and the amount of stake delegated.

Parameters:

vec_u8 (List[int]) –

Return type:

List[Tuple[DelegateInfo, bittensor.utils.balance.Balance]]

classmethod fix_decoded_values(decoded)#

Fixes the decoded values.

Parameters:

decoded (Any) –

Return type:

DelegateInfo

classmethod from_vec_u8(vec_u8)#

Returns a DelegateInfo object from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

Optional[DelegateInfo]

classmethod list_from_vec_u8(vec_u8)#

Returns a list of DelegateInfo objects from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

List[DelegateInfo]

class bittensor.IPInfo#

Dataclass for associated IP Info.

ip: str#
ip_type: int#
protocol: int#
encode()#

Returns a dictionary of the IPInfo object that can be encoded.

Return type:

Dict[str, Any]

classmethod fix_decoded_values(decoded)#

Returns a SubnetInfo object from a decoded IPInfo dictionary.

Parameters:

decoded (Dict) –

Return type:

IPInfo

classmethod from_parameter_dict(parameter_dict)#

Returns a IPInfo object from a torch parameter_dict.

Parameters:

parameter_dict (torch.nn.ParameterDict) –

Return type:

IPInfo

classmethod from_vec_u8(vec_u8)#

Returns a IPInfo object from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

Optional[IPInfo]

classmethod list_from_vec_u8(vec_u8)#

Returns a list of IPInfo objects from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

List[IPInfo]

to_parameter_dict()#

Returns a torch tensor of the subnet info.

Return type:

torch.nn.ParameterDict

exception bittensor.IdentityError#

Bases: ChainTransactionError

Error raised when an identity transaction fails.

exception bittensor.InternalServerError#

Bases: Exception

This exception is raised when the requested function fails on the server. Indicates a server error.

exception bittensor.InvalidConfigFile#

Bases: Exception

In place of YAMLError

exception bittensor.InvalidRequestNameError#

Bases: Exception

This exception is raised when the request name is invalid. Ususally indicates a broken URL.

exception bittensor.KeyFileError#

Bases: Exception

Error thrown when the keyfile is corrupt, non-writable, non-readable or the password used to decrypt is invalid.

exception bittensor.KeyFileError#

Bases: Exception

Error thrown when the keyfile is corrupt, non-writable, non-readable or the password used to decrypt is invalid.

exception bittensor.MetadataError#

Bases: ChainTransactionError

Error raised when metadata commitment transaction fails.

class bittensor.Mockkeyfile(path)#

The Mockkeyfile is a mock object representing a keyfile that does not exist on the device.

It is designed for use in testing scenarios and simulations where actual filesystem operations are not required. The keypair stored in the Mockkeyfile is treated as non-encrypted and the data is stored as a serialized string.

Parameters:

path (str) –

property data#

Returns the serialized keypair data stored in the keyfile.

Returns:

The serialized keypair data.

Return type:

bytes

property keypair#

Returns the mock keypair stored in the keyfile.

Returns:

The mock keypair.

Return type:

bittensor.Keypair

__repr__()#

Returns a string representation of the Mockkeyfile, same as __str__().

Returns:

The string representation of the Mockkeyfile.

Return type:

str

__str__()#

Returns a string representation of the Mockkeyfile. The representation will indicate if the keyfile is empty, encrypted, or decrypted.

Returns:

The string representation of the Mockkeyfile.

Return type:

str

check_and_update_encryption(no_prompt=None, print_result=False)#
decrypt(password=None)#

Returns without doing anything since the mock keyfile is not encrypted.

Parameters:

password (str, optional) – Ignored in this context. Defaults to None.

encrypt(password=None)#

Raises a ValueError since encryption is not supported for the mock keyfile.

Parameters:

password (str, optional) – Ignored in this context. Defaults to None.

Raises:

ValueError – Always raises this exception for Mockkeyfile.

exists_on_device()#

Returns True indicating that the mock keyfile exists on the device (although it is not created on the actual file system).

Returns:

Always returns True for Mockkeyfile.

Return type:

bool

get_keypair(password=None)#

Returns the mock keypair stored in the keyfile. The password parameter is ignored.

Parameters:

password (str, optional) – Ignored in this context. Defaults to None.

Returns:

The mock keypair stored in the keyfile.

Return type:

bittensor.Keypair

is_encrypted()#

Returns False indicating that the mock keyfile is not encrypted.

Returns:

Always returns False for Mockkeyfile.

Return type:

bool

is_readable()#

Returns True indicating that the mock keyfile is readable (although it is not read from the actual file system).

Returns:

Always returns True for Mockkeyfile.

Return type:

bool

is_writable()#

Returns True indicating that the mock keyfile is writable (although it is not written to the actual file system).

Returns:

Always returns True for Mockkeyfile.

Return type:

bool

make_dirs()#

Creates the directories for the mock keyfile. Does nothing in this class, since no actual filesystem operations are needed.

set_keypair(keypair, encrypt=True, overwrite=False, password=None)#

Sets the mock keypair in the keyfile. The encrypt and overwrite parameters are ignored.

Parameters:
  • keypair (bittensor.Keypair) – The mock keypair to be set.

  • encrypt (bool, optional) – Ignored in this context. Defaults to True.

  • overwrite (bool, optional) – Ignored in this context. Defaults to False.

  • password (str, optional) – Ignored in this context. Defaults to None.

bittensor.NACL_SALT = b'\x13q\x83\xdf\xf1Z\t\xbc\x9c\x90\xb5Q\x879\xe9\xb1'#
class bittensor.NeuronInfo#

Dataclass for neuron metadata.

active: int#
axon_info: AxonInfo#
bonds: List[List[int]]#
coldkey: str#
consensus: float#
dividends: float#
emission: float#
hotkey: str#
incentive: float#
is_null: bool = False#
last_update: int#
netuid: int#
prometheus_info: PrometheusInfo#
pruning_score: int#
rank: float#
stake: bittensor.utils.balance.Balance#
stake_dict: Dict[str, bittensor.utils.balance.Balance]#
total_stake: bittensor.utils.balance.Balance#
trust: float#
uid: int#
validator_permit: bool#
validator_trust: float#
weights: List[List[int]]#
static _neuron_dict_to_namespace(neuron_dict)#
Return type:

NeuronInfo

static _null_neuron()#
Return type:

NeuronInfo

classmethod fix_decoded_values(neuron_info_decoded)#

Fixes the values of the NeuronInfo object.

Parameters:

neuron_info_decoded (Any) –

Return type:

NeuronInfo

classmethod from_vec_u8(vec_u8)#

Returns a NeuronInfo object from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

NeuronInfo

classmethod from_weights_bonds_and_neuron_lite(neuron_lite, weights_as_dict, bonds_as_dict)#
Parameters:
Return type:

NeuronInfo

classmethod list_from_vec_u8(vec_u8)#

Returns a list of NeuronInfo objects from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

List[NeuronInfo]

class bittensor.NeuronInfoLite#

Dataclass for neuron metadata, but without the weights and bonds.

active: int#
axon_info: NeuronInfoLite.axon_info#
coldkey: str#
consensus: float#
dividends: float#
emission: float#
hotkey: str#
incentive: float#
is_null: bool = False#
last_update: int#
netuid: int#
prometheus_info: PrometheusInfo#
pruning_score: int#
rank: float#
stake: bittensor.utils.balance.Balance#
stake_dict: Dict[str, bittensor.utils.balance.Balance]#
total_stake: bittensor.utils.balance.Balance#
trust: float#
uid: int#
validator_permit: bool#
validator_trust: float#
static _neuron_dict_to_namespace(neuron_dict)#
Return type:

NeuronInfoLite

static _null_neuron()#
Return type:

NeuronInfoLite

classmethod fix_decoded_values(neuron_info_decoded)#

Fixes the values of the NeuronInfoLite object.

Parameters:

neuron_info_decoded (Any) –

Return type:

NeuronInfoLite

classmethod from_vec_u8(vec_u8)#

Returns a NeuronInfoLite object from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

NeuronInfoLite

classmethod list_from_vec_u8(vec_u8)#

Returns a list of NeuronInfoLite objects from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

List[NeuronInfoLite]

exception bittensor.NominationError#

Bases: ChainTransactionError

Error raised when a nomination transaction fails.

exception bittensor.NotDelegateError#

Bases: StakeError

Error raised when a hotkey you are trying to stake to is not a delegate.

exception bittensor.NotRegisteredError#

Bases: ChainTransactionError

Error raised when a neuron is not registered, and the transaction requires it to be.

exception bittensor.NotVerifiedException#

Bases: Exception

This exception is raised when the request is not verified.

exception bittensor.PostProcessException#

Bases: Exception

This exception is raised when the response headers cannot be updated.

exception bittensor.PriorityException#

Bases: Exception

This exception is raised when the request priority is not met.

class bittensor.PriorityThreadPoolExecutor(maxsize=-1, max_workers=None, thread_name_prefix='', initializer=None, initargs=())#

Bases: concurrent.futures._base.Executor

Base threadpool executor with a priority queue

property is_empty#
_counter#
_adjust_thread_count()#
_initializer_failed()#
classmethod add_args(parser, prefix=None)#

Accept specific arguments from parser

Parameters:
classmethod config()#

Get config from the argument parser.

Return: bittensor.config() object.

Return type:

bittensor.config

shutdown(wait=True)#

Clean-up the resources associated with the Executor.

It is safe to call this method several times. Otherwise, no other methods can be called after this one.

Parameters:
  • wait – If True then shutdown will not return until all running futures have finished executing and the resources used by the executor have been reclaimed.

  • cancel_futures – If True then shutdown will cancel all pending futures. Futures that are completed or running will not be cancelled.

submit(fn, *args, **kwargs)#

Submits a callable to be executed with the given arguments.

Schedules the callable to be executed as fn(*args, **kwargs) and returns a Future instance representing the execution of the callable.

Returns:

A Future representing the given call.

Parameters:

fn (Callable) –

Return type:

concurrent.futures._base.Future

class bittensor.PrometheusInfo#

Dataclass for prometheus info.

block: int#
ip: str#
ip_type: int#
port: int#
version: int#
classmethod fix_decoded_values(prometheus_info_decoded)#

Returns a PrometheusInfo object from a prometheus_info_decoded dictionary.

Parameters:

prometheus_info_decoded (Dict) –

Return type:

PrometheusInfo

bittensor.ProposalCallData#
class bittensor.ProposalVoteData#

Bases: TypedDict

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:

d = {} for k, v in iterable:

d[k] = v

dict(**kwargs) -> new dictionary initialized with the name=value pairs

in the keyword argument list. For example: dict(one=1, two=2)

ayes: List[str]#
end: int#
index: int#
nays: List[str]#
threshold: int#
bittensor.RAOPERTAO = 1000000000.0#
exception bittensor.RegistrationError#

Bases: ChainTransactionError

Error raised when a neuron registration transaction fails.

exception bittensor.RunException#

Bases: Exception

This exception is raised when the requested function cannot be executed. Indicates a server error.

exception bittensor.StakeError#

Bases: ChainTransactionError

Error raised when a stake transaction fails.

class bittensor.StakeInfo#

Dataclass for stake info.

coldkey_ss58: str#
hotkey_ss58: str#
stake: bittensor.utils.balance.Balance#
classmethod fix_decoded_values(decoded)#

Fixes the decoded values.

Parameters:

decoded (Any) –

Return type:

StakeInfo

classmethod from_vec_u8(vec_u8)#

Returns a StakeInfo object from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

Optional[StakeInfo]

classmethod list_from_vec_u8(vec_u8)#

Returns a list of StakeInfo objects from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

List[StakeInfo]

classmethod list_of_tuple_from_vec_u8(vec_u8)#

Returns a list of StakeInfo objects from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

Dict[str, List[StakeInfo]]

class bittensor.StreamingSynapse#

Bases: bittensor.Synapse, abc.ABC

The StreamingSynapse() class is designed to be subclassed for handling streaming responses in the Bittensor network. It provides abstract methods that must be implemented by the subclass to deserialize, process streaming responses, and extract JSON data. It also includes a method to create a streaming response object.

class BTStreamingResponse(model, **kwargs)#

Bases: starlette.responses.StreamingResponse

BTStreamingResponse() is a specialized subclass of the Starlette StreamingResponse designed to handle the streaming of tokens within the Bittensor network. It is used internally by the StreamingSynapse class to manage the response streaming process, including sending headers and calling the token streamer provided by the subclass.

This class is not intended to be directly instantiated or modified by developers subclassing StreamingSynapse. Instead, it is used by the create_streaming_response() method to create a response object based on the token streamer provided by the subclass.

Parameters:

model (BTStreamingResponseModel) –

async __call__(scope, receive, send)#

Asynchronously calls the stream_response method, allowing the BTStreamingResponse object to be used as an ASGI application.

This method is part of the ASGI interface and is called by the ASGI server to handle the request and send the response. It delegates to the stream_response() method to perform the actual streaming process.

Parameters:
  • scope (starlette.types.Scope) – The scope of the request, containing information about the client, server, and request itself.

  • receive (starlette.types.Receive) – A callable to receive the request, provided by the ASGI server.

  • send (starlette.types.Send) – A callable to send the response, provided by the ASGI server.

async stream_response(send)#

Asynchronously streams the response by sending headers and calling the token streamer.

This method is responsible for initiating the response by sending the appropriate headers, including the content type for event-streaming. It then calls the token streamer to generate the content and sends the response body to the client.

Parameters:

send (starlette.types.Send) – A callable to send the response, provided by the ASGI server.

class Config#
validate_assignment = True#
create_streaming_response(token_streamer)#

Creates a streaming response using the provided token streamer. This method can be used by the subclass to create a response object that can be sent back to the client. The token streamer should be implemented to generate the content of the response according to the specific requirements of the subclass.

Parameters:

token_streamer (Callable[[starlette.types.Send], Awaitable[None]]) – A callable that takes a send function and returns an awaitable. It’s responsible for generating the content of the response.

Returns:

The streaming response object, ready to be sent to the client.

Return type:

BTStreamingResponse

abstract extract_response_json(response)#

Abstract method that must be implemented by the subclass. This method should provide logic to extract JSON data from the response, including headers and content. It is called after the response has been processed and is responsible for retrieving structured data that can be used by the application.

Parameters:

response (starlette.responses.Response) – The response object from which to extract JSON data.

Return type:

dict

abstract async process_streaming_response(response)#

Abstract method that must be implemented by the subclass. This method should provide logic to handle the streaming response, such as parsing and accumulating data. It is called as the response is being streamed from the network, and should be implemented to handle the specific streaming data format and requirements of the subclass.

Parameters:

response (starlette.responses.Response) – The response object to be processed, typically containing chunks of data.

class bittensor.SubnetHyperparameters#

Dataclass for subnet hyperparameters.

activity_cutoff: int#
adjustment_interval: int#
bonds_moving_avg: int#
immunity_period: int#
kappa: int#
max_burn: int#
max_difficulty: int#
max_regs_per_block: int#
max_validators: int#
max_weight_limit: float#
min_allowed_weights: int#
min_burn: int#
min_difficulty: int#
registration_allowed: bool#
rho: int#
serving_rate_limit: int#
target_regs_per_interval: int#
tempo: int#
weights_rate_limit: int#
weights_version: int#
classmethod fix_decoded_values(decoded)#

Returns a SubnetInfo object from a decoded SubnetInfo dictionary.

Parameters:

decoded (Dict) –

Return type:

SubnetHyperparameters

classmethod from_parameter_dict(parameter_dict)#

Returns a SubnetHyperparameters object from a torch parameter_dict.

Parameters:

parameter_dict (torch.nn.ParameterDict) –

Return type:

SubnetInfo

classmethod from_vec_u8(vec_u8)#

Returns a SubnetHyperparameters object from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

Optional[SubnetHyperparameters]

classmethod list_from_vec_u8(vec_u8)#

Returns a list of SubnetHyperparameters objects from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

List[SubnetHyperparameters]

to_parameter_dict()#

Returns a torch tensor of the subnet hyperparameters.

Return type:

torch.nn.ParameterDict

class bittensor.SubnetInfo#

Dataclass for subnet info.

blocks_since_epoch: int#
burn: bittensor.utils.balance.Balance#
connection_requirements: Dict[str, float]#
difficulty: int#
emission_value: float#
immunity_period: int#
kappa: int#
max_allowed_validators: int#
max_n: int#
max_weight_limit: float#
min_allowed_weights: int#
modality: int#
netuid: int#
owner_ss58: str#
rho: int#
scaling_law_power: float#
subnetwork_n: int#
tempo: int#
classmethod fix_decoded_values(decoded)#

Returns a SubnetInfo object from a decoded SubnetInfo dictionary.

Parameters:

decoded (Dict) –

Return type:

SubnetInfo

classmethod from_parameter_dict(parameter_dict)#

Returns a SubnetInfo object from a torch parameter_dict.

Parameters:

parameter_dict (torch.nn.ParameterDict) –

Return type:

SubnetInfo

classmethod from_vec_u8(vec_u8)#

Returns a SubnetInfo object from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

Optional[SubnetInfo]

classmethod list_from_vec_u8(vec_u8)#

Returns a list of SubnetInfo objects from a vec_u8.

Parameters:

vec_u8 (List[int]) –

Return type:

List[SubnetInfo]

to_parameter_dict()#

Returns a torch tensor of the subnet info.

Return type:

torch.nn.ParameterDict

class bittensor.Synapse(**data)#

Bases: pydantic.BaseModel

Represents a Synapse in the Bittensor network, serving as a communication schema between neurons (nodes).

Synapses ensure the format and correctness of transmission tensors according to the Bittensor protocol. Each Synapse type is tailored for a specific machine learning (ML) task, following unique compression and communication processes. This helps maintain sanitized, correct, and useful information flow across the network.

The Synapse class encompasses essential network properties such as HTTP route names, timeouts, request sizes, and terminal information. It also includes methods for serialization, deserialization, attribute setting, and hash computation, ensuring secure and efficient data exchange in the network.

The class includes Pydantic validators and root validators to enforce data integrity and format. Additionally, properties like is_success, is_failure, is_timeout, etc., provide convenient status checks based on dendrite responses.

Think of Bittensor Synapses as glorified pydantic wrappers that have been designed to be used in a distributed network. They provide a standardized way to communicate between neurons, and are the primary mechanism for communication between neurons in Bittensor.

Key Features:

  1. HTTP Route Name (name attribute):

    Enables the identification and proper routing of requests within the network. Essential for users defining custom routes for specific machine learning tasks.

  2. Query Timeout (timeout attribute):

    Determines the maximum duration allowed for a query, ensuring timely responses and network efficiency. Crucial for users to manage network latency and response times, particularly in time-sensitive applications.

  3. Request Sizes (total_size, header_size attributes):

    Keeps track of the size of request bodies and headers, ensuring efficient data transmission without overloading the network. Important for users to monitor and optimize the data payload, especially in bandwidth-constrained environments.

  4. Terminal Information (dendrite, axon attributes):

    Stores information about the dendrite (receiving end) and axon (sending end), facilitating communication between nodes. Users can access detailed information about the communication endpoints, aiding in debugging and network analysis.

  5. Body Hash Computation (computed_body_hash, required_hash_fields):

    Ensures data integrity and security by computing hashes of transmitted data. Provides users with a mechanism to verify data integrity and detect any tampering during transmission.

  6. Serialization and Deserialization Methods:

    Facilitates the conversion of Synapse objects to and from a format suitable for network transmission. Essential for users who need to customize data formats for specific machine learning models or tasks.

