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Basic inference node setup

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.DS_Store
__pycache__
*.pyc
.lake_cache/*
logs/*
.env
keys
data
worker-data
head-data
lib

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Any contribution that you make to this repository will
be under the Apache 2 License, as dictated by that
[license](http://www.apache.org/licenses/LICENSE-2.0.html):
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# Use an official Python runtime as the base image
FROM amd64/python:3.9-buster as project_env
# Set the working directory in the container
WORKDIR /app
# Install dependencies
COPY requirements.txt requirements.txt
RUN pip install --upgrade pip setuptools \
&& pip install -r requirements.txt
FROM project_env
COPY . /app/
# Set the entrypoint command
CMD ["gunicorn", "--conf", "/app/gunicorn_conf.py", "main:app"]

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FROM --platform=linux/amd64 696230526504.dkr.ecr.us-east-1.amazonaws.com/allora-inference-base:latest
# FROM --platform=linux/amd64 allora-inference-base:dev
USER root
RUN pip install requests
USER appuser
COPY main.py /app/

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# Basic ETH price prediction node
Example Allora network worker node: a node to provide price predictions of ETH.
One of the primary objectives is to demonstrate the utilization of a basic inference model operating within a dedicated container. The purpose is to showcase its seamless integration with the Allora network infrastructure, enabling it to contribute with valuable inferences.
# Components
* **head**: An Allora network head node. This is not required for running your node in the Allora network, but it will help for testing your node emulating a network.
* **worker**: The node that will respond to inference requests from the Allora network heads.
* **inference**: A container that conducts inferences, maintains the model state, and responds to internal inference requests via a Flask application. The node operates with a basic linear regression model for price predictions.
* **updater**: An example of a cron-like container, that will update the data of the inference node.
Check the `docker-compose.yml` file (or see docker-compose section below) to see separate components:
# Inference request flow
When a request is made to the head, it relays this request to a number of workers associated with this head. The request specifies a function to run which will execute a wasm code that will call the `main.py` file in the worker. The worker will check the argument (the coin to predict for), and make a request to the `inference` node, and return this value to the `head`, which prepares the response from all of its nodes and sends it back to the requestor.
# Docker setup
## Structure
- head and worker nodes are built upon `Dockerfile_b7s` file
- inference and updater nodes are built with `Dockerfile`. This works as an example on how to reuse your current model containers, just by setting up a Flask web application in front with minimal integration work with the Allora network nodes.
The `Dockerfile_b7s` file is functional but simple, so you may want to change it to fit your needs, if you attempt to expand upon the current setup.
For further details, please check the base repo [allora-inference-base](https://github.com/allora-network/allora-inference-base).
### Application path
By default, the application runtime lives under `/app`, as well as the Python code the worker provides (`/app/main.py`). The current user needs to have write permissions on `/app/runtime`.
### Data volume and permissions
It is recommended to mount `/data` as a volume, to persist the node databases of peers, functions, etc. which are defined in the flags passed to the worker.
You can create this folder e.g. `mkdir data` in the repo root directory.
It is recommended to set up two different `/data` volumes. It is suggested to use `data-worker` for the worker, `data-head` for the head.
Troubleshooting: A conflict may happen between the uid/gid of the user inside the container(1001) with the permissions of your own user.
To make the container user have permissions to write on the `/data` volume, you may need to set the UID/GID from the user running the container. You can get those in linux/osx via `id -u` and `id -g`.
The current `docker-compose.yml` file shows the `worker` service setting UID and GID. As well, the `Dockerfile` also sets UID/GID values.
# docker-compose
A full working example is provided in `docker-compose`.
## Structure
There is a docker-compose.yml provided that sets up one head node, one worker node, one inference node, and an updater node.
Please find details about options on the [allora-inference-base](https://github.com/allora-network/allora-inference-base) repo.
## Dependencies
Ensure the following dependencies are in place before proceeding:
- **Docker Image**: Have an available image of the `allora-inference-base`, and reference it as a base on the `FROM` of the `Dockerfile_b7s` file.
- **Keys Setup**: Create a set of keys for your head and worker and use them in the head and worker configuration. If no keys are specified in the volumes, new keys are created. However, the worker will need to specify the `peer_id` of the head for defining it as a `BOOT_NODES`.
## Connecting to the Allora network
In order to connect the an Allora network to provide inferences, both the head and the worker need to register against it. More details on [allora-inference-base](https://github.com/allora-network/allora-inference-base) repo.
The following optional flags are used in the `command:` section of the `docker-compose.yml` file to define the connectivity with the Allora network.
```
--allora-chain-key-name=index-provider # your local key name in your keyring
--allora-chain-restore-mnemonic='pet sock excess ...' # your node's Allora address mnemonic
--allora-node-rpc-address= # RPC address of a node in the chain
--allora-chain-topic-id= # The topic id from the chain that you want to provide predictions for
```
In order for the nodes to register with the chain, a funded address is needed first.
If these flags are not provided, the nodes will not register to the appchain and will not attempt to connect to the appchain.
# Setup
Once this is set up, run `docker compose up head worker inference`
This will bring up the head, the worker and the inference nodes (which will run an initial update). The `updater` node is a companion for updating the inference node state and it's meant to hit the /update endpoint on the inference service. It is expected to run periodically, being crucial for maintaining the accuracy of the inferences.
## Testing docker-compose setup
The head node has the only open port, and responds to requests in port 6000.
Example request:
```
curl --location 'http://localhost:6000/api/v1/functions/execute' \
--header 'Content-Type: application/json' \
--data '{
"function_id": "bafybeigpiwl3o73zvvl6dxdqu7zqcub5mhg65jiky2xqb4rdhfmikswzqm",
"method": "allora-inference-function.wasm",
"parameters": null,
"topic": "1",
"config": {
"env_vars": [
{
"name": "BLS_REQUEST_PATH",
"value": "/api"
},
{
"name": "ALLORA_ARG_PARAMS",
"value": "ETH"
}
],
"number_of_nodes": -1,
"timeout": 2
}
}'
```
Response:
```
{"code":"200","request_id":"e3daeda0-c849-4b68-b21d-8f51e42bb9d3","results":[{"result":{"stdout":"{\"value\":\"2564.250058819078\"}\n\n\n","stderr":"","exit_code":0},"peers":["12D3KooWG8dHctRt6ctakJfG5masTnLaKM6xkudoR5BxLDRSrgVt"],"frequency":100}],"cluster":{"peers":["12D3KooWG8dHctRt6ctakJfG5masTnLaKM6xkudoR5BxLDRSrgVt"]}}
```
# Testing inference only
This setup allows to develop your model without need for bringing up the head and worker.
To only test the inference model, you can just:
- In docker-compose.yml, under `inference` service, uncomment the lines:
```
ports:
- "8000:8000"
```
- Run `docker compose up --build inference` and wait for the initial data load.
- Requests can now be sent, e.g. request ETH price inferences as in:
```
$ curl http://localhost:8000/inference/ETH
{"value":"2564.2513659239594"}
```
or update the node's internal state (download pricing data, train and update the model):
```
$ curl http://localhost:8000/update
0
```

