repromodel

repromodel

Boosting the AI research efficiency

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ReproModel is an open-source toolbox designed to boost AI research efficiency by enabling researchers to reproduce, compare, train, and test AI models faster. It provides standardized models, dataloaders, and processing procedures, allowing researchers to focus on new datasets and model development. With a no-code solution, users can access benchmark and SOTA models and datasets, utilize training visualizations, extract code for publication, and leverage an LLM-powered automated methodology description writer. The toolbox helps researchers modularize development, compare pipeline performance reproducibly, and reduce time for model development, computation, and writing. Future versions aim to facilitate building upon state-of-the-art research by loading previously published study IDs with verified code, experiments, and results stored in the system.

README:

ReproModel - Open Source Toolbox for Boosting the AI Research Efficiency

ReproModel

Open Source Toolbox for Boosting the AI Research Efficiency

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ReproModel helps the AI research community to reproduce, compare, train, and test AI models faster.

ReproModel toolbox revolutionizes research efficiency by providing standardized models, dataloaders, and processing procedures. It features a comprehensive suite of pre-existing experiments, a code extractor, and an LLM descriptor. This toolbox allows researchers to focus on new datasets and model development, significantly reducing time and computational costs.

With this no-code solution, you'll have access to a collection of benchmark and SOTA models and datasets. Dive into training visualizations, effortlessly extract code for publication, and let our LLM-powered automated methodology description writer do the heavy lifting.

The current prototype helps researchers to modularize their development and compare the performance of each step in the pipeline in a reproducible way. This prototype version helped us reduce the time for model development, computation, and writing by at least 40%. Watch our demo.

The coming versions will help researchers build upon state-of-the-art research faster by just loading the previously published study ID. All code, experiments, and results will already be verified and stored in our system.

https://repromodel.netlify.app

Feature Roadmap

✅ Standard Models Included
✅ Benchmark Datasets
✅ Metrics (100+)
✅ Losses (20+)
✅ Data Splitting
✅ Augmentations
✅ Optimizers (10+)
✅ Learning Rate Schedulers
✅ Early Stopping Criterion
✅ Training Device Selection
✅ Logging (Tensorboard ...)
✅ AI Experiment Description Generator
✅ Code Extractor
✅ Custom Script Editor
✅ Docker image
🔲 GUI augmentation builder
🔲 Conventional ML models workflow
🔲 Parallel training
🔲 Statistical testing
🔲 Explainability
🔲 Interpretability

Documentation

For examples and step-by-step instructions, please visit our full documentation at https://www.repromodel.com/docs.

Running Locally

Docker (Option 1)

Please verify that you have Docker or Docker CLI installed on your system.

Pull the docker image:
docker pull dsitnik1612/repromodel

Run the container:
docker run --name ReproModel -p 5173:5173 -p 6006:6006 -p 5005:5005 dsitnik1612/repromodel

Then open the frontend under: http://localhost:5173/

Source code (Option 2)

In case you want to run the ReproModel directly from the source code, here are the steps:

You will need to have Node.js installed.

Combines npm install, creation of a virtual environment, as well as the launch of the frontend and backend:

npm run repromodel            // Mac and Linux
npm run repromodel-windows    // Windows

If you want to launch the frontend only:

npm install
npm run dev

For using the Methodology Generator, you need to have Ollama installed You can get Ollama from their website and pull the model of your choice.

npm install
npm run repromodel-with-llm            // Mac and Linux
npm run repromodel-with-llm-windows    // Windows

Then open the frontend under: http://localhost:5173/

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create.

Any contributions you make are greatly appreciated. If you have a suggestion that would make this better, please read our Contribution Guidelines and Code of Conduct.

List of contributors

Dario Sitnik
Dario Sitnik, PhD
AI Scientist
GitHub
Mint Owl
Mint Owl
ML Engineer
GitHub
Martin Schumakher
Martin Schumakher
Developer
GitHub
Tomonari Feehan
Tomonari Feehan
Developer
GitHub

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Questions & Support

For questions or any type of support, you can reach out to me via [email protected]

License

This project is licensed under the MIT License.

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