Auto-PyTorch vs Lale
Auto-PyTorch
automl.github.ioLale
lale.readthedocs.ioSide by side
| Figure | Auto-PyTorch | Lale |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | — |
| Plans published | 1 | 0 |
| Spec score · AutoML Software | 8.4 (#5) | 7.2 (#19) |
| Installation | The installation documentation specifies Linux, Python 3.7 or later, a C++11-capable compiler, and SWIG 3.0.*, and also documents a Docker image. | Lale can be installed from PyPI with pip install lale, and the documentation describes Core and Full setup targets. |
| Integrations | The documentation describes using Dask.distributed for parallel Bayesian optimization and sklearn column transformers for data preprocessing. | Its operator libraries include support based on scikit-learn, AI Fairness 360, category_encoders, imbalanced-learn, LightGBM, Snap ML, and XGBoost. |
| Purpose | Auto-PyTorch is an automated machine learning toolkit based on PyTorch that automates algorithm selection and hyperparameter tuning. | Lale is a Python library for semi-automated data science that helps select algorithms and tune pipeline hyperparameters in a type-safe way. |
| What it does | Auto-PyTorch is an automated machine-learning toolkit based on PyTorch that helps users automate algorithm 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
Auto-PyTorch
Auto-PyTorch is a free, self-hosted machine-learning toolkit based on PyTorch. It automates algorithm selection and hyperparameter tuning, including neural-network architecture and training settings, within a user-defined search budget. Documented search methods include Bayesian optimisation, meta-learning, SMAC and Hyperband. Supported work includes tabular classification, tabular regression and time-series forecasting. Tabular preprocessing can perform imputation, categorical encoding, scaling and feature processing, while related hyperparameters are tuned. The toolkit can build ensembles from validation-set predictions, with controls for ensemble size and candidate limits. Users can set estimator memory limits and total wall time. Parallel Bayesian optimisation uses Dask.distributed; workers need shared filesystem access to training data and models. Installation documentation specifies Linux, Python 3.7 or later, a C++11-capable compiler and SWIG 3.0. SWIG 4.0 or later is unsupported. Forecasting needs extra dependencies. A Docker image and PyPI installation are documented, under the 3-clause BSD license.