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content/tutorials/compute-to-data-algorithms.md
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content/tutorials/compute-to-data-algorithms.md
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---
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title: Writing Algorithms for Compute to Data
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description: Learn how to write algorithms for use in Ocean Protocol's Compute-to-Data feature.
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---
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## Overview
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An algorithm in the Ocean Protocol stack is another asset type, in addition to data sets. An algorithm for Compute to Data is composed of the following:
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- an algorithm code
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- a Docker image (base image + tag)
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- an entry point
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## Environment
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When creating an algorithm asset in Ocean Protocol, the additional `algorithm` object needs to be included in its metadata service to define the Docker container environment:
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```json
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{
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"algorithm": {
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"container": {
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"entrypoint": "node $ALGO",
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"image": "node",
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"tag": "latest"
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}
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}
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}
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```
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| Variable | Usage |
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| ------------ | --------------------------------------------------------------------------------------------------------------------------------------- |
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| `image` | The Docker image name the algorithm will run with. |
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| `tag` | The Docker image tag that you are going to use. |
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| `entrypoint` | The Docker entrypoint. `$ALGO` is a macro that gets replaced inside the compute job, depending where your algorithm code is downloaded. |
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When publishing an algorithm through the [Ocean Market](https://market.oceanprotoco.com), these properties can be set via the publish UI.
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### Environment Examples
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Run an algorithm written in JavaScript/Node.js, based on Node.js v14:
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```json
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{
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"algorithm": {
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"container": {
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"entrypoint": "node $ALGO",
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"image": "node",
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"tag": "14"
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}
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}
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}
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```
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Run an algorithm written in Python, based on Python v3.9:
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```json
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{
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"algorithm": {
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"container": {
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"entrypoint": "python3.9 $ALGO",
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"image": "python",
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"tag": "3.9.4-alpine3.13"
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}
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}
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}
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```
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Be aware that you might need a lot of dependencies, so it's a lot faster if you are going to build your own image and publish your algorithm with that custom image. We also collect some [example images](https://github.com/oceanprotocol/algo_dockers).
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### Data Storage
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As part of a compute job, every algorithm runs in a K8s pod with these volumes mounted:
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| Path | Permissions | Usage |
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| --------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `/data/inputs` | read | Storage for input data sets, accessible only to the algorithm running in the pod. |
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| `/data/ddos` | read | Storage for all DDOs involved in compute job (input data set + algorithm). |
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| `/data/outputs` | read/write | Storage for all of the algorithm's output files. They are uploaded on some form of cloud storage, and URLs are sent back to the consumer. |
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| `/data/logs/` | read/write | All algorithm output (such as `print`, `console.log`, etc.) is stored in a file located in this folder. They are stored and sent to the consumer as well. |
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### Environment variables available to algorithms
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For every algorithm pod, the Compute to Data environment provides the following environment variables:
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| Variable | Usage |
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| -------------------- | ------------------------------------------------------ |
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| `DIDS` | An array of DID strings containing the input datasets. |
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| `TRANSFORMATION_DID` | The DID of the algorithm. |
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## Example: JavaScript/Node.js
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The following is a simple JavaScript/Node.js algorithm, doing a line count for ALL input datasets. The algorithm is not using any environment variables, but instead it's scanning the `/data/inputs` folder.
