AutoKeras vs TPOT
AutoKeras
autokeras.comTPOT
epistasislab.github.ioSide by side
| Figure | AutoKeras | TPOT |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · AutoML Software | 8.9 (#2) | 7.3 (#18) |
| Installation | The official installation method is pip install autokeras, with Python 3 required. | TPOT requires Python and can be installed in a conda environment or manually; the documented Python range is >=3.10 and <3.14. |
| Purpose | AutoKeras is an AutoML system based on Keras, developed to make machine learning accessible to everyone. | TPOT is a Python automated machine learning tool that optimizes machine learning pipelines using genetic programming. |
| Support | The project directs users to GitHub Discussions to ask questions. | The project directs users to its GitHub issues to report bugs or suggest enhancements and to discuss extensions. |
Plans
In their own words
AutoKeras
AutoKeras is a free, open-source AutoML system based on Keras, designed to make machine learning accessible through code. It supports image and text classification and regression, as well as classification and regression for structured data. Its documentation also includes a tutorial for multi-task or multimodal datasets. Users can assemble custom models from AutoKeras blocks and let the system search for detailed configurations. Image workflows can set architecture type, normalization and data augmentation, or leave those choices for automatic tuning. The best model found can be exported as a Keras Model. The Python package is installed with pip and supports Linux, macOS and Windows, including self-hosted use. The installation guide supports Python 3 and recommends PyTorch version 2.3.0 or later with the PyTorch backend. By default, the final 20% of training data is used for validation, though users can set a different split or supply validation data. Model selection uses validation loss by default, with support for customized metrics and objectives. The project directs users to GitHub Discussions for questions.