commit ce1316c2022692a9dcb5bb15cbf42add3eec8581 Author: conache Date: Wed Mar 6 17:53:48 2024 +0200 Basic inference node setup diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e865921 --- /dev/null +++ b/.gitignore @@ -0,0 +1,11 @@ +.DS_Store +__pycache__ +*.pyc +.lake_cache/* +logs/* +.env +keys +data +worker-data +head-data +lib \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..cfba094 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,18 @@ +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): + +~~~ +5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. +~~~ + +Contributors must sign-off each commit by adding a `Signed-off-by: ...` +line to commit messages to certify that they have the right to submit +the code they are contributing to the project according to the +[Developer Certificate of Origin (DCO)](https://developercertificate.org/). diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..df82d46 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,17 @@ +# 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"] diff --git a/Dockerfile_b7s b/Dockerfile_b7s new file mode 100644 index 0000000..acf35d8 --- /dev/null +++ b/Dockerfile_b7s @@ -0,0 +1,8 @@ +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/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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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 + ``` + diff --git a/app.py b/app.py new file mode 100644 index 0000000..32424c5 --- /dev/null +++ b/app.py @@ -0,0 +1,62 @@ +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/") +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) diff --git a/config.py b/config.py new file mode 100644 index 0000000..c1b91db --- /dev/null +++ b/config.py @@ -0,0 +1,5 @@ +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") diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..02bf686 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,93 @@ +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: diff --git a/gunicorn_conf.py b/gunicorn_conf.py new file mode 100644 index 0000000..759df0b --- /dev/null +++ b/gunicorn_conf.py @@ -0,0 +1,12 @@ +# 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" diff --git a/main.py b/main.py new file mode 100644 index 0000000..4a6b890 --- /dev/null +++ b/main.py @@ -0,0 +1,25 @@ +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) diff --git a/model.py b/model.py new file mode 100644 index 0000000..2cea035 --- /dev/null +++ b/model.py @@ -0,0 +1,101 @@ +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}") diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..ae25853 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +flask[async] +gunicorn[gthread] +numpy==1.26.2 +pandas==2.1.3 +Requests==2.31.0 +scikit_learn==1.3.2 \ No newline at end of file diff --git a/update_app.py b/update_app.py new file mode 100644 index 0000000..0b3efd6 --- /dev/null +++ b/update_app.py @@ -0,0 +1,20 @@ +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) diff --git a/updater.py b/updater.py new file mode 100644 index 0000000..2eb4332 --- /dev/null +++ b/updater.py @@ -0,0 +1,54 @@ +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)