Enables single machine or distributed training and evaluation of deep learning models.
Open source
24 alternativesFEDOT
FEDOT automates machine learning model development through its modeling framework.
About FEDOT
FEDOT is an open-source Python framework for automated design of machine learning pipelines. It uses an evolutionary approach to build graph-based pipelines that connect data preprocessing and model blocks for classification, regression, clustering, and time series prediction.
It provides a fit/predict API that accepts NumPy arrays, Pandas DataFrames, or file paths, and can export resulting pipelines as JSON or with input data as a ZIP archive. It installs with pip or Docker, with separate optional dependencies for image, text processing, and DNNs.
Key features
- Automatically designs composite machine learning pipelines using evolutionary optimization
- Supports classification, regression, clustering, and time series prediction
- Builds graph-based pipelines linking preprocessing and model blocks
- Works with scikit-learn, CatBoost, XGBoost, and custom models
- Tunes hyperparameters with custom metrics and search spaces
- Exports pipelines as JSON or ZIP with input data
Best for: Python users who want to automate pipeline construction for classification, regression, clustering, or time series tasks.
Good for
- Open-source under the 3-Clause BSD license
- Handles table, text, and image data types
- Provides fit/predict API with tutorial notebooks
Watch out for
- Extra installation needed for image, text processing, and DNN use
FEDOT at a glance
- Website
- github.com/aimclub/FEDOT
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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FEDOT FAQ
How is FEDOT installed?
Install with `pip install fedot`, or with `pip install fedot[extra]` for image and text processing and DNNs. Docker images are also available.
What input formats can be used?
NumPy arrays, Pandas DataFrames, and file paths can be used as sources of input data.
How can results be saved for reproducibility?
Resulting pipelines can be exported separately as JSON or together with input data as a ZIP archive.
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