docs/user-guides/compute-to-data/make-a-boss-c2d-algorithm.md

description
How to construct the beginnings of an awesome algorithm for C2D compute jobs on datasets

Make a Boss C2D Algorithm

The beginning of any great algorithm for Compute-to-Data starts by referencing the dataset asset correctly on the Docker container. Read on, anon.

Open the local dataset file

This code goes at the top of your algorithm file for your algorithm NFT asset to use with Compute-to-Data. It references your data NFT asset file on the Docker container you selected.

{% tabs %} {% tab title="Python" %}

import csv
import json
import os

def get_input(local=False):

    dids = os.getenv("DIDS", None)

    if not dids:
        print("No DIDs found in the environment. Aborting.")
        return

    dids = json.loads(dids)

    for did in dids:
        filename = f"data/inputs/{did}/0"  # 0 for metadata service
        print(f"Reading asset file {filename}.")
        return filename

# Get the input filename using the get_input function
input_filename = get_input()

if not input_filename:
    # No input filename returned
    exit()
    
# Open the file & run your code
with open(input_filename, 'r') as file:
    # Read the CSV file
    csv_reader = csv.DictReader(file)
    
    <YOUR CODE GOES HERE>

{% endtab %}

{% tab title="Javascript" %}

const fs = require("fs");

var input_folder = "/data/inputs";
var output_folder = "/data/outputs";

async function processfolder(Path) {
    var files = fs.readdirSync(Path);
    for (var i =0; i < files.length; i++) {
        var file = files[i];
        var fullpath = Path + "/" + file;
        if (fs.statSync(fullpath).isDirectory()) {
            await processfolder(fullpath);
        } else {
            <YOUR CODE GOES HERE>
        }
    }
}

<YOUR CODE & FUNCTION DEFINITIONS GO HERE>

// Open the file & run your code
processfolder(input_folder);
    

{% endtab %} {% endtabs %}

Note: Here are the following Python libraries that you can use in your code:

// Python modules
numpy==1.16.3
pandas==0.24.2
python-dateutil==2.8.0
pytz==2019.1
six==1.12.0
sklearn
xlrd == 1.2.0
openpyxl >= 3.0.3
wheel
matplotlib