  7. Status Check Properties (is_success, is_failure, is_timeout, etc.):

    Provides quick and easy methods to check the status of a request, improving error handling and response management. Users can efficiently handle different outcomes of network requests, enhancing the robustness of their applications.

Example usage:

# Creating a Synapse instance with default values
synapse = Synapse()

# Setting properties and input
synapse.timeout = 15.0
synapse.name = "MySynapse"
# Not setting fields that are not defined in your synapse class will result in an error, e.g.:
synapse.dummy_input = 1 # This will raise an error because dummy_input is not defined in the Synapse class

# Get a dictionary of headers and body from the synapse instance
synapse_dict = synapse.json()

# Get a dictionary of headers from the synapse instance
headers = synapse.to_headers()

# Reconstruct the synapse from headers using the classmethod 'from_headers'
synapse = Synapse.from_headers(headers)

# Deserialize synapse after receiving it over the network, controlled by `deserialize` method
deserialized_synapse = synapse.deserialize()

# Checking the status of the request
if synapse.is_success:
    print("Request succeeded")

# Checking and setting the status of the request
print(synapse.axon.status_code)
synapse.axon.status_code = 408 # Timeout
Parameters:
  • name (str) – HTTP route name, set on axon.attach().

  • timeout (float) – Total query length, set by the dendrite terminal.

  • total_size (int) – Total size of request body in bytes.

  • header_size (int) – Size of request header in bytes.

  • dendrite (TerminalInfo) – Information about the dendrite terminal.

  • axon (TerminalInfo) – Information about the axon terminal.

  • computed_body_hash (str) – Computed hash of the request body.

  • required_hash_fields (List[str]) – Fields required to compute the body hash.

  • data (Any) –

deserialize()#

Custom deserialization logic for subclasses.

Return type:

Synapse

__setattr__()#

Override method to make required_hash_fields read-only.

Parameters:
  • name (str) –

  • value (Any) –

get_total_size()#

Calculates and returns the total size of the object.

Return type:

int

to_headers()#

Constructs a dictionary of headers from instance properties.

Return type:

dict

body_hash()#

Computes a SHA3-256 hash of the serialized body.

Return type:

str

parse_headers_to_inputs()#

Parses headers to construct an inputs dictionary.

Parameters:

headers (dict) –

Return type:

dict

from_headers()#

Creates an instance from a headers dictionary.

Parameters:

headers (dict) –

Return type:

Synapse

This class is a cornerstone in the Bittensor framework, providing the necessary tools for secure, efficient, and standardized communication in a decentralized environment.

class Config#
validate_assignment = True#
property body_hash: str#

Computes a SHA3-256 hash of the serialized body of the Synapse instance.

This hash is used to ensure the data integrity and security of the Synapse instance when it’s transmitted across the network. It is a crucial feature for verifying that the data received is the same as the data sent.

Process:

  1. Iterates over each required field as specified in required_fields_hash.

  2. Concatenates the string representation of these fields.

  3. Applies SHA3-256 hashing to the concatenated string to produce a unique fingerprint of the data.

Example:

synapse = Synapse(name="ExampleRoute", timeout=10)
hash_value = synapse.body_hash
# hash_value is the SHA3-256 hash of the serialized body of the Synapse instance
Returns:

The SHA3-256 hash as a hexadecimal string, providing a fingerprint of the Synapse instance’s data for integrity checks.

Return type:

str

property failed_verification: bool#

Checks if the dendrite’s status code indicates failed verification.

This method returns True if the status code of the dendrite is 401, which is the HTTP status code for unauthorized access.

Returns:

True if dendrite’s status code is 401, False otherwise.

Return type:

bool

property is_blacklist: bool#

Checks if the dendrite’s status code indicates a blacklisted request.

This method returns True if the status code of the dendrite is 403, which is the HTTP status code for a forbidden request.

Returns:

True if dendrite’s status code is 403, False otherwise.

Return type:

bool

property is_failure: bool#

Checks if the dendrite’s status code indicates failure.

This method returns True if the status code of the dendrite is not 200, which would mean the HTTP request was not successful.

Returns:

True if dendrite’s status code is not 200, False otherwise.

Return type:

bool

property is_success: bool#

Checks if the dendrite’s status code indicates success.

This method returns True if the status code of the dendrite is 200, which typically represents a successful HTTP request.

Returns:

True if dendrite’s status code is 200, False otherwise.

Return type:

bool

property is_timeout: bool#

Checks if the dendrite’s status code indicates a timeout.

This method returns True if the status code of the dendrite is 408, which is the HTTP status code for a request timeout.

Returns:

True if dendrite’s status code is 408, False otherwise.

Return type:

bool

_extract_header_size#
_extract_timeout#
_extract_total_size#
axon: TerminalInfo | None#
computed_body_hash: str | None#
dendrite: TerminalInfo | None#
header_size: int | None#
name: str | None#
required_hash_fields: List[str] | None#
timeout: float | None#
total_size: int | None#
__setattr__(name, value)#

Override the __setattr__() method to make the required_hash_fields property read-only.

This is a security mechanism such that the required_hash_fields property cannot be overridden by the user or malicious code.

Parameters:
  • name (str) –

  • value (Any) –

deserialize()#

Deserializes the Synapse object.

This method is intended to be overridden by subclasses for custom deserialization logic. In the context of the Synapse superclass, this method simply returns the instance itself. When inheriting from this class, subclasses should provide their own implementation for deserialization if specific deserialization behavior is desired.

By default, if a subclass does not provide its own implementation of this method, the Synapse’s deserialize method will be used, returning the object instance as-is.

In its default form, this method simply returns the instance of the Synapse itself without any modifications. Subclasses of Synapse can override this method to add specific deserialization behaviors, such as converting serialized data back into complex object types or performing additional data integrity checks.

Example:

class CustomSynapse(Synapse):
    additional_data: str

    def deserialize(self) -> "CustomSynapse":
        # Custom deserialization logic
        # For example, decoding a base64 encoded string in 'additional_data'
        if self.additional_data:
            self.additional_data = base64.b64decode(self.additional_data).decode('utf-8')
        return self

serialized_data = '{"additional_data": "SGVsbG8gV29ybGQ="}'  # Base64 for 'Hello World'
custom_synapse = CustomSynapse.parse_raw(serialized_data)
deserialized_synapse = custom_synapse.deserialize()

# deserialized_synapse.additional_data would now be 'Hello World'
Returns:

The deserialized Synapse object. In this default implementation, it returns the object itself.

Return type:

Synapse

classmethod from_headers(headers)#

Constructs a new Synapse instance from a given headers dictionary, enabling the re-creation of the Synapse’s state as it was prior to network transmission.

This method is a key part of the deserialization process in the Bittensor network, allowing nodes to accurately reconstruct Synapse objects from received data.

Example:

received_headers = {
    'bt_header_axon_address': '127.0.0.1',
    'bt_header_dendrite_port': '8080',
    # Other headers...
}
synapse = Synapse.from_headers(received_headers)
# synapse is a new Synapse instance reconstructed from the received headers
Parameters:

headers (dict) – The dictionary of headers containing serialized Synapse information.

Returns:

A new instance of Synapse, reconstructed from the parsed header information, replicating the original instance’s state.

Return type:

Synapse

get_total_size()#

Get the total size of the current object.

This method first calculates the size of the current object, then assigns it to the instance variable self.total_size() and finally returns this value.

Returns:

The total size of the current object.

Return type:

int

classmethod parse_headers_to_inputs(headers)#

Interprets and transforms a given dictionary of headers into a structured dictionary, facilitating the reconstruction of Synapse objects.

This method is essential for parsing network-transmitted data back into a Synapse instance, ensuring data consistency and integrity.

Process:

  1. Separates headers into categories based on prefixes (axon, dendrite, etc.).

  2. Decodes and deserializes input_obj headers into their original objects.

  3. Assigns simple fields directly from the headers to the input dictionary.

Example:

received_headers = {
    'bt_header_axon_address': '127.0.0.1',
    'bt_header_dendrite_port': '8080',
    # Other headers...
}
inputs = Synapse.parse_headers_to_inputs(received_headers)
# inputs now contains a structured representation of Synapse properties based on the headers

Note

This is handled automatically when calling Synapse.from_headers(headers)() and does not need to be called directly.

Parameters:

headers (dict) – The headers dictionary to parse.

Returns:

A structured dictionary representing the inputs for constructing a Synapse instance.

Return type:

dict

set_name_type(values)#
Return type:

dict

to_headers()#

Converts the state of a Synapse instance into a dictionary of HTTP headers.

This method is essential for packaging Synapse data for network transmission in the Bittensor framework, ensuring that each key aspect of the Synapse is represented in a format suitable for HTTP communication.

Process:

  1. Basic Information: It starts by including the name and timeout of the Synapse, which are fundamental for identifying the query and managing its lifespan on the network.

  2. Complex Objects: The method serializes the axon and dendrite objects, if present, into strings. This serialization is crucial for preserving the state and structure of these objects over the network.

  3. Encoding: Non-optional complex objects are serialized and encoded in base64, making them safe for HTTP transport.

  4. Size Metrics: The method calculates and adds the size of headers and the total object size, providing valuable information for network bandwidth management.

Example Usage:

synapse = Synapse(name="ExampleSynapse", timeout=30)
headers = synapse.to_headers()
# headers now contains a dictionary representing the Synapse instance
Returns:

A dictionary containing key-value pairs representing the Synapse’s properties, suitable for HTTP communication.

Return type:

dict

exception bittensor.SynapseParsingError#

Bases: Exception

This exception is raised when the request headers are unable to be parsed into the synapse type.

bittensor.T#
bittensor.TORCH_DTYPES#
class bittensor.Tensor(**data)#

Bases: pydantic.BaseModel

Represents a Tensor object.

Parameters:
  • buffer (Optional[str]) – Tensor buffer data.

  • dtype (str) – Tensor data type.

  • shape (List[int]) – Tensor shape.

  • data (Any) –

class Config#
validate_assignment = True#
_extract_dtype#
_extract_shape#
buffer: str | None#
dtype: str#
shape: List[int]#
deserialize()#

Deserializes the Tensor object.

Returns:

The deserialized tensor object.

Return type:

torch.Tensor

Raises:

Exception – If the deserialization process encounters an error.

numpy()#
Return type:

numpy.ndarray

static serialize(tensor)#

Serializes the given tensor.

Parameters:

tensor (torch.Tensor) – The tensor to serialize.

Returns:

The serialized tensor.

Return type:

Tensor

Raises:

Exception – If the serialization process encounters an error.

tensor()#
Return type:

torch.Tensor

tolist()#
Return type:

List[object]

class bittensor.TerminalInfo(**data)#

Bases: pydantic.BaseModel

TerminalInfo encapsulates detailed information about a network synapse (node) involved in a communication process.

This class serves as a metadata carrier, providing essential details about the state and configuration of a terminal during network interactions. This is a crucial class in the Bittensor framework.

The TerminalInfo class contains information such as HTTP status codes and messages, processing times, IP addresses, ports, Bittensor version numbers, and unique identifiers. These details are vital for maintaining network reliability, security, and efficient data flow within the Bittensor network.

This class includes Pydantic validators and root validators to enforce data integrity and format. It is designed to be used natively within Synapses, so that you will not need to call this directly, but rather is used as a helper class for Synapses.

Parameters:
  • status_code (int) – HTTP status code indicating the result of a network request. Essential for identifying the outcome of network interactions.

  • status_message (str) – Descriptive message associated with the status code, providing additional context about the request’s result.

  • process_time (float) – Time taken by the terminal to process the call, important for performance monitoring and optimization.

  • ip (str) – IP address of the terminal, crucial for network routing and data transmission.

  • port (int) – Network port used by the terminal, key for establishing network connections.

  • version (int) – Bittensor version running on the terminal, ensuring compatibility between different nodes in the network.

  • nonce (int) – Unique, monotonically increasing number for each terminal, aiding in identifying and ordering network interactions.

  • uuid (str) – Unique identifier for the terminal, fundamental for network security and identification.

  • hotkey (str) – Encoded hotkey string of the terminal wallet, important for transaction and identity verification in the network.

  • signature (str) – Digital signature verifying the tuple of nonce, axon_hotkey, dendrite_hotkey, and uuid, critical for ensuring data authenticity and security.

  • data (Any) –

Usage:

# Creating a TerminalInfo instance
terminal_info = TerminalInfo(
    status_code=200,
    status_message="Success",
    process_time=0.1,
    ip="198.123.23.1",
    port=9282,
    version=111,
    nonce=111111,
    uuid="5ecbd69c-1cec-11ee-b0dc-e29ce36fec1a",
    hotkey="5EnjDGNqqWnuL2HCAdxeEtN2oqtXZw6BMBe936Kfy2PFz1J1",
    signature="0x0813029319030129u4120u10841824y0182u091u230912u"
)

# Accessing TerminalInfo attributes
ip_address = terminal_info.ip
processing_duration = terminal_info.process_time

# TerminalInfo can be used to monitor and verify network interactions, ensuring proper communication and security within the Bittensor network.

TerminalInfo plays a pivotal role in providing transparency and control over network operations, making it an indispensable tool for developers and users interacting with the Bittensor ecosystem.

class Config#
validate_assignment = True#
_extract_nonce#
_extract_port#
_extract_process_time#
_extract_status_code#
_extract_version#
hotkey: str | None#
ip: str | None#
nonce: int | None#
port: int | None#
process_time: float | None#
signature: str | None#
status_code: int | None#
status_message: str | None#
uuid: str | None#
version: int | None#
exception bittensor.TransferError#

Bases: ChainTransactionError

Error raised when a transfer transaction fails.

bittensor.U16_MAX = 65535#
bittensor.U64_MAX = 18446744073709551615#
exception bittensor.UnknownSynapseError#

Bases: Exception

This exception is raised when the request name is not found in the Axon’s forward_fns dictionary.

exception bittensor.UnstakeError#

Bases: ChainTransactionError

Error raised when an unstake transaction fails.

bittensor.__archive_entrypoint__ = 'wss://archive.chain.opentensor.ai:443/'#
bittensor.__bellagene_entrypoint__ = 'wss://parachain.opentensor.ai:443'#
bittensor.__blocktime__ = 12#
bittensor.__console__#
bittensor.__delegates_details_url__: str = 'https://raw.githubusercontent.com/opentensor/bittensor-delegates/main/public/delegates.json'#
bittensor.__finney_entrypoint__ = 'wss://entrypoint-finney.opentensor.ai:443'#
bittensor.__finney_test_entrypoint__ = 'wss://test.finney.opentensor.ai:443/'#
bittensor.__local_entrypoint__ = 'ws://127.0.0.1:9944'#
bittensor.__network_explorer_map__#
bittensor.__networks__ = ['local', 'finney', 'test', 'archive']#
bittensor.__new_signature_version__ = 360#
bittensor.__pipaddress__ = 'https://pypi.org/pypi/bittensor/json'#
bittensor.__rao_symbol__: str#
bittensor.__ss58_address_length__ = 48#
bittensor.__ss58_format__ = 42#
bittensor.__tao_symbol__: str#
bittensor.__type_registry__#
bittensor.__use_console__ = True#
bittensor.__version__ = '6.6.1'#
bittensor.__version_as_int__#
bittensor.ask_password_to_encrypt()#

Prompts the user to enter a password for key encryption.

Returns:

The valid password entered by the user.

Return type:

password (str)

class bittensor.axon(wallet=None, config=None, port=None, ip=None, external_ip=None, external_port=None, max_workers=None)#

The axon class in Bittensor is a fundamental component that serves as the server-side interface for a neuron within the Bittensor network.

This class is responsible for managing incoming requests from other neurons and implements various mechanisms to ensure efficient and secure network interactions.

An axon relies on a FastAPI router to create endpoints for different message types. These endpoints are crucial for handling various request types that a neuron might receive. The class is designed to be flexible and customizable, allowing users to specify custom rules for forwarding, blacklisting, prioritizing, and verifying incoming requests. The class also includes internal mechanisms to manage a thread pool, supporting concurrent handling of requests with defined priority levels.

Methods in this class are equipped to deal with incoming requests from various scenarios in the network and serve as the server face for a neuron. It accepts multiple arguments, like wallet, configuration parameters, ip address, server binding port, external ip, external port and max workers. Key methods involve managing and operating the FastAPI application router, including the attachment and operation of endpoints.

Key Features:

  • FastAPI router integration for endpoint creation and management.

  • Customizable request handling including forwarding, blacklisting, and prioritization.

  • Verification of incoming requests against custom-defined functions.

  • Thread pool management for concurrent request handling.

  • Command-line argument support for user-friendly program interaction.

Example Usage:

import bittensor
# Define your custom synapse class
class MySyanpse( bittensor.Synapse ):
    input: int = 1
    output: int = None

# Define a custom request forwarding function using your synapse class
def forward( synapse: MySyanpse ) -> MySyanpse:
    # Apply custom logic to synapse and return it
    synapse.output = 2
    return synapse

# Define a custom request verification function
def verify_my_synapse( synapse: MySyanpse ):
    # Apply custom verification logic to synapse
    # Optionally raise Exception
    assert synapse.input == 1
    ...

# Define a custom request blacklist fucntion
def blacklist_my_synapse( synapse: MySyanpse ) -> bool:
    # Apply custom blacklist
    return False ( if non blacklisted ) or True ( if blacklisted )

# Define a custom request priority fucntion
def prioritize_my_synape( synapse: MySyanpse ) -> float:
    # Apply custom priority
    return 1.0

# Initialize Axon object with a custom configuration
my_axon = bittensor.axon(
    config=my_config,
    wallet=my_wallet,
    port=9090,
    ip="192.0.2.0",
    external_ip="203.0.113.0",
    external_port=7070
)

# Attach the endpoint with the specified verification and forward functions.
my_axon.attach(
    forward_fn = forward_my_synapse,
    verify_fn = verify_my_synapse,
    blacklist_fn = blacklist_my_synapse,
    priority_fn = prioritize_my_synape
)

# Serve and start your axon.
my_axon.serve(
    netuid = ...
    subtensor = ...
).start()

# If you have multiple forwarding functions, you can chain attach them.
my_axon.attach(
    forward_fn = forward_my_synapse,
    verify_fn = verify_my_synapse,
    blacklist_fn = blacklist_my_synapse,
    priority_fn = prioritize_my_synape
).attach(
    forward_fn = forward_my_synapse_2,
    verify_fn = verify_my_synapse_2,
    blacklist_fn = blacklist_my_synapse_2,
    priority_fn = prioritize_my_synape_2
).serve(
    netuid = ...
    subtensor = ...
).start()
Parameters:
  • wallet (bittensor.wallet, optional) – Wallet with hotkey and coldkeypub.

  • config (bittensor.config, optional) – Configuration parameters for the axon.

  • port (int, optional) – Port for server binding.

  • ip (str, optional) – Binding IP address.

  • external_ip (str, optional) – External IP address to broadcast.

  • external_port (int, optional) – External port to broadcast.

  • max_workers (int, optional) – Number of active threads for request handling.

Returns:

An instance of the axon class configured as per the provided arguments.

Return type:

bittensor.axon

Note

This class is a core part of Bittensor’s decentralized network for machine intelligence, allowing neurons to communicate effectively and securely.

Importance and Functionality
Endpoint Registration

This method dynamically registers API endpoints based on the Synapse used, allowing the Axon to respond to specific types of requests and synapses.

Customization of Request Handling

By attaching different functions, the Axon can customize how it handles, verifies, prioritizes, and potentially blocks incoming requests, making it adaptable to various network scenarios.

Security and Efficiency

The method contributes to both the security (via verification and blacklisting) and efficiency (via prioritization) of request handling, which are crucial in a decentralized network environment.

