| title | emoji | colorFrom | colorTo | sdk | sdk_version | app_file | pinned | license |
|---|---|---|---|---|---|---|---|---|
Decenter.streamlit.app |
🏢 |
pink |
red |
streamlit |
1.28.2 |
app.py |
false |
mit |
Decentralized AI Model Training Infrastructure Visit: https://decenter-ai.streamlit.app
DeCenter AI is a PaaS infrastructure that empowers machine learning engineers to train AI models more quickly and affordably through decentralized parallel training mechanisms.
- decenter-ai.streamlit.app
- DeCenter AI
- Description
- Table of Contents
- Prerequisites
- Overview
- How to use the demo
- How to Contribute
- License
- Contributors
- Support
- Links
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.txtfile 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
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 runTo run the project using Docker, follow these steps:
docker build -t decenter.streamlit .
docker run -p 8501:8501 decenter.streamlitFor 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.
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.
- Intuitive AI model deployment UI
- Customizable node configuration
- Private decentralized infrastructure
- Scheduled model training
- Data scientists
- Machine learning engineers
- AI Engineers View our Customer profile.
- Rapid iteration
- Cost effective
- Seamless deployment
- Automated resource management
- Visit https://decenter-ai.streamlit.app
- Enter model name
- Upload training dataset
- Select training script
- Click train
- Model training commences
- After training is done , you can download your trained model
We welcome contributions from the community! To get started, follow these steps:
- Fork the repository on GitHub.
- Clone your fork of the repository to your local machine.
- Create a new branch for your changes:
git checkout -b <your-username>/your-feature-branch. - Make your changes and commit them to your branch.
- Push your changes to your fork on GitHub.
- 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.
DeCenter AI is released under the MIT License.
- Victor Kaycee Email. Linkedln.
- Hiro Nasfame Email. Linkedln.
- William Ikeji Email. Linkedln.
- Dinesh Email. Linkedln.
For any inquiries or assistance, please contact our support team at admin@decenterai.com or visit our website.
Deck.