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import json
import pickle
import pandas as pd
import numpy as np
from datetime import datetime
from flask import Flask, jsonify, Response
from model import download_data, format_data, train_model
from config import model_file_path
app = Flask(__name__)
def update_data():
"""Download price data, format data and train model."""
download_data()
format_data()
train_model()
def get_eth_inference():
"""Load model and predict current price."""
with open(model_file_path, "rb") as f:
loaded_model = pickle.load(f)
now_timestamp = pd.Timestamp(datetime.now()).timestamp()
X_new = np.array([now_timestamp]).reshape(-1, 1)
current_price_pred = loaded_model.predict(X_new)
return current_price_pred[0][0]
@app.route("/inference/<string:token>")
def generate_inference(token):
"""Generate inference for given token."""
if not token or token != "ETH":
error_msg = "Token is required" if not token else "Token not supported"
return Response(
json.dumps({"error": error_msg}), status=400, mimetype="application/json"
)
try:
inference = get_eth_inference()
return jsonify({"value": str(inference)})
except Exception as e:
return Response(
json.dumps({"error": str(e)}), status=500, mimetype="application/json"
)
@app.route("/update")
def update():
"""Update data and return status."""
try:
update_data()
return "0"
except Exception:
return "1"
if __name__ == "__main__":
update_data()
app.run(host="0.0.0.0", port=8000)

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import os
app_base_path = os.getenv("APP_BASE_PATH", default=os.getcwd())
data_base_path = os.path.join(app_base_path, "data")
model_file_path = os.path.join(data_base_path, "model.pkl")