Flexibility

The ability to define custom functions for different aspects of request handling provides great flexibility, allowing the Axon to be tailored to specific needs and use cases within the Bittensor network.

Error Handling and Validation

The method ensures that the attached functions meet the required signatures, providing error handling to prevent runtime issues.

__del__()#

This magic method is called when the Axon object is about to be destroyed. It ensures that the Axon server shuts down properly.

__repr__()#

Provides a machine-readable (unambiguous) representation of the Axon instance. It is made identical to __str__ in this case.

Return type:

str

__str__()#

Provides a human-readable representation of the Axon instance.

Return type:

str

classmethod add_args(parser, prefix=None)#

Adds AxonServer-specific command-line arguments to the argument parser.

Parameters:
  • parser (argparse.ArgumentParser) – Argument parser to which the arguments will be added.

  • prefix (str, optional) – Prefix to add to the argument names. Defaults to None.

Note

Environment variables are used to define default values for the arguments.

attach(forward_fn, blacklist_fn=None, priority_fn=None, verify_fn=None)#

Attaches custom functions to the Axon server for handling incoming requests. This method enables the Axon to define specific behaviors for request forwarding, verification, blacklisting, and prioritization, thereby customizing its interaction within the Bittensor network.

Registers an API endpoint to the FastAPI application router. It uses the name of the first argument of the forward_fn() function as the endpoint name.

The attach method in the Bittensor framework’s axon class is a crucial function for registering API endpoints to the Axon’s FastAPI application router. This method allows the Axon server to define how it handles incoming requests by attaching functions for forwarding, verifying, blacklisting, and prioritizing requests. It’s a key part of customizing the server’s behavior and ensuring efficient and secure handling of requests within the Bittensor network.

Parameters:
  • forward_fn (Callable) – Function to be called when the API endpoint is accessed. It should have at least one argument.

  • blacklist_fn (Callable, optional) – Function to filter out undesired requests. It should take the same arguments as forward_fn() and return a boolean value. Defaults to None, meaning no blacklist filter will be used.

  • priority_fn (Callable, optional) – Function to rank requests based on their priority. It should take the same arguments as forward_fn() and return a numerical value representing the request’s priority. Defaults to None, meaning no priority sorting will be applied.

  • verify_fn (Callable, optional) – Function to verify requests. It should take the same arguments as forward_fn() and return a boolean value. If None, self.default_verify() function will be used.

Return type:

bittensor.axon

Note

The methods forward_fn(), blacklist_fn(), priority_fn(), and verify_fn() should be designed to receive the same parameters.

Raises:
  • AssertionError – If forward_fn() does not have the signature: forward( synapse: YourSynapse ) -> synapse.

  • AssertionError – If blacklist_fn() does not have the signature: blacklist( synapse: YourSynapse ) -> bool.

  • AssertionError – If priority_fn() does not have the signature: priority( synapse: YourSynapse ) -> float.

  • AssertionError – If verify_fn() does not have the signature: verify( synapse: YourSynapse ) -> None.

Returns:

Returns the instance of the AxonServer class for potential method chaining.

Return type:

self

Parameters:
  • forward_fn (Callable) –

  • blacklist_fn (Callable) –

  • priority_fn (Callable) –

  • verify_fn (Callable) –

Example Usage:

def forward_custom(synapse: MyCustomSynapse) -> MyCustomSynapse:
    # Custom logic for processing the request
    return synapse

def blacklist_custom(synapse: MyCustomSynapse) -> Tuple[bool, str]:
    return True, "Allowed!"

def priority_custom(synapse: MyCustomSynapse) -> float:
    return 1.0

def verify_custom(synapse: MyCustomSynapse):
    # Custom logic for verifying the request
    pass

my_axon = bittensor.axon(...)
my_axon.attach(forward_fn=forward_custom, verify_fn=verify_custom)

Note

The attach() method is fundamental in setting up the Axon server’s request handling capabilities, enabling it to participate effectively and securely in the Bittensor network. The flexibility offered by this method allows developers to tailor the Axon’s behavior to specific requirements and use cases.

classmethod check_config(config)#

This method checks the configuration for the axon’s port and wallet.

Parameters:

config (bittensor.config) – The config object holding axon settings.

Raises:

AssertionError – If the axon or external ports are not in range [1024, 65535]

classmethod config()#

Parses the command-line arguments to form a Bittensor configuration object.

Returns:

Configuration object with settings from command-line arguments.

Return type:

bittensor.config

async default_verify(synapse)#

This method is used to verify the authenticity of a received message using a digital signature.

It ensures that the message was not tampered with and was sent by the expected sender.

The default_verify() method in the Bittensor framework is a critical security function within the Axon server. It is designed to authenticate incoming messages by verifying their digital signatures. This verification ensures the integrity of the message and confirms that it was indeed sent by the claimed sender. The method plays a pivotal role in maintaining the trustworthiness and reliability of the communication within the Bittensor network.

Key Features
Security Assurance

The default_verify method is crucial for ensuring the security of the Bittensor network. By verifying digital signatures, it guards against unauthorized access and data manipulation.

Preventing Replay Attacks

The method checks for increasing nonce values, which is a vital step in preventing replay attacks. A replay attack involves an adversary reusing or delaying the transmission of a valid data transmission to deceive the receiver.

Authenticity and Integrity Checks

By verifying that the message’s digital signature matches its content, the method ensures the message’s authenticity (it comes from the claimed sender) and integrity (it hasn’t been altered during transmission).

Trust in Communication

This method fosters trust in the network communication. Neurons (nodes in the Bittensor network) can confidently interact, knowing that the messages they receive are genuine and have not been tampered with.

Cryptographic Techniques

The method’s reliance on asymmetric encryption techniques is a cornerstone of modern cryptographic security, ensuring that only entities with the correct cryptographic keys can participate in secure communication.

Parameters:

synapse (bittensor.Synapse) – bittensor.Synapse bittensor request synapse.

Raises:
  • Exception – If the receiver_hotkey doesn’t match with self.receiver_hotkey.

  • Exception – If the nonce is not larger than the previous nonce for the same endpoint key.

  • Exception – If the signature verification fails.

After successful verification, the nonce for the given endpoint key is updated.

Note

The verification process assumes the use of an asymmetric encryption algorithm, where the sender signs the message with their private key and the receiver verifies the signature using the sender’s public key.

classmethod help()#

Prints the help text (list of command-line arguments and their descriptions) to stdout.

info()#

Returns the axon info object associated with this axon.

Return type:

bittensor.AxonInfo

serve(netuid, subtensor=None)#

Serves the Axon on the specified subtensor connection using the configured wallet. This method registers the Axon with a specific subnet within the Bittensor network, identified by the netuid. It links the Axon to the broader network, allowing it to participate in the decentralized exchange of information.

Parameters:
  • netuid (int) – The unique identifier of the subnet to register on. This ID is essential for the Axon to correctly position itself within the Bittensor network topology.

  • subtensor (bittensor.subtensor, optional) – The subtensor connection to use for serving. If not provided, a new connection is established based on default configurations.

Returns:

The Axon instance that is now actively serving on the specified subtensor.

Return type:

bittensor.axon

Example:

my_axon = bittensor.axon(...)
subtensor = bt.subtensor(network="local") # Local by default
my_axon.serve(netuid=1, subtensor=subtensor)  # Serves the axon on subnet with netuid 1

Note

The serve method is crucial for integrating the Axon into the Bittensor network, allowing it to start receiving and processing requests from other neurons.

start()#

Starts the Axon server and its underlying FastAPI server thread, transitioning the state of the Axon instance to started. This method initiates the server’s ability to accept and process incoming network requests, making it an active participant in the Bittensor network.

The start method triggers the FastAPI server associated with the Axon to begin listening for incoming requests. It is a crucial step in making the neuron represented by this Axon operational within the Bittensor network.

Returns:

The Axon instance in the ‘started’ state.

Return type:

bittensor.axon

Example:

my_axon = bittensor.axon(...)
... # setup axon, attach functions, etc.
my_axon.start()  # Starts the axon server

Note

After invoking this method, the Axon is ready to handle requests as per its configured endpoints and custom logic.

stop()#

Stops the Axon server and its underlying GRPC server thread, transitioning the state of the Axon instance to stopped. This method ceases the server’s ability to accept new network requests, effectively removing the neuron’s server-side presence in the Bittensor network.

By stopping the FastAPI server, the Axon ceases to listen for incoming requests, and any existing connections are gracefully terminated. This function is typically used when the neuron is being shut down or needs to temporarily go offline.

Returns:

The Axon instance in the ‘stopped’ state.

Return type:

bittensor.axon

Example:

my_axon = bittensor.axon(...)
my_axon.start()
...
my_axon.stop()  # Stops the axon server

Note

It is advisable to ensure that all ongoing processes or requests are completed or properly handled before invoking this method.

to_string()#

Provides a human-readable representation of the AxonInfo for this Axon.

async verify_body_integrity(request)#

The verify_body_integrity method in the Bittensor framework is a key security function within the Axon server’s middleware. It is responsible for ensuring the integrity of the body of incoming HTTP requests.

It asynchronously verifies the integrity of the body of a request by comparing the hash of required fields with the corresponding hashes provided in the request headers. This method is critical for ensuring that the incoming request payload has not been altered or tampered with during transmission, establishing a level of trust and security between the sender and receiver in the network.

Parameters:

request (Request) – The incoming FastAPI request object containing both headers and the request body.

Returns:

Returns the parsed body of the request as a dictionary if all the hash comparisons match,

indicating that the body is intact and has not been tampered with.

Return type:

dict

Raises:

JSONResponse – Raises a JSONResponse with a 400 status code if any of the hash comparisons fail, indicating a potential integrity issue with the incoming request payload. The response includes the detailed error message specifying which field has a hash mismatch.

This method performs several key functions:

  1. Decoding and loading the request body for inspection.

  2. Gathering required field names for hash comparison from the Axon configuration.

  3. Loading and parsing the request body into a dictionary.

  4. Reconstructing the Synapse object and recomputing the hash for verification and logging.

  5. Comparing the recomputed hash with the hash provided in the request headers for verification.

Note

The integrity verification is an essential step in ensuring the security of the data exchange within the Bittensor network. It helps prevent tampering and manipulation of data during transit, thereby maintaining the reliability and trust in the network communication.

bittensor.cast_dtype(raw)#

Casts the raw value to a string representing the torch data type.

Parameters:

raw (Union[None, torch.dtype, str]) – The raw value to cast.

Returns:

The string representing the torch data type.

Return type:

str

Raises:

Exception – If the raw value is of an invalid type.

bittensor.cast_float(raw)#

Converts a string to a float, if the string is not None.

This function attempts to convert a string to a float. If the string is None, it simply returns None.

Parameters:

raw (str) – The string to convert.

Returns:

The converted float, or None if the input was None.

Return type:

float or None

bittensor.cast_int(raw)#

Converts a string to an integer, if the string is not None.

This function attempts to convert a string to an integer. If the string is None, it simply returns None.

Parameters:

raw (str) – The string to convert.

Returns:

The converted integer, or None if the input was None.

Return type:

int or None

bittensor.cast_shape(raw)#

Casts the raw value to a string representing the tensor shape.

Parameters:

raw (Union[None, List[int], str]) – The raw value to cast.

Returns:

The string representing the tensor shape.

Return type:

str

Raises:

Exception – If the raw value is of an invalid type or if the list elements are not of type int.

class bittensor.cli(config=None, args=None)#

Implementation of the Command Line Interface (CLI) class for the Bittensor protocol. This class handles operations like key management (hotkey and coldkey) and token transfer.

Parameters:
static __create_parser__()#

Creates the argument parser for the Bittensor CLI.

Returns:

An argument parser object for Bittensor CLI.

Return type:

argparse.ArgumentParser

static check_config(config)#

Checks if the essential configuration exists under different command

Parameters:

config (bittensor.config) – The configuration settings for the CLI.

static create_config(args)#

From the argument parser, add config to bittensor.executor and local config

Parameters:

args (List[str]) – List of command line arguments.

Returns:

The configuration object for Bittensor CLI.

Return type:

bittensor.config

run()#

Executes the command from the configuration.

class bittensor.config(parser=None, args=None, strict=False, default=None)#

Bases: munch.DefaultMunch

Implementation of the config class, which manages the configuration of different Bittensor modules.

Parameters:
__is_set: Dict[str, bool]#

Translates the passed parser into a nested Bittensor config.

Parameters:
  • parser (argparse.ArgumentParser) – Command line parser object.

  • strict (bool) – If true, the command line arguments are strictly parsed.

  • args (list of str) – Command line arguments.

  • default (Optional[Any]) – Default value for the Config. Defaults to None. This default will be returned for attributes that are undefined.

Returns:

Nested config object created from parser arguments.

Return type:

config (bittensor.config)

__deepcopy__(memo)#
Return type:

config

static __parse_args__(args, parser=None, strict=False)#

Parses the passed args use the passed parser.

Parameters:
  • args (List[str]) – List of arguments to parse.

  • parser (argparse.ArgumentParser) – Command line parser object.

  • strict (bool) – If true, the command line arguments are strictly parsed.

Returns:

Namespace object created from parser arguments.

Return type:

Namespace

__repr__()#

Invertible* string-form of a Munch.

>>> b = Munch(foo=Munch(lol=True), hello=42, ponies='are pretty!')
>>> print (repr(b))
Munch({'ponies': 'are pretty!', 'foo': Munch({'lol': True}), 'hello': 42})
>>> eval(repr(b))
Munch({'ponies': 'are pretty!', 'foo': Munch({'lol': True}), 'hello': 42})
>>> with_spaces = Munch({1: 2, 'a b': 9, 'c': Munch({'simple': 5})})
>>> print (repr(with_spaces))
Munch({'a b': 9, 1: 2, 'c': Munch({'simple': 5})})
>>> eval(repr(with_spaces))
Munch({'a b': 9, 1: 2, 'c': Munch({'simple': 5})})

(*) Invertible so long as collection contents are each repr-invertible.

Return type:

str

static __split_params__(params, _config)#
Parameters:
__str__()#

Return str(self).

Return type:

str

classmethod _merge(a, b)#

Merge two configurations recursively. If there is a conflict, the value from the second configuration will take precedence.

static _remove_private_keys(d)#
copy()#

D.copy() -> a shallow copy of D

Return type:

config

is_set(param_name)#

Returns a boolean indicating whether the parameter has been set or is still the default.

Parameters:

param_name (str) –

Return type:

bool

merge(b)#

Merges the current config with another config.

Parameters:

b – Another config to merge.

classmethod merge_all(configs)#

Merge all configs in the list into one config. If there is a conflict, the value from the last configuration in the list will take precedence.

Parameters:

configs (list of config) – List of configs to be merged.

Returns:

Merged config object.

Return type:

config

to_string(items)#

Get string from items

Return type:

str

update_with_kwargs(kwargs)#

Add config to self

bittensor.configs#
bittensor.custom_rpc_type_registry#
bittensor.debug(on=True)#
Parameters:

on (bool) –

bittensor.decrypt_keyfile_data(keyfile_data, password=None, coldkey_name=None)#

Decrypts the passed keyfile data using ansible vault.

Parameters:
  • keyfile_data (bytes) – The bytes to decrypt.

  • password (str, optional) – The password used to decrypt the data. If None, asks for user input.

  • coldkey_name (str, optional) – The name of the cold key. If provided, retrieves the password from environment variables.

Returns:

The decrypted data.

Return type:

decrypted_data (bytes)

Raises:

KeyFileError – Raised if the file is corrupted or if the password is incorrect.

bittensor.defaults#
class bittensor.dendrite(wallet=None)#

Bases: torch.nn.Module

The Dendrite class, inheriting from PyTorch’s Module class, represents the abstracted implementation of a network client module.

In the brain analogy, dendrites receive signals from other neurons (in this case, network servers or axons), and the Dendrite class here is designed to send requests to those endpoint to recieve inputs.

This class includes a wallet or keypair used for signing messages, and methods for making HTTP requests to the network servers. It also provides functionalities such as logging network requests and processing server responses.

Parameters:
  • keypair – The wallet or keypair used for signing messages.

  • external_ip (str) – The external IP address of the local system.

  • synapse_history (list) – A list of Synapse objects representing the historical responses.

  • wallet (Optional[Union[bittensor.wallet, bittensor.keypair]]) –

__str__()#

Returns a string representation of the Dendrite object.

Return type:

str

__repr__()#

Returns a string representation of the Dendrite object, acting as a fallback for __str__().

Return type:

str

query(self, *args, **kwargs) bittensor.Synapse | List[bittensor.Synapse]#

Makes synchronous requests to one or multiple target Axons and returns responses.

Return type:

Union[bittensor.Synapse, List[bittensor.Synapse], bittensor.StreamingSynapse, List[bittensor.StreamingSynapse]]

forward(self, axons, synapse=bittensor.Synapse(), timeout=12, deserialize=True, run_async=True, streaming=False) bittensor.Synapse#

Asynchronously sends requests to one or multiple Axons and collates their responses.

Parameters:
Return type:

List[Union[AsyncGenerator[Any], bittenst.Synapse, bittensor.StreamingSynapse]]

call(self, target_axon, synapse=bittensor.Synapse(), timeout=12.0, deserialize=True) bittensor.Synapse#

Asynchronously sends a request to a specified Axon and processes the response.

Parameters:
Return type:

bittensor.Synapse

call_stream(self, target_axon, synapse=bittensor.Synapse(), timeout=12.0, deserialize=True) AsyncGenerator[bittensor.Synapse, None]#

Sends a request to a specified Axon and yields an AsyncGenerator that contains streaming response chunks before finally yielding the filled Synapse as the final element.

Parameters:
Return type:

AsyncGenerator[Any]

preprocess_synapse_for_request(self, target_axon_info, synapse, timeout=12.0) bittensor.Synapse#

Preprocesses the synapse for making a request, including building headers and signing.

Parameters:
Return type:

bittensor.Synapse

process_server_response(self, server_response, json_response, local_synapse)#

Processes the server response, updates the local synapse state, and merges headers.

Parameters:
close_session(self)#

Synchronously closes the internal aiohttp client session.

aclose_session(self)#

Asynchronously closes the internal aiohttp client session.

Note

When working with async aiohttp client sessions, it is recommended to use a context manager.

Example with a context manager:

>>> aysnc with dendrite(wallet = bittensor.wallet()) as d:
>>>     print(d)
>>>     d( <axon> ) # ping axon
>>>     d( [<axons>] ) # ping multiple
>>>     d( bittensor.axon(), bittensor.Synapse )

However, you are able to safely call dendrite.query() without a context manager in a synchronous setting.

Example without a context manager:

>>> d = dendrite(wallet = bittensor.wallet() )
>>> print(d)
>>> d( <axon> ) # ping axon
>>> d( [<axons>] ) # ping multiple
>>> d( bittensor.axon(), bittensor.Synapse )
property session: aiohttp.ClientSession#

An asynchronous property that provides access to the internal aiohttp client session.

This property ensures the management of HTTP connections in an efficient way. It lazily initializes the aiohttp.ClientSession on its first use. The session is then reused for subsequent HTTP requests, offering performance benefits by reusing underlying connections.

This is used internally by the dendrite when querying axons, and should not be used directly unless absolutely necessary for your application.

Returns:

The active aiohttp client session instance. If no session exists, a new one is created and returned. This session is used for asynchronous HTTP requests within the dendrite, adhering to the async nature of the network interactions in the Bittensor framework.