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```js
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const fs = require('fs')
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const inputFolder = '/data/inputs'
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const outputFolder = '/data/outputs'
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async function countrows(file) {
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console.log('Start counting for ' + file)
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const fileBuffer = fs.readFileSync(file)
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const toString = fileBuffer.toString()
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const splitLines = toString.split('\n')
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const rows = splitLines.length - 1
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fs.appendFileSync(outputFolder + '/output.log', file + ',' + rows + '\r\n')
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console.log('Finished. We have ' + rows + ' lines')
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}
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async function processfolder(folder) {
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const files = fs.readdirSync(folder)
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for (const i = 0; i < files.length; i++) {
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const file = files[i]
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const fullpath = folder + '/' + 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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await countrows(fullpath)
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}
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}
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}
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processfolder(inputFolder)
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```
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This snippet will create and expose the following files as compute job results to the consumer:
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- `/data/outputs/output.log`
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- `/data/logs/algo.log`
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To run this, use the following container object:
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```json
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{
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"algorithm": {
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"container": {
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"entrypoint": "node $ALGO",
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"image": "node",
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"tag": "12"
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}
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}
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}
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```
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## Example: Python
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A more advanced line counting in Python, which relies on environment variables and constructs a job object, containing all the input files & DDOs
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```python
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import pandas as pd
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import numpy as np
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import os
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import time
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import json
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def get_job_details():
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"""Reads in metadata information about assets used by the algo"""
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job = dict()
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job['dids'] = json.loads(os.getenv('DIDS', None))
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job['metadata'] = dict()
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job['files'] = dict()
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job['algo'] = dict()
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job['secret'] = os.getenv('secret', None)
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algo_did = os.getenv('TRANSFORMATION_DID', None)
