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Signed-off-by: Nasfame <laciferin@gmail.com> Took 3 minutes
2 parents e2b12bb + 131fa0e commit 00af202

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.github/pull_request_template.md

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## Related Issue(s)
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Fixes #
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Resolves #
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Closes #
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Fixes # Resolves # Closes #
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## Proposed Changes
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.github/release-drafter.yml

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template: |
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## What’s Changed
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$CHANGES

.github/wip.yml

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locations:
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- title
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- label_name
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- commit_subject
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terms:
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- do not merge
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-
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# https://github.com/wip/app#configuration

.github/workflows/cross.yaml

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- name: Build
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run: npm run build
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test:
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timeout-minutes: 10
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strategy:
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max-parallel: 2
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fail-fast: false
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matrix:
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os: [ ubuntu-latest, macos-latest, windows-latest ]
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node_version: [ 18, 19, 20 ]
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architecture: [ x64 ] #, arm64 ] # arm,
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os: [ubuntu-latest, macos-latest, windows-latest]
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node_version: [18, 19, 20]
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architecture: [x64] #, arm64 ] # arm,
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runs-on: ${{ matrix.os }}
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name: Node ${{ matrix.node_version }} - ${{ matrix.architecture }} on ${{ matrix.os }}
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run: |
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echo $envfile > .env.tmp
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sed 's/ /\n/g' .env.tmp > .env
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# source .env for windows
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# source .env for windows
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- name: Install dependencies
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run: pnpm install
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# - name: Run tests for ${{runner.os}} TODO: once jest tests pass
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# run: npm run test

.github/workflows/prettier.yaml

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name: Prettier
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on:
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# push:
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# branches:
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# - main
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# branches-ignore:
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# - main
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# push:
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# branches:
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# - main
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# branches-ignore:
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# - main
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# pull_request_review:
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# types: [ submitted ]
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# pull_request_review:
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# types: [ submitted ]
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pull_request:
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branches:
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- opened
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branches:
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- 'main'
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# paths: ['**.yaml','**.js', '**.jsx', '**.ts', '**.tsx', '**.css', '**.scss', '**.html', '**.vue', '**.json', '**.md']
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# paths: ['**.yaml','**.js', '**.jsx', '**.ts', '**.tsx', '**.css', '**.scss', '**.html', '**.vue', '**.json', '**.md']
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workflow_dispatch:
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inputs:
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# contents: |
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# .github/workflows/${ github.workflow}.yaml
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# - name: Check if workflow was dispatched manually
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# run: |
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# if [[ "${{ github.event }}" == "workflow_dispatch" ]]; then
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# echo "Workflow was dispatched manually."
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# export ONLY_CHANGED=false
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# else
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# echo "Workflow was triggered by another event."
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# export ONLY_CHANGED=true
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# fi
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# - name: Check if workflow was dispatched manually
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# run: |
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# if [[ "${{ github.event }}" == "workflow_dispatch" ]]; then
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# echo "Workflow was dispatched manually."
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# export ONLY_CHANGED=false
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# else
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# echo "Workflow was triggered by another event."
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# export ONLY_CHANGED=true
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# fi
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- name: Prettify code
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# continue-on-error: true
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uses: creyD/prettier_action@v4.3
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# env:
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# GITHUB_ACTOR: ${{github.actor}}
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env:
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GITHUB_ACTOR: Baymax
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with:
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dry: false
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github_token: ${{ github.token }}
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same_commit: false
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commit_message: 'style: pretty code'
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commit_description: 'automated via ci'
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# file_pattern: '*'
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# file_pattern: '*'
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only_changed: ${{ env.ONLY_CHANGED }}
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prettier_options: '--write . --ignore-path .prettierignore.ci'
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# ignore path not necessary

