How to Create and Deploy an Inference Generation Function

Worker nodes invoke this function to gather inferences during inference solicitation

What Is an Inference Generation Function?

This function will serve as the gateway through which the inferences from worker nodes will be sent. This function typically has the role of triggering your model, and for that purpose, a Blockless CGI Extension is used inside the function.

Allora Recommended Inference Function

Allora provides a recommended inference function that will enable the main.py file in the worker. This is the recommended function to use in topics.

Current data for the recommended inference function is:

  • function_id : bafybeigpiwl3o73zvvl6dxdqu7zqcub5mhg65jiky2xqb4rdhfmikswzqm
  • method: allora-inference-function.wasm

However, it is possible to create a custom function. Example of use cases that would need it would be a function that instead of triggering the execution of a python file, it would trigger a bash script, a go file, etc.

How to Create and Deploy an Inference Generation Function?

To create an inference function for your topic, follow the steps outlined in the tutorial below to create your first function on Blockless:

Blockless Quick Start Guide

Once you have installed the Blockless CLI and created your initial sample function, you will need to modify it to trigger your model to generate the inferences and return them in the Stdout.

You can use the example below:
Inference Generation Logic

To make tests in your function you can invoke the function locally using:

bls function invoke

Your function needs to follow the same output as the example above. An example of output:

{
	"value": "2543.3176470588235294"
}

After testing now, you can deploy your function using:

bls function deploy

After testing and deploying your function, save the function_id and the method (the name of the Wasm file) to use when creating your topic.