automl/Auto-PyTorch

Health v2 · maintenance only

Adoption not part of the score

2541 stars · 302 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Auto-PyTorch is an AutoML framework that jointly performs neural architecture search and hyperparameter optimization for PyTorch models, using SMAC and multi-fidelity meta-learning. It primarily targets tabular classification/regression and time series forecasting tasks.

Use cases

• automatically find the best neural network architecture for my tabular dataset
• hyperparameter optimization for a PyTorch model without manual tuning
• automated deep learning for classification and regression on tabular data
• auto-tune a deep learning model for time series forecasting
• compare automated deep learning pipelines against sklearn baselines

When to choose

• you want fully automated deep learning on tabular or time series data
• you need joint architecture search and hyperparameter optimization with SMAC
• you prefer a research-grade AutoML tool built on PyTorch

When to avoid

• you need image, text, or other non-tabular data support
• you want a rapidly evolving tool with frequent releases
• you need lightweight manual control over model architecture

Facets

library · maturity maintenance

machine-learning deep-learning llm-training machine-learning deep-learning data-science python automl neural-architecture-search hyperparameter-optimization tabular-data time-series-forecasting smac pytorch

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markdown · JSON · MCP: product_card(name="automl/Auto-PyTorch")