Subtensor API package for bittensor
Project description
Subtensor API Python package
A python wrapper around the @polkadot/api
node library to query the bittensor chain.
Install from pypi
pip install subtensorapi==1.0.0
Running the CLI
Usage
View usage
python3 -m subtensorapi --help
Specify chain endpoint url
python3 -m subtensorapi [sync_and_save|blockAtReg_and_save|sync_and_save_historical] --endpoint_url ENDPOINT_URL
Specify filename to save to
python3 -m subtensorapi [sync_and_save|blockAtReg_and_save|sync_and_save_historical] --filename FILENAME.json
Specify blockhash to sync the chain at, default "latest"
python3 -m subtensorapi [sync_and_save|blockAtReg_and_save|sync_and_save_historical] --block_hash BLOCK_HASH
Sync And Save Metagraph
Pulls the neurons
storage map and saves it to a JSON file.
View usage
python3 -m subtensorapi sync_and_save --help
Run sync of metagraph and save to JSON file
python3 -m subtensorapi sync_and_save -f metagraph.json
Sync And Save Neurons from Metagraph at historical blocks
Pulls the neurons
storage map at each block for each UID and saves it to a JSON file.
View usage
python3 -m subtensorapi sync_async_and_save_historicalnd_save --help
Run sync of metagraph at blocks 2706651 2706652 2706653
and UIDs 1 2 3
and save to JSON file
python3 -m subtensorapi sync_and_save_historical -i 1 2 3 -b 2706651 2706652 2706653 -f history.json
Grab blockAtRegistration
Pulls the blockAtRegistration
storage map and saves it to a JSON file.
View usage
python3 -m subtensorapi blockAtReg_and_save --help
Using the library
FastSync class
Setup of a FastSync instance
from subtensorapi import FastSync
# check if fast sync is available on the platform
FastSync.verify_fast_sync_support()
# specify the chain endpoint_url
fast_sync: FastSync = FastSync(endpoint_url)
Run the metagraph sync
Pulls the neurons
storage map.
# specify block_hash to sync at. Default is "latest"
block_hash = "0xb2fa081[...]"
# run the sync command and save to JSON file at block_hash
fast_sync.sync_and_save(block_hash)
# load neurons in from JSON file
neurons = fast_sync.load_neurons()
Run the historical metagraph sync
Pulls the neurons
storage map for each block and each UID
# specify blockNumbers to sync at. Default is "latest"
blockNumbers = ["latest", 2706652]
# specify UIDs to sync
UIDs = [1, 2, 3, 4095]
# run the sync command and save to JSON file at block_hash
fast_sync.sync_and_save_historical(blockNumbers, UIDs)
# load neurons in from JSON file
historical_neurons = fast_sync.load_historical_neurons()
Run blockAtRegistration pull
Pulls the blockAtRegistration
storage map.
# specify block_hash to sync at. Default is "latest"
block_hash = "0xb2fa081[...]"
# run the pull command and save to JSON file at block_hash
fast_sync.get_blockAtRegistration_for_all_and_save(block_hash)
# load blockAtRegistration_all from JSON file
blockAtRegistration_all = fast_sync.load_blockAtRegistration_for_all()
Using the FastSync class without writing to a file
You can redirect the file writing to a pipe instead of a file.
This removes the need to write to disk and provides a speedup.
Example:
# all neurons
all_neurons: List[SimpleNamespace] = fast_sync.sync_fd(block_hash)
# historical neurons
historical_neurons: Dict[str, Dict[str, SimpleNamespace]] = fast_sync.sync_historical_fd(blockNumbers, UIDs)
# blockAtRegistration_all
blockAtRegistration_all: List[int] = fast_sync.get_blockAtRegistration_for_all_fd(block_hash)
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