Return type:

aiohttp.ClientSession

Example usage:

import bittensor as bt                    # Import bittensor
wallet = bt.wallet( ... )                 # Initialize a wallet
dendrite = bt.dendrite( wallet )          # Initialize a dendrite instance with the wallet

async with (await dendrite.session).post( # Use the session to make an HTTP POST request
    url,                                  # URL to send the request to
    headers={...},                        # Headers dict to be sent with the request
    json={...},                           # JSON body data to be sent with the request
    timeout=10,                           # Timeout duration in seconds
) as response:
    json_response = await response.json() # Extract the JSON response from the server
async __aenter__()#

Asynchronous context manager entry method.

Enables the use of the async with statement with the Dendrite instance. When entering the context, the current instance of the class is returned, making it accessible within the asynchronous context.

Returns:

The current instance of the Dendrite class.

Return type:

Dendrite

Usage:

async with Dendrite() as dendrite:
    await dendrite.some_async_method()
async __aexit__(exc_type, exc_value, traceback)#

Asynchronous context manager exit method.

Ensures proper cleanup when exiting the async with context. This method will close the aiohttp client session asynchronously, releasing any tied resources.

Parameters:
  • exc_type (Type[BaseException], optional) – The type of exception that was raised.

  • exc_value (BaseException, optional) – The instance of exception that was raised.

  • traceback (TracebackType, optional) – A traceback object encapsulating the call stack at the point where the exception was raised.

Usage:

async with bt.dendrite( wallet ) as dendrite:
    await dendrite.some_async_method()

Note

This automatically closes the session by calling __aexit__() after the context closes.

__del__()#

Dendrite destructor.

This method is invoked when the Dendrite instance is about to be destroyed. The destructor ensures that the aiohttp client session is closed before the instance is fully destroyed, releasing any remaining resources.

Note

Relying on the destructor for cleanup can be unpredictable. It is recommended to explicitly close sessions using the provided methods or the async with context manager.

Usage:

dendrite = Dendrite()
# ... some operations ...
del dendrite  # This will implicitly invoke the __del__ method and close the session.
__repr__()#

Returns a string representation of the Dendrite object, acting as a fallback for __str__().

Returns:

The string representation of the Dendrite object in the format dendrite(<user_wallet_address>)().

Return type:

str

__str__()#

Returns a string representation of the Dendrite object.

Returns:

The string representation of the Dendrite object in the format dendrite(<user_wallet_address>)().

Return type:

str

_get_endpoint_url(target_axon, request_name)#

Constructs the endpoint URL for a network request to a target axon.

This internal method generates the full HTTP URL for sending a request to the specified axon. The URL includes the IP address and port of the target axon, along with the specific request name. It differentiates between requests to the local system (using ‘0.0.0.0’) and external systems.

Parameters:
  • target_axon – The target axon object containing IP and port information.

  • request_name – The specific name of the request being made.

Returns:

A string representing the complete HTTP URL for the request.

Return type:

str

_handle_request_errors(synapse, request_name, exception)#

Handles exceptions that occur during network requests, updating the synapse with appropriate status codes and messages.

This method interprets different types of exceptions and sets the corresponding status code and message in the synapse object. It covers common network errors such as connection issues and timeouts.

Parameters:
  • synapse – The synapse object associated with the request.

  • request_name – The name of the request during which the exception occurred.

  • exception – The exception object caught during the request.

Note

This method updates the synapse object in-place.

_log_incoming_response(synapse)#

Logs information about incoming responses for debugging and monitoring.

Similar to _log_outgoing_request(), this method logs essential details of the incoming responses, including the size of the response, synapse name, axon details, status code, and status message. This logging is vital for troubleshooting and understanding the network interactions in Bittensor.

Parameters:

synapse – The synapse object representing the received response.

_log_outgoing_request(synapse)#

Logs information about outgoing requests for debugging purposes.

This internal method logs key details about each outgoing request, including the size of the request, the name of the synapse, the axon’s details, and a success indicator. This information is crucial for monitoring and debugging network activity within the Bittensor network.

To turn on debug messages, set the environment variable BITTENSOR_DEBUG to 1, or call the bittensor debug method like so:

import bittensor
bittensor.debug()
Parameters:

synapse – The synapse object representing the request being sent.

async aclose_session()#

Asynchronously closes the internal aiohttp client session.

This method is the asynchronous counterpart to the close_session() method. It should be used in asynchronous contexts to ensure that the aiohttp client session is closed properly. The method releases resources associated with the session, such as open connections and internal buffers, which is essential for resource management in asynchronous applications.

Usage:

When finished with dendrite in an asynchronous context await dendrite_instance.aclose_session().

Example:

async with dendrite_instance:
    # Operations using dendrite
    pass
# The session will be closed automatically after the above block
async call(target_axon, synapse=bittensor.Synapse(), timeout=12.0, deserialize=True)#

Asynchronously sends a request to a specified Axon and processes the response.

This function establishes a connection with a specified Axon, sends the encapsulated data through the Synapse object, waits for a response, processes it, and then returns the updated Synapse object.

Parameters:
  • target_axon (Union['bittensor.AxonInfo', 'bittensor.axon']) – The target Axon to send the request to.

  • synapse (bittensor.Synapse, optional) – The Synapse object encapsulating the data. Defaults to a new bittensor.Synapse() instance.

  • timeout (float, optional) – Maximum duration to wait for a response from the Axon in seconds. Defaults to 12.0.

  • deserialize (bool, optional) – Determines if the received response should be deserialized. Defaults to True.

Returns:

The Synapse object, updated with the response data from the Axon.

Return type:

bittensor.Synapse

async call_stream(target_axon, synapse=bittensor.Synapse(), timeout=12.0, deserialize=True)#

Sends a request to a specified Axon and yields streaming responses.

Similar to call, but designed for scenarios where the Axon sends back data in multiple chunks or streams. The function yields each chunk as it is received. This is useful for processing large responses piece by piece without waiting for the entire data to be transmitted.

Parameters:
  • target_axon (Union['bittensor.AxonInfo', 'bittensor.axon']) – The target Axon to send the request to.

  • synapse (bittensor.Synapse, optional) – The Synapse object encapsulating the data. Defaults to a new bittensor.Synapse() instance.

  • timeout (float, optional) – Maximum duration to wait for a response (or a chunk of the response) from the Axon in seconds. Defaults to 12.0.

  • deserialize (bool, optional) – Determines if each received chunk should be deserialized. Defaults to True.

Yields:

object – Each yielded object contains a chunk of the arbitrary response data from the Axon. bittensor.Synapse: After the AsyncGenerator has been exhausted, yields the final filled Synapse.

Return type:

AsyncGenerator[Any]

close_session()#

Closes the internal aiohttp client session synchronously.

This method ensures the proper closure and cleanup of the aiohttp client session, releasing any resources like open connections and internal buffers. It is crucial for preventing resource leakage and should be called when the dendrite instance is no longer in use, especially in synchronous contexts.

Note

This method utilizes asyncio’s event loop to close the session asynchronously from a synchronous context. It is advisable to use this method only when asynchronous context management is not feasible.

Usage:

When finished with dendrite in a synchronous context dendrite_instance.close_session().

async forward(axons, synapse=bittensor.Synapse(), timeout=12, deserialize=True, run_async=True, streaming=False)#

Asynchronously sends requests to one or multiple Axons and collates their responses.

This function acts as a bridge for sending multiple requests concurrently or sequentially based on the provided parameters. It checks the type of the target Axons, preprocesses the requests, and then sends them off. After getting the responses, it processes and collates them into a unified format.

When querying an Axon that sends a single response, this function returns a Synapse object containing the response data. If multiple Axons are queried, a list of Synapse objects is returned, each containing the response from the corresponding Axon.

For example:

>>> ...
>>> wallet = bittensor.wallet()                   # Initialize a wallet
>>> synapse = bittensor.Synapse(...)              # Create a synapse object that contains query data
>>> dendrte = bittensor.dendrite(wallet = wallet) # Initialize a dendrite instance
>>> axons = metagraph.axons                       # Create a list of axons to query
>>> responses = await dendrite(axons, synapse)    # Send the query to all axons and await the responses

When querying an Axon that sends back data in chunks using the Dendrite, this function returns an AsyncGenerator that yields each chunk as it is received. The generator can be iterated over to process each chunk individually.

For example:

>>> ...
>>> dendrte = bittensor.dendrite(wallet = wallet)
>>> async for chunk in dendrite.forward(axons, synapse, timeout, deserialize, run_async, streaming):
>>>     # Process each chunk here
>>>     print(chunk)
Parameters:
  • axons (Union[List[Union['bittensor.AxonInfo', 'bittensor.axon']], Union['bittensor.AxonInfo', 'bittensor.axon']]) – The target Axons to send requests to. Can be a single Axon or a list of Axons.

  • synapse (bittensor.Synapse, optional) – The Synapse object encapsulating the data. Defaults to a new bittensor.Synapse() instance.

  • timeout (float, optional) – Maximum duration to wait for a response from an Axon in seconds. Defaults to 12.0.

  • deserialize (bool, optional) – Determines if the received response should be deserialized. Defaults to True.

  • run_async (bool, optional) – If True, sends requests concurrently. Otherwise, sends requests sequentially. Defaults to True.

  • streaming (bool, optional) – Indicates if the response is expected to be in streaming format. Defaults to False.

Returns:

If a single Axon is targeted, returns its response. If multiple Axons are targeted, returns a list of their responses.

Return type:

Union[AsyncGenerator, bittensor.Synapse, List[bittensor.Synapse]]

preprocess_synapse_for_request(target_axon_info, synapse, timeout=12.0)#

Preprocesses the synapse for making a request. This includes building headers for Dendrite and Axon and signing the request.

Parameters:
  • target_axon_info (bittensor.AxonInfo) – The target axon information.

  • synapse (bittensor.Synapse) – The synapse object to be preprocessed.

  • timeout (float, optional) – The request timeout duration in seconds. Defaults to 12.0 seconds.

Returns:

The preprocessed synapse.

Return type:

bittensor.Synapse

process_server_response(server_response, json_response, local_synapse)#

Processes the server response, updates the local synapse state with the server’s state and merges headers set by the server.

Parameters:
  • server_response (object) –

    The aiohttp response object from the server.

  • json_response (dict) – The parsed JSON response from the server.

  • local_synapse (bittensor.Synapse) – The local synapse object to be updated.

Raises:

None – But errors in attribute setting are silently ignored.

query(*args, **kwargs)#

Makes a synchronous request to multiple target Axons and returns the server responses.

Cleanup is automatically handled and sessions are closed upon completed requests.

Parameters:
  • axons (Union[List[Union['bittensor.AxonInfo', 'bittensor.axon']], Union['bittensor.AxonInfo', 'bittensor.axon']]) – The list of target Axon information.

  • synapse (bittensor.Synapse, optional) – The Synapse object. Defaults to bittensor.Synapse().

  • timeout (float, optional) – The request timeout duration in seconds. Defaults to 12.0 seconds.

Returns:

If a single target axon is provided, returns the response from that axon. If multiple target axons are provided, returns a list of responses from all target axons.

Return type:

Union[bittensor.Synapse, List[bittensor.Synapse]]

bittensor.deserialize_keypair_from_keyfile_data(keyfile_data)#

Deserializes Keypair object from passed keyfile data.

Parameters:

keyfile_data (bytes) – The keyfile data as bytes to be loaded.

Returns:

The Keypair loaded from bytes.

Return type:

keypair (bittensor.Keypair)

Raises:

KeyFileError – Raised if the passed bytes cannot construct a keypair object.

bittensor.display_mnemonic_msg(keypair, key_type)#

Display the mnemonic and a warning message to keep the mnemonic safe.

Parameters:
  • keypair (Keypair) – Keypair object.

  • key_type (str) – Type of the key (coldkey or hotkey).

bittensor.encrypt_keyfile_data(keyfile_data, password=None)#

Encrypts the passed keyfile data using ansible vault.

Parameters:
  • keyfile_data (bytes) – The bytes to encrypt.

  • password (str, optional) – The password used to encrypt the data. If None, asks for user input.

Returns:

The encrypted data.

Return type:

encrypted_data (bytes)

bittensor.from_scale_encoding(input, type_name, is_vec=False, is_option=False)#
Parameters:
Return type:

Optional[Dict]

bittensor.from_scale_encoding_using_type_string(input, type_string)#
Parameters:
  • input (Union[List[int], bytes, scalecodec.base.ScaleBytes]) –

  • type_string (str) –

Return type:

Optional[Dict]

bittensor.get_coldkey_password_from_environment(coldkey_name)#

Retrieves the cold key password from the environment variables.

Parameters:

coldkey_name (str) – The name of the cold key.

Returns:

The password retrieved from the environment variables, or None if not found.

Return type:

password (str)

bittensor.get_size(obj, seen=None)#

Recursively finds size of objects.

This function traverses every item of a given object and sums their sizes to compute the total size.

Parameters:
  • obj (any type) – The object to get the size of.

  • seen (set) – Set of object ids that have been calculated.

Returns:

The total size of the object.

Return type:

int

class bittensor.keyfile(path)#

Defines an interface for a substrate interface keypair stored on device.

Parameters:

path (str) –

property data: bytes#

Returns the keyfile data under path.

Returns:

The keyfile data stored under the path.

Return type:

keyfile_data (bytes)

Raises:

KeyFileError – Raised if the file does not exist, is not readable, or writable.

property keyfile_data: bytes#

Returns the keyfile data under path.

Returns:

The keyfile data stored under the path.

Return type:

keyfile_data (bytes)

Raises:

KeyFileError – Raised if the file does not exist, is not readable, or writable.

property keypair: bittensor.Keypair#

Returns the keypair from path, decrypts data if the file is encrypted.

Returns:

The keypair stored under the path.

Return type:

keypair (bittensor.Keypair)

Raises:

KeyFileError – Raised if the file does not exist, is not readable, writable, corrupted, or if the password is incorrect.

__repr__()#

Return repr(self).

__str__()#

Return str(self).

_may_overwrite()#

Asks the user if it is okay to overwrite the file.

Returns:

True if the user allows overwriting the file.

Return type:

may_overwrite (bool)

_read_keyfile_data_from_file()#

Reads the keyfile data from the file.

Returns:

The keyfile data stored under the path.

Return type:

keyfile_data (bytes)

Raises:

KeyFileError – Raised if the file does not exist or is not readable.

_write_keyfile_data_to_file(keyfile_data, overwrite=False)#

Writes the keyfile data to the file.

Parameters:
  • keyfile_data (bytes) – The byte data to store under the path.

  • overwrite (bool, optional) – If True, overwrites the data without asking for permission from the user. Default is False.

Raises:

KeyFileError – Raised if the file is not writable or the user responds No to the overwrite prompt.

check_and_update_encryption(print_result=True, no_prompt=False)#

Check the version of keyfile and update if needed.

Parameters:
  • print_result (bool) – Print the checking result or not.

  • no_prompt (bool) – Skip if no prompt.

Raises:

KeyFileError – Raised if the file does not exists, is not readable, writable.

Returns:

Return True if the keyfile is the most updated with nacl, else False.

Return type:

result (bool)

decrypt(password=None)#

Decrypts the file under the path.

Parameters:

password (str, optional) – The password for decryption. If None, asks for user input.

Raises:

KeyFileError – Raised if the file does not exist, is not readable, writable, corrupted, or if the password is incorrect.

encrypt(password=None)#

Encrypts the file under the path.

Parameters:

password (str, optional) – The password for encryption. If None, asks for user input.

Raises:

KeyFileError – Raised if the file does not exist, is not readable, or writable.

exists_on_device()#

Returns True if the file exists on the device.

Returns:

True if the file is on the device.

Return type:

on_device (bool)

get_keypair(password=None)#

Returns the keypair from the path, decrypts data if the file is encrypted.

Parameters:

password (str, optional) – The password used to decrypt the file. If None, asks for user input.

Returns:

The keypair stored under the path.

Return type:

keypair (bittensor.Keypair)

Raises:

KeyFileError – Raised if the file does not exist, is not readable, writable, corrupted, or if the password is incorrect.

is_encrypted()#

Returns True if the file under path is encrypted.

Returns:

True if the file is encrypted.

Return type:

encrypted (bool)

is_readable()#

Returns True if the file under path is readable.

Returns:

True if the file is readable.

Return type:

readable (bool)

is_writable()#

Returns True if the file under path is writable.

Returns:

True if the file is writable.

Return type:

writable (bool)

make_dirs()#

Creates directories for the path if they do not exist.

set_keypair(keypair, encrypt=True, overwrite=False, password=None)#

Writes the keypair to the file and optionally encrypts data.

Parameters:
  • keypair (bittensor.Keypair) – The keypair to store under the path.

  • encrypt (bool, optional) – If True, encrypts the file under the path. Default is True.

  • overwrite (bool, optional) – If True, forces overwrite of the current file. Default is False.

  • password (str, optional) – The password used to encrypt the file. If None, asks for user input.

Raises:

KeyFileError – Raised if the file does not exist, is not readable, writable, or if the password is incorrect.

bittensor.keyfile_data_encryption_method(keyfile_data)#

Returns true if the keyfile data is encrypted.

Parameters:

keyfile_data (bytes, required) – Bytes to validate

Returns:

True if data is encrypted.

Return type:

encryption_method (bool)

bittensor.keyfile_data_is_encrypted(keyfile_data)#

Returns true if the keyfile data is encrypted.

Parameters:

keyfile_data (bytes) – The bytes to validate.

Returns:

True if the data is encrypted.

Return type:

is_encrypted (bool)

bittensor.keyfile_data_is_encrypted_ansible(keyfile_data)#

Returns true if the keyfile data is ansible encrypted.

Parameters:

keyfile_data (bytes) – The bytes to validate.

Returns:

True if the data is ansible encrypted.

Return type:

is_ansible (bool)

bittensor.keyfile_data_is_encrypted_legacy(keyfile_data)#

Returns true if the keyfile data is legacy encrypted. :param keyfile_data: The bytes to validate. :type keyfile_data: bytes

Returns:

True if the data is legacy encrypted.

Return type:

is_legacy (bool)

Parameters:

keyfile_data (bytes) –

bittensor.keyfile_data_is_encrypted_nacl(keyfile_data)#

Returns true if the keyfile data is NaCl encrypted.

Parameters:

keyfile_data (bytes, required) – Bytes to validate.

Returns:

True if data is ansible encrypted.

Return type:

is_nacl (bool)

bittensor.legacy_encrypt_keyfile_data(keyfile_data, password=None)#
Parameters:
  • keyfile_data (bytes) –

  • password (str) –

Return type:

bytes

class bittensor.logging#

Standardized logging for Bittensor.

__debug_on__: bool = False#
__file_sink__: int#
__has_been_inited__: bool = False#
__std_sink__: int#
__trace_on__: bool = False#
classmethod _format(prefix, sufix=None)#

Format logging message

Parameters:
classmethod add_args(parser, prefix=None)#

Accept specific arguments fro parser

Parameters:
classmethod check_config(config)#

Check config

Parameters:

config (bittensor.config) –

classmethod config()#

Get config from the argument parser.

