Building a Worker Node with the allocmd CLI

The allocmd is a CLI tool that handles worker nodes' seamless creation and deployment. With this tool, you do not need to write the worker node from scratch, the CLI tool will help you bootstrap all the needed components to get worker nodes working. All you have to do is to update the config.yaml file with your custom parameters, update the providedmain.pyto communicate with your inference server, run the deploy command, and your worker should be up and running.

To build a worker node with allocmd, you will need to follow the following steps:

1. Install allocmd CLI

You will begin with installing the tool on your machine.

pip install allocmd

you can run allocmd --help to get general help or allocmd [command] --help to get help relating to a particular command.

2. Initialize the worker for development

The next step is initializing the CLI to bootstrap all the needed components to get your worker running. The following command will handle the initialization process. It will create all the files in the appropriate directories and generate identities for your node to be used for local development.

allocmd init --name <preffered name> --topic <topic id> --env dev

Before running this command you will have to pick the topic Id you wish to generate inference for after which you can run this command with the topic Id. The command will auto-create some files, the most important of which is the dev-docker-compose.yamlfile which is an already complete docker-compose that you can run immediately to see your worker and head nodes running perfectly on your local machine. You can edit the files as you wish. for instance the main.py is meant for you to call your inference server, hence you will have to edit the sample code with actual URLs and logic as you prefer.

When you run the docker-compose (docker-compose -f dev-docker-compose.yaml up --build), maybe after you have written and tested your logic in main.py, you then should be seeing the logs from the nodes, and you should be able to make a request to your head node and see it get a response from the worker node. Note that in production, you won't be the one to make the inference request, as the Allora chain will do this at the cadence provided by the topic creator.

You can test your node by running the following curl command:

curl --location 'http://localhost:6000/api/v1/functions/execute' --header 'Accept: application/json, text/plain, */*' --header 'Content-Type: application/json;charset=UTF-8' --data '{
    "function_id": "bafybeigpiwl3o73zvvl6dxdqu7zqcub5mhg65jiky2xqb4rdhfmikswzqm",
    "method": "allora-inference-function.wasm",
    "parameters": null,
    "topic": "<TOPIC_ID>",
    "config": {
        "env_vars": [
            {                              
                "name": "BLS_REQUEST_PATH",
                "value": "/api"
            },
            {                              
                "name": "ALLORA_ARG_PARAMS",
                "value": "<argument>"
            }
        ],
        "number_of_nodes": -1,
        "timeout" : 2
    }
}' | jq

The <TOPIC_ID> needs to be an existing topic on the chain. The <argument> is what the topic is expecting to receive to perform the inference (as an indication to test, you can use the DefaultArg value from the topic on-chain, e.g. for ETH prediction topic, it should be "ETH").

3. Initialize the worker for production

Your worker node is now ready to be deployed, the main.py has been modified, all env variables passed, and the worker node is running locally and you are now ready to deploy your worker to run in the production environment. The following command will handle the generation of the prod-docker-compose.yaml file which contains all the keys and parameters needed for your worker to function perfectly in production.

allocmd init --env prod

By running this command, prod-docker-compose.yaml will be generated with appropriate keys and parameters. You can now run the docker-compose file or deploy the whole codebase in your preferred cloud instance. At this stage, your worker should be responding to inference request from the Allora Chain.