decenter-ai/decenter-ai.streamlit.app

Decenteralized AI training platform for all

title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	license
Decenter.streamlit.app	🏢	pink	red	streamlit	1.28.2	app.py	false	mit

decenter-ai.streamlit.app

DeCenter AI

Decentralized AI Model Training Infrastructure Visit: https://decenter-ai.streamlit.app

Description

DeCenter AI is a PaaS infrastructure that empowers machine learning engineers to train AI models more quickly and affordably through decentralized parallel training mechanisms.

Table of Contents

• decenter-ai.streamlit.app
• DeCenter AI
• Description
• Table of Contents
• Prerequisites
• Overview
• How to use the demo
• How to Contribute
• License
• Contributors
• Support
• Links

Prerequisites

To run project, you need to have the following prerequisites:

• Python (version 3.10 or higher) installed on your machine.
• The necessary Python packages and dependencies installed. You can find the required packages in the requirements.txt file of the project repository.
• Download and extract the sample python code (model training code), Dataset, requirement text and pre-trained model for the demo sample1.zip

How to Run with Python3

To run using Python and make, follow these steps:

   git clone https://github.com/DeCenter-AI/decenter-ai.streamlit.app decenter.app
   cd decenter.app
   make install
#   create .env file and fill in the environment variables
   make run

How to Run via Docker

To run the project using Docker, follow these steps:

docker build -t decenter.streamlit .
docker run -p 8501:8501 decenter.streamlit

For developers, I recommend
docker run -it -e "mode=development" -p 8501:8501 decenter
playaround and test your code!

Please note that you will need to replace <repository_url> with the actual URL of this/forked repo

I hope this helps! Raise issues to clarify your doubts and notify bugs.

Overview

DeCenter AI functions as a PaaS infrastructure, empowering machine learning engineers to expedite and make the training of AI models more cost-effective through decentralized parallel training methods.

The core objective of DeCenter AI is to democratize and decentralize AI model training. By offering a distributed training platform, it allows data scientists, machine learning engineers, researchers, and AI specialists to collaboratively contribute to the training of AI models. Structured around a distributed parallel training mechanism, DeCenter AI has been designed to facilitate the training of various ML and DL models in a significantly reduced time frame and cost compared to the current norms. Our platform incorporates a built-in incentive system, fueled by DCEN Tokens. This system not only rewards contributors and participants but also encourages them to undertake tasks such as reviewing, testing, and rating AI models.

Features

• Intuitive AI model deployment UI
• Customizable node configuration
• Private decentralized infrastructure
• Scheduled model training

Target Customers

• Data scientists
• Machine learning engineers
• AI Engineers View our Customer profile.

Benefits

• Rapid iteration
• Cost effective
• Seamless deployment
• Automated resource management

How to use the demo

1. Visit https://decenter-ai.streamlit.app
2. Enter model name
3. Upload training dataset
4. Select training script
5. Click train
6. Model training commences
7. After training is done , you can download your trained model

How to Contribute

We welcome contributions from the community! To get started, follow these steps:

1. Fork the repository on GitHub.
2. Clone your fork of the repository to your local machine.
3. Create a new branch for your changes: git checkout -b <your-username>/your-feature-branch.
4. Make your changes and commit them to your branch.
5. Push your changes to your fork on GitHub.
6. Open a pull request from your fork's branch to the main repository.

Please make sure to follow the Code of Conduct when contributing to this project.

License

DeCenter AI is released under the MIT License.

Contributors

• Victor Kaycee Email. Linkedln.
• Hiro Nasfame Email. Linkedln.
• William Ikeji Email. Linkedln.
• Dinesh Email. Linkedln.

Support

For any inquiries or assistance, please contact our support team at [email protected] or visit our website.

Links

Deck.