Returns:

bittensor.config object

classmethod debug(prefix, sufix=None)#

Info logging

Parameters:
classmethod error(prefix, sufix=None)#

Error logging

Parameters:
classmethod exception(prefix, sufix=None)#

Exception logging with traceback

Parameters:
classmethod get_level()#
Return type:

int

classmethod help()#

Print help to stdout

classmethod info(prefix, sufix=None)#

Info logging

Parameters:
classmethod log_filter(record)#

Filter out debug log if debug is not on

classmethod log_formatter(record)#

Log with different format according to record[‘extra’]

classmethod log_save_filter(record)#
classmethod log_save_formatter(record)#
classmethod set_debug(debug_on=True)#

Set debug for the specific cls class

Parameters:

debug_on (bool) –

classmethod set_trace(trace_on=True)#

Set trace back for the specific cls class

Parameters:

trace_on (bool) –

classmethod success(prefix, sufix=None)#

Success logging

Parameters:
classmethod trace(prefix, sufix=None)#

Info logging

Parameters:
classmethod warning(prefix, sufix=None)#

Warning logging

Parameters:
class bittensor.metagraph(netuid, network='finney', lite=True, sync=True)#

Bases: torch.nn.Module

The metagraph class is a core component of the Bittensor network, representing the neural graph that forms the backbone of the decentralized machine learning system.

The metagraph is a dynamic representation of the network’s state, capturing the interconnectedness and attributes of neurons (participants) in the Bittensor ecosystem. This class is not just a static structure but a live reflection of the network, constantly updated and synchronized with the state of the blockchain.

In Bittensor, neurons are akin to nodes in a distributed system, each contributing computational resources and participating in the network’s collective intelligence. The metagraph tracks various attributes of these neurons, such as stake, trust, and consensus, which are crucial for the network’s incentive mechanisms and the Yuma Consensus algorithm as outlined in the NeurIPS paper. These attributes govern how neurons interact, how they are incentivized, and their roles within the network’s decision-making processes.

Parameters:
  • netuid (int) – A unique identifier that distinguishes between different instances or versions of the Bittensor network.

  • network (str) – The name of the network, signifying specific configurations or iterations within the Bittensor ecosystem.

  • version (torch.nn.parameter.Parameter) – The version number of the network, formatted for compatibility with PyTorch models, integral for tracking network updates.

  • n (torch.nn.Parameter) – The total number of neurons in the network, reflecting its size and complexity.

  • block (torch.nn.Parameter) – The current block number in the blockchain, crucial for synchronizing with the network’s latest state.

  • stake – Represents the cryptocurrency staked by neurons, impacting their influence and earnings within the network.

  • total_stake – The cumulative stake across all neurons.

  • ranks – Neuron rankings as per the Yuma Consensus algorithm, influencing their incentive distribution and network authority.

  • trust – Scores indicating the reliability of neurons, mainly miners, within the network’s operational context.

  • consensus – Scores reflecting each neuron’s alignment with the network’s collective decisions.

  • validator_trust – Trust scores for validator neurons, crucial for network security and validation.

  • incentive – Rewards allocated to neurons, particularly miners, for their network contributions.

  • emission – The rate at which rewards are distributed to neurons.

  • dividends – Rewards received primarily by validators as part of the incentive mechanism.

  • active – Status indicating whether a neuron is actively participating in the network.

  • last_update – Timestamp of the latest update to a neuron’s data.

  • validator_permit – Indicates if a neuron is authorized to act as a validator.

  • weights – Inter-neuronal weights set by each neuron, influencing network dynamics.

  • bonds – Represents speculative investments by neurons in others, part of the reward mechanism.

  • uids – Unique identifiers for each neuron, essential for network operations.

  • axons (List) – Details about each neuron’s axon, critical for facilitating network communication.

  • lite (bool) –

  • sync (bool) –

The metagraph plays a pivotal role in Bittensor’s decentralized AI operations, influencing everything from data propagation to reward distribution. It embodies the principles of decentralized governance and collaborative intelligence, ensuring that the network remains adaptive, secure, and efficient.

Example Usage:

Initializing the metagraph to represent the current state of the Bittensor network:

metagraph = bt.metagraph(netuid=config.netuid, network=subtensor.network, sync=False)

Synchronizing the metagraph with the network to reflect the latest state and neuron data:

metagraph.sync(subtensor=subtensor)

Accessing metagraph properties to inform network interactions and decisions:

total_stake = metagraph.S
neuron_ranks = metagraph.R
neuron_incentives = metagraph.I
...

Maintaining a local copy of hotkeys for querying and interacting with network entities:

hotkeys = deepcopy(metagraph.hotkeys)
property B: torch.FloatTensor#

Bonds in the Bittensor network represent a speculative reward mechanism where neurons can accumulate bonds in other neurons. Bonds are akin to investments or stakes in other neurons, reflecting a belief in their future value or performance. This mechanism encourages correct weighting and collaboration among neurons while providing an additional layer of incentive.

Returns:

A tensor representing the bonds held by each neuron, where each value signifies the proportion of bonds owned by one neuron in another.

Return type:

torch.FloatTensor

property C: torch.FloatTensor#

Represents the consensus values of neurons in the Bittensor network. Consensus is a measure of how much a neuron’s contributions are trusted and agreed upon by the majority of the network. It is calculated based on a staked weighted trust system, where the network leverages the collective judgment of all participating peers. Higher consensus values indicate that a neuron’s contributions are more widely trusted and valued across the network.

Returns:

A tensor of consensus values, where each element reflects the level of trust and agreement a neuron has achieved within the network.

Return type:

torch.FloatTensor

property D: torch.FloatTensor#

Represents the dividends received by neurons in the Bittensor network. Dividends are a form of reward or distribution, typically given to neurons based on their stake, performance, and contribution to the network. They are an integral part of the network’s incentive structure, encouraging active and beneficial participation.

Returns:

A tensor of dividend values, where each element indicates the dividends received by a neuron, reflecting their share of network rewards.

Return type:

torch.FloatTensor

property E: torch.FloatTensor#

Denotes the emission values of neurons in the Bittensor network. Emissions refer to the distribution or release of rewards (often in the form of cryptocurrency) to neurons, typically based on their stake and performance. This mechanism is central to the network’s incentive model, ensuring that active and contributing neurons are appropriately rewarded.

Returns:

A tensor where each element represents the emission value for a neuron, indicating the amount of reward distributed to that neuron.

Return type:

torch.FloatTensor

property I: torch.FloatTensor#

Incentive values of neurons represent the rewards they receive for their contributions to the network. The Bittensor network employs an incentive mechanism that rewards neurons based on their informational value, stake, and consensus with other peers. This ensures that the most valuable and trusted contributions are incentivized.

Returns:

A tensor of incentive values, indicating the rewards or benefits accrued by each neuron based on their contributions and network consensus.

Return type:

torch.FloatTensor

property R: torch.FloatTensor#

Contains the ranks of neurons in the Bittensor network. Ranks are determined by the network based on each neuron’s performance and contributions. Higher ranks typically indicate a greater level of contribution or performance by a neuron. These ranks are crucial in determining the distribution of incentives within the network, with higher-ranked neurons receiving more incentive.

Returns:

A tensor where each element represents the rank of a neuron. Higher values indicate higher ranks within the network.

Return type:

torch.FloatTensor

property S: torch.FloatTensor#

Represents the stake of each neuron in the Bittensor network. Stake is an important concept in the Bittensor ecosystem, signifying the amount of network weight (or “stake”) each neuron holds, represented on a digital ledger. The stake influences a neuron’s ability to contribute to and benefit from the network, playing a crucial role in the distribution of incentives and decision-making processes.

Returns:

A tensor representing the stake of each neuron in the network. Higher values signify a greater stake held by the respective neuron.

Return type:

torch.FloatTensor

property T: torch.FloatTensor#

Represents the trust values assigned to each neuron in the Bittensor network. Trust is a key metric that reflects the reliability and reputation of a neuron based on its past behavior and contributions. It is an essential aspect of the network’s functioning, influencing decision-making processes and interactions between neurons.

The trust matrix is inferred from the network’s inter-peer weights, indicating the level of trust each neuron has in others. A higher value in the trust matrix suggests a stronger trust relationship between neurons.

Returns:

A tensor of trust values, where each element represents the trust level of a neuron. Higher values denote a higher level of trust within the network.

Return type:

torch.FloatTensor

property Tv: torch.FloatTensor#

Contains the validator trust values of neurons in the Bittensor network. Validator trust is specifically associated with neurons that act as validators within the network. This specialized form of trust reflects the validators’ reliability and integrity in their role, which is crucial for maintaining the network’s stability and security.

Validator trust values are particularly important for the network’s consensus and validation processes, determining the validators’ influence and responsibilities in these critical functions.

Returns:

A tensor of validator trust values, specifically applicable to neurons serving as validators, where higher values denote greater trustworthiness in their validation roles.

Return type:

torch.FloatTensor

property W: torch.FloatTensor#

Represents the weights assigned to each neuron in the Bittensor network. In the context of Bittensor, weights are crucial for determining the influence and interaction between neurons. Each neuron is responsible for setting its weights, which are then recorded on a digital ledger. These weights are reflective of the neuron’s assessment or judgment of other neurons in the network.

The weight matrix \(W = [w_{ij}]\) is a key component of the network’s architecture, where the \(i^{th}\) row is set by neuron \(i\) and represents its weights towards other neurons. These weights influence the ranking and incentive mechanisms within the network. Higher weights from a neuron towards another can imply greater trust or value placed on that neuron’s contributions.

Returns:

A tensor of inter-peer weights, where each element \(w_{ij}\) represents the weight assigned by neuron \(i\) to neuron \(j\). This matrix is fundamental to the network’s functioning, influencing the distribution of incentives and the inter-neuronal dynamics.

Return type:

torch.FloatTensor

property addresses: List[str]#

Provides a list of IP addresses for each neuron in the Bittensor network. These addresses are used for network communication, allowing neurons to connect, interact, and exchange information with each other. IP addresses are fundamental for the network’s peer-to-peer communication infrastructure.

Returns:

A list of IP addresses, with each string representing the address of a neuron. These addresses enable the decentralized, distributed nature of the network, facilitating direct communication and data exchange among neurons.

Return type:

List[str]

Note

While IP addresses are a basic aspect of network communication, specific details about their use in the Bittensor network may not be covered in the NeurIPS paper. They are, however, integral to the functioning of any distributed network.

property coldkeys: List[str]#

Contains a list of coldkeys for each neuron in the Bittensor network.

Coldkeys are similar to hotkeys but are typically used for more secure, offline activities such as storing assets or offline signing of transactions. They are an important aspect of a neuron’s security, providing an additional layer of protection for sensitive operations and assets.

Returns:

A list of coldkeys, each string representing the coldkey of a neuron. These keys play a vital role in the secure management of assets and sensitive operations within the network.

Return type:

List[str]

Note

The concept of coldkeys, while not explicitly covered in the NeurIPS paper, is a standard practice in blockchain and decentralized networks for enhanced security and asset protection.

property hotkeys: List[str]#

Represents a list of hotkeys for each neuron in the Bittensor network.

Hotkeys are unique identifiers used by neurons for active participation in the network, such as sending and receiving information or transactions. They are akin to public keys in cryptographic systems and are essential for identifying and authenticating neurons within the network’s operations.

Returns:

A list of hotkeys, with each string representing the hotkey of a corresponding neuron.

These keys are crucial for the network’s security and integrity, ensuring proper identification and authorization of network participants.

Return type:

List[str]

Note

While the NeurIPS paper may not explicitly detail the concept of hotkeys, they are a fundamental of decentralized networks for secure and authenticated interactions.

__repr__()#

Provides a detailed string representation of the metagraph object, intended for unambiguous understanding and debugging purposes. This method simply calls the __str__() method, ensuring consistency between the informal and formal string representations of the metagraph.

Returns:

The same string representation as provided by the __str__() method, detailing the metagraph’s key attributes including network UID, number of neurons, block number, and network name.

Return type:

str

Example

The __repr__() output can be used in debugging to get a clear and concise description of the metagraph:

metagraph_repr = repr(metagraph)
print(metagraph_repr)  # Output mirrors that of __str__
__str__()#

Provides a human-readable string representation of the metagraph object. This representation includes key identifiers and attributes of the metagraph, making it easier to quickly understand the state and configuration of the metagraph in a simple format.

Returns:

A string that succinctly represents the metagraph, including its network UID, the total number of neurons (n), the current block number, and the network’s name. This format is particularly useful for logging, debugging, and displaying the metagraph in a concise manner.

Return type:

str

Example

When printing the metagraph object or using it in a string context, this method is automatically invoked:

print(metagraph)  # Output: "metagraph(netuid:1, n:100, block:500, network:finney)"
_assign_neurons(block, lite, subtensor)#

Assigns neurons to the metagraph based on the provided block number and the lite flag.

This method is responsible for fetching and setting the neuron data in the metagraph, which includes neuron attributes like UID, stake, trust, and other relevant information.

Parameters:
  • block – The block number for which the neuron data needs to be fetched. If None, the latest block data is used.

  • lite – A boolean flag indicating whether to use a lite version of the neuron data. The lite version typically includes essential information and is quicker to fetch and process.

  • subtensor – The subtensor instance used for fetching neuron data from the network.

Internal Usage:

Used internally during the sync process to fetch and set neuron data:

self._assign_neurons(block, lite, subtensor)
_create_tensor(data, dtype)#

Creates a tensor parameter with the given data and data type. This method is a utility function used internally to encapsulate data into a PyTorch tensor, making it compatible with the metagraph’s PyTorch model structure.

Parameters:
  • data – The data to be included in the tensor. This could be any numeric data, like stakes, ranks, etc.

  • dtype – The data type for the tensor, typically a PyTorch data type like torch.float32 or torch.int64.

Returns:

A tensor parameter encapsulating the provided data.

Return type:

torch.nn.Parameter

Internal Usage:

Used internally to create tensor parameters for various metagraph attributes:

self.stake = self._create_tensor(neuron_stakes, dtype=torch.float32)
_initialize_subtensor(subtensor)#

Initializes the subtensor to be used for syncing the metagraph.

This method ensures that a subtensor instance is available and properly set up for data retrieval during the synchronization process.

If no subtensor is provided, this method is responsible for creating a new instance of the subtensor, configured according to the current network settings.

Parameters:

subtensor – The subtensor instance provided for initialization. If None, a new subtensor instance is created using the current network configuration.

Returns:

The initialized subtensor instance, ready to be used for syncing the metagraph.

Return type:

subtensor

Internal Usage:

Used internally during the sync process to ensure a valid subtensor instance is available:

subtensor = self._initialize_subtensor(subtensor)
_process_root_weights(data, attribute, subtensor)#

Specifically processes the root weights data for the metagraph. This method is similar to _process_weights_or_bonds() but is tailored for processing root weights, which have a different structure and significance in the network.

Parameters:
  • data – The raw root weights data to be processed.

  • attribute (str) – A string indicating the attribute type, here it’s typically weights.

  • subtensor (bittensor.subtensor) – The subtensor instance used for additional data and context needed in processing.

Returns:

A tensor parameter encapsulating the processed root weights data.

Return type:

torch.nn.Parameter

Internal Usage:

Used internally to process and set root weights for the metagraph:

self.root_weights = self._process_root_weights(
    raw_root_weights_data, "weights", subtensor
    )
_process_weights_or_bonds(data, attribute)#

Processes the raw weights or bonds data and converts it into a structured tensor format. This method handles the transformation of neuron connection data (weights or bonds) from a list or other unstructured format into a tensor that can be utilized within the metagraph model.

Parameters:
  • data – The raw weights or bonds data to be processed. This data typically comes from the subtensor.

  • attribute (str) – A string indicating whether the data is weights or bonds, which determines the specific processing steps to be applied.

Returns:

A tensor parameter encapsulating the processed weights or bonds data.

Return type:

torch.nn.Parameter

Internal Usage:

Used internally to process and set weights or bonds for the neurons:

self.weights = self._process_weights_or_bonds(raw_weights_data, "weights")
_set_metagraph_attributes(block, subtensor)#

Sets various attributes of the metagraph based on the latest network data fetched from the subtensor.

This method updates parameters like the number of neurons, block number, stakes, trusts, ranks, and other neuron-specific information.

Parameters:
  • block – The block number for which the metagraph attributes need to be set. If None, the latest block data is used.

  • subtensor – The subtensor instance used for fetching the latest network data.

Internal Usage:

Used internally during the sync process to update the metagraph’s attributes:

self._set_metagraph_attributes(block, subtensor)
_set_weights_and_bonds(subtensor=None)#

Computes and sets the weights and bonds for each neuron in the metagraph. This method is responsible for processing the raw weight and bond data obtained from the network and converting it into a structured format suitable for the metagraph model.

Parameters:

subtensor (bittensor.subtensor) – The subtensor instance used for fetching weights and bonds data. If None, the weights and bonds are not updated.

Internal Usage:

Used internally during the sync process to update the weights and bonds of the neurons:

self._set_weights_and_bonds(subtensor=subtensor)
load()#

Loads the state of the metagraph from the default save directory. This method is instrumental for restoring the metagraph to its last saved state. It automatically identifies the save directory based on the network and netuid properties of the metagraph, locates the latest block file in that directory, and loads all metagraph parameters from it.

This functionality is particularly beneficial when continuity in the state of the metagraph is necessary across different runtime sessions, or after a restart of the system. It ensures that the metagraph reflects the exact state it was in at the last save point, maintaining consistency in the network’s representation.

The method delegates to load_from_path, supplying it with the directory path constructed from the metagraph’s current network and netuid properties. This abstraction simplifies the process of loading the metagraph’s state for the user, requiring no direct path specifications.

Returns:

The metagraph instance after loading its state from the default directory.

Return type:

metagraph

Example

Load the metagraph state from the last saved snapshot in the default directory:

metagraph.load()

After this operation, the metagraph’s parameters and neuron data are restored to their state at the time of the last save in the default directory.

Note

The default save directory is determined based on the metagraph’s network and netuid attributes. It is important to ensure that these attributes are set correctly and that the default save directory contains the appropriate state files for the metagraph.

load_from_path(dir_path)#

Loads the state of the metagraph from a specified directory path. This method is crucial for restoring the metagraph to a specific state based on saved data. It locates the latest block file in the given directory and loads all metagraph parameters from it. This is particularly useful for analyses that require historical states of the network or for restoring previous states of the metagraph in different execution environments.

The method first identifies the latest block file in the specified directory, then loads the metagraph state including neuron attributes and parameters from this file. This ensures that the metagraph is accurately reconstituted to reflect the network state at the time of the saved block.

Parameters:

dir_path (str) – The directory path where the metagraph’s state files are stored. This path should contain one or more saved state files, typically named in a format that includes the block number.

Returns:

The metagraph instance after loading its state from the specified directory path.

Return type:

metagraph

Example

Load the metagraph state from a specific directory:

dir_path = "/path/to/saved/metagraph/states"
metagraph.load_from_path(dir_path)

The metagraph is now restored to the state it was in at the time of the latest saved block in the specified directory.

Note

This method assumes that the state files in the specified directory are correctly formatted and contain valid data for the metagraph. It is essential to ensure that the directory path and the state files within it are accurate and consistent with the expected metagraph structure.

metadata()#

Retrieves the metadata of the metagraph, providing key information about the current state of the Bittensor network. This metadata includes details such as the network’s unique identifier (netuid), the total number of neurons (n), the current block number, the network’s name, and the version of the Bittensor network.

Returns:

A dictionary containing essential metadata about the metagraph, including:

  • netuid: The unique identifier for the network.

  • n: The total number of neurons in the network.

  • block: The current block number in the network’s blockchain.

  • network: The name of the Bittensor network.

  • version: The version number of the Bittensor software.

Return type:

dict

Note

This metadata is crucial for understanding the current state and configuration of the network, as well as for tracking its evolution over time.

save()#

Saves the current state of the metagraph to a file on disk. This function is crucial for persisting the current state of the network’s metagraph, which can later be reloaded or analyzed. The save operation includes all neuron attributes and parameters, ensuring a complete snapshot of the metagraph’s state.

Returns:

The metagraph instance after saving its state.