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version: '3'
services:
inference:
container_name: inference-basic-eth-pred
build:
context: .
command: python -u /app/app.py
ports:
- "8000:8000"
networks:
eth-model-local:
aliases:
- inference
ipv4_address: 172.22.0.4
worker:
container_name: worker-basic-eth-pred
environment:
- INFERENCE_API_ADDRESS=http://inference:8000
- HOME=/data
build:
context: .
dockerfile: Dockerfile_b7s
entrypoint:
- "/bin/bash"
- "-c"
- |
if [ ! -f /data/keys/priv.bin ]; then
echo "Generating new private keys..."
mkdir -p /data/keys
cd /data/keys
allora-keys
fi
# Change boot-nodes below to the key advertised by your head
allora-node --role=worker --peer-db=/data/peerdb --function-db=/data/function-db \
--runtime-path=/app/runtime --runtime-cli=bls-runtime --workspace=/data/workspace \
--private-key=/data/keys/priv.bin --log-level=debug --port=9011 \
--boot-nodes=/ip4/172.22.0.100/tcp/9010/p2p/12D3KooWSBJucc8S3YdLH8n5UqTQpSbNjwEjcnYCW8zWuPhDAFHY \
--topic=1
volumes:
- ./worker-data:/data
working_dir: /data
depends_on:
- inference
- head
networks:
eth-model-local:
aliases:
- worker
ipv4_address: 172.22.0.10
head:
# 12D3KooWSBJucc8S3YdLH8n5UqTQpSbNjwEjcnYCW8zWuPhDAFHY
container_name: head-basic-eth-pred
image: 696230526504.dkr.ecr.us-east-1.amazonaws.com/allora-inference-base-head:latest
environment:
- HOME=/data
entrypoint:
- "/bin/bash"
- "-c"
- |
if [ ! -f /data/keys/priv.bin ]; then
echo "Generating new private keys..."
mkdir -p /data/keys
cd /data/keys
allora-keys
fi
allora-node --role=head --peer-db=/data/peerdb --function-db=/data/function-db \
--runtime-path=/app/runtime --runtime-cli=bls-runtime --workspace=/data/workspace \
--private-key=/data/keys/priv.bin --log-level=debug --port=9010 --rest-api=:6000
ports:
- "6000:6000"
volumes:
- ./head-data:/data
working_dir: /data
networks:
eth-model-local:
aliases:
- head
ipv4_address: 172.22.0.100
networks:
eth-model-local:
driver: bridge
ipam:
config:
- subnet: 172.22.0.0/24
volumes:
worker-data:
head-data:

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# Gunicorn config variables
loglevel = "info"
errorlog = "-" # stderr
accesslog = "-" # stdout
worker_tmp_dir = "/dev/shm"
graceful_timeout = 120
timeout = 30
keepalive = 5
worker_class = "gthread"
workers = 1
threads = 8
bind = "0.0.0.0:9000"

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import os
import requests
import sys
import json
INFERENCE_ADDRESS = os.environ["INFERENCE_API_ADDRESS"]
def process(token_name):
response = requests.get(f"{INFERENCE_ADDRESS}/inference/{token_name}")
content = response.text
print(content)
if __name__ == "__main__":
# Your code logic with the parsed argument goes here
try:
if len(sys.argv) >= 2:
token_name = sys.argv[1]
else:
token_name = "ETH"
process(token_name=token_name)
except Exception as e:
response = json.dumps({"error": {str(e)}})
print(response)

