The provided code analysis is of a miner template for benchmarking using the Bittensor framework. It's designed to be a starting point for creating a custom miner, and measures its performance with respect to query execution. The template extends the
QueryBenchmark class and provides methods for running the miner and configuring it.
miner_name(): This method returns the miner's name as a string, which is set to 'template_miner' in this case.
run_neuron(config): This method takes a Bittensor configuration object as input and runs the neuron using the
config(): This method returns the configuration object for the miner.
benchmark_custom(): This method runs the custom benchmark by performing a sequence of queries with a specified number of calls, batch size, and block size. It then prints the query analysis and saves the results to a CSV file.
mainfunction initializes a
Benchmarkobject and runs the benchmark.
To adapt this template to your own miner, you need to make the following changes:
miner_name()method to return your custom miner's name.
run_neuron(config)method to use your custom miner's neuron implementation. Replace
bittensor.neurons.text.template_miner.neuronwith your own miner's neuron class.
config()method to return the configuration object for your custom miner. Replace
bittensor.neurons.text.template_miner.neuron.config()with the configuration method for your miner's neuron class.
benchmark_custom()method if you have specific benchmarking requirements. You can modify the number of calls, batch size, and block size, or implement additional benchmarking tests.
If your custom miner requires additional configuration or setup, you can add those steps in the
mainfunction or in a separate method.
After making these changes, you can run the benchmarking script for your custom miner and analyze its performance.