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if job['dids'] is not None:
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for did in job['dids']:
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# get the ddo from disk
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filename = '/data/ddos/' + did
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print(f'Reading json from {filename}')
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with open(filename) as json_file:
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ddo = json.load(json_file)
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# search for metadata service
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for service in ddo['service']:
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if service['type'] == 'metadata':
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job['files'][did] = list()
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index = 0
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for file in service['attributes']['main']['files']:
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job['files'][did].append(
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'/data/inputs/' + did + '/' + str(index))
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index = index + 1
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if algo_did is not None:
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job['algo']['did'] = algo_did
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job['algo']['ddo_path'] = '/data/ddos/' + algo_did
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return job
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def line_counter(job_details):
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"""Executes the line counter based on inputs"""
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print('Starting compute job with the following input information:')
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print(json.dumps(job_details, sort_keys=True, indent=4))
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""" Now, count the lines of the first file in first did """
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first_did = job_details['dids'][0]
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filename = job_details['files'][first_did][0]
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non_blank_count = 0
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with open(filename) as infp:
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for line in infp:
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if line.strip():
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non_blank_count += 1
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print ('number of non-blank lines found %d' % non_blank_count)
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""" Print that number to output to generate algo output"""
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f = open("/data/outputs/result", "w")
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f.write(str(non_blank_count))
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f.close()
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if __name__ == '__main__':
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line_counter(get_job_details())
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```
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To run this algorithm, use the following `container` object:
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```json
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{
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"algorithm": {
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"container": {
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"entrypoint": "python3.6 $ALGO",
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"image": "oceanprotocol/algo_dockers",
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"tag": "python-sql"
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}
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}
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}
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```
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@ -1,216 +0,0 @@
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---
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title: Writing C2D Algorithms
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description:
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---