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import os
import pickle
from zipfile import ZipFile
from datetime import datetime
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from updater import download_binance_monthly_data, download_binance_daily_data
from config import app_base_path, model_file_path
binance_data_path = os.path.join(app_base_path, "binance/futures-klines")
training_price_data_path = os.path.join(app_base_path, "eth_price_data.csv")
def download_data():
cm_or_um = "um"
symbols = ["ETHUSDT"]
intervals = ["1d"]
years = ["2020", "2021", "2022", "2023", "2024"]
months = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
download_path = binance_data_path
download_binance_monthly_data(
cm_or_um, symbols, intervals, years, months, download_path
)
print(f"Downloaded monthly data to {download_path}.")
current_datetime = datetime.now()
current_year = current_datetime.year
current_month = current_datetime.month
download_binance_daily_data(
cm_or_um, symbols, intervals, current_year, current_month, download_path
)
print(f"Downloaded daily data to {download_path}.")
def format_data():
files = sorted([x for x in os.listdir(binance_data_path)])
# No files to process
if len(files) == 0:
return
price_df = pd.DataFrame()
for file in files:
zip_file_path = os.path.join(binance_data_path, file)
if not zip_file_path.endswith(".zip"):
continue
myzip = ZipFile(zip_file_path)
with myzip.open(myzip.filelist[0]) as f:
line = f.readline()
header = 0 if line.decode("utf-8").startswith("open_time") else None
df = pd.read_csv(myzip.open(myzip.filelist[0]), header=header).iloc[:, :11]
df.columns = [
"start_time",
"open",
"high",
"low",
"close",
"volume",
"end_time",
"volume_usd",
"n_trades",
"taker_volume",
"taker_volume_usd",
]
df.index = [pd.Timestamp(x + 1, unit="ms") for x in df["end_time"]]
df.index.name = "date"
price_df = pd.concat([price_df, df])
price_df.sort_index().to_csv(training_price_data_path)
def train_model():
# Load the eth price data
price_data = pd.read_csv(training_price_data_path)
df = pd.DataFrame()
# Convert 'date' to a numerical value (timestamp) we can use for regression
df["date"] = pd.to_datetime(price_data["date"])
df["date"] = df["date"].map(pd.Timestamp.timestamp)
df["price"] = price_data[["open", "close", "high", "low"]].mean(axis=1)
# Reshape the data to the shape expected by sklearn
x = df["date"].values.reshape(-1, 1)
y = df["price"].values.reshape(-1, 1)
# Split the data into training set and test set
x_train, _, y_train, _ = train_test_split(x, y, test_size=0.2, random_state=0)
# Train the model
model = LinearRegression()
model.fit(x_train, y_train)
# Save the trained model to a file
with open(model_file_path, "wb") as f:
pickle.dump(model, f)
print(f"Trained model saved to {model_file_path}")

6
requirements.txt Normal file
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flask[async]
gunicorn[gthread]
numpy==1.26.2
pandas==2.1.3
Requests==2.31.0
scikit_learn==1.3.2

20
update_app.py Normal file
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import os
import requests
inference_address = os.environ["INFERENCE_API_ADDRESS"]
url = f"{inference_address}/update"
response = requests.get(url)
if response.status_code == 200:
# Request was successful
content = response.text
if content == "0":
print("Response content is '0'")
exit(0)
else:
exit(1)
else:
# Request failed
print(f"Request failed with status code: {response.status_code}")
exit(1)

54
updater.py Normal file
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import os
import requests
from concurrent.futures import ThreadPoolExecutor
# Function to download the URL, called asynchronously by several child processes
def download_url(url, download_path):
response = requests.get(url)
if response.status_code == 404:
print(f"File not exist: {url}")
else:
file_name = os.path.join(download_path, os.path.basename(url))
# create the entire path if it doesn't exist
os.makedirs(os.path.dirname(file_name), exist_ok=True)
with open(file_name, "wb") as f:
f.write(response.content)
print(f"Downloaded: {url} to {file_name}")
def download_binance_monthly_data(
cm_or_um, symbols, intervals, years, months, download_path
):
# Verify if CM_OR_UM is correct, if not, exit
if cm_or_um not in ["cm", "um"]:
print("CM_OR_UM can be only cm or um")
return
base_url = f"https://data.binance.vision/data/futures/{cm_or_um}/monthly/klines"
# Main loop to iterate over all the arrays and launch child processes
with ThreadPoolExecutor() as executor:
for symbol in symbols:
for interval in intervals:
for year in years:
for month in months:
url = f"{base_url}/{symbol}/{interval}/{symbol}-{interval}-{year}-{month}.zip"
executor.submit(download_url, url, download_path)
def download_binance_daily_data(
cm_or_um, symbols, intervals, year, month, download_path
):
if cm_or_um not in ["cm", "um"]:
print("CM_OR_UM can be only cm or um")
return
base_url = f"https://data.binance.vision/data/futures/{cm_or_um}/daily/klines"
with ThreadPoolExecutor() as executor:
for symbol in symbols:
for interval in intervals:
for day in range(1, 32): # Assuming days range from 1 to 31
url = f"{base_url}/{symbol}/{interval}/{symbol}-{interval}-{year}-{month:02d}-{day:02d}.zip"
executor.submit(download_url, url, download_path)