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99 lines
2.2 KiB
Markdown
99 lines
2.2 KiB
Markdown
---
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description: >-
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How to construct the beginnings of an awesome algorithm for C2D compute jobs
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on datasets
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---
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# Make a Boss C2D Algorithm
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<figure><img src="../../.gitbook/assets/like-a-boss.gif" alt=""><figcaption></figcaption></figure>
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The beginning of any great algorithm for Compute-to-Data starts by referencing the dataset asset correctly on the Docker container. Read on, anon.
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### Open the local dataset file
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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.
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{% tabs %}
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{% tab title="Python" %}
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```python
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import csv
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import json
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import os
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def get_input(local=False):
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dids = os.getenv("DIDS", None)
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if not dids:
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print("No DIDs found in the environment. Aborting.")
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return
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dids = json.loads(dids)
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for did in dids:
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filename = f"data/inputs/{did}/0" # 0 for metadata service
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print(f"Reading asset file {filename}.")
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return filename
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# Get the input filename using the get_input function
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input_filename = get_input()
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if not input_filename:
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# No input filename returned
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exit()
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# Open the file & run your code
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with open(input_filename, 'r') as file:
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# Read the CSV file
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csv_reader = csv.DictReader(file)
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<YOUR CODE GOES HERE>
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```
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{% endtab %}
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{% tab title="Javascript" %}
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```javascript
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const fs = require("fs");
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var input_folder = "/data/inputs";
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var output_folder = "/data/outputs";
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async function processfolder(Path) {
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var files = fs.readdirSync(Path);
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for (var i =0; i < files.length; i++) {
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var file = files[i];
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var fullpath = Path + "/" + file;
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if (fs.statSync(fullpath).isDirectory()) {
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await processfolder(fullpath);
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} else {
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<YOUR CODE GOES HERE>
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}
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}
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}
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<YOUR CODE & FUNCTION DEFINITIONS GO HERE>
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// Open the file & run your code
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processfolder(input_folder);
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```
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{% endtab %}
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{% endtabs %}
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**Note:** Here are the following Python libraries that you can use in your code:
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```python
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// Python modules
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numpy==1.16.3
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pandas==0.24.2
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python-dateutil==2.8.0
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pytz==2019.1
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six==1.12.0
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sklearn
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xlrd == 1.2.0
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openpyxl >= 3.0.3
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wheel
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matplotlib
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```
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