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## Overview
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An C2D algorithm is composed of the following:
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- a docker image (base image + tag)
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- an algorithm code
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- a entry point
|
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|
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That's why, while creating the algorithm asset in ocean, we need the additional object "algorithm" defined in the metadata service:
|
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```json
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"algorithm": {
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"container": {
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"entrypoint": "node $ALGO",
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"image": "node",
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"tag": "latest"
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}
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}
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```
|
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Most important attributes are the following:
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- image: this is the docker image that your are going to use
|
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- tag: this is the docker image tag that you are going to use
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- entrypoint: this is the entrypoint. $ALGO is a macro that gets replaced inside C2D, depending where your algo code is downloaded
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|
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Here are some examples:
|
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|
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- to run a JS algo, based on node 14:
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```json
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"algorithm": {
|
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"container": {
|
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"entrypoint": "node $ALGO",
|
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"image": "node",
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"tag": "14"
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}
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}
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```
|
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- to run a python algo, based on python:3.9.4-alpine3.13:
|
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```json
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"algorithm": {
|
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"container": {
|
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"entrypoint": "python3.9 $ALGO",
|
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"image": "python",
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"tag": "3.9.4-alpine3.13"
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}
|
|
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}
|
|
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```
|
|
||||||
|
|
||||||
Be aware that you might need a lot of dependencies, so it's a lot faster if you are going to build your own images (we build some [HERE](https://github.com/oceanprotocol/algo_dockers))
|
|
||||||
|
|
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|
|
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|
|
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## Data structure
|
|
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|
|
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Every algorithm pod will have some volumes mounted:
|
|
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|
|
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- /data/inputs (read only) - this is where the datasets are going to be stored
|
|
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- /data/ddos (read only) - this is where all DDOs (input + algorithm) are going to be stored
|
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- /data/outputs - this is where the algorithm should store all output files (they are going to be uploaded on storage and URLs sent back to the consumer)
|
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|
|
