> ML_LIBRARY // AUTO-SKLEARN_v1.0
auto-sklearn
AutoML Freiburg — Groundbreaking academic automated machine learning framework built on scikit-learn and SMAC.
automl-hpov0.15.0BSD-3-Clausequalified
Model Training
Accelerators:
CPU
Distributed Training:No
Model Inference
Inference Accelerators:
CPU
Deployment Targets:server
What It Does
- +Automated pipeline search over scikit-learn preprocessors, classifiers, and regressors
- +Meta-learning warm-starts using similarity to 140+ OpenML benchmark datasets
- +Sequential Model-based Algorithm Configuration (SMAC3) Bayesian optimization
- +Post-hoc ensemble construction combining the best-performing evaluated pipelines into a weighted ensemble
What It Does Not Do
- -Natively execute on native Windows OS without Docker or WSL2 (requires POSIX resource limits)
- -Train modern multi-layer vision or transformer architectures on GPUs
- -Scale across multi-node clusters out of the box
>Suitable Work Types
- Academic benchmarking of automated tabular machine learning
- Establishing competitive baseline ensembles on standard classification and regression problems
- AutoML research comparing Bayesian optimization strategies
>Unsuitable Work Types
- Production deployments on native Windows servers without containerization
- Large datasets (>10GB) where modern tabular gradient boosting AutoML (AutoGluon/FLAML) is 10x faster
Data Residency Implications
Runs strictly locally in POSIX memory. Zero telemetry or external transmission.
Security Considerations
BSD-3-Clause license. Restrict execution to Linux/WSL2 environments.
Operational Profile & Known Limitations
Maturity:mature
Learning Curve:low
Ops Complexity:moderate
Cost Tier:free-oss
> Known Limitations:
- Strict dependency on Linux POSIX resource modules (resource.setrlimit) makes it impossible to install directly on native Windows.
Associated Incident Patterns (Incidentpedia)
Enforce safeguards and monitoring to guard against these documented real-world failure modes:
> Primary Evidence & Benchmark Citations
auto-sklearn Documentationofficial-docs • 0.15.0
2026-09-25HIGH