Auto-PyTorch

Auto-PyTorch is an AutoML toolkit for PyTorch-based tabular classification, tabular regression, and time series forecasting. Use this skill when you need to search over pipeline components, tune hyperparameters, inspect ensembles, or validate data shapes before a run.

Install

Use the public package name:

pip install autoPyTorch
pip install autoPyTorch[forecasting]

Use the forecasting extra whenever you need the time-series APIs, validators, or forecasting metrics.

Quick smoke check

Run the bundled install check script after installation:

python scripts/check_install.py

That script prints the installed version, importability of the public modules, and the most important API signatures.

Route map

sub-skills/tabular-automl/

Use this route for:

• TabularClassificationTask and TabularRegressionTask
• get_dataset(...), get_search_space(...), fit_pipeline(...), search(...), and refit(...)
• feature typing, categorical handling, dataset compression, and resampling strategy selection
• custom search spaces via include_components, exclude_components, and search_space_updates
• traditional learners such as LightGBM, CatBoost, RandomForest, ExtraTrees, SVM, and KNN
• ensemble inspection with show_models(), sprint_statistics(), and plot_perf_over_time()

Read sub-skills/tabular-automl/SKILL.md when the task is about tabular classification or regression, even if the user also wants custom configs, portfolios, or result inspection.

sub-skills/forecasting/

Use this route for:

• TimeSeriesForecastingTask
• uni-variant and multi-variant sequence layouts
• start_times, freq, series_idx, known_future_features, and n_prediction_steps
• forecasting validators, sequence construction, and forecast-horizon handling
• forecasting-specific metrics such as mean_MASE_forecasting and mean_MAPE_forecasting

Read sub-skills/forecasting/SKILL.md when the task names time series forecasting, sequence validation, known future features, or forecast horizon setup.

Core usage pattern

Most tasks follow the same shape:

1. Build or load dataset inputs.
2. Let the validator normalize the data.
3. Create the task object.
4. Search, fit a single configuration, or refit the selected model.
5. Inspect predictions, scores, and ensemble output.

If you need the detailed API surface, open references/package-overview.md first for a compact summary, then open the closest sub-skill reference instead of trying to infer it from the router.

Common inputs and outputs

• Inputs can be NumPy arrays, pandas objects, or Python lists for the supported task type.
• Tabular workflows may need feat_types when dtype inference is ambiguous.
• Forecasting workflows may need series_idx, start_times, and known_future_features to preserve sequence identity.
• search(...) returns the fitted task object after the search completes.
• fit_pipeline(...) returns a fitted pipeline plus run metadata for a single configuration.
• predict(...) and score(...) operate on the fitted task or pipeline.

Common failures

Read references/troubleshooting.md for cross-cutting install, import, data-shape, optional-dependency, and package-resolution issues.

The most common problems are:

• missing forecasting extras when importing forecasting APIs
• missing or uninitialized automl_common submodule when using a source checkout
• OpenML or other network-backed example data not being available
• scikit-learn, torch, or compiled dependency version mismatches

Related references

• references/package-overview.md — compact package API and task summary
• references/troubleshooting.md — cross-cutting install and import problems
• references/repo-provenance.md — source commit and package version snapshot
• references/repo-routing-metadata.json — router metadata for managed imports
• scripts/check_install.py — lightweight install and import smoke check
• sub-skills/tabular-automl/ — tabular classification and regression workflows
• sub-skills/forecasting/ — time series forecasting workflows