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All algorithm output (such as print, console.log, etc) are going to be stored in a file located in /data/logs/. They are going to be stored and sent to the customer as well
|
|
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|
|
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|
|
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## ENV variables available to algorithms
|
|
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|
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|
|
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For every algorithm pod, C2D is going to provide the following ENVs:
|
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|
|
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- DIDS: this is an array containing the input datasets
|
|
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- TRANSFORMATION_DID: this is the algorithm did
|
|
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|
|
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|
|
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## Sample Algorithms
|
|
||||||
|
|
||||||
## JS example
|
|
||||||
|
|
||||||
The following is a simple js algorithm, that does a line count for ALL input datasets. The algo is not using any ENVS, but instead it's scanning the /data/inputs folder.
|
|
||||||
|
|
||||||
```js
|
|
||||||
const fs = require("fs")
|
|
||||||
const path = require("path")
|
|
||||||
|
|
||||||
|
|
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var input_folder="/data/inputs";
|
|
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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 {
|
|
||||||
await countrows(fullpath)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
async function countrows(file){
|
|
||||||
console.log("Start counting for "+file)
|
|
||||||
var fileBuffer = fs.readFileSync(file);
|
|
||||||
var to_string = fileBuffer.toString();
|
|
||||||
var split_lines = to_string.split("\n");
|
|
||||||
var rows=split_lines.length-1;
|
|
||||||
fs.appendFileSync(output_folder+'/output.log', file+','+rows+"\r\n");
|
|
||||||
console.log('Finished. We have '+rows+' lines')
|
|
||||||
}
|
|
||||||
|
|
||||||
processfolder(input_folder)
|
|
||||||
```
|
|
||||||
|
|
||||||
To run this, use the following container object:
|
|
||||||
|
|
||||||
```json
|
|
||||||
"algorithm": {
|
|
||||||
"container": {
|
|
||||||
"entrypoint": "node $ALGO",
|
|
||||||
"image": "node",
|
|
||||||
"tag": "10"
|
|
||||||
}
|
|
||||||
}
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
## Advanced Python example
|
|
||||||
|
|
||||||
A more advanced python line counting, which relies on ENVs and constructs a job object, containing all the input files & ddos
|
|
||||||
|
|
||||||
```python
|
|
||||||
import pandas as pd
|
|
||||||
import numpy as np
|
|
||||||
import os
|
|
||||||
import time
|
|
||||||
import json
|
|
||||||
|
|
||||||
def get_job_details():
|
|
||||||
"""Reads in metadata information about assets used by the algo"""
|
|
||||||
job = dict()
|
|
||||||
job['dids'] = json.loads(os.getenv('DIDS', None))
|
|
||||||
job['metadata'] = dict()
|
|
||||||
job['files'] = dict()
|
|
||||||
job['algo'] = dict()
|
|
||||||
job['secret'] = os.getenv('secret', None)
|
|
||||||
algo_did = os.getenv('TRANSFORMATION_DID', None)
|
|
||||||
if job['dids'] is not None:
|
|
||||||
for did in job['dids']:
|
|
||||||
# get the ddo from disk
|
|
||||||
filename = '/data/ddos/' + did
|
|
||||||
print(f'Reading json from {filename}')
|
|
||||||
with open(filename) as json_file:
|
|
||||||
ddo = json.load(json_file)
|
|
||||||
# search for metadata service
|
|
||||||
for service in ddo['service']:
|
|
||||||
if service['type'] == 'metadata':
|
|
||||||
job['files'][did] = list()
|
|
||||||
index = 0
|
|
||||||
for file in service['attributes']['main']['files']:
|
|
||||||
job['files'][did].append(
|
|
||||||
'/data/inputs/' + did + '/' + str(index))
|
|
||||||
index = index + 1
|
|
||||||
if algo_did is not None:
|
|
||||||
job['algo']['did'] = algo_did
|
|
||||||
job['algo']['ddo_path'] = '/data/ddos/' + algo_did
|
|
||||||
return job
|
|
||||||
|
|
||||||
|
|
||||||
def line_counter(job_details):
|
|
||||||
"""Executes the line counter based on inputs"""
|
|
||||||
print('Starting compute job with the following input information:')
|
|
||||||
print(json.dumps(job_details, sort_keys=True, indent=4))
|
|
||||||
|
|
||||||
""" Now, count the lines of the first file in first did """
|
|
||||||
first_did = job_details['dids'][0]
|
|
||||||
filename = job_details['files'][first_did][0]
|
|
||||||
non_blank_count = 0