README.md

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## Description
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DeCenter AI is a PaaS infrastructure that empowers machine learning engineers to train AI models more quickly and
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affordably through decentralized parallel training mechanisms.
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DeCenter AI is a PaaS infrastructure that empowers machine learning engineers to train AI
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models more quickly and affordably through decentralized parallel training mechanisms.
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## Table of Contents
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- [DeCenter AI](#decenter-ai)
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- [Description](#description)
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- [Table of Contents](#table-of-contents)
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## Overview
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DeCenter AI functions as a PaaS infrastructure, empowering machine learning engineers to expedite and make the training
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of AI models more cost-effective through decentralized parallel training methods.
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DeCenter AI functions as a PaaS infrastructure, empowering machine learning engineers to
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expedite and make the training of AI models more cost-effective through decentralized
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parallel training methods.
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The core objective of DeCenter AI is to democratize and decentralize AI model training. By offering a distributed
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training platform, it allows data scientists, machine learning engineers, researchers, and AI specialists to
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collaboratively contribute to the training of AI models. Structured around a distributed parallel training mechanism,
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DeCenter AI has been designed to facilitate the training of various ML and DL models in a significantly reduced time
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frame and cost compared to the current norms.
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Our platform incorporates a built-in incentive system, fueled by DCEN Tokens. This system not only rewards contributors
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and participants but also encourages them to undertake tasks such as reviewing, testing, and rating AI models.
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The core objective of DeCenter AI is to democratize and decentralize AI model training. By
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offering a distributed training platform, it allows data scientists, machine learning
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engineers, researchers, and AI specialists to collaboratively contribute to the training
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of AI models. Structured around a distributed parallel training mechanism, DeCenter AI has
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been designed to facilitate the training of various ML and DL models in a significantly
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reduced time frame and cost compared to the current norms. Our platform incorporates a
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built-in incentive system, fueled by DCEN Tokens. This system not only rewards
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contributors and participants but also encourages them to undertake tasks such as
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reviewing, testing, and rating AI models.
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### Features
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- Decenter teams
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- Model repository
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- Model customization
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- Auto training and testing
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- Incentivisation and governance
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View our [Features List](https://www.canva.com/design/DAFttOVmaYg/cfT1zEfkCmqLt4JiDqdMmw/view?utm_content=DAFttOVmaYg&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink).
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- Incentivisation and governance View our
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[Features List](https://www.canva.com/design/DAFttOVmaYg/cfT1zEfkCmqLt4JiDqdMmw/view?utm_content=DAFttOVmaYg&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink).
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### Target Customers
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- Data scientists
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- Machine learning engineers
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- AI Engineers
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View our [Customer profile](https://www.canva.com/design/DAFri_nB4wo/eI4WrI2aQGyfy6T1bx4ZTQ/view?utm_content=DAFri_nB4wo&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink).
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- AI Engineers View our
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[Customer profile](https://www.canva.com/design/DAFri_nB4wo/eI4WrI2aQGyfy6T1bx4ZTQ/view?utm_content=DAFri_nB4wo&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink).
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### Benefits
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- Automated resource management
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- Seamless deployment and scalability
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## How to Contribute
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We welcome contributions from the community! To get started, follow these steps:
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2. Clone your fork of the repository to your local machine.
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3. Create a new branch for your changes: `git checkout -b <your-username>/your-feature-branch`.
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3. Create a new branch for your changes:
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`git checkout -b <your-username>/your-feature-branch`.
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4. Make your changes and commit them to your branch.
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5. Push your changes to your fork on GitHub.
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6. Open a pull request from your fork's branch to the main repository.
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Please make sure to follow the [Code of Conduct](./CODE_OF_CONDUCT.md) when contributing to this project.
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Please make sure to follow the [Code of Conduct](./CODE_OF_CONDUCT.md) when contributing
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to this project.
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## License
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DeCenter AI is released under the [MIT License](https://opensource.org/licenses/MIT).
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## Support
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For any inquiries or assistance, please contact our support team at admin@decenterai.com or visit
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our [website](https://decenterai.com/).
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For any inquiries or assistance, please contact our support team at admin@decenterai.com
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or visit our [website](https://decenterai.com/).
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## Links
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[Deck](https://www.canva.com/design/DAFvisezgFA/GCYskdQ4GaltJ2v6YGIgjQ/view?utm_content=DAFvisezgFA&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink ). [Project Roadmap](https://www.canva.com/design/DAFttYsFcCE/NsHO_5cm8pPfHf8_dAFELA/view?utm_content=DAFttYsFcCE&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink). [Go-to-market](https://www.canva.com/design/DAFttc0AxfY/unRA-Piq6dXOhxNGF2_3Ag/view?utm_content=DAFttc0AxfY&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink).
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[Deck](https://www.canva.com/design/DAFvisezgFA/GCYskdQ4GaltJ2v6YGIgjQ/view?utm_content=DAFvisezgFA&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink).
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[Project Roadmap](https://www.canva.com/design/DAFttYsFcCE/NsHO_5cm8pPfHf8_dAFELA/view?utm_content=DAFttYsFcCE&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink).
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[Go-to-market](https://www.canva.com/design/DAFttc0AxfY/unRA-Piq6dXOhxNGF2_3Ag/view?utm_content=DAFttc0AxfY&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink).

bacalhau.js

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const axios = require("axios");
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const axios = require('axios')
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let data = {
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Engine: "Docker",
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Engine: 'Docker',
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Docker: {
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Image: "ubuntu",
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Entrypoint: ["echo"],
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Parameters: ["hello"],
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Image: 'ubuntu',
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Entrypoint: ['echo'],
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Parameters: ['hello'],
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},
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},
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Verifier: "Noop",
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Verifier: 'Noop',
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Type: 'IPFS',
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};
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.post('http://dashboard.bacalhau.org:1000/api/v1/run', data)
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console.log(response.data);
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console.log(response.data)
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console.error(error);
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console.error(error)
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})

decs.d.ts

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declare module 'web3.storage'

enums/app.ts

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export const AppName = "Decenter AI"
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export const AppName = 'Decenter AI'
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export const AppDescription = `
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DecenterAI: Decentralized Infra for AI Model Training
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`.trim()
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export const AppSiteManifest = '/site.webmanifest'
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export const WagmiProjectId = "db1b8a46ffa835bd9a48a89ff540f990"
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export const WagmiProjectId = 'db1b8a46ffa835bd9a48a89ff540f990'

environment.d.ts

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namespace NodeJS {
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interface ProcessEnv {
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NEXT_PUBLIC_AUTH_CID: string,
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NEXT_PUBLIC_GOOGLE_CID: string;
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}
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}
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interface ProcessEnv {
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NEXT_PUBLIC_AUTH_CID: string
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NEXT_PUBLIC_GOOGLE_CID: string
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}
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}

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