AutoKeras vs Lale
AutoKeras
autokeras.comLale
lale.readthedocs.ioSide by side
| Figure | AutoKeras | Lale |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 2 | 0 |
| Spec score · AutoML Software | 8.9 (#2) | 7.2 (#19) |
| Installation | The official installation method is pip install autokeras, with Python 3 required. | Lale can be installed from PyPI with pip install lale, and the documentation describes Core and Full setup targets. |
| Purpose | AutoKeras is an AutoML system based on Keras, developed to make machine learning accessible to everyone. | Lale is a Python library for semi-automated data science that helps select algorithms and tune pipeline hyperparameters in a type-safe way. |
| Requirements | The installation guide says AutoKeras supports Python 3 and recommends PyTorch version 2.3.0 or later with the PyTorch backend. | The installation documentation specifies Python 3.7 or later and lists Linux, Windows 10, and Mac OS X as supported operating systems. |
| Tasks | Its supported tasks include image and text classification and regression, plus structured data classification and regression. | The FAQ says Lale is used for classification and regression, and users can define custom scoring metrics for automation tools. |
| What it does | AutoKeras is an AutoML library that automates deep-learning model selection and hyperparameter tuning. | Lale is a Python library for semi-automated data science that helps select algorithms and tune pipeline hyperparameters. |
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.