|
|
||||||
with open(filename) as infp:
|
|
||||||
for line in infp:
|
|
||||||
if line.strip():
|
|
||||||
non_blank_count += 1
|
|
||||||
print ('number of non-blank lines found %d' % non_blank_count)
|
|
||||||
""" Print that number to output to generate algo output"""
|
|
||||||
f = open("/data/outputs/result", "w")
|
|
||||||
f.write(str(non_blank_count))
|
|
||||||
f.close()
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
line_counter(get_job_details())
|
|
||||||
|
|
||||||
```
|
|
||||||
|
|
||||||
To run this, use the following container object:
|
|
||||||
|
|
||||||
```json
|
|
||||||
"algorithm": {
|
|
||||||
"container": {
|
|
||||||
"entrypoint": "python3.6 $ALGO",
|
|
||||||
"image": "oceanprotocol/algo_dockers",
|
|
||||||
"tag": "python-sql"
|
|
||||||
}
|
|
||||||
}
|
|
||||||
```
|
|
||||||
|
|
@ -24,10 +24,10 @@
|
|||||||
|
|
||||||
- group: Compute-to-Data
|
- group: Compute-to-Data
|
||||||
items:
|
items:
|
||||||
|
- title: Writing Algorithms
|
||||||
|
link: /tutorials/compute-to-data-algorithms/
|
||||||
- title: Run a Compute-to-Data Environment
|
- title: Run a Compute-to-Data Environment
|
||||||
link: /tutorials/compute-to-data/
|
link: /tutorials/compute-to-data/
|
||||||
- title: Writing C2D Algorithms
|
|
||||||
link: /tutorials/write-c2d-algorithms/
|
|
||||||
|
|
||||||
- group: Storage Setup
|
- group: Storage Setup
|
||||||
items:
|
items:
|
||||||
|
64
package-lock.json
generated
64
package-lock.json
generated
@ -7248,9 +7248,9 @@
|
|||||||
}
|
}
|
||||||
},
|
},
|
||||||
"classnames": {
|
"classnames": {
|
||||||
"version": "2.2.6",
|
"version": "2.3.1",
|
||||||
"resolved": "https://registry.npmjs.org/classnames/-/classnames-2.2.6.tgz",
|
"resolved": "https://registry.npmjs.org/classnames/-/classnames-2.3.1.tgz",
|
||||||
"integrity": "sha512-JR/iSQOSt+LQIWwrwEzJ9uk0xfN3mTVYMwt1Ir5mUcSN6pU+V4zQFFaJsclJbPuAUQH+yfWef6tm7l1quW3C8Q=="
|
"integrity": "sha512-OlQdbZ7gLfGarSqxesMesDa5uz7KFbID8Kpq/SxIoNGDqY8lSYs0D+hhtBXhcdB3rcbXArFr7vlHheLk1voeNA=="
|
||||||
},
|
},
|
||||||
"clean-stack": {
|
"clean-stack": {
|
||||||
"version": "2.2.0",
|
"version": "2.2.0",
|
||||||
@ -9980,9 +9980,9 @@
|
|||||||
}
|
}
|
||||||
},
|
},
|
||||||
"eslint-config-prettier": {
|
"eslint-config-prettier": {
|
||||||
"version": "8.1.0",
|
"version": "8.2.0",
|
||||||
"resolved": "https://registry.npmjs.org/eslint-config-prettier/-/eslint-config-prettier-8.1.0.tgz",
|
"resolved": "https://registry.npmjs.org/eslint-config-prettier/-/eslint-config-prettier-8.2.0.tgz",
|
||||||
"integrity": "sha512-oKMhGv3ihGbCIimCAjqkdzx2Q+jthoqnXSP+d86M9tptwugycmTFdVR4IpLgq2c4SHifbwO90z2fQ8/Aio73yw==",
|
"integrity": "sha512-dWV9EVeSo2qodOPi1iBYU/x6F6diHv8uujxbxr77xExs3zTAlNXvVZKiyLsQGNz7yPV2K49JY5WjPzNIuDc2Bw==",
|
||||||
"dev": true
|
"dev": true
|
||||||
},
|
},
|
||||||
"eslint-config-react-app": {
|
"eslint-config-react-app": {
|
||||||
@ -10379,9 +10379,9 @@
|
|||||||
}
|
}
|
||||||
},
|
},
|
||||||
"eslint-plugin-prettier": {
|
"eslint-plugin-prettier": {
|
||||||
"version": "3.3.1",
|
"version": "3.4.0",
|
||||||
"resolved": "https://registry.npmjs.org/eslint-plugin-prettier/-/eslint-plugin-prettier-3.3.1.tgz",
|
"resolved": "https://registry.npmjs.org/eslint-plugin-prettier/-/eslint-plugin-prettier-3.4.0.tgz",
|
||||||
"integrity": "sha512-Rq3jkcFY8RYeQLgk2cCwuc0P7SEFwDravPhsJZOQ5N4YI4DSg50NyqJ/9gdZHzQlHf8MvafSesbNJCcP/FF6pQ==",
|
"integrity": "sha512-UDK6rJT6INSfcOo545jiaOwB701uAIt2/dR7WnFQoGCVl1/EMqdANBmwUaqqQ45aXprsTGzSa39LI1PyuRBxxw==",
|
||||||
"dev": true,
|
"dev": true,
|
||||||
"requires": {
|
"requires": {
|
||||||
"prettier-linter-helpers": "^1.0.0"
|
"prettier-linter-helpers": "^1.0.0"
|
||||||
@ -13675,9 +13675,9 @@
|
|||||||
}
|
}
|
||||||
},
|
},
|
||||||
"gatsby-remark-vscode": {
|
"gatsby-remark-vscode": {
|
||||||
"version": "3.2.0",
|
"version": "3.2.1",
|
||||||
"resolved": "https://registry.npmjs.org/gatsby-remark-vscode/-/gatsby-remark-vscode-3.2.0.tgz",
|
"resolved": "https://registry.npmjs.org/gatsby-remark-vscode/-/gatsby-remark-vscode-3.2.1.tgz",
|
||||||
"integrity": "sha512-KsI47oc5SfjZkcyNo+P6IZx3ouMWpKGuPXEDpnG70R9QOmwcrSGUJAmI37RfGP2RSTMnla+28sxzWOe1vXnL9g==",
|
"integrity": "sha512-txzIOhfkBg49YLAw49L8PnkTu9ZK8gu61p/WbXelL0R9Abw96pmP+R4Bu1RJx3NSwikhC0nqwgORZl/qeaWwXQ==",
|
||||||
"requires": {
|
"requires": {
|
||||||
"decompress": "^4.2.0",
|
"decompress": "^4.2.0",
|
||||||
"json5": "^2.1.1",
|
"json5": "^2.1.1",
|
||||||
@ -15481,9 +15481,9 @@
|
|||||||
"integrity": "sha512-SEQu7vl8KjNL2eoGBLF3+wAjpsNfA9XMlXAYj/3EdaNfAlxKthD1xjEQfGOUhllCGGJVNY34bRr6lPINhNjyZw=="
