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24 alternativesREP
REP provides an IPython-based machine learning environment for consistent, reproducible data-driven research.
About REP
REP (Reproducible Experiment Platform) is an IPython-based environment for conducting data-driven research in a consistent and reproducible way. It is described as a Machine Learning toolbox for Humans. REP does not try to substitute scikit-learn, but extends it with wrappers that follow an extended scikit-learn interface.
With REP you can train classifiers using unified Python wrappers for libraries including Scikit-learn, XGBoost, TMVA, uBoost, Theanets, Pybrain and Neurolab. You can run parallel training on a cluster, run grid search with parallel execution, and create classification and regression reports with plots including ROC curves and learning curves. Research can be versioned with git, and custom quality metrics and meta-algorithms can be added. Installation is available via Docker image or manual setup, and how-to notebooks are provided in the repository.
REP suits Python users working with IPython notebooks who need reproducible machine learning experiments across different libraries. It is relevant for researchers and students working on Linux or Windows who use version control and shared experiment workflows.
Key features
- Unified Python wrappers for multiple ML libraries
- Parallel classifier training on a cluster
- Classification and regression reports with plots
- Grid search with parallel execution
- Research versioning using git
- Docker image with REP and dependencies
Best for: Researchers and Python users who need reproducible experiments across different machine learning libraries.
Good for
- Extends scikit-learn interface rather than replacing it
- Supports multiple ML libraries through one interface
- Provides Docker installation option and example notebooks
- Licensed under Apache 2.0
Watch out for
- Example code written in Python 2
- MatrixNet service available only to CERN
REP at a glance
- Website
- github.com/yandex/rep
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Platforms
- Linux, Windows
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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REP FAQ
What is REP?
REP is an IPython-based environment for consistent and reproducible data-driven research with unified wrappers for different machine learning libraries.
How do I install REP?
You can install it using the provided Docker image or by manually installing REP and its dependencies on your machine.
Does REP replace scikit-learn?
No. REP extends scikit-learn and provides wrappers that follow an extended scikit-learn interface.
Where are REP usage examples?
Example notebooks are in the /howto/ folder and can be viewed online through nbviewer.
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