Auto-PyTorch
6.1#11 in automl softwareFree planFree planLinuxSelf-hosted
VisitAuto-PyTorch vs MLJAR AutoML
MLJAR AutoML
7.1#3 in automl softwareFrom $20 a monthFree planWebLinuxmacOSSelf-hosted
VisitWhat both publish
| Fact | Auto-PyTorch | MLJAR AutoML |
|---|---|---|
| Deployment | The project provides a Docker image and can be installed from PyPI or manually in a Python environment. | Generated Mercury apps can be previewed locally, published through MLJAR Platform, or self-hosted with Docker. |
| Integrations | The documentation describes using Dask.distributed for parallel Bayesian optimization and sklearn column transformers for data preprocessing. | The Python package integrates with Mercury to generate prediction apps that support single predictions and batch CSV predictions. |
| Purpose | Auto-PyTorch is an automated machine learning toolkit based on PyTorch that automates algorithm selection and hyperparameter tuning. | MLJAR AutoML automates supervised machine learning for tabular data, including binary classification, multiclass classification, and regression. |
Auto-PyTorch plans
- Auto-PyTorchFree
MLJAR AutoML plans
- FreeFree
- mljar-supervised Python packageFree
- Pro$20 a month
- Business$60 a month
- Perpetual license$199, paid once
Pick Auto-PyTorch if…
Auto-PyTorch suits machine-learning practitioners who want to automate model search in a code-based, self-hosted workflow. Its documented tasks cover tabular data and time-series forecasting.
Pick MLJAR AutoML if…
MLJAR AutoML suits people building supervised models from tabular data who want notebook or Python workflows. It is not aimed at forecasting or time-series modeling, and users should account for variation in reports and explanations.