|
"integrity": "sha512-SEQu7vl8KjNL2eoGBLF3+wAjpsNfA9XMlXAYj/3EdaNfAlxKthD1xjEQfGOUhllCGGJVNY34bRr6lPINhNjyZw=="
|
||||||
},
|
},
|
||||||
"husky": {
|
"husky": {
|
||||||
"version": "5.1.3",
|
"version": "6.0.0",
|
||||||
"resolved": "https://registry.npmjs.org/husky/-/husky-5.1.3.tgz",
|
"resolved": "https://registry.npmjs.org/husky/-/husky-6.0.0.tgz",
|
||||||
"integrity": "sha512-fbNJ+Gz5wx2LIBtMweJNY1D7Uc8p1XERi5KNRMccwfQA+rXlxWNSdUxswo0gT8XqxywTIw7Ywm/F4v/O35RdMg==",
|
"integrity": "sha512-SQS2gDTB7tBN486QSoKPKQItZw97BMOd+Kdb6ghfpBc0yXyzrddI0oDV5MkDAbuB4X2mO3/nj60TRMcYxwzZeQ==",
|
||||||
"dev": true
|
"dev": true
|
||||||
},
|
},
|
||||||
"iconv-lite": {
|
"iconv-lite": {
|
||||||
@ -19636,13 +19636,13 @@
|
|||||||
}
|
}
|
||||||
},
|
},
|
||||||
"plist": {
|
"plist": {
|
||||||
"version": "3.0.1",
|
"version": "3.0.2",
|
||||||
"resolved": "https://registry.npmjs.org/plist/-/plist-3.0.1.tgz",
|
"resolved": "https://registry.npmjs.org/plist/-/plist-3.0.2.tgz",
|
||||||
"integrity": "sha512-GpgvHHocGRyQm74b6FWEZZVRroHKE1I0/BTjAmySaohK+cUn+hZpbqXkc3KWgW3gQYkqcQej35FohcT0FRlkRQ==",
|
"integrity": "sha512-MSrkwZBdQ6YapHy87/8hDU8MnIcyxBKjeF+McXnr5A9MtffPewTs7G3hlpodT5TacyfIyFTaJEhh3GGcmasTgQ==",
|
||||||
"requires": {
|
"requires": {
|
||||||
"base64-js": "^1.2.3",
|
"base64-js": "^1.5.1",
|
||||||
"xmlbuilder": "^9.0.7",
|
"xmlbuilder": "^9.0.7",
|
||||||
"xmldom": "0.1.x"
|
"xmldom": "^0.5.0"
|
||||||
},
|
},
|
||||||
"dependencies": {
|
"dependencies": {
|
||||||
"xmlbuilder": {
|
"xmlbuilder": {
|
||||||
@ -22621,9 +22621,9 @@
|
|||||||
}
|
}
|
||||||
},
|
},
|
||||||
"slugify": {
|
"slugify": {
|
||||||
"version": "1.4.7",
|
"version": "1.5.0",
|
||||||
"resolved": "https://registry.npmjs.org/slugify/-/slugify-1.4.7.tgz",
|
"resolved": "https://registry.npmjs.org/slugify/-/slugify-1.5.0.tgz",
|
||||||
"integrity": "sha512-tf+h5W1IrjNm/9rKKj0JU2MDMruiopx0jjVA5zCdBtcGjfp0+c5rHw/zADLC3IeKlGHtVbHtpfzvYA0OYT+HKg=="
|
"integrity": "sha512-Q2UPZ2udzquy1ElHfOLILMBMqBEXkiD3wE75qtBvV+FsDdZZjUqPZ44vqLTejAVq+wLLHacOMcENnP8+ZbzmIA=="
|
||||||
},
|
},
|
||||||
"smoothscroll-polyfill": {
|
"smoothscroll-polyfill": {
|
||||||
"version": "0.4.4",
|
"version": "0.4.4",
|
||||||
@ -24873,14 +24873,14 @@
|
|||||||
"integrity": "sha512-2ham8XPWTONajOR0ohOKOHXkm3+gaBmGut3SRuu75xLd/RRaY6vqgh8NBYYk7+RW3u5AtzPQZG8F10LHkl0lAQ=="
|
"integrity": "sha512-2ham8XPWTONajOR0ohOKOHXkm3+gaBmGut3SRuu75xLd/RRaY6vqgh8NBYYk7+RW3u5AtzPQZG8F10LHkl0lAQ=="
|
||||||
},
|
},
|
||||||
"vscode-oniguruma": {
|
"vscode-oniguruma": {
|
||||||
"version": "1.4.0",
|
"version": "1.5.1",
|
||||||
"resolved": "https://registry.npmjs.org/vscode-oniguruma/-/vscode-oniguruma-1.4.0.tgz",
|
"resolved": "https://registry.npmjs.org/vscode-oniguruma/-/vscode-oniguruma-1.5.1.tgz",
|
||||||
"integrity": "sha512-VvTl/jIAADEqWWpEYRsOI1sXiYOTDA8KYNgK60+Mb3T+an9zPz3Cqc6RVJeYgOx/P5G+4M4jygB3X5xLLfYD0g=="
|
"integrity": "sha512-JrBZH8DCC262TEYcYdeyZusiETu0Vli0xFgdRwNJjDcObcRjbmJP+IFcA3ScBwIXwgFHYKbAgfxtM/Cl+3Spjw=="
|
||||||
},
|
},
|
||||||
"vscode-textmate": {
|
"vscode-textmate": {
|
||||||
"version": "5.2.0",
|
"version": "5.4.0",
|
||||||
"resolved": "https://registry.npmjs.org/vscode-textmate/-/vscode-textmate-5.2.0.tgz",
|
"resolved": "https://registry.npmjs.org/vscode-textmate/-/vscode-textmate-5.4.0.tgz",
|
||||||
"integrity": "sha512-Uw5ooOQxRASHgu6C7GVvUxisKXfSgW4oFlO+aa+PAkgmH89O3CXxEEzNRNtHSqtXFTl0nAC1uYj0GMSH27uwtQ=="
|
"integrity": "sha512-c0Q4zYZkcLizeYJ3hNyaVUM2AA8KDhNCA3JvXY8CeZSJuBdAy3bAvSbv46RClC4P3dSO9BdwhnKEx2zOo6vP/w=="
|
||||||
},
|
},
|
||||||
"warning": {
|
"warning": {
|
||||||
"version": "4.0.3",
|
"version": "4.0.3",
|
||||||
@ -26282,9 +26282,9 @@
|
|||||||
"integrity": "sha512-fDlsI/kFEx7gLvbecc0/ohLG50fugQp8ryHzMTuW9vSa1GJ0XYWKnhsUx7oie3G98+r56aTQIUB4kht42R3JvA=="
|
"integrity": "sha512-fDlsI/kFEx7gLvbecc0/ohLG50fugQp8ryHzMTuW9vSa1GJ0XYWKnhsUx7oie3G98+r56aTQIUB4kht42R3JvA=="
|
||||||
},
|
},
|
||||||
"xmldom": {
|
"xmldom": {
|
||||||
"version": "0.1.31",
|
"version": "0.5.0",
|
||||||
"resolved": "https://registry.npmjs.org/xmldom/-/xmldom-0.1.31.tgz",
|
"resolved": "https://registry.npmjs.org/xmldom/-/xmldom-0.5.0.tgz",
|
||||||
"integrity": "sha512-yS2uJflVQs6n+CyjHoaBmVSqIDevTAWrzMmjG1Gc7h1qQ7uVozNhEPJAwZXWyGQ/Gafo3fCwrcaokezLPupVyQ=="
|
"integrity": "sha512-Foaj5FXVzgn7xFzsKeNIde9g6aFBxTPi37iwsno8QvApmtg7KYrr+OPyRHcJF7dud2a5nGRBXK3n0dL62Gf7PA=="
|
||||||
},
|
},
|
||||||
"xmlhttprequest-ssl": {
|
"xmlhttprequest-ssl": {
|
||||||
"version": "1.5.5",
|
"version": "1.5.5",
|
||||||
|
Loading…
Reference in New Issue
Block a user