Return type:

metagraph

Example

Save the current state of the metagraph to the default directory:

metagraph.save()

The saved state can later be loaded to restore or analyze the metagraph’s state at this point.

If using the default save path:

metagraph.load()

If using a custom save path:

metagraph.load_from_path(dir_path)
sync(block=None, lite=True, subtensor=None)#

Synchronizes the metagraph with the Bittensor network’s current state. It updates the metagraph’s attributes to reflect the latest data from the network, ensuring the metagraph represents the most current state of the network.

Parameters:
  • block (Optional[int]) – A specific block number to synchronize with. If None, the metagraph syncs with the latest block. This allows for historical analysis or specific state examination of the network.

  • lite (bool) – If True, a lite version of the metagraph is used for quicker synchronization. This is beneficial when full detail is not necessary, allowing for reduced computational and time overhead.

  • subtensor (Optional[bittensor.subtensor]) – An instance of the subtensor class from Bittensor, providing an interface to the underlying blockchain data. If provided, this instance is used for data retrieval during synchronization.

Returns:

The metagraph instance, updated to the state of the specified block or the latest network state.

Return type:

metagraph

Example

Sync the metagraph with the latest block from the subtensor, using the lite version for efficiency:

metagraph.sync(subtensor=subtensor)

Sync with a specific block number for detailed analysis:

metagraph.sync(block=12345, lite=False, subtensor=subtensor)

Note

If attempting to access data beyond the previous 300 blocks, you must use the archive network for subtensor. Light nodes are configured only to store the previous 300 blocks if connecting to finney or test networks.

For example:

subtensor = bittensor.subtensor(network='archive')
bittensor.serialized_keypair_to_keyfile_data(keypair)#

Serializes keypair object into keyfile data.

Parameters:

keypair (bittensor.Keypair) – The keypair object to be serialized.

Returns:

Serialized keypair data.

Return type:

data (bytes)

class bittensor.subtensor(network=None, config=None, _mock=False, log_verbose=True)#

The Subtensor class in Bittensor serves as a crucial interface for interacting with the Bittensor blockchain, facilitating a range of operations essential for the decentralized machine learning network.

This class enables neurons (network participants) to engage in activities such as registering on the network, managing staked weights, setting inter-neuronal weights, and participating in consensus mechanisms.

The Bittensor network operates on a digital ledger where each neuron holds stakes (S) and learns a set of inter-peer weights (W). These weights, set by the neurons themselves, play a critical role in determining the ranking and incentive mechanisms within the network. Higher-ranked neurons, as determined by their contributions and trust within the network, receive more incentives.

The Subtensor class connects to various Bittensor networks like the main finney network or local test networks, providing a gateway to the blockchain layer of Bittensor. It leverages a staked weighted trust system and consensus to ensure fair and distributed incentive mechanisms, where incentives (I) are primarily allocated to neurons that are trusted by the majority of the network.

Additionally, Bittensor introduces a speculation-based reward mechanism in the form of bonds (B), allowing neurons to accumulate bonds in other neurons, speculating on their future value. This mechanism aligns with market-based speculation, incentivizing neurons to make judicious decisions in their inter-neuronal investments.

Parameters:
  • network (str) – The name of the Bittensor network (e.g., ‘finney’, ‘test’, ‘archive’, ‘local’) the instance is connected to, determining the blockchain interaction context.

  • chain_endpoint (str) – The blockchain node endpoint URL, enabling direct communication with the Bittensor blockchain for transaction processing and data retrieval.

  • config (bittensor.config) –

  • _mock (bool) –

  • log_verbose (bool) –

Example Usage:

# Connect to the main Bittensor network (Finney).
finney_subtensor = subtensor(network='finney')

# Close websocket connection with the Bittensor network.
finney_subtensor.close()

# (Re)creates the websocket connection with the Bittensor network.
finney_subtensor.connect_websocket()

# Register a new neuron on the network.
wallet = bittensor.wallet(...)  # Assuming a wallet instance is created.
success = finney_subtensor.register(wallet=wallet, netuid=netuid)

# Set inter-neuronal weights for collaborative learning.
success = finney_subtensor.set_weights(wallet=wallet, netuid=netuid, uids=[...], weights=[...])

# Speculate by accumulating bonds in other promising neurons.
success = finney_subtensor.delegate(wallet=wallet, delegate_ss58=other_neuron_ss58, amount=bond_amount)

# Get the metagraph for a specific subnet using given subtensor connection
metagraph = subtensor.metagraph(netuid=netuid)

By facilitating these operations, the Subtensor class is instrumental in maintaining the decentralized intelligence and dynamic learning environment of the Bittensor network, as envisioned in its foundational principles and mechanisms described in the NeurIPS paper. paper.

property block: int#

Returns current chain block. :returns: Current chain block. :rtype: block (int)

Return type:

int

get_proposal_vote_data#
__repr__()#

Return repr(self).

Return type:

str

__str__()#

Return str(self).

Return type:

str

_do_associate_ips(wallet, ip_info_list, netuid, wait_for_inclusion=False, wait_for_finalization=True)#

Sends an associate IPs extrinsic to the chain.

Parameters:
  • wallet (bittensor.wallet()) – Wallet object.

  • ip_info_list (List[IPInfo]()) – List of IPInfo objects.

  • netuid (int) – Netuid to associate IPs to.

  • wait_for_inclusion (bool) – If true, waits for inclusion.

  • wait_for_finalization (bool) – If true, waits for finalization.

Returns:

True if associate IPs was successful. error (Optional[str]()): Error message if associate IPs failed, None otherwise.

Return type:

success (bool)

_do_burned_register(netuid, wallet, wait_for_inclusion=False, wait_for_finalization=True)#
Parameters:
  • netuid (int) –

  • wallet (bittensor.wallet) –

  • wait_for_inclusion (bool) –

  • wait_for_finalization (bool) –

Return type:

Tuple[bool, Optional[str]]

_do_delegation(wallet, delegate_ss58, amount, wait_for_inclusion=True, wait_for_finalization=False)#
Parameters:
  • wallet (bittensor.wallet) –

  • delegate_ss58 (str) –

  • amount (bittensor.utils.balance.Balance) –

  • wait_for_inclusion (bool) –

  • wait_for_finalization (bool) –

Return type:

bool

_do_nominate(wallet, wait_for_inclusion=True, wait_for_finalization=False)#
Parameters:
  • wallet (bittensor.wallet) –

  • wait_for_inclusion (bool) –

  • wait_for_finalization (bool) –

Return type:

bool

_do_pow_register(netuid, wallet, pow_result, wait_for_inclusion=False, wait_for_finalization=True)#

Sends a (POW) register extrinsic to the chain.

Parameters:
  • netuid (int) – The subnet to register on.

  • wallet (bittensor.wallet) – The wallet to register.

  • pow_result (POWSolution) – The PoW result to register.

  • wait_for_inclusion (bool) – If true, waits for the extrinsic to be included in a block.

  • wait_for_finalization (bool) – If true, waits for the extrinsic to be finalized.

Returns:

True if the extrinsic was included in a block. error (Optional[str]): None on success or not waiting for inclusion/finalization, otherwise the error message.

Return type:

success (bool)

_do_root_register(wallet, wait_for_inclusion=False, wait_for_finalization=True)#
Parameters:
  • wallet (bittensor.wallet) –

  • wait_for_inclusion (bool) –

  • wait_for_finalization (bool) –

Return type:

Tuple[bool, Optional[str]]

_do_serve_axon(wallet, call_params, wait_for_inclusion=False, wait_for_finalization=True)#

Internal method to submit a serve axon transaction to the Bittensor blockchain. This method creates and submits a transaction, enabling a neuron’s Axon to serve requests on the network.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron.

  • call_params (AxonServeCallParams) – Parameters required for the serve axon call.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

Returns:

A tuple containing a success flag and an optional error message.

Return type:

Tuple[bool, Optional[str]]

This function is crucial for initializing and announcing a neuron’s Axon service on the network, enhancing the decentralized computation capabilities of Bittensor.

_do_serve_prometheus(wallet, call_params, wait_for_inclusion=False, wait_for_finalization=True)#

Sends a serve prometheus extrinsic to the chain. :param wallet: Wallet object. :type wallet: bittensor.wallet() :param call_params: Prometheus serve call parameters. :type call_params: PrometheusServeCallParams() :param wait_for_inclusion: If true, waits for inclusion. :type wait_for_inclusion: bool :param wait_for_finalization: If true, waits for finalization. :type wait_for_finalization: bool

Returns:

True if serve prometheus was successful. error (Optional[str]()): Error message if serve prometheus failed, None otherwise.

Return type:

success (bool)

Parameters:
_do_set_weights(wallet, uids, vals, netuid, version_key=bittensor.__version_as_int__, wait_for_inclusion=False, wait_for_finalization=True)#

Internal method to send a transaction to the Bittensor blockchain, setting weights for specified neurons. This method constructs and submits the transaction, handling retries and blockchain communication.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron setting the weights.

  • uids (List[int]) – List of neuron UIDs for which weights are being set.

  • vals (List[int]) – List of weight values corresponding to each UID.

  • netuid (int) – Unique identifier for the network.

  • version_key (int, optional) – Version key for compatibility with the network.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

Returns:

A tuple containing a success flag and an optional error message.

Return type:

Tuple[bool, Optional[str]]

This method is vital for the dynamic weighting mechanism in Bittensor, where neurons adjust their trust in other neurons based on observed performance and contributions.

_do_stake(wallet, hotkey_ss58, amount, wait_for_inclusion=True, wait_for_finalization=False)#

Sends a stake extrinsic to the chain.

Parameters:
  • wallet (bittensor.wallet()) – Wallet object that can sign the extrinsic.

  • hotkey_ss58 (str) – Hotkey ss58 address to stake to.

  • amount (Balance()) – Amount to stake.

  • wait_for_inclusion (bool) – If true, waits for inclusion before returning.

  • wait_for_finalization (bool) – If true, waits for finalization before returning.

Returns:

True if the extrinsic was successful.

Return type:

success (bool)

Raises:

StakeError – If the extrinsic failed.

_do_swap_hotkey(wallet, new_wallet, wait_for_inclusion=False, wait_for_finalization=True)#
Parameters:
  • wallet (bittensor.wallet) –

  • new_wallet (bittensor.wallet) –

  • wait_for_inclusion (bool) –

  • wait_for_finalization (bool) –

Return type:

Tuple[bool, Optional[str]]

_do_transfer(wallet, dest, transfer_balance, wait_for_inclusion=True, wait_for_finalization=False)#

Sends a transfer extrinsic to the chain.

Parameters:
  • wallet (bittensor.wallet()) – Wallet object.

  • dest (str) – Destination public key address.

  • transfer_balance (Balance()) – Amount to transfer.

  • wait_for_inclusion (bool) – If true, waits for inclusion.

  • wait_for_finalization (bool) – If true, waits for finalization.

Returns:

True if transfer was successful. block_hash (str): Block hash of the transfer. On success and if wait_for_ finalization/inclusion is True. error (str): Error message if transfer failed.

Return type:

success (bool)

_do_undelegation(wallet, delegate_ss58, amount, wait_for_inclusion=True, wait_for_finalization=False)#
Parameters:
  • wallet (bittensor.wallet) –

  • delegate_ss58 (str) –

  • amount (bittensor.utils.balance.Balance) –

  • wait_for_inclusion (bool) –

  • wait_for_finalization (bool) –

Return type:

bool

_do_unstake(wallet, hotkey_ss58, amount, wait_for_inclusion=True, wait_for_finalization=False)#

Sends an unstake extrinsic to the chain.

Parameters:
  • wallet (bittensor.wallet()) – Wallet object that can sign the extrinsic.

  • hotkey_ss58 (str) – Hotkey ss58 address to unstake from.

  • amount (Balance()) – Amount to unstake.

  • wait_for_inclusion (bool) – If true, waits for inclusion before returning.

  • wait_for_finalization (bool) – If true, waits for finalization before returning.

Returns:

True if the extrinsic was successful.

Return type:

success (bool)

Raises:

StakeError – If the extrinsic failed.

_encode_params(call_definition, params)#

Returns a hex encoded string of the params using their types.

Parameters:
Return type:

str

static _null_neuron()#
Return type:

bittensor.chain_data.NeuronInfo

classmethod add_args(parser, prefix=None)#
Parameters:
add_stake(wallet, hotkey_ss58=None, amount=None, wait_for_inclusion=True, wait_for_finalization=False, prompt=False)#

Adds the specified amount of stake to a neuron identified by the hotkey SS58 address. Staking is a fundamental process in the Bittensor network that enables neurons to participate actively and earn incentives.

Parameters:
  • wallet (bittensor.wallet) – The wallet to be used for staking.

  • hotkey_ss58 (Optional[str]) – The SS58 address of the hotkey associated with the neuron.

  • amount (Union[Balance, float]) – The amount of TAO to stake.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • prompt (bool, optional) – If True, prompts for user confirmation before proceeding.

Returns:

True if the staking is successful, False otherwise.

Return type:

bool

This function enables neurons to increase their stake in the network, enhancing their influence and potential rewards in line with Bittensor’s consensus and reward mechanisms.

add_stake_multiple(wallet, hotkey_ss58s, amounts=None, wait_for_inclusion=True, wait_for_finalization=False, prompt=False)#

Adds stakes to multiple neurons identified by their hotkey SS58 addresses. This bulk operation allows for efficient staking across different neurons from a single wallet.

Parameters:
  • wallet (bittensor.wallet) – The wallet used for staking.

  • hotkey_ss58s (List[str]) – List of SS58 addresses of hotkeys to stake to.

  • amounts (List[Union[Balance, float]], optional) – Corresponding amounts of TAO to stake for each hotkey.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • prompt (bool, optional) – If True, prompts for user confirmation before proceeding.

Returns:

True if the staking is successful for all specified neurons, False otherwise.

Return type:

bool

This function is essential for managing stakes across multiple neurons, reflecting the dynamic and collaborative nature of the Bittensor network.

associated_validator_ip_info(netuid, block=None)#

Retrieves the list of all validator IP addresses associated with a specific subnet in the Bittensor network. This information is crucial for network communication and the identification of validator nodes.

Parameters:
  • netuid (int) – The network UID of the subnet to query.

  • block (Optional[int]) – The blockchain block number for the query.

Returns:

A list of IPInfo objects for validator nodes in the subnet, or None if no validators are associated.

Return type:

Optional[List[IPInfo]]

Validator IP information is key for establishing secure and reliable connections within the network, facilitating consensus and validation processes critical for the network’s integrity and performance.

blocks_since_epoch(netuid, block=None)#

Returns network BlocksSinceLastStep hyper parameter

Parameters:
  • netuid (int) –

  • block (Optional[int]) –

Return type:

int

bonds(netuid, block=None)#

Retrieves the bond distribution set by neurons within a specific subnet of the Bittensor network. Bonds represent the investments or commitments made by neurons in one another, indicating a level of trust and perceived value. This bonding mechanism is integral to the network’s market-based approach to measuring and rewarding machine intelligence.

Parameters:
  • netuid (int) – The network UID of the subnet to query.

  • block (Optional[int]) – The blockchain block number for the query.

Returns:

A list of tuples mapping each neuron’s UID to its bonds with other neurons.

Return type:

List[Tuple[int, List[Tuple[int, int]]]]

Understanding bond distributions is crucial for analyzing the trust dynamics and market behavior within the subnet. It reflects how neurons recognize and invest in each other’s intelligence and contributions, supporting diverse and niche systems within the Bittensor ecosystem.

burn(netuid, block=None)#

Retrieves the ‘Burn’ hyperparameter for a specified subnet. The ‘Burn’ parameter represents the amount of Tao that is effectively removed from circulation within the Bittensor network.

Parameters:
  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The value of the ‘Burn’ hyperparameter if the subnet exists, None otherwise.

Return type:

Optional[Balance]

Understanding the ‘Burn’ rate is essential for analyzing the network’s economic model, particularly how it manages inflation and the overall supply of its native token Tao.

burned_register(wallet, netuid, wait_for_inclusion=False, wait_for_finalization=True, prompt=False)#

Registers a neuron on the Bittensor network by burning TAO. This method of registration involves recycling TAO tokens, contributing to the network’s deflationary mechanism.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron to be registered.

  • netuid (int) – The unique identifier of the subnet.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • prompt (bool, optional) – If True, prompts for user confirmation before proceeding.

Returns:

True if the registration is successful, False otherwise.

Return type:

bool

This function offers an alternative registration path, aligning with the network’s principles of token circulation and value conservation.

close()#

Cleans up resources for this subtensor instance like active websocket connection and active extensions

commit(wallet, netuid, data)#
Parameters:
  • netuid (int) –

  • data (str) –

static config()#
Return type:

bittensor.config

connect_websocket()#

(Re)creates the websocket connection, if the URL contains a ‘ws’ or ‘wss’ scheme

delegate(wallet, delegate_ss58=None, amount=None, wait_for_inclusion=True, wait_for_finalization=False, prompt=False)#

Becomes a delegate for the hotkey associated with the given wallet. This method is used to nominate a neuron (identified by the hotkey in the wallet) as a delegate on the Bittensor network, allowing it to participate in consensus and validation processes.

Parameters:
  • wallet (bittensor.wallet) – The wallet containing the hotkey to be nominated.

  • wait_for_finalization (bool, optional) – If True, waits until the transaction is finalized on the blockchain.

  • wait_for_inclusion (bool, optional) – If True, waits until the transaction is included in a block.

  • delegate_ss58 (Optional[str]) –

  • amount (Union[bittensor.utils.balance.Balance, float]) –

  • prompt (bool) –

Returns:

True if the nomination process is successful, False otherwise.

Return type:

bool

This function is a key part of the decentralized governance mechanism of Bittensor, allowing for the dynamic selection and participation of validators in the network’s consensus process.

static determine_chain_endpoint_and_network(network)#

Determines the chain endpoint and network from the passed network or chain_endpoint.

Parameters:
  • network (str) – The network flag. The choices are: -- finney (main network), -- archive (archive network +300 blocks), -- local (local running network), -- test (test network).

  • chain_endpoint (str) – The chain endpoint flag. If set, overrides the network argument.

Returns:

The network flag. chain_endpoint (str): The chain endpoint flag. If set, overrides the network argument.

Return type:

network (str)

difficulty(netuid, block=None)#

Retrieves the ‘Difficulty’ hyperparameter for a specified subnet in the Bittensor network. This parameter is instrumental in determining the computational challenge required for neurons to participate in consensus and validation processes.

Parameters:
  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The value of the ‘Difficulty’ hyperparameter if the subnet exists, None otherwise.

Return type:

Optional[int]

The ‘Difficulty’ parameter directly impacts the network’s security and integrity by setting the computational effort required for validating transactions and participating in the network’s consensus mechanism.

does_hotkey_exist(hotkey_ss58, block=None)#

Returns true if the hotkey is known by the chain and there are accounts.

Parameters:
  • hotkey_ss58 (str) –

  • block (Optional[int]) –

Return type:

bool

get_all_neurons_for_pubkey(hotkey_ss58, block=None)#

Retrieves information about all neuron instances associated with a given public key (hotkey SS58 address) across different subnets of the Bittensor network. This function aggregates neuron data from various subnets to provide a comprehensive view of a neuron’s presence and status within the network.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A list of NeuronInfo objects detailing the neuron’s presence across various subnets.

Return type:

List[NeuronInfo]

This function is valuable for analyzing a neuron’s overall participation, influence, and contributions across the Bittensor network.

get_all_subnet_netuids(block=None)#

Retrieves the list of all subnet unique identifiers (netuids) currently present in the Bittensor network.

Parameters:

block (Optional[int], optional) – The blockchain block number at which to retrieve the subnet netuids.

Returns:

A list of subnet netuids.

Return type:

List[int]

This function provides a comprehensive view of the subnets within the Bittensor network, offering insights into its diversity and scale.

get_all_subnets_info(block=None)#

Retrieves detailed information about all subnets within the Bittensor network. This function provides comprehensive data on each subnet, including its characteristics and operational parameters.

Parameters:

block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A list of SubnetInfo objects, each containing detailed information about a subnet.

Return type:

List[SubnetInfo]

Gaining insights into the subnets’ details assists in understanding the network’s composition, the roles of different subnets, and their unique features.

get_all_uids_for_hotkey(hotkey_ss58, block=None)#

Retrieves all unique identifiers (UIDs) associated with a given hotkey across different subnets within the Bittensor network. This function helps in identifying all the neuron instances that are linked to a specific hotkey.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

Returns:

A list of UIDs associated with the given hotkey across various subnets.

Return type:

List[int]

This function is important for tracking a neuron’s presence and activities across different subnets within the Bittensor ecosystem.

get_axon_info(netuid, hotkey_ss58, block=None)#

Returns the axon information for this hotkey account

Parameters:
  • netuid (int) –

  • hotkey_ss58 (str) –

  • block (Optional[int]) –

Return type:

Optional[bittensor.chain_data.AxonInfo]

get_balance(address, block=None)#

Retrieves the token balance of a specific address within the Bittensor network. This function queries the blockchain to determine the amount of Tao held by a given account.

Parameters:
  • address (str) – The Substrate address in ss58 format.

  • block (int, optional) – The blockchain block number at which to perform the query.

Returns:

The account balance at the specified block, represented as a Balance object.

Return type:

Balance

This function is important for monitoring account holdings and managing financial transactions within the Bittensor ecosystem. It helps in assessing the economic status and capacity of network participants.

get_balances(block=None)#

Retrieves the token balances of all accounts within the Bittensor network as of a specific blockchain block. This function provides a comprehensive view of the token distribution among different accounts.

Parameters:

block (int, optional) – The blockchain block number at which to perform the query.

Returns:

A dictionary mapping each account’s ss58 address to its balance.

Return type:

Dict[str, Balance]

This function is valuable for analyzing the overall economic landscape of the Bittensor network, including the distribution of financial resources and the financial status of network participants.

get_block_hash(block_id)#

Retrieves the hash of a specific block on the Bittensor blockchain. The block hash is a unique identifier representing the cryptographic hash of the block’s content, ensuring its integrity and immutability.

Parameters:

block_id (int) – The block number for which the hash is to be retrieved.

Returns:

The cryptographic hash of the specified block.

Return type:

str

The block hash is a fundamental aspect of blockchain technology, providing a secure reference to each block’s data. It is crucial for verifying transactions, ensuring data consistency, and maintaining the trustworthiness of the blockchain.

get_commitment(netuid, uid, block=None)#
Parameters:
  • netuid (int) –

  • uid (int) –

  • block (Optional[int]) –

Return type:

str

get_current_block()#

Returns the current block number on the Bittensor blockchain. This function provides the latest block number, indicating the most recent state of the blockchain.

Returns:

The current chain block number.

Return type:

int

Knowing the current block number is essential for querying real-time data and performing time-sensitive operations on the blockchain. It serves as a reference point for network activities and data synchronization.

get_delegate_by_hotkey(hotkey_ss58, block=None)#

Retrieves detailed information about a delegate neuron based on its hotkey. This function provides a comprehensive view of the delegate’s status, including its stakes, nominators, and reward distribution.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the delegate’s hotkey.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

Detailed information about the delegate neuron, None if not found.

Return type:

Optional[DelegateInfo]

This function is essential for understanding the roles and influence of delegate neurons within the Bittensor network’s consensus and governance structures.

get_delegate_take(hotkey_ss58, block=None)#

Retrieves the delegate ‘take’ percentage for a neuron identified by its hotkey. The ‘take’ represents the percentage of rewards that the delegate claims from its nominators’ stakes.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The delegate take percentage, None if not available.

Return type:

Optional[float]

The delegate take is a critical parameter in the network’s incentive structure, influencing the distribution of rewards among neurons and their nominators.

get_delegated(coldkey_ss58, block=None)#

Retrieves a list of delegates and their associated stakes for a given coldkey. This function identifies the delegates that a specific account has staked tokens on.

Parameters:
  • coldkey_ss58 (str) – The SS58 address of the account’s coldkey.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A list of tuples, each containing a delegate’s information and staked amount.

Return type:

List[Tuple[DelegateInfo, Balance]]

This function is important for account holders to understand their stake allocations and their involvement in the network’s delegation and consensus mechanisms.

get_delegates(block=None)#

Retrieves a list of all delegate neurons within the Bittensor network. This function provides an overview of the neurons that are actively involved in the network’s delegation system.

Parameters:

block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A list of DelegateInfo objects detailing each delegate’s characteristics.

Return type:

List[DelegateInfo]

Analyzing the delegate population offers insights into the network’s governance dynamics and the distribution of trust and responsibility among participating neurons.

get_emission_value_by_subnet(netuid, block=None)#

Retrieves the emission value of a specific subnet within the Bittensor network. The emission value represents the rate at which the subnet emits or distributes the network’s native token (Tao).

Parameters:
  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The emission value of the subnet, None if not available.

Return type:

Optional[float]

The emission value is a critical economic parameter, influencing the incentive distribution and reward mechanisms within the subnet.

get_existential_deposit(block=None)#

Retrieves the existential deposit amount for the Bittensor blockchain. The existential deposit is the minimum amount of TAO required for an account to exist on the blockchain. Accounts with balances below this threshold can be reaped to conserve network resources.

Parameters:

block (Optional[int], optional) – Block number at which to query the deposit amount. If None, the current block is used.

Returns:

The existential deposit amount, or None if the query fails.

Return type:

Optional[Balance]

The existential deposit is a fundamental economic parameter in the Bittensor network, ensuring efficient use of storage and preventing the proliferation of dust accounts.

get_hotkey_owner(hotkey_ss58, block=None)#

Returns the coldkey owner of the passed hotkey

Parameters:
  • hotkey_ss58 (str) –

  • block (Optional[int]) –

Return type:

Optional[str]

get_netuids_for_hotkey(hotkey_ss58, block=None)#

Retrieves a list of subnet UIDs (netuids) for which a given hotkey is a member. This function identifies the specific subnets within the Bittensor network where the neuron associated with the hotkey is active.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

Returns:

A list of netuids where the neuron is a member.

Return type:

List[int]

get_neuron_for_pubkey_and_subnet(hotkey_ss58, netuid, block=None)#

Retrieves information about a neuron based on its public key (hotkey SS58 address) and the specific subnet UID (netuid). This function provides detailed neuron information for a particular subnet within the Bittensor network.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

Returns:

Detailed information about the neuron if found, None otherwise.

Return type:

Optional[NeuronInfo]

This function is crucial for accessing specific neuron data and understanding its status, stake, and other attributes within a particular subnet of the Bittensor ecosystem.

get_nominators_for_hotkey(hotkey_ss58, block=None)#

Retrieves a list of nominators and their stakes for a neuron identified by its hotkey. Nominators are neurons that stake their tokens on a delegate to support its operations.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A list of tuples containing each nominator’s address and staked amount.

Return type:

List[Tuple[str, Balance]]

This function provides insights into the neuron’s support network within the Bittensor ecosystem, indicating its trust and collaboration relationships.

get_prometheus_info(netuid, hotkey_ss58, block=None)#

Returns the prometheus information for this hotkey account

Parameters:
  • netuid (int) –

  • hotkey_ss58 (str) –

  • block (Optional[int]) –

Return type:

Optional[bittensor.chain_data.AxonInfo]

get_proposal_call_data(proposal_hash, block=None)#

Retrieves the call data of a specific proposal on the Bittensor blockchain. This data provides detailed information about the proposal, including its purpose and specifications.

Parameters:
  • proposal_hash (str) – The hash of the proposal.

  • block (Optional[int], optional) – The blockchain block number at which to query the proposal call data.

Returns:

An object containing the proposal’s call data, or None if not found.

Return type:

Optional[bittensor.ProposalCallData]

This function is crucial for analyzing the types of proposals made within the network and the specific changes or actions they intend to implement or address.

get_proposal_hashes(block=None)#

Retrieves the list of proposal hashes currently present on the Bittensor blockchain. Each hash uniquely identifies a proposal made within the network.

Parameters:

block (Optional[int], optional) – The blockchain block number to query the proposal hashes.

Returns:

A list of proposal hashes, or None if not available.

Return type:

Optional[List[str]]

This function enables tracking and reviewing the proposals made in the network, offering insights into the active governance and decision-making processes.

get_proposals(block=None)#

Retrieves all active proposals on the Bittensor blockchain, along with their call and voting data. This comprehensive view allows for a thorough understanding of the proposals and their reception by the senate.

Parameters:

block (Optional[int], optional) – The blockchain block number to query the proposals.

Returns:

A dictionary mapping proposal hashes to their corresponding call and vote data, or None if not available.

Return type:

Optional[Dict[str, Tuple[bittensor.ProposalCallData, bittensor.ProposalVoteData]]]

This function is integral for analyzing the governance activity on the Bittensor network, providing a holistic view of the proposals and their impact or potential changes within the network.

get_senate_members(block=None)#

Retrieves the list of current senate members from the Bittensor blockchain. Senate members are responsible for governance and decision-making within the network.

Parameters:

block (Optional[int], optional) – The blockchain block number at which to retrieve the senate members.

Returns:

A list of SS58 addresses of current senate members, or None if not available.

Return type:

Optional[List[str]]

Understanding the composition of the senate is key to grasping the governance structure and decision-making authority within the Bittensor network.

get_stake(hotkey_ss58, block=None)#

Returns a list of stake tuples (coldkey, balance) for each delegating coldkey including the owner

Parameters:
  • hotkey_ss58 (str) –

  • block (Optional[int]) –

Return type:

List[Tuple[str, bittensor.utils.balance.Balance]]

get_stake_for_coldkey_and_hotkey(hotkey_ss58, coldkey_ss58, block=None)#

Returns the stake under a coldkey - hotkey pairing

Parameters:
  • hotkey_ss58 (str) –

  • coldkey_ss58 (str) –

  • block (Optional[int]) –

Return type:

Optional[bittensor.utils.balance.Balance]

get_stake_info_for_coldkey(coldkey_ss58, block=None)#

Retrieves stake information associated with a specific coldkey. This function provides details about the stakes held by an account, including the staked amounts and associated delegates.

Parameters:
  • coldkey_ss58 (str) – The SS58 address of the account’s coldkey.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A list of StakeInfo objects detailing the stake allocations for the account.

Return type:

List[StakeInfo]

Stake information is vital for account holders to assess their investment and participation in the network’s delegation and consensus processes.

get_stake_info_for_coldkeys(coldkey_ss58_list, block=None)#

Retrieves stake information for a list of coldkeys. This function aggregates stake data for multiple accounts, providing a collective view of their stakes and delegations.

Parameters:
  • coldkey_ss58_list (List[str]) – A list of SS58 addresses of the accounts’ coldkeys.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A dictionary mapping each coldkey to a list of its StakeInfo objects.

Return type:

Dict[str, List[StakeInfo]]

This function is useful for analyzing the stake distribution and delegation patterns of multiple accounts simultaneously, offering a broader perspective on network participation and investment strategies.

get_subnet_burn_cost(block=None)#

Retrieves the burn cost for registering a new subnet within the Bittensor network. This cost represents the amount of Tao that needs to be locked or burned to establish a new subnet.

Parameters:

block (Optional[int]) – The blockchain block number for the query.

Returns:

The burn cost for subnet registration.

Return type:

int

The subnet burn cost is an important economic parameter, reflecting the network’s mechanisms for controlling the proliferation of subnets and ensuring their commitment to the network’s long-term viability.

get_subnet_connection_requirement(netuid_0, netuid_1, block=None)#
Parameters:
  • netuid_0 (int) –

  • netuid_1 (int) –

  • block (Optional[int]) –

Return type:

Optional[int]

get_subnet_connection_requirements(netuid, block=None)#

Retrieves the connection requirements for a specific subnet within the Bittensor network. This function provides details on the criteria that must be met for neurons to connect to the subnet.

Parameters:
  • netuid (int) – The network UID of the subnet to query.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A dictionary detailing the connection requirements for the subnet.

Return type:

Dict[str, int]

Understanding these requirements is crucial for neurons looking to participate in or interact with specific subnets, ensuring compliance with their connection standards.

get_subnet_hyperparameters(netuid, block=None)#

Retrieves the hyperparameters for a specific subnet within the Bittensor network. These hyperparameters define the operational settings and rules governing the subnet’s behavior.

Parameters:
  • netuid (int) – The network UID of the subnet to query.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The subnet’s hyperparameters, or None if not available.

Return type:

Optional[SubnetHyperparameters]

Understanding the hyperparameters is crucial for comprehending how subnets are configured and managed, and how they interact with the network’s consensus and incentive mechanisms.

get_subnet_info(netuid, block=None)#

Retrieves detailed information about a specific subnet within the Bittensor network. This function provides key data on the subnet, including its operational parameters and network status.

Parameters:
  • netuid (int) – The network UID of the subnet to query.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

Detailed information about the subnet, or None if not found.

Return type:

Optional[SubnetInfo]

This function is essential for neurons and stakeholders interested in the specifics of a particular subnet, including its governance, performance, and role within the broader network.

get_subnet_modality(netuid, block=None)#
Parameters:
  • netuid (int) –

  • block (Optional[int]) –

Return type:

Optional[int]

get_subnet_owner(netuid, block=None)#

Retrieves the owner’s address of a specific subnet within the Bittensor network. The owner is typically the entity responsible for the creation and maintenance of the subnet.

Parameters:
  • netuid (int) – The network UID of the subnet to query.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The SS58 address of the subnet’s owner, or None if not available.

Return type:

Optional[str]

Knowing the subnet owner provides insights into the governance and operational control of the subnet, which can be important for decision-making and collaboration within the network.

get_subnets(block=None)#

Retrieves a list of all subnets currently active within the Bittensor network. This function provides an overview of the various subnets and their identifiers.

Parameters:

block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A list of network UIDs representing each active subnet.

Return type:

List[int]

This function is valuable for understanding the network’s structure and the diversity of subnets available for neuron participation and collaboration.

get_total_stake_for_coldkey(ss58_address, block=None)#

Returns the total stake held on a coldkey across all hotkeys including delegates

Parameters:
  • ss58_address (str) –

  • block (Optional[int]) –

Return type:

Optional[bittensor.utils.balance.Balance]

get_total_stake_for_hotkey(ss58_address, block=None)#

Returns the total stake held on a hotkey including delegative

Parameters:
  • ss58_address (str) –

  • block (Optional[int]) –

Return type:

Optional[bittensor.utils.balance.Balance]

get_total_subnets(block=None)#

Retrieves the total number of subnets within the Bittensor network as of a specific blockchain block.

Parameters:

block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The total number of subnets in the network.

Return type:

int

Understanding the total number of subnets is essential for assessing the network’s growth and the extent of its decentralized infrastructure.

get_transfer_fee(wallet, dest, value)#

Calculates the transaction fee for transferring tokens from a wallet to a specified destination address. This function simulates the transfer to estimate the associated cost, taking into account the current network conditions and transaction complexity.

Parameters:
  • wallet (bittensor.wallet) – The wallet from which the transfer is initiated.

  • dest (str) – The SS58 address of the destination account.

  • value (Union[Balance, float, int]) – The amount of tokens to be transferred, specified as a Balance object, or in Tao (float) or Rao (int) units.

Returns:

The estimated transaction fee for the transfer, represented as a Balance object.

Return type:

Balance

Estimating the transfer fee is essential for planning and executing token transactions, ensuring that the wallet has sufficient funds to cover both the transfer amount and the associated costs. This function provides a crucial tool for managing financial operations within the Bittensor network.

get_uid_for_hotkey_on_subnet(hotkey_ss58, netuid, block=None)#

Retrieves the unique identifier (UID) for a neuron’s hotkey on a specific subnet.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The UID of the neuron if it is registered on the subnet, None otherwise.

Return type:

Optional[int]

The UID is a critical identifier within the network, linking the neuron’s hotkey to its operational and governance activities on a particular subnet.

get_vote_data(proposal_hash, block=None)#

Retrieves the voting data for a specific proposal on the Bittensor blockchain. This data includes information about how senate members have voted on the proposal.

Parameters:
  • proposal_hash (str) – The hash of the proposal for which voting data is requested.

  • block (Optional[int], optional) – The blockchain block number to query the voting data.

Returns:

An object containing the proposal’s voting data, or None if not found.

Return type:

Optional[ProposalVoteData]

This function is important for tracking and understanding the decision-making processes within the Bittensor network, particularly how proposals are received and acted upon by the governing body.

classmethod help()#

Print help to stdout

immunity_period(netuid, block=None)#

Retrieves the ‘ImmunityPeriod’ hyperparameter for a specific subnet. This parameter defines the duration during which new neurons are protected from certain network penalties or restrictions.

Parameters:
  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The value of the ‘ImmunityPeriod’ hyperparameter if the subnet exists, None otherwise.

Return type:

Optional[int]

The ‘ImmunityPeriod’ is a critical aspect of the network’s governance system, ensuring that new participants have a grace period to establish themselves and contribute to the network without facing immediate punitive actions.

incentive(netuid, block=None)#

Retrieves the list of incentives for neurons within a specific subnet of the Bittensor network. This function provides insights into the reward distribution mechanisms and the incentives allocated to each neuron based on their contributions and activities.

Parameters:
  • netuid (int) – The network UID of the subnet to query.

  • block (Optional[int]) – The blockchain block number for the query.

Returns:

The list of incentives for neurons within the subnet, indexed by UID.

Return type:

List[int]

Understanding the incentive structure is crucial for analyzing the network’s economic model and the motivational drivers for neuron participation and collaboration.

is_hotkey_delegate(hotkey_ss58, block=None)#

Determines whether a given hotkey (public key) is a delegate on the Bittensor network. This function checks if the neuron associated with the hotkey is part of the network’s delegation system.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

True if the hotkey is a delegate, False otherwise.

Return type:

bool

Being a delegate is a significant status within the Bittensor network, indicating a neuron’s involvement in consensus and governance processes.

is_hotkey_registered(hotkey_ss58, netuid=None, block=None)#

Determines whether a given hotkey (public key) is registered in the Bittensor network, either globally across any subnet or specifically on a specified subnet. This function checks the registration status of a neuron identified by its hotkey, which is crucial for validating its participation and activities within the network.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • netuid (Optional[int], optional) – The unique identifier of the subnet to check the registration. If None, the registration is checked across all subnets.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

Returns:

True if the hotkey is registered in the specified context (either any subnet or a specific subnet), False otherwise.

Return type:

bool

This function is important for verifying the active status of neurons in the Bittensor network. It aids in understanding whether a neuron is eligible to participate in network processes such as consensus, validation, and incentive distribution based on its registration status.

is_hotkey_registered_any(hotkey_ss58, block=None)#

Checks if a neuron’s hotkey is registered on any subnet within the Bittensor network.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • block (Optional[int], optional) – The blockchain block number at which to perform the check.

Returns:

True if the hotkey is registered on any subnet, False otherwise.

Return type:

bool

This function is essential for determining the network-wide presence and participation of a neuron.

is_hotkey_registered_on_subnet(hotkey_ss58, netuid, block=None)#

Checks if a neuron’s hotkey is registered on a specific subnet within the Bittensor network.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number at which to perform the check.

Returns:

True if the hotkey is registered on the specified subnet, False otherwise.

Return type:

bool

This function helps in assessing the participation of a neuron in a particular subnet, indicating its specific area of operation or influence within the network.

is_senate_member(hotkey_ss58, block=None)#

Checks if a given neuron (identified by its hotkey SS58 address) is a member of the Bittensor senate. The senate is a key governance body within the Bittensor network, responsible for overseeing and approving various network operations and proposals.

Parameters:
  • hotkey_ss58 (str) – The SS58 address of the neuron’s hotkey.

  • block (Optional[int], optional) – The blockchain block number at which to check senate membership.

Returns:

True if the neuron is a senate member at the given block, False otherwise.

Return type:

bool

This function is crucial for understanding the governance dynamics of the Bittensor network and for identifying the neurons that hold decision-making power within the network.

kappa(netuid, block=None)#

Retrieves the ‘Kappa’ hyperparameter for a specified subnet. ‘Kappa’ is a critical parameter in the Bittensor network that controls the distribution of stake weights among neurons, impacting their rankings and incentive allocations.

Parameters:
  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

The value of the ‘Kappa’ hyperparameter if the subnet exists, None otherwise.

Return type:

Optional[float]

Mathematical Context:

Kappa (κ) is used in the calculation of neuron ranks, which determine their share of network incentives. It is derived from the softmax function applied to the inter-neuronal weights set by each neuron. The formula for Kappa is: κ_i = exp(w_i) / Σ(exp(w_j)), where w_i represents the weight set by neuron i, and the denominator is the sum of exponential weights set by all neurons. This mechanism ensures a normalized and probabilistic distribution of ranks based on relative weights.

Understanding ‘Kappa’ is crucial for analyzing stake dynamics and the consensus mechanism within the network, as it plays a significant role in neuron ranking and incentive allocation processes.

leave_senate(wallet, wait_for_inclusion=True, wait_for_finalization=False, prompt=False)#

Removes a specified amount of stake from a single hotkey account. This function is critical for adjusting individual neuron stakes within the Bittensor network.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron from which the stake is being removed.

  • hotkey_ss58 (Optional[str]) – The SS58 address of the hotkey account to unstake from.

  • amount (Union[Balance, float], optional) – The amount of TAO to unstake. If not specified, unstakes all.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • prompt (bool, optional) – If True, prompts for user confirmation before proceeding.

Returns:

True if the unstaking process is successful, False otherwise.

Return type:

bool

This function supports flexible stake management, allowing neurons to adjust their network participation and potential reward accruals.

max_allowed_validators(netuid, block=None)#

Returns network MaxAllowedValidators hyper parameter

Parameters:
  • netuid (int) –

  • block (Optional[int]) –

Return type:

Optional[int]

max_n(netuid, block=None)#

Returns network MaxAllowedUids hyper parameter

Parameters:
  • netuid (int) –

  • block (Optional[int]) –

Return type:

Optional[int]

max_weight_limit(netuid, block=None)#

Returns network MaxWeightsLimit hyper parameter

Parameters:
  • netuid (int) –

  • block (Optional[int]) –

Return type:

Optional[float]

metagraph(netuid, lite=True, block=None)#

Returns a synced metagraph for a specified subnet within the Bittensor network. The metagraph represents the network’s structure, including neuron connections and interactions.

Parameters:
  • netuid (int) – The network UID of the subnet to query.

  • lite (bool, default=True) – If true, returns a metagraph using a lightweight sync (no weights, no bonds).

  • block (Optional[int]) – Block number for synchronization, or None for the latest block.

Returns:

The metagraph representing the subnet’s structure and neuron relationships.

Return type:

bittensor.Metagraph

The metagraph is an essential tool for understanding the topology and dynamics of the Bittensor network’s decentralized architecture, particularly in relation to neuron interconnectivity and consensus processes.

min_allowed_weights(netuid, block=None)#

Returns network MinAllowedWeights hyper parameter

Parameters:
  • netuid (int) –

  • block (Optional[int]) –

Return type:

Optional[int]

neuron_for_uid(uid, netuid, block=None)#

Retrieves detailed information about a specific neuron identified by its unique identifier (UID) within a specified subnet (netuid) of the Bittensor network. This function provides a comprehensive view of a neuron’s attributes, including its stake, rank, and operational status.

Parameters:
  • uid (int) – The unique identifier of the neuron.

  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

Detailed information about the neuron if found, None otherwise.

Return type:

Optional[NeuronInfo]

This function is crucial for analyzing individual neurons’ contributions and status within a specific subnet, offering insights into their roles in the network’s consensus and validation mechanisms.

neuron_for_uid_lite(uid, netuid, block=None)#

Retrieves a lightweight version of information about a neuron in a specific subnet, identified by its UID. The ‘lite’ version focuses on essential attributes such as stake and network activity.

Parameters:
  • uid (int) – The unique identifier of the neuron.

  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A simplified version of neuron information if found, None otherwise.

Return type:

Optional[NeuronInfoLite]

This function is useful for quick and efficient analyses of neuron status and activities within a subnet without the need for comprehensive data retrieval.

neuron_for_wallet(wallet, netuid, block=None)#

Retrieves information about a neuron associated with a given wallet on a specific subnet. This function provides detailed data about the neuron’s status, stake, and activities based on the wallet’s hotkey address.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron.

  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

Returns:

Detailed information about the neuron if found, None otherwise.

Return type:

Optional[NeuronInfo]

This function is important for wallet owners to understand and manage their neuron’s presence and activities within a particular subnet of the Bittensor network.

neuron_has_validator_permit(uid, netuid, block=None)#

Checks if a neuron, identified by its unique identifier (UID), has a validator permit on a specific subnet within the Bittensor network. This function determines whether the neuron is authorized to participate in validation processes on the subnet.

Parameters:
  • uid (int) – The unique identifier of the neuron.

  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

True if the neuron has a validator permit, False otherwise.

Return type:

Optional[bool]

This function is essential for understanding a neuron’s role and capabilities within a specific subnet, particularly regarding its involvement in network validation and governance.

neurons(netuid, block=None)#

Retrieves a list of all neurons within a specified subnet of the Bittensor network. This function provides a snapshot of the subnet’s neuron population, including each neuron’s attributes and network interactions.

Parameters:
  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A list of NeuronInfo objects detailing each neuron’s characteristics in the subnet.

Return type:

List[NeuronInfo]

Understanding the distribution and status of neurons within a subnet is key to comprehending the network’s decentralized structure and the dynamics of its consensus and governance processes.

neurons_lite(netuid, block=None)#

Retrieves a list of neurons in a ‘lite’ format from a specific subnet of the Bittensor network. This function provides a streamlined view of the neurons, focusing on key attributes such as stake and network participation.

Parameters:
  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number for the query.

Returns:

A list of simplified neuron information for the subnet.

Return type:

List[NeuronInfoLite]

This function offers a quick overview of the neuron population within a subnet, facilitating efficient analysis of the network’s decentralized structure and neuron dynamics.

nominate(wallet, wait_for_finalization=False, wait_for_inclusion=True)#

Becomes a delegate for the hotkey associated with the given wallet. This method is used to nominate a neuron (identified by the hotkey in the wallet) as a delegate on the Bittensor network, allowing it to participate in consensus and validation processes.

Parameters:
  • wallet (bittensor.wallet) – The wallet containing the hotkey to be nominated.

  • wait_for_finalization (bool, optional) – If True, waits until the transaction is finalized on the blockchain.

  • wait_for_inclusion (bool, optional) – If True, waits until the transaction is included in a block.

Returns:

True if the nomination process is successful, False otherwise.

Return type:

bool

This function is a key part of the decentralized governance mechanism of Bittensor, allowing for the dynamic selection and participation of validators in the network’s consensus process.

query_constant(module_name, constant_name, block=None)#

Retrieves a constant from the specified module on the Bittensor blockchain. This function is used to access fixed parameters or values defined within the blockchain’s modules, which are essential for understanding the network’s configuration and rules.

Parameters:
  • module_name (str) – The name of the module containing the constant.

  • constant_name (str) – The name of the constant to retrieve.

  • block (Optional[int], optional) – The blockchain block number at which to query the constant.

Returns:

The value of the constant if found, None otherwise.

Return type:

Optional[object]

Constants queried through this function can include critical network parameters such as inflation rates, consensus rules, or validation thresholds, providing a deeper understanding of the Bittensor network’s operational parameters.

query_identity(key, block=None)#

Queries the identity of a neuron on the Bittensor blockchain using the given key. This function retrieves detailed identity information about a specific neuron, which is a crucial aspect of the network’s decentralized identity and governance system.

Note

See the Bittensor CLI documentation for supported identity parameters.

Parameters:
  • key (str) – The key used to query the neuron’s identity, typically the neuron’s SS58 address.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

Returns:

An object containing the identity information of the neuron if found, None otherwise.

Return type:

Optional[object]

The identity information can include various attributes such as the neuron’s stake, rank, and other network-specific details, providing insights into the neuron’s role and status within the Bittensor network.

query_map(module, name, block=None, params=[])#

Queries map storage from any module on the Bittensor blockchain. This function retrieves data structures that represent key-value mappings, essential for accessing complex and structured data within the blockchain modules.

Parameters:
  • module (str) – The name of the module from which to query the map storage.

  • name (str) – The specific storage function within the module to query.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

  • params (Optional[List[object]], optional) – Parameters to be passed to the query.

Returns:

A data structure representing the map storage if found, None otherwise.

Return type:

Optional[object]

This function is particularly useful for retrieving detailed and structured data from various blockchain modules, offering insights into the network’s state and the relationships between its different components.

query_map_subtensor(name, block=None, params=[])#

Queries map storage from the Subtensor module on the Bittensor blockchain. This function is designed to retrieve a map-like data structure, which can include various neuron-specific details or network-wide attributes.

Parameters:
  • name (str) – The name of the map storage function to query.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

  • params (Optional[List[object]], optional) – A list of parameters to pass to the query function.

Returns:

An object containing the map-like data structure, or None if not found.

Return type:

QueryMapResult

This function is particularly useful for analyzing and understanding complex network structures and relationships within the Bittensor ecosystem, such as inter-neuronal connections and stake distributions.

query_module(module, name, block=None, params=[])#

Queries any module storage on the Bittensor blockchain with the specified parameters and block number. This function is a generic query interface that allows for flexible and diverse data retrieval from various blockchain modules.

Parameters:
  • module (str) – The name of the module from which to query data.

  • name (str) – The name of the storage function within the module.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

  • params (Optional[List[object]], optional) – A list of parameters to pass to the query function.

Returns:

An object containing the requested data if found, None otherwise.

Return type:

Optional[object]

This versatile query function is key to accessing a wide range of data and insights from different parts of the Bittensor blockchain, enhancing the understanding and analysis of the network’s state and dynamics.

query_runtime_api(runtime_api, method, params, block=None)#

Queries the runtime API of the Bittensor blockchain, providing a way to interact with the underlying runtime and retrieve data encoded in Scale Bytes format. This function is essential for advanced users who need to interact with specific runtime methods and decode complex data types.

Parameters:
  • runtime_api (str) – The name of the runtime API to query.

  • method (str) – The specific method within the runtime API to call.

  • params (Optional[List[ParamWithTypes]], optional) – The parameters to pass to the method call.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

Returns:

The Scale Bytes encoded result from the runtime API call, or None if the call fails.

Return type:

Optional[bytes]

This function enables access to the deeper layers of the Bittensor blockchain, allowing for detailed and specific interactions with the network’s runtime environment.

query_subtensor(name, block=None, params=[])#

Queries named storage from the Subtensor module on the Bittensor blockchain. This function is used to retrieve specific data or parameters from the blockchain, such as stake, rank, or other neuron-specific attributes.

Parameters:
  • name (str) – The name of the storage function to query.

  • block (Optional[int], optional) – The blockchain block number at which to perform the query.

  • params (Optional[List[object]], optional) – A list of parameters to pass to the query function.

Returns:

An object containing the requested data if found, None otherwise.

Return type:

Optional[object]

This query function is essential for accessing detailed information about the network and its neurons, providing valuable insights into the state and dynamics of the Bittensor ecosystem.

register(wallet, netuid, wait_for_inclusion=False, wait_for_finalization=True, prompt=False, max_allowed_attempts=3, output_in_place=True, cuda=False, dev_id=0, tpb=256, num_processes=None, update_interval=None, log_verbose=False)#

Registers a neuron on the Bittensor network using the provided wallet. Registration is a critical step for a neuron to become an active participant in the network, enabling it to stake, set weights, and receive incentives.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron to be registered.

  • netuid (int) – The unique identifier of the subnet.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • arguments (Other) – Various optional parameters to customize the registration process.

  • prompt (bool) –

  • max_allowed_attempts (int) –

  • output_in_place (bool) –

  • cuda (bool) –

  • dev_id (Union[List[int], int]) –

  • tpb (int) –

  • num_processes (Optional[int]) –

  • update_interval (Optional[int]) –

  • log_verbose (bool) –

Returns:

True if the registration is successful, False otherwise.

Return type:

bool

This function facilitates the entry of new neurons into the network, supporting the decentralized growth and scalability of the Bittensor ecosystem.

register_senate(wallet, wait_for_inclusion=True, wait_for_finalization=False, prompt=False)#

Removes a specified amount of stake from a single hotkey account. This function is critical for adjusting individual neuron stakes within the Bittensor network.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron from which the stake is being removed.

  • hotkey_ss58 (Optional[str]) – The SS58 address of the hotkey account to unstake from.

  • amount (Union[Balance, float], optional) – The amount of TAO to unstake. If not specified, unstakes all.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • prompt (bool, optional) – If True, prompts for user confirmation before proceeding.

Returns:

True if the unstaking process is successful, False otherwise.

Return type:

bool

This function supports flexible stake management, allowing neurons to adjust their network participation and potential reward accruals.

register_subnetwork(wallet, wait_for_inclusion=False, wait_for_finalization=True, prompt=False)#

Registers a new subnetwork on the Bittensor network using the provided wallet. This function is used for the creation and registration of subnetworks, which are specialized segments of the overall Bittensor network.

Parameters:
  • wallet (bittensor.wallet) – The wallet to be used for registration.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • prompt (bool, optional) – If True, prompts for user confirmation before proceeding.

Returns:

True if the subnetwork registration is successful, False otherwise.

Return type:

bool

This function allows for the expansion and diversification of the Bittensor network, supporting its decentralized and adaptable architecture.

rho(netuid, block=None)#

Retrieves the ‘Rho’ hyperparameter for a specified subnet within the Bittensor network. ‘Rho’ represents the global inflation rate, which directly influences the network’s token emission rate and economic model.

Note

This is currently fixed such that the Bittensor blockchain emmits 7200 Tao per day.

Parameters:
  • netuid (int) – The unique identifier of the subnet.

  • block (Optional[int], optional) – The blockchain block number at which to query the parameter.

Returns:

The value of the ‘Rho’ hyperparameter if the subnet exists, None otherwise.

Return type:

Optional[int]

Mathematical Context:

Rho (p) is calculated based on the network’s target inflation and actual neuron staking. It adjusts the emission rate of the TAO token to balance the network’s economy and dynamics. The formula for Rho is defined as: p = (Staking_Target / Staking_Actual) * Inflation_Target. Here, Staking_Target and Staking_Actual represent the desired and actual total stakes in the network, while Inflation_Target is the predefined inflation rate goal.

‘Rho’ is essential for understanding the network’s economic dynamics, affecting the reward distribution and incentive structures across the network’s neurons.

root_register(wallet, wait_for_inclusion=False, wait_for_finalization=True, prompt=False)#

Registers the neuron associated with the wallet on the root network. This process is integral for participating in the highest layer of decision-making and governance within the Bittensor network.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron to be registered on the root network.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • prompt (bool, optional) – If True, prompts for user confirmation before proceeding.

Returns:

True if the registration on the root network is successful, False otherwise.

Return type:

bool

This function enables neurons to engage in the most critical and influential aspects of the network’s governance, signifying a high level of commitment and responsibility in the Bittensor ecosystem.

root_set_weights(wallet, netuids, weights, version_key=0, wait_for_inclusion=False, wait_for_finalization=False, prompt=False)#

Sets the weights for neurons on the root network. This action is crucial for defining the influence and interactions of neurons at the root level of the Bittensor network.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron setting the weights.

  • netuids (Union[torch.LongTensor, list]) – The list of neuron UIDs for which weights are being set.

  • weights (Union[torch.FloatTensor, list]) – The corresponding weights to be set for each UID.

  • version_key (int, optional) – Version key for compatibility with the network.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • prompt (bool, optional) – If True, prompts for user confirmation before proceeding.

Returns:

True if the setting of root-level weights is successful, False otherwise.

Return type:

bool

This function plays a pivotal role in shaping the root network’s collective intelligence and decision-making processes, reflecting the principles of decentralized governance and collaborative learning in Bittensor.

run_faucet(wallet, wait_for_inclusion=False, wait_for_finalization=True, prompt=False, max_allowed_attempts=3, output_in_place=True, cuda=False, dev_id=0, tpb=256, num_processes=None, update_interval=None, log_verbose=False)#

Facilitates a faucet transaction, allowing new neurons to receive an initial amount of TAO for participating in the network. This function is particularly useful for newcomers to the Bittensor network, enabling them to start with a small stake on testnet only.

Parameters:
  • wallet (bittensor.wallet) – The wallet for which the faucet transaction is to be run.

  • arguments (Other) – Various optional parameters to customize the faucet transaction process.

  • wait_for_inclusion (bool) –

  • wait_for_finalization (bool) –

  • prompt (bool) –

  • max_allowed_attempts (int) –

  • output_in_place (bool) –

  • cuda (bool) –

  • dev_id (Union[List[int], int]) –

  • tpb (int) –

  • num_processes (Optional[int]) –

  • update_interval (Optional[int]) –

  • log_verbose (bool) –

Returns:

True if the faucet transaction is successful, False otherwise.

Return type:

bool

This function is part of Bittensor’s onboarding process, ensuring that new neurons have the necessary resources to begin their journey in the decentralized AI network.

Note

This is for testnet ONLY and is disabled currently. You must build your own staging subtensor chain with the --features pow-faucet argument to enable this.

scaling_law_power(netuid, block=None)#

Returns network ScalingLawPower hyper parameter

Parameters:
  • netuid (int) –

  • block (Optional[int]) –

Return type:

Optional[float]

serve(wallet, ip, port, protocol, netuid, placeholder1=0, placeholder2=0, wait_for_inclusion=False, wait_for_finalization=True, prompt=False)#

Registers a neuron’s serving endpoint on the Bittensor network. This function announces the IP address and port where the neuron is available to serve requests, facilitating peer-to-peer communication within the network.

Parameters:
  • wallet (bittensor.wallet) – The wallet associated with the neuron being served.

  • ip (str) – The IP address of the serving neuron.

  • port (int) – The port number on which the neuron is serving.

  • protocol (int) – The protocol type used by the neuron (e.g., GRPC, HTTP).

  • netuid (int) – The unique identifier of the subnetwork.

  • arguments (Other) – Placeholder parameters for future extensions.

  • wait_for_inclusion (bool, optional) – Waits for the transaction to be included in a block.

  • wait_for_finalization (bool, optional) – Waits for the transaction to be finalized on the blockchain.

  • prompt (bool, optional) – If True, prompts for user confirmation before proceeding.

  • placeholder1 (int) –

  • placeholder